第三届国际物联网学术大会征稿(IoT2012)

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广东海洋大学课程学习报告物联网工程导论学习报告信息时代下的物联网学号班级姓名指导老师2013-12-4评价：目录一.物联网的简介 ------------------------------------------------------------------------------------------------- 21.1物联网的概念 ------------------------------------------------------------------------------------------- 2二.物联网走上现代舞台---------------------------------------------------------------------------------------- 32. 大放异彩-智能系列 ----------------------------------------------------------------------------------- 32.1智慧城市-城市发展的方向 -------------------------------------------------------------------- 32.2未来校园-智慧校园------------------------------------------------------------------------------ 42.3生活的梦想-智能家居--------------------------------------------------------------------------- 6 三相关的技术和平台 ------------------------------------------------------------------------------------------- 83.1 射频识别（RFID） ----------------------------------------------------------------------------------- 83.1.1 什么是RFID技术？ ------------------------------------------------------------------------ 83.1.2 RFID的分类 ---------------------------------------------------------------------------------- 83.1.3 RFID的组成部分 ---------------------------------------------------------------------------- 83.1.4 RFID的工作原理 ---------------------------------------------------------------------------- 93.2 红外感应器 --------------------------------------------------------------------------------------------- 93.3全球定位系统（GPS） ------------------------------------------------------------------------------- 93.4 激光扫描器 -------------------------------------------------------------------------------------------- 103.5 EPC ------------------------------------------------------------------------------------------------------- 103.6 因特网技术 -------------------------------------------------------------------------------------------- 103.7zigbee技术 ---------------------------------------------------------------------------------------------- 103.8 传感器网络技术 -------------------------------------------------------------------------------------- 123.9 嵌入式技术 -------------------------------------------------------------------------------------------- 123.10 信息安全技术---------------------------------------------------------------------------------------- 12 四物联网的原理及构建--------------------------------------------------------------------------------------- 134.1 物联网的原理简介 ----------------------------------------------------------------------------------- 134.2 物联网的三层构架 ---------------------------------------------------------------------------------- 144.3.1 传感器 ---------------------------------------------------------------------------------------- 144.3.2电子标签(ID)-------------------------------------------------------------------------------- 14一.物联网的简介1.1物联网的概念物联网的概念是在1999年提出的。

An Efficient Distributed Verification Protocol for Data Storage Security in Cloud Computing

An Efficient Distributed Verification Protocol for Data Storage Security in Cloud ComputingAbstract— Data storage is an important application of cloud computing, where clients can remotely store their data into the cloud. By uploading their data into the cloud, clients can be relieved from the burden of local data storage and maintenance. This new paradigm of data storage service also introduces new security challenges. One of these risks that can attack the cloud computing is the integrity of the data stored in the cloud. In order to overcome the threat of integrity of data, the client must be able to use the assistance of a Third Party A uditor (TPA), in such a way that the TPA verifies the integrity of data stored in cloud with the client’s public key on the behalf of the client. The existing schemes with single verifier (TPA) may not scale well for this purpose. In this paper, we propose A n Efficient Distributed Verification Protocol (EDVP) to verify the integrity of data in a distributed manner with support of multiple verifiers (Multiple TPA s) instead of single Verifier (TPA). Through the extensive security, performance and experimental results, we show that our scheme is more efficient than single verifier based scheme. Keywords: cloud storage, Integrity, Client, TPA, SUBTPAs, Verification, cloud computing.I.I NTRODUCTIONCloud computing is a large-scale distributed computing paradigm in which a pool of computing resources is available to Clients via the Internet. The Cloud Computing resources are accessible as public utility services, such as processing power, storage, software, and network bandwidth etc. Cloud storage is a new business solution for remote backup outsourcing, as it offers an abstraction of infinite storage space for clients to host data backups in a pay-as-you-go manner [1]. It helps enterprises and government agencies significantly reduce their financial overhead of data management, since they can now archive their data backups remotely to third-party cloud storage providersrather than maintaining local computers on their own. For example, Amazon S3 is a well known storage service.The increasing of data storage in the cloud has brought a lot of attention and concern over security issues of this data. One of important issue is with cloud data storage is that of data integrity verification at untrusted cloud servers. For example, the storage service provider, which experiences Byzantine failures occasionally, may decide to hide the data loss incidents from the clients for the benefit of their own. What is more serious is that for saving money and storage space the service provider might neglect to keep or deliberately delete rarely accessed data files which belong to thin clients. Consider the large size of the outsourced data and the client’s constrained resource capability, the main problem can be generalized as how can the client find an efficient way to perform periodical integrity verifications without local copy of data files.To verify the integrity of data in cloud without having local copy of data files, recently several integrity verification protocols have been developed under different systems [2-13].A ll these protocols have verified the integrity of data with single verifier (TPA). However, in single auditor verification systems, they use only one Third Party A uditor (TPA) to verify the Integrity of data based Challenge-Response Protocol. In that verification process, the TPA stores the metadata corresponding to the file blocks and creates a challenge and sends to the CSP. The CSP generates the Integrity proof for corresponding challenge, and send back to the TPA. Then, TPA verifies the response with the previously stored metadata and gives the final audit result to the client. However, in this single A uditor system, if TPA system will crash due to heavy workload then whole verification process will be aborted. In addition, during the verification process, the network traffic will be very high near the TPA organization and may create network congestion. Thus, the performance will be degrading in single auditor verification schemes. Therefore, we need an efficient distributed verification protocol to verify the integrity of data in cloud.In this paper, we propose an Efficient Distributed Verification Protocol (EDVP) to verify the integrity of data in a distributed manner with support of multiple verifiers (Multiple TPAs) instead of single Verifier (TPA), which were discussed in existing prior works[2-13]. In our protocol, many number of SUBTPA s concurrently works under the single TPA and workload also must be uniformly distribute among the SUBTPA s, so that each SUBTPA will verify over the whole part, Suppose if TPA fails, one of the SUBTPA will act as TPA. Our protocol would detect the data corruptions in the cloud efficiently when compared to single verifier based protocols.Our protocol design is based on RSA-based Dynamic Public Audit Service for Integrity Verification of data in cloud proposed by Syam et al.[11] in a distributed manner. Here, the n verifiers challenge the n servers uniformly and if m server’s response is correct out of n servers then, we can say that Integrity of data is ensured. To verify the Integrity of the data, our verification process uses multiple TPA s, among theSyam Kumar.P1Dept.of Computer ScinceIFHE(Deemed University)Hyderabad, Indiashyam.553@1,Subramanian. R2, Thamizh Selvam.D3Dept.of Computer Science School of Engineering and Technology,Pondicherry University, Puducherry, India, rsmanian.csc@.in2,dthamizhselvam@32013 Second International Conference on Advanced Computing, Networking and Securitymultiple TPAs, one TPA will act as main TPA and remaining are SUBTPA s. The main TPA uses all SUBTPA s to detect data corruptions efficiently, if main TPA fails, then one of the SUBTPA will act as main TPA. The SUBTPA s do not communicate with each other and they would like to verify the Integrity of the stored data in cloud, and consistency of the provider’s responses. The propose system guarantee the atomic operations to all TPA s; this means that TPA which observe each SUBTPA operations are consistent, in the sense that their own operations plus those operations whose effects they see have occurred atomically in same sequence.In Centrally Controlled and Distributed Data paradigm, where all SUBTPA s are controlled by the TPA and SUBTPA’s communicate to any Cloud Data Storage Server, we consider a synchronous distributed system with multiple TPA s and Servers. Every SUBTPA is connected to Server through a synchronous reliable channel that delivers a challenge to the server. The SUBTPA and the server together are called parties P. A protocol specifies the behaviours of all parties. An execution of P is a sequence of alternating states and state transitions, called events, which occur according to the specification of the system components. A ll SUBTPA s follow the protocol; in particular, they do not crash. Every SUBTPA has some small local trusted memory, which serves to store distribution keys and authentication values. The server might be faulty or malicious and deviate arbitrarily from the protocol; such behaviour is also called Byzantine failure.The Synchronous system comes down to assuming the following two properties:1. Synchronous computation. There is a known upper bound on processing delays. That is, the time taken by any process to execute a step is always less than this bound. Remember that a step gathers the delivery of a message (possibly nil) sent by some other process, a local computation (possibly involving interaction among several layers of the same process), and the sending of a message to some other process.2. Synchronous communication. There is a known upper bound on challenge/response transmission delays. That is, the time period between the instant at which a challenge is sent and the time at which the response is delivered by the destination process is less than this bound.II.RELATED WORKBowers et al. [2] introduced a High Availability Integrity Layer (HAIL) protocol to solve the Availability and Integrity problems in cloud computing using error correcting codes and Universal Hash Functions (UHFs). This scheme achieves the A vailability and Integrity of data. However, this scheme supports private verifiability.To support public verifiability of data integrity, Barsoum et al. [3] proposed a Dynamic Multiple Data Copies over the Cloud Servers, which is based on multiple replicas. This scheme achieves the Availability and Integrity of data stored in cloud. Public verification enables a third party auditor (TPA) to verify the integrity of data in cloud with the data owner's public key on the behalf of the data owner,. Wang et al. [4] designed an Enabling Public Auditability and Data Dynamics for data storage security in cloud computing using Merkle Hash Tree (MHT). It achieves the guarantee of the data Integrity with efficient data dynamic operations and public verifiability. Similarly,Wang et al. [5] proposed a flexible distributed verification protocol to ensure the dependability, reliability and correctness of outsourced data in the cloud by utilizing homomorpic token and distributed erasure coded data. This scheme allow users to audit the outsourced data with less communication and computation cost. Simultaneously, it detects the malfunctioning servers. In their subsequent work, Wang et al. [6] developed a privacy-preserving data storage security in cloud computing. Their construction utilizes and uniquely combines the public key based homomorpic authenticator with random masking while achieving the Integrity and privacy from the auditor. Similarly, Hao et al. [7] proposed a privacy-preserving remote data Integrity checking protocol with data dynamics and public verifiability. This protocol achives the deterministic guaranty of Integrity and does not leak any information to third party auditors. Zhuo et al. [8] designed a dynamic audit service to verify the Integrity of outsourced data at untrusted cloud servers. Their audit system can support public verifiability and timely abnormal detection with help of fragment structure, random sampling and index hash table. Yang et al. [9] proposed a provable data possession of resource-constrained mobile devices in cloud computing. In their framework, the mobile terminal devices only need to generate some secret keys and random numbers with the help of trusted platform model (TPM) chips, and the needed computing workload and storage space is fit for the mobile devices by using bilinear signature and Merkle hash tree (MHT), this scheme aggregates the verification tokens of the data file into one small signature to reduce the communication and storage burden.Although, all these schemes achieved the Integrity of remote data assurance under different systems, they do not provide a strong integrity assurance to the clients because their verification process using pseudorandom sequence. If we use pseudorandom sequence to verify the remote data Integrity, sometimes they may not detect the data modifications on data blocks. Since pseudorandom sequence is not uniform (uncorrelated numbers), it does not cover the entire file while generating Integrity proof for a challenge. Therefore, probabilistic Integrity checking methods using pseudorandom sequence may not provide strong Integrity assurance to user’s data stored in remotely.To provide better Integrity assurance, Syam et al. [10] proposed a homomorpic distributed verification protocol using Sobol sequence instead of pseudorandom sequence [2-9]. Their protocol ensures the A vailability, Integrity of data and also detects the data corruption efficiently. In their subsequent work, Syam et al. [11] described a RSA-based Dynamic Public Audit protocol for integrity verification of data stored in cloud. This scheme gives probabilistic proofs based on random challenges and like [10] it also detects the data modification on file. Similarly, Syam et al. [12] developed an Efficient and Secure protocol for both Confidentiality andIntegrity of data with public verifiability and dynamic operations. Their construction uses Elliptic Curve Cryptography instead of RSA because ECC offers same security as RSA with small key size. Later, Syam et al.[13] proposed a publicly verifiable Dynamic secret sharing protocol for A vailability, Integrity, Confidentiality of data with public verifiability.Although all these schemes achieved the integrity of remote data under different systems with Single TPA, but in single auditor verification protocols, they use only one Third Party A uditor (TPA) to verify the Integrity of data based Challenge-Response Protocol. However, in this single Auditor system, if TPA system will crash due to heavy workload then whole verification process will be aborted.III.PROBLEM STATEMENTA.Problem DefinitionIn cloud data storage, the client stores the data in cloud via cloud service provider. Once data moves to cloud he has no control over it i.e. no security for outsourced data stored in cloud, even if Cloud Service Provider (CSP) provides some standard security mechanism to protect the data from attackers but still there is a possibility threats from attackers to cloud data storage, since it is under the control of third party provider, such as data leakage, data corruption and data loss. Thus, how can user efficiently and frequently verify that whether cloud server storing data correctly or not? A nd will not be tampered with it. We note that the client can verify the integrity of data stored in cloud without having a local copy of data and any knowledge of the entire data. In case clients do not have the time to verify the security of data stored in cloud, they can assign this task to trusted Third Party Auditor (TPA). The TPA verifies the integrity of data on behalf of clients using their public key.B.System ArchitectureThe network representation architecture for cloud data storage, which consists four parts: those are Client, Cloud Service Provider (CSP), Third Party A uditors (TPA s) and SUBTPAS as depicted in Fig 1:Fig 1: Cloud Data Storage Architecture Client: - Clients are those who have data to be stored, and accessing the data with help of Cloud Service Provider (CSP). They are typically desktop computers, laptops, mobile phones, tablet computers, etc.Cloud Service Provider (CSP):- Cloud Service Providers (CSPs) are those who have major resources and expertise in building, managing distributed cloud storage servers and provide applications, infrastructure, hardware, enabling technology to customers via internet as a service.Third Party Auditor (TPA):- Third Party Auditor (TPA) who has expertise and capabilities that users may not have and he verify the security of cloud data storage on behalf of users. SUBTPAS: the SUBTPA s verifies the integrity of data concurrently under the control of TPAThroughout this paper, terms verifier or TPA and server or CSP are used interchangeablyC.Security ThreatsThe cloud data storage mainly facing data corruption challenge:Data Corruption: cloud service provider or malicious cloud user or other unauthorized users are self interested to alter the user data or deleting.There are two types of attackers are disturbing the data storage in cloud:1) Internal Attackers: malicious cloud user, malicious third party user (either cloud provider or customer organizations) are self interested to altering the user’s personal data or deleting the user data stored in cloud. Moreover they decide to hide the data loss by server hacks or Byzantine Failure to maintain its reputation2) External Attackers: we assume that an external attacker can compromise all storage servers, so that he can intentionally modify or delete the user’s data as long as they are internally consistent.D.GoalsIn order to address the data integrity stored in cloud computing, we propose an Efficient Distribution Verification Protocol for ensuring data storage integrity to achieve the following goals:Integrity: the data stored safely in cloud and maintain all the time in cloud without any alteration.Low-Overhead: the proposed scheme verifies the security of data stored in cloud with less overhead.E.Preliminaries and Notations•f key(.)- Sobol Random Function (SRF) indexed on some key, which is defined asf : {0,1}* ×key-GF (2w).•ʌkey– Sobol Random Permutation (SRP) indexed under key, which is defined asʌ : {0,1}log2(l) × key –{0,1}log2(l) .IV. EFFICENT DISTRIBUTION VERIFICATIONPROTOCOL:EDVP The EDVP protocol is designed based on RSA -based Dynamic Public A udit Protocol (RSA -DPA P), which is proposed by Syam et al.[11]. In EDVP, we are mainly concentrating on verification phase of RSA -DPA P. The EDVP contains three phases: 1) Key Distribution, 2) Verification Process 3) Validating Integrity. The process of EDVP is: first, the TPA generates the keys and distribute to SUBTPA s. Then the SUBTPA s verify the integrity of data and gives result to main TPA. Finally, the main TPA validates the integrity by observing the report from SUBTPAs.A. Key DistributionIn key distribution, the TPA generates the random keyand distributes it to his SUBTPAs as follows:The TPA first generates the Random key by using SobolRandom Function [15] then Compute)(1i f K k =Where1 i n and the key is indexed on some (usually secret) key: f :{0,1}*× keyĺZ p Then, employ (m, n ) secret sharing scheme [14] andpartition the random key K into n pieces. To divide K into npieces, the client select a polynomial a(x) with degree m-1andcomputes the n pieces: 1221....−++++=m j i i a i a i a K K (2)¦−=+=11m j j j i i a K K (3)A fter that TPA chooses nSUBTPA s and distributes n pieces to them. The procedure of key distribution is given in algorithm 1.Algorithm 1: Key Distribution1.1. Generates a random key K using Sobol Sequence. )(1i f K k =2. Then, the TPA partition the K into n pieces using (m,n) secret sharing scheme3. TPA select the Number of SUBTPAs: n, and threshold value m;4. for i ĸ1 to n do5. TPA sends k i to the all SUBTPA i s6. end for7. endB. Verification ProcessIn verification process, all SUBTPAs verify the Integrity of data and give results to the TPA, if m SUBTPAs responses meet the threshold value then TPA says that Integrity of data is valid. At a high level, the protocol operates like this: A TPA assigns a local timestamp to every SUBTPA of its operations. Then, every SUBTPA maintains a timestamp vector T in itstrusted memory. A t SUBTPA i , entry T[j] is equal to thetimestamp of the most recently executed operation by SUBTPA j in some view of SUBTPA i .To verify the Integrity of data, each SUBTPA creates a challenge and sends to the CSP as follows: first SUBTPA generates set of Random indices c of set [1, n] using Sobol Random Permutation (SRP) with random key)(c j j K π= (4) Where 1 c l and ʌkey (.) is a Sobol Random Permutation (SRP), which is indexed under key: ʌ : {0,1}log2(l ) ×key–{0,1} log2(l ).Next, each SUBTPA also chooses a fresh random key r j, wherer j = )(2l f k (5)Then, creates a challenge chal ={j, r j } is pairs of random indices and random values. Each SUBTPA sends a challenge to the CSP and waits for the response. The CSP computes a response to the corresponding SUBTPA challenges and send responses back to SUBTPAs.When the SUBTPA receives the response message, first he checks the timestamp, it make sure that V T (using vectorcomparison) and that V [i] = T[i]. If not, the TPA aborts theoperation and halts; this means that server has violated the consistency of the service. Otherwise, the SUBTP COMMITS the operation and check if stored metadata and response (integrity proof) is correct or not? If it is correct,then stores TRUE in its table and sends true message to TPA, otherwise store FALSE and send a false signal to the TPA for corrupted file blocks. The detailed procedure of verification processes is given in algorithm 2. Algorithm 2: Verification Process 1. Procedure: Verification Process 2. Timestamp T3. Each SUBTPA i computes4. Compute )(c j SRPk π=5. the Generate the sobol random key r j6. Send (Chal=(j, r j ) as a challenge to the CSP;7. the server computes the Proof PR i send back to theSUBTPAs;8. PR i ĸReceive(V);9. If (V T V [i] = T[i]) 10. return COMMIT then11. if PR i equals to Stored Metadata then 12. return TRUE;13. Send Signal, (Packet j , TRUE i ) to theTPA14. else15. return FALSE;16. Send Signal, (Packet i , FALSE i ) to the TPA; 17. end if 18. else19. ABORT and halt the process 20. e nd if 21. e nd(1)C.Validating IntegrityTo validate the Integrity of the data, the TPA will receive the report from any subset m out of n SUBTPAs and validates the Integrity. If the m SUBTPA s give the TRUE signal to TPA, then the TPA decides that data is not corrupted otherwise he decides that data has been corrupted. In the final step, the TPA will give an A udit result to the Client. In algorithm 3, we given the process of validating the Integrity, in which, we generalize the Integrity of the verification protocol in a distributed manner. Therefore, we can use distribution verification on scheme [11].Algorithm 3: Validating Integrity1.Procedure: validation(i)2.TPA receives the response from the m SUBTPAs3.for iĸ1 to m do4.If(response==TRUE)5. Integrity of data is valid6. else if(response==FALSE)7. Integrity is not valid8.end if9.end for10.endV.A NALYSIS OF EDVPIn this section, we analyse the security, and performance of EDVP.A.Security AnalysisIn security analysis, we analyze the Integrity of the data in terms of probability detection.Probability Detection:It is very natural that verification activities would increase the communication and computational overheads of the system. To enhance the performance, we used Secret sharing technique [14] to distribute the Key k that provides minimum communication and tractable computational complexity. Thus, it reduces the communication overhead between TPA and SUBTPAs. For a new verification, the TPA can change the Key K for any SUBTPA and send only the different part of the multiset elements to the SUBTPA. In addition, we used probabilistic verification scheme based on Sobol Sequences that provides uniformity not only for whole sequences but also for each subsequences, so each SUBTPA will independently verify over the entire file blocks. Thus, there is a high probability to detect fault location very quickly. Therefore, a Sobol sequence provides strong Integrity proof for the remotely stored data.The probability detections of data corruptions of this protocol same as previous protocols [9-12].In EDVP, we use Sobol random sequence generator to generate the file block number, because sequence are uniformly distributed over [0, 1] and cover the whole region. To make integers, we multiply constant powers of two with the generated sequences. Here, we consider one concrete example, taking 32 numbers from the Sobol sequences.B. B. Performance Analysis and Experimental ResultsIn this section, we evaluate the performance of theverification time for validating Integrity and compare theexperimental results with previous single verifier basedprotocol [11] as shown in Tables 1-3. In Table 4 and 5, wehave shown that the Computation cost of the Verifier and CSPrespectively.Table 1: Veri ication times (Sec) with 5 veri iers whendifferent percentages of 100000 blocks are corruptedCorruption data in percentageSingle Verifierbased Protocols[11]EDVP[5 verifiers]1% 25.99 12.145% 53.23 26.55 10% 70.12 38.6315% 96.99 51.2220% 118.83 86.4430% 135.63 102.8940% 173.45 130.8550% 216.11 153.81 Table 2: Verif ication times (Sec) with 10 Verif ierswhen di f f erent percentages o f 100000 blocks are corruptedCorruption data in percentage Single Verifier basedProtocols[11]EDVP[10verifiers]1% 25.9908.14 5% 53.2318.55 10% 70.12 29.63 15% 96.99 42.22 20% 118.83 56.44 30% 135.63 65.89 40% 173.45 80.85 50% 216.11 98.81T able 3: Verification times (Sec) with 20 verifiers when different percentages of 100000 blocks are corruptedCorruption data in percentage Single VerifierbasedProtocols[11]EDVP[20verifiers]1% 25.9904.145% 53.2314.5510% 70.12 21.6315% 96.99 32.2220% 118.83 46.4430% 135.63 55.8940% 173.45 68.8550% 216.11 85.81From Tables 1-3, we can observe that verification time is lessfor detecting data corruptions in cloud when compared to single verifier based protocol [11]Table 4:Verifier computation Time (ms) for the differentfile sizesFile Size Single Verifier basedProtocol[11]EDVP1MB 148.26 80.07 2MB 274.05 192.65 4MB 526.25 447.23 6MB 784.43 653.44 8MB 1083.9 820.87 10MB 2048.26 1620.06Table 5:CSP computation Time (ms) for the different filesizesFile Size Single Verifier basedProtocols[11]EDVP1MB 488.16 356.272MB 501.23 392.554MB 542.11 421.116MB 572.17 448.678MB 594. 15 465.1710MB 640.66 496. 02 From the table 4 & 5, we can observe that computation cost of verifier and CSP is less compared existing scheme[11]VI.C ONCLUSIONIn this paper, we presented an EDVP scheme to verify the Integrity of data stored in the cloud in a distributed manner with support of multiple verifiers (Multiple TPAs) instead of single Verifier (TPA). This protocol use many number of SUBTPA s concurrently works under the single TPA and workload also must be uniformly distribute among SUBTPAs, so that each SUBTPA will verify the integrity of data over the whole part. Through the security and performance analysis, we have proved that an EDVP verification protocol would detect the data corruptions in the cloud efficiently when compared to single verifier verification based scheme.R EFERENCES[1]R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I.Brandic.“Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5thUtility,” Future Generation Computer Systems, vol. 25, no. 6,June 2009, pp 599–616, Elsevier Science, A msterdam, TheNetherlands.[2]Bowers K. D., Juels A., and Oprea A., (2008) “HAIL: A High-vailability and Integrity Layer for Cloud Storage,”Cryptology ePrint Archive, Report 2008/489.[3]Barsoum, A. F., and Hasan, M. A., “On Verifying DynamicMultiple Data Copies over Cloud Servers”, Technical Report, Department of Electrical and Computer Engineering University of Waterloo, Ontario, Canada, Aug 2011.[4]Wang Q., Wang C., Li J., Ren K., and Lou W., “Enablingpublic veri¿ability and data dynamics for storage security incloud computing”, IEEE Trans. Parallel and Distributed Systems. VOL.22, NO.5. May, 2011,pp.[5]Wang C., Wang Q., Ren K., cao N., and Lou W.,(2012)“Towards Secure and Dependable Storage Services in CloudComputing”, IEEE Trans. Service Computing. VOL. 5, NO. 2,APRIL -JUNE 2012, pp.220-232.[6]Wang, C., Ren, K., Lou,W., and Li, J., “Toward publiclyauditable secure cloud data storage services”, IEEE Networks,Vol. 24, No. 4, 2010, pp. 19–24.[7]Hao Z., Zhong S., Yu N.,(2011) “A Privacy-Preserving RemoteData Integrity Checking Protocol with Data Dynamics andPublic Verifiability”, IEEE Trans Knowledge and DataEngineering,Vol.23, Issue 9,pp.1432 –1437.[8]Zhu Y., Wang H., Hu Z., Ahn G., Hu H., Stephen, and Yau S.,“Dynamic A udit Services for Integrity Verification of Outsourced Storages in Clouds”, Proc. of the 26th A CMSymposium on Applied Computing (SAC), March 21-24, 2011,Tunghai University, TaiChung, Taiwan.[9]Yang J., Wang H., Wang J., Tan C., and Yu D., (2011)“Provable Data Possession of Resource-constrained MobileDevices in Cloud Computing”, JOURNA L OF NETWORKS,VOL. 6, NO. 7, July,, 2011,pp.1033-1040[10]P. Syam Kumar, R. Subramanian, “Homomorpic DistributedVerification Ptorotocol for Ensuring Data Storage in CloudComputing”. Journal of Information, Vol. 14, No.10, Oct-2011, pp.3465-3476.[11]P. Syam Kumar, R. Subramanian, “RSA-based DynamicPublic A udit Service for Integrity Verification of DataStorage in Cloud Computing using Sobol Sequence” SpecialIssue Security, Privacy and Trust in Cloud Systems, International Journal of Cloud Computing(IJCC) in InderScience Publications, Vol. 1 No.2/3, 2012, pp.167-200. [12]P. Syam Kumar, R. Subramanian, “A n effiecent and Secureprotocol for Ensuring Data Storage Security inCloud Computing” publication in International Journal of computerScience Issues(IJCSI), Volume 8, Issue 6, Nov-2011, pp.261-274.[13]P. Syam Kumar, Marie Stanislas Ashok, Subramanian. R, “APublicly Verifiable Dynamic Secret Sharing Protocol forSecure and Dependable Data Storage in Cloud Computing”Communicated for Publication in International Journal ofCloud Applications and Computing (IJCAC).[14]Shamir A.,“How to Share a Secret”, Comm. A CM, vol.22.1979.[15]Brately P and Fox B L (1988) Algorithm 659: ImplementingSobol’s Quasi-random Sequence Generator ACM Trans. Math.Software 14 (1) 88–100.。

新时代下的智慧社区建设路径研究

新时代下的智慧社区建设路径研究随着智慧城市的纵深发展，传统社区建设的不适应和不匹配日益显现，构建以人为核心，以社会服务为导向、以技术为手段的智慧社区迫在眉睫。

新时代下的智慧社区作为目前最具可持续发展能力及创造力的现代化社区治理模式，已成为全球城市社区发展的战略选择。

文章剖析了三明市智慧社区的建设现状，深究了其建设的制约因素，力图从建设规划、治理模式、监督反馈、管理导向、人才培养等方面，为新时代下的三明市智慧社区建设寻找一条可持续发展路径，以期为后续智慧社区的建设与发展提供参考。

标签：智慧社区;建设路径;城市建设一、新时代下的智慧社区建设概述智慧社区是一个具有动态性、开放性和复杂性的系统。

随着现代信息技术迅猛发展和城市化的快速推进，智慧社区将成为未来智慧城市的基本单元。

智慧社区是指以人为核心，以社区服务为导向，以新一代信息技术为手段，整合数据、深入分析，促进智慧技术高度集成、智慧管理科学高效、智慧服务便捷普惠为主要特征的现代化社区治理模式。

习近平提出了“实施国家大数据战略，加快建设数字中国”的要求，各级政府积极响应，将智慧城市、智慧社区的建设作为重要抓手。

智慧社区已成为我国推进数字中国建设和城市信息化进程中的前沿探索和实践应用，是我国传统社区转型与升级的迫切要求，也是创建品质社区和提升核心竞争力的必然选择。

二、新时代下三明市智慧社区的建设现状（一）缺少统一规划，未制定出实施标准智慧社区的建设是一个系统工程，统一规划、制定标准是社区智慧化建设的总体指向，是智慧社区建设和实施的前提与依据。

目前，三明市政府还未制定出符合本市的智慧社区规划和实施标准，各方存在行动上的交叉关系，且未能充分领会智慧社区建设的内涵，仅停留在技术层面或在个别领域上盲目开发，造成内耗严重，效率低下的问题。

（二）治理模式单一，层次化需求难满足社区的建设目前主要是由政府主导，治理模式单一，难以应对社区中大规模的人口流动、资源信息的整合、居民诉求多样化等问题。

物联网智能设备网络安全的国际规范和标准发展最新介绍(英文)

IoT Device Security White Paper2019IoT Security Lab1.I oT Device SummaryA.IoT Device Development1.Applications are like a hundred flowers in bloom, IoTdevices grow exponentiallyIn recent years, new IoT applications come out in anendless stream, like a hundred flowers in bloom. IoTdevices appear in the fields of intelligent transportation,intelligent medical treatment, intelligent power grid,intelligent agriculture, and etc. IoT devices come into thepeople’s daily life and world of work, growingexponentially.Globally, the number of IoT devices grows rapidly. By 2019, the global IoT connected equipment reach 11 billion in total. Among them, consumer IoT devices reach 6 billion, industrial IoT devices 5 billion. According to a forcast from GSMA, 25 billion devices will be connected to IoT by 2025. Among them, consumer IoT 11 billion, industrial 14 billion. In the future, industrial IoT connection will lead the growth, from 2017 to 2025, the number will increase by 4.7 times, with an annual increase of 21%.In China, the evolution and breakthrough of telecom and information technologies, (eg: NB-IoT, eMTC, LoRa, LPWA…) boosts the rapid growth of IoT industry.Recent years, the expansion and coverage of IoT applications boosts the rapid growth furthermore. According to IoT-Analytics, in 2018, Among the announced 1600 IoT projects worldwidely, the intelligent city projects make up 23%, Industrial IoT 17%, the intelligent building, automobile, energy take up 12%, 11%, 10% respectively.2.Pressing need for cellular communications, IoT network cards grow rapidlyIoT network cards are the important media for all types of cellular IoT networks. In China, the 3 telecom infrastructure enterprises all issued IoT network cards. By Oct. 2019, nearly 900 million cards were issued by the 3 ones. Among them, China Telecom issued 150 million, China Mobile 600 million and China Unicom 140 million. Compared to 2018, the total number increased 300 million, that’s a 50% increase.3.The commercial deployment of 5th Generation network, making the network cards and devices a coupleJun.6th2019, China MIIT(Ministry of Industry and Information Technology) issued 4 commercial licenses respectively to China Telecom, China Mobile, China Unicom, and China Radio & Television. Oct. 31th 2019, together with China Telecom, China Mobile, China Unicom and China Tower, MIIT announced the launch of 5th G network commercial service, which means China enters a new era of5th G networks. Currently, the 3 telecom infrastructure enterprises(China Mobile, China Telecom, China Unicom) has started 5th G networking tests and business demonstrations in the cities of Beijing, Shanghai, Chengdu, Wuhan, Hangzhou, Nanjing, Shenzhen, etc. China Radio & Television also plans to build trial networks in 16 cities including Beijing, Tianjin and Shanghai.5G networks are of high speed, low latency and wide connections, satisfying the needs of IoT better. 5G networks’ commercialization boosts the scaled deployments and applications of IoT devices greatly.B.IoT Device Frameworks and Classifications1.Oriented to be general and universal, IoT devicesframeworks are of light load and adjustable.As the nerve endings of IoT, the functions of the devices include: a) Information collecting, analyzing and controlling of objects in the physical world. B) Through the telecom modules in the devices, transmitting information to servers and receiving orders.To realize the above functions, IoT devices normally have 5 functional modules, namely hardware, firmware, applications, data, and connection access.The 1st one is hardware modules, they are the basic modules of IoT devices, including all hardware components and parts. Hardware modules are the physical media of firmware modules, application modules and data modules, and provide hardware bases for telecom modules. Normally in IoT devices, hardware modules include mainboards, which bear processor units, connection modules, debugging interfaces and all other parts and components.The 2nd one is firmware modules, which mainly include system kernels, component drivers, management modules, and etc.Firmware are given with 3 abilities: hardware controlling, remote controlling and computing. The Hardware controlling enables IoT devices to control various other devices, therefore collect, analyze and control information in the physical world. For the unattended devices, remote controlling realizes updates and controlling of firmware andapplication modules. Computing assures the accuracy of the data collected and calculated.The 3rd one is application modules, they are programs or instruction sets, pre-set in the firmware modules to realize certain purposes. Normally in an embedded software system, data collected by hardware modules are preprocessed, and then interact with servers or other devices after transmitted through connection modules.The 4th one is data modules, which run through all device modules. Data are collected through hardware modules, stored in the firmware modules. Data are analyzed and processed by application modules, transmitted and reported by connection modules. Data modules include parameters of hardware, system and configuration data of firmware, and application data of application modules.The 5th one is connection access modules, which transmit data via cellular telecom or non-cellular telecom systems, to the servers directly or indirectly through gateways.IoT device frameworks are designed general and universal, as well as flexible and adjustable. In some cases, IoT device frameworks are adjusted upon certain applications. For example, SCM devices has no firmware modules, but simple application modules.2.By network access classification, IoT devices include card and non-card ones, they coexist.a)Network card IoT devices, or cellular devices, which access the networks of authorized frequency bands such as NB-IoT, eMTC, etc., through IoT network cards provided by telecom infrastructure enterprises.b)Non-network card devices, which access the networks of unauthorized frequency bands such as WiFi, BLE, LoRa, Sigfox, Zigbee, etc, through the embedded modules in the devices.3.By applications, IoT devices are classified into 3 types.a)Consumer IoT devicesAccording to IDC, consumer IoT will be the 2nd largest expense in all IoT industries for the 1st time. At present, various types of smart devices appear continuously. For example, wearable devices, smart hardware, smart home items, smart vehicles, smart facilities for the aged, and etc. Enterprises such as Huawei, Xiaomi and China Mobile are aggressively design and introduce various types of wearable smart devices.b)Public facility IoT devicesPublic facility IoT devices are mostly made for smart city applications, with smart sensors and low energy WAN such as NB-IoT, LoRa. The devices are applied to smart cities, smart securities, smart transportations & parkings, smart lightings, smart garbage disposals, etc. HIkvision, Huawei, and Haier are the industrial enterprise examples.c)Production IoT devicesProduction IoT devices are made for the fields of industry, agriculture and energy, and etc. Now it becomes the key element for conventional industry upgrades. For example, industrial IoT devices are installed in the equipment offactories, collecting data from displacement sensors, GPS,vibration sensors, liquid level sensors, pressure sensors,temperature sensors, etc., and transmitting them toservers for processing, therefor monitoring and controllingthe equipment timely. The example enterprises of it areHoneywell, Siemens, Sanyi, and etc.2.IoT Security Situation and TrendA.Low Capability of IoT Device Securityand Frequent Security IncidentsIn recent years, globally, IoT security incidents occurred frequently, caused by device security problems. Faults and Vulnerabilities are be used by criminals to maliciously attach or control the devices, and steal or tamper with data. The daily operations of connections and the applications are affected seriously. According to the attacking types, IoT security incidents can be classified into the following 3:a)Denial of service attacksOct. of 2016, the attackers took advantage of thevulnerabilities in cameras and controlled large quantity of cameras in the U.S. west coach with the malicious software. They attacked the DNS servers with DDoS.Telecom services were paralyzed on a large scale, including the telecom networks, public services and social platforms, etc. Feb. of 2017, more than 5000 IoT devices were infected by malicious software. They were remotely controlled and turned into a botnet, causing massive service exceptions to the automatic vending machines on the campuses.b)Devices under malicious controlApril of 2017, Samsung Tizen OS were exposed with more than 40 security vulnerabilities, including access bypasses, info leakages, command executions, DoSs, and etc.30 million smart TVs and 10 million smart phones were involved in the incident. Malicious hackers could take advantage of the above vulnerabilities, attack and control the above devices remotely.c)Sensitive data leakagesJul. of 2017, American vending machine maker Avanti Markets were hacked with malicious software in their payment devices.More than 1.6 million users’ personal data were leaked, including credit card accounts, bio-recognition information, etc.B.Vast Influences of Security Incidents And Its Coverage of Vertical IndustriesWith the IoT applications go more and more popular, IoT devices mix together deeply with vertical industries. IoT devices of huge quantities, various types, are gradually covering the industries of healthcare, transportation, power, and agriculture, etc. Security incidents of IoT devices directly affect the daily operation of the vertical industries. Typically the incidents occur in the following 3 industries:1. Smart healthcareIn Oct. 2018, China SFDA (State Food and Drug Administration) issued an announcement of recalling for large quantities of medical equipment. The cause of the recalling was theinsufficient software security, which was very vulnerable to hacking attacks. The vendors on the recalling list included Medtronic, GE, Abbott, and etc. The equipment included MRIs, Anesthesia systems, heart-lung machines, and etc., which was about 240 thousand in total by number.2．Internet of vehiclesJun. 2018, the RobustNet team of University of Michigan found that they could hack into the vehicle terminals and tamper with real-time traffic information, so as to cause traffic jams. Sept. of 2018, researchers in the KU Leuven University of Belgium found that there was a hidden danger in Pektron remote key system. The Tesla Model S cars could be stolen by a special device, which could intercept the signals from the remote keys and capture the codes.3.Smart homeJun. 2017, China CCTV reported that massive home cameras were intruded in by malicious hackers, who stole the camera IP addresses with a piece of scanning software, exploited the weak passwords and remotely controlled the cameras, captured the images in the cameras, peeped people’s daily life,disclosed their personal information. In addition, the IP addresses, user names, passwords and personal information were put up for sale publically.4.Smart citiesAug. 2018, a research team from IBM found 17 vulnerabilities (including default passwords, bypass IDs, etc.) in the smart city systems of Libelium, Echelon and Battelle.5.Smart electricity metersOct. 2014, security researchers found vulnerabilities in smart electricity meters in Spain, which could be used for electricity bill cheatings, and even for shutting down the whole power system. That’s caused by insufficient security protections inside the meters, the electrical system could be attacked and controlled easily.C.Difficulty of Real Name Registration, Making IoT A Hotbed of DefraudingWith the development of mobile internet applications, criminals started network and telecom defrauding directly orindirectly, by taking advantage of the difficulty of real name registration for IoT network cards. Their typical approaches are:a)Indirect network and telecom swindles by networkregistration. Though the real-name registration is requested by the Central Network Information Office, IoT network cards are difficult to register with real-names. Mar.10th of 2019, the Shandong Rizhao police captured a swindling gang of click farming, who registered network IDs with unregistered IoT cards, publishing fake click-faming advertisement. Lots of victims were swindled.b) Direct telecom swindlesAccording to China Business News, there are over 400 thousand malicious network hackers in China. About 40 million “black cards” are abused every year, among them, 80% are IoT network cards. One “black card” could cause RMB100 illegal income, which means a 3.2 billion loss every year.D.Sprouting IoT Device Security Industry, Breeding diversified EcosystemsChina’s rapid development of IoT devices catches the great attention from both domestic and foreign companies. They are speeding up involving in China’s IoT security industry, and aggressively setting up their own ecosystems, and making the overall ecosystem diversified and co-existed.The players in the ecosystem include telecom infrastructure enterprises, internet enterprises and telecom product makers, and etc.Telecom infrastructure enterprises are accelerating to set up their ecosystem alliances. From abroad, Feb. 2017, American AT&T allied itself with Nokia, IBM, Qualcomm, Symantec, Palo Alto networks, and Trustonic, founded the IoT Network Security Alliance. The alliance is set up for communications and cooperation between the alliance members, facing the IoT security challenges side by side with the strength of each, therefore developing the IoT security ecosystem.In China, the 3 telecom infrastructure enterprises have all set up IoT security organizations of their own. By founding IoT industry alliances, the 3 enterprises is seeking partners toachieve industrial consensus and build up IoT security ecosystems. China Telecom has set up the “Security Ecosystem Promotion Team of Tianyi IoT Industrial Alliance” in Sep. 2019. The team is to carry out researches for IoT security, to promote R&D for IoT security technologies, industries and applications.China Mobile has set up the “Mobile IoT Industrial Alliance”. By now, 928 members are in the alliance. The members come from the industries of chipset, module, device, network, platform, application, and the related. The “IoT Security Executive Committee has been founded, to promote the applications of IoT security related products.China Unicom has formed the “IoT Industrial Alliance”, together with a “Public Security Committee”. There are 30 well-known members including CASIC(China Aerospace Science and Industry Group), CECT(China Electronic Tech. Group).Internet enterprises are actively setting up their dominant ecosystems. In China, Mar. 2018, Alibaba announced to marchtoward the field of IoT comprehensively. They founded “IoT Business Division”, positioning on “the Builder of IoT Infrastructure”. In order to dominate the ecosystem development and pushing forward the standardization constructions, Alibaba organized the “IoT Connectivity Alliance” (ICA), in order to set up security standardizations, and to gather players in the IoT security industrial chain, such as device manufacturers, security chipset makers, module makers, testing labs and etc. Alibaba focuses itself on setting up safe IoT connections, realizing safe solutions for cross-industry integrations, leading IoT security industry development, co-founding a win-win security ecosystem. At present, the security standardization team is active to carry out standardization work for IoT security chipsets, IoT ID recognition systems and the smart lock safety ranking system, and etc. 10 reports of alliance standardizations were issued.In the meantime, Telecom enterprises are carrying out farsighted platform ecosystems. Sep. 2017, Cisco united Bosch, Bank of New York Mellon, blockchain service provider Consensys and Skuchain, info security maker Gemalto and etc, in order to build safe, expandable, inter-operative andtrustable IoT and platforms, and upgrade the overall security of IoT industry.In China, Sep. 2017, Huawei brought out an IoT development strategy named “Platform + Connection + Ecosystem”, aiming to become the builder of the intelligent platforms, the innovator of various connections and the promoter of IoT ecosystems. Huawei has set up a number of open labs globally, orientated to provide end-to-end IoT security tests and certificate service for vertical industries, and to upgrade the security capability of the overall IoT industry.From 2015, Xiaomi started to carry out a comprehensive consumer IoT ecosystem (“Xiaomi Ecosystem”). Xiaomi is dedicated to promote the industrial cooperation, by building “the Xiaomi IoT platform” for developers. By now, the platform has connected 151 million devices (excluding mobile phones or PCs), with over 800 different types. There are over 500 partners on the platform, it is claimed to be the largest consumer IoT platform in the world.3. Risk Analysis on IoT Device SecurityIoT is an expansion to the internet, with a perceptual layer as the most outstanding difference to the internet. The core of the perceptual layer is the IoT devices which are of large numbers, various types, low cost, low energy, and of perception and communication abilities.Compared to conventional network devices, the IoT devices are inferior in security. Therefore, in the IoT circle, IoT device security is the key, and is the importance of the importance.A.Risk Point Analysis on IoT Devicesrge Scale Devices, Difficult to Managea)Difficult to unify and standardizeTo the fragmented demands of the various and complicated IoT applications, devices are designed for various types, various features, various functions, and upon various security requirements, which makes the security unification and standardization difficult.b)Difficult to manage in real-timeIoT devices are normally of large scale, scattered around geographically, furthermore, they are often unattended. The fact is that even there are thefts or damages to the devices, it is difficult to be found and managed in real-time.2.Low device security capability, difficult to resist attacksLimited by the reality of low cost, low energy, low computing capability, it is difficult to set up the security system of PCs or smart phones (such as security policies, encryption algorithms) to the IoT devices. As a result, IoT devices are not well security protected, vulnerable to be hacked or attacked, and hard to resist.The risk points of IoT devices mainly include:a)Hardware risks, vulnerable to damageb)Firmware risks, vulnerable to attacksIoT devices universally have the following problems:No legitimate or integrity verification in start-up code, no in-time fixings for system vulnerabilities, too many open accesses, loose access permissions, weak authentications for IDs or authorizations, etc. Therefore the devices are vulnerable to attacks of user IDs and information disclosures, which could be used for forging IDs or connection nodes, and intruding into other devices or gateways.c)Telecom security risks, vulnerable to be controlledThe weak security protections and the large-sales make the IoT devices the breakthrough points for DDoS attackers. For example, when one single device is attacked and controlled, millions of devices in the connection could be infected. A resulted large-scale botnet initiates service requests to the backbone networks or servers, causing resources overloads and finally service interrupts or paralysis.d)Application security risks, vulnerable to exploitsThe apps in IoT devices generally have problems as logic flaws or coding vulnerabilities. Some device makers just use the third-party components to save costs, which brings in open source vulnerabilities. Attackers exploit the software vulnerabilities, implant Trojans or viruses to intrude into thedevices, and finally causing application failures.e)Data security risks, vulnerable to thefts and tamperingMassive data of personal and industrial information are sensed, collected and stored in huge amount of IoT devices. Limited by hardware resources, IoT devices cannot directly be armed with conventional data security systems. As a result, the sensitive data lack security regulations, sometimes they are transmitted in plain text. Consequently, data are stolen or tampered with, privacies are disclosed. On the other hand, the data resources are polluted.4. IoT Device Security Assurance SystemConfronted by the IoT device security risks, we are going tobuild a full-process working mechanism of “Assessing security risks, enhancing security capabilities, comprehensivemonitoring & control”.Through “Applying new technologies, taking new approaches, building new platforms”, we are going to mitigate the security risks one by one, build up the IoT device security assurancesystem, comprehensively enhance the security capabilities. A.Assessing Security RisksAs for the fact that “large quantities, various types and difficulty to manage” of IoT devices, and the “difficulty to implement real-name registrations and vulnerability to abuse” of IoT network cards, comprehensive security risk assessments are required to be carried out to the IoT devices.Firstly, for the IoT devices, we are going to set up a classification system for the assessments. Considering the current situation of the security capabilities and the security demands, we are going to raise differentiated and targeted requirements, enhance security capabilities accordingly.The 1st step: Based on sufficient and comprehensive investigations and the realities, we are going to set up classification standards, including classification indexes and methods, and assessment methods. The IoT devices are going to be managed by classifications, namely “high risk, medium risk, and low risk”.The 2nd step, we are going to practice the “management by different classifications”. For the high risk devices, the security capabilities are going to be enhanced, they are going be monitored in real-time and equipped with situation awareness system.For the medium risk ones, the security capabilities are going to be enhanced as well, they are going to be monitored at regular time.And for the low risk ones, they are going to be monitored occasionally.Secondly, as for the assessments themselves, we are going to take actions on the hardware modules, firmware modules, applications modules, data modules and the network access modules, and etc.For the IoT network cards, we are going to set up management system for the whole chain. Through technical approaches, the telecom infrastructure enterprises are going to solve thefollowing 5 problems:1. The related parties shall coordinate to collect the dataflow at each link in the network card chain, to reflect the actual procedures.2. Data are not to be tampered with.3. A management and control Center is to be set up.4. Different links in the chain shall have different privacy protection capabilities.5. A real-time and clear dataflow recording mechanism is to be set up in the whole chain.The blockchain technology is naturally suitable for IoT the whole-chain network card management, which can realize the real-name registrations, behavior monitoring, credit assessments and verifications. The core of the blockchain management is to ensure authentic and integral data.The whole data collection and registration procedure includes 4 roles, namely “telecom enterprises – middle companies – sales terminals – consumers”. The data are to upload after the following 3 requirements:1, initial registrations,2, sales qualifications,3, card activationsB.Enhancing Security CapabilitiesAs for the fact that “large quantities, various types and difficulty to manage” of IoT devices, and the “difficulty to implement real-name registrations and vulnerability to abuse” of IoT network cards, security capabilities are required to be enhanced to the IoT devices according to the assessment results. The enhancement is to be realized by the principles of “Interior strengthening, exterior empowerment, safe and trustworthiness”.1)Interior strengthening for IoT network cardsBased on the security capability of network cards, together with the capabilities of devices, networks, and platforms, a security assurance system that combines securityauthentications, data encryptions, and security storages, is to be established.By now, the SE-SIM of China Mobile is qualified with above mentioned capabilities. In addition, some vendors’ chipsets are also qualified with hardware security environment, such as Qualcomm, Hisilicon, etc.2)Exterior empowerment for IoT devicesAs the security risks of “low security capabilities of IoT devices” and the “difficult traffic oriented access of network cards”, devices security can be enhanced by exterior empowerment.Empowering the security capabilities to the devices, and building the security management platforms, putting them together, enables to realize both traffic oriented access and device security management.The “Exterior empowerment” means: Integrating security kits such as modules, SDK, and etc., to the devices or gateways. That is to make the devices or gateways capable of receiving and executing security polices, as well as reporting and accessing behavioral data, and etc. The security policies include setting up black and white lists, controlling andlimiting accesses, etc.The IoT security management platform realizes the traffic oriented access by the following technical ideas:Devices bearing the network cards connect to the security management platform and the application platform. The security management platform dispatches the security policies to the devices. Equipped with the security policies, the devices visit the application platform, distinguishing the coming IP addresses, URIs from the black and white list.For the devices without network cards or security policies, oriented accesses are realized through the connected gateways with security policies.The IoT security management platforms work by the following 3 stages:Stage 1, Prior warning before incidentsThe security management platforms dispatch security policies to the devices, control and limit the accesses. By bigdata analysis and data mining technologies, the platforms obtain feature information, process with statistical analysis, therefore warn on any unusual situations.Stage 2, Monitoring during incidentsThe security capabilities report the execution results and access information to the platforms when they carry out security policies. Simultaneously, the access information of the devices are monitored in real time. The new ID registration information is supervised and reported in regular time.Stage 3, Managing and Controlling after incidentsThe platforms take direct and full control of the devices to avoid further damages when serious incidents or access violations occur.At present, there are a number of similar products, including the “Anlianbao” from China Mobile, the “Security Control SDK” from Qihoo 360, the “IoT Security Center” from Anhen Info, and etc.。

国际会议级别

Asian Control Conference (ASCC)
European Association for Signal Processing 18.
(EURASIP)
European Signal Processing Conference (EUSIPCO)
19. European Graphics Society
The Optoelectronics and Communications Conference (OECC)光電與通訊工程國際研討會
International Symposlum on Growth of
19. Association for "Optoelectronics Frontier by Nitride Ⅲ-Nitrides(ISGN)三族氮基半導體生長國際研討
23. European Union Control Association (EUCA)
European Control Conference (ECC)
Innovative Computing, Information and Control 24.
(ICIC)
International Symposium on Intelligent Informatics (ISII)
6. Society (WSEAS)
八)
Administered by UCMSS Universal Conference The International Conference on e-Learning,
7. Management Systems & Support/The University of e-Business, Enterprise Information Systems, and

冷链物流毕业论文

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维普数据《中文科技期刊数据库》使

《中文科技期刊数据库》中文科技期刊数据库》
• 《中文科技期刊数据库》是我国最大的数字期刊数据库，该库受到国内图书情报界的广泛关注和普遍赞誉，目前已拥有包括港澳台地区在内2000 余家大型机构用户，是我国数字图书馆建设的核心资源之一，高校图书馆文献保障系统的重要组成部分，也是科研工作者进行科技查证和科技查新的必备数据库。
中文科技期刊数据库
--检索应用技巧及最新研发方向
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• 重庆维普资讯有限公司是科学技术部西南信息中心下属的一家大型的专业化数据公司，是中文期刊数据库建设事业的奠基人，主要致力于对海量的报刊数据进行科学严谨的研究、分析、采集、加工等深层次开发和推广应用。 • 1989年，维普资讯开发建设了我国第一个期刊数据库—— 《中文科技期刊数据库》。今天，《中文科技期刊数据库》收录期刊12000余种，文献总量超过1700万篇，广泛被我国高等院校、公共图书馆、科研机构所采用，成为文献保障系统的重要组成部分，科技工作者进行科技查新和科技查证的必备数据库。目前，该数据库在全国已经拥有2000余家大型机构用户。
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产品独特功能介绍
1、《中文科技期刊数据库》采用国内一流检索内核“尚唯全文检索系统”实现数据库的检索管理。“尚唯全文检索系统”是经国内专家团队鉴定一致认为达到“国内领先、国际先进”水平的检索系统，各种指标及其综合性能均大大领先于其它同类产品。
产品独特功能介绍
• 2、是国内首家采用OpenURL技术规范的大型数据库产品，OpenURL(Open Uniform resource Locators)协议是一种上下文相关的开放链接框架，它实现同时对不同的异构数据库或信息资源进行数据关联，方便地为用户单位提供资源的二次开发利用，例如与图书馆OPAC 系统的数据关联。OpenURL协议已经成为美国国家标准。维普是国内首家应用OpenURL协议的数据库厂商，已经在中国科学院、国家图书馆、北方航空航天大学、中国生物医学文献数据库成功应用，效果明显，深受欢迎。

异质性研发、知识溢出与企业创新产出——基于创新链视角的实证分析

异质性研发、知识溢出与企业创新产出基于创新链视角的实证分析蒋欣娟1,吴福象1,丛海彬2(1.南京大学商学院,江苏南京210093;2.宁波大学商学院,浙江宁波315000)收稿日期:2020-07-28基金项目:国家自然科学基金项目(71803078);国家社会科学基金重大项目(14Z D A 024);江苏省333人才支持计划项目(B R A 2017358)作者简介:蒋欣娟(1995-),女,吉林长春人,南京大学商学院博士研究生,研究方向为企业创新㊁产业经济;吴福象(1966-),男,安徽安庆人,博士,南京大学商学院教授㊁博士生导师,研究方向为产业经济㊁区域经济;丛海彬(1978-),男,吉林洮南人,博士,宁波大学商学院副教授㊁硕士生导师,研究方向为区域经济㊂摘要:畅通创新链的首要前提,是科学认识嵌入创新链的不同类型企业所承载的创新功能差异及企业间技术经济联系㊂利用2012 2018年我国上市公司授权专利的前向索引数据进行统计分析发现,不同所有制企业存在异质性研发行为,国有企业研发活动更多承担了基础研究创新功能,而民营企业研发活动更多承担了应用研究创新功能㊂运用面板T o b i t 模型实证分析异质性研发知识溢出对企业创新产出的影响,结果表明,国有企业知识溢出对企业发明专利与非发明专利申请均表现为促进作用,民营企业知识溢出对企业发明专利申请表现为促进作用,而对非发明专利申请表现为抑制作用,且作用大小在不同所有制以及不同生命周期的企业间存在差异㊂由此,基于创新链构建新型国家创新体系过程中,应引导国有企业优先布局高度依赖基础研究的科学领域,解决市场失灵问题并充分发挥其创新促进效应;鼓励民营企业在共性技术研究领域展开协作,规避同业竞争所形成的创新抑制效应;因企制宜㊁分类施策,实现创新资源高效配置和综合集成㊂关键词:异质性研发;知识溢出;创新产出;创新链D O I :10.6049/k j j b yd c .2020060395 开放科学(资源服务)标识码(O S I D ):中图分类号:F 273.1 文献标识码:A 文章编号:1001-7348(2020)24-0080-10I d i o s y n c r a t i c R&D ,K n o w l e d g e S p i l l o v e r a n d t h e I n n o v a t i o n O u t p u t o f E n t e r pr i s e s A n E m p i r i c a l S t u d y f r o m t h e P e r s pe c t i v e of I n n o v a t i o n C h a i n J i a ng X i n j u a n 1,W u F u x i a n g 1,C o n g Ha ib i n 2(1.E c o n o m i c S c h o o l ,N a n j i n g U n i v e r s i t y ,N a n j i n g 210093,C h i n a ;2.B u s i n e s s S c h o o l ,N i n g b o U n i v e r s i t y ,N i n gb o 315000,C h i n a )A b s t r ac t :T h e p r i m a r y p r e m i s e f o r s m o o t h i n g t h e i n n o v a t i o n c h a i n i s t o f o r m a t s c i e n t i f i c c o gn i t i o n o f t h e d i f f e r e n t i a l i n n o v a -t i v e f u n c t i o n u n d e r t o o k b y d i f f e r e n t t y p e s o f e n t e r p r i s e s a n d t h e t e c h n i c a l e c o n o m i c r e l a t i o n s b e t w e e n t h e e n t e r p r i s e s .B a s e d o n t h e d a t a o f t h e l i s t e d m a n u f a c t u r i n g c o m p a n i e s 'p a t e n t f o r w a r d c i t a t i o n s f r o m 2012t o 2018,t h i s p a p e r f o u n d t h a t t h e r e e x i s t i d i o s y n c r a t i c R&D b e h a v i o r s a m o n g d i f f e r e n t o w n e r s h i p e n t e r pr i s e s .T h e R&D a c t i v i t i e s o f s t a t e -o w n e d e n t e r -p r i s e s h a v e m o r e a s s u m e d t h e i n n o v a t i v e f u n c t i o n o f b a s i c r e s e a r c h ,w h i l e t h e R&D a c t i v i t i e s o f p r i v a t e e n t e r pr i s e s h a v e m o r e a s s u m e d t h e i n n o v a t i v e f u n c t i o n o f a p p l i e d r e s e a r c h .T h i s p a p e r u s e d t h e p a n e l T o b i t m o d e l t o e m p i r i c a l l y a n a l y z e t h e i m p a c t o f i d i o s y n c r a t i c R&D 's k n o w l e d g e s p i l l o v e r o n e n t e r p r i s e s 'i n n o v a t i o n o u t pu t .T h e r e s u l t s s h o w e d t h a t t h e k n o w l -e d g e s p i l l o v e r o f s t a t e -o w n e d e n t e r p r i s e s c a n s i g n i f i c a n t l y p r o m o t e t h e a p p l i c a t i o n s o f a l l k i n d s o f pa t e n t ,w h i l e t h e k n o w l -e d g e s p i l l o v e r o f p r i v a t e e n t e r p r i s e s s h o w s a p r o m o t i n g e f f e c t o n t h e a p p l i c a t i o n s o f i n v e n t i o n p a t e n t s a n d a n i n h ib i t o r y e f f ec t o n t h e a p p l i c a t i o n s o f n o n -i n v e n t i o n p a t e n t s .A nd t he l e v e l of i n f l u e n c e v a r i e s a m o ng th e e n t e r p ri s e s w i t h d i f f e r e n t o w n e r s h i p o r d i f f e r e n t s t a g e s o f l i f e -c y c l e .T h i s p a p e r c o n t a i n s t h e f o l l o w i n g p o l i c y i m p l i c a t i o n s .D u r i n gt h e p r o c e s s o f c o n -s t r u c t i n g a n e w n a t i o n a l i n n o v a t i o n s y s t e m b a s e d o n t h e i n n o v a t i o n c h a i n ,s t a t e -o w n e d e n t e r pr i s e s s h o u l d b e g u i d e d i n t o t h e s c i e n t i f i c f i e l d s w h i c h a r e h i g h l y d e p e n d e n t o n b a s i c r e s e a r c h i n o r d e r t o s o l v e t h e p r o b l e m o f m a r k e t i n e f f i c i e n c y a n d g i v e f u l l p l a y t o i t s i n n o v a t i o n p r o m o t i o n e f f e c t .P r i v a t e e n t e r p r i s e s s h o u l d b e e n c o u r a g e d t o c o l l a b o r a t e i n t h e f i e l d o f c o m -m o n t e c h n o l o g y i n o r d e r t o a v o i d t h e i n n o v a t i o n i n h i b i t i o n e f f e c t f o r m e d b y h o r i z o n t a l c o m p e t i t i o n .M a k i n g a p p r o p r i a t e p o l -i c i e s a c c o r d i n g t o t h e e n t e r p r i s e s 's i t u a t i o n s w i l l a l s o b e c o n d u c i v e t o t h e e f f i c i e n t a l l o c a t i o n a n d c o m p r e h e n s i v e i n t e g r a t i o n o f i n n o v a t i v e r e s o u r c e s .K e y Wo r d s :I d i o s y n c r a t i c R&D ;K n o w l e d g e S p i l l o v e r ;I n n o v a t i o n O u t p u t ;I n n o v a t i o n C h a i n0引言近年来,我国在由发达国家主导的全球价值链分工体系中,持续推动中间产品创新升级,实现国际分工地位攀升和贸易利得增加[1,2]㊂然而,随着我国科技水平与世界前沿差距不断缩小,发达国家对我国采取的贸易保护措施愈加激烈,针对我国产品的贸易摩擦频发,美国甚至不惜通过阻滞供应链循环打压我国高技术企业发展[3]㊂从本质上说,发达国家的上述行为就是意图通过削弱以中间品及资本品贸易为主要渠道的国际知识溢出效应,遏制我国技术创新突破,避免我国通过占据产业技术制高点获取超额利润㊂跨国创新知识吸收渠道受限,意味着我国只有通过强化本土创新主体间的技术经济联系,提高技术创新绩效并促进产业自主发展,才能在国际技术竞争与产业竞争中谋得话语权㊂2020年,中央政府工作报告的战略部署也体现出这一思想,不仅提出稳定支持基础研究和应用基础研究,还强调畅通创新链㊂基于创新链构建系统技术政策体系,不仅是推动我国企业创新产出水平提升㊁重塑我国制造业全球竞争优势的关键环节,更是在国际经济大变局下落实创新驱动发展战略㊁构建国内国际双循环相互促进的新发展格局的重要支撑㊂根据不同的创新功能,创新链可分为基础研究㊁应用研究与产品创新3个环节[4,5]㊂明确嵌入创新链各类创新主体的功能定位及技术经济联系,是系统构建技术政策体系,促进科学技术化㊁技术工程化㊁工程产业化的首要前提[6]㊂2018年,我国各类企业的R&D经费支出在全国R&D经费投入总量中的占比达76.6%,企业已成为最重要的创新载体㊂因此,本文主要围绕各类企业展开研究㊂已有研究从组织双元创新视角揭示企业在开展R&D活动时存在异质性研发行为[7,8],但鲜有研究从创新功能视角考察嵌入创新链的企业是否存在异质性研发行为㊂此外,以往围绕不同所有制企业创新展开的研究,大多仅关注国有企业创新绩效低下问题[9,10],忽视了国有资本介入可能在解决技术创新市场失灵问题方面发挥的重要作用㊂基于此,本文从创新链视角出发,通过统计分析对嵌入创新链的不同所有制企业所承载的创新功能加以区分,在此基础上,进一步通过实证分析异质性研发对企业创新产出的知识溢出效应㊂本研究既加深了对企业间技术经济联系的认识,也为基于创新链构建新型国家创新体系提供政策参考㊂1文献回顾1.1创新链与企业异质性研发自M a r s h a l l&V r e d e n b u r g[11]提出创新链概念以来,国内外学者围绕创新链内涵㊁构成环节㊁功能节点及其技术经济联系展开了系列研究㊂所谓创新链,是指在推动以满足市场需求为目标的创新成果产出过程中,创新主体间形成的用于创新要素传递与转化的链接结构[12]㊂从构成环节看,创新链可依据创新功能分为基础研究㊁应用研究与产品创新3个环节[4,5]㊂其中,基础研究是不以特定应用为目的㊁仅为探索知识原理而开展的研究;应用研究是为满足特定应用需求而围绕相关技术㊁方法或工艺开展的研究;产品创新囊括了将研究结果转移到产品环节所涉及的技术活动[13]㊂从创新主体看,政府部门㊁行业协会㊁金融机构是创新链的主要支撑点,高校㊁科研院所与企业则是创新链上的重要功能节点[14]㊂围绕功能节点间技术经济联系展开的研究,大多将公共研发部门视为基础研究的创新载体,将各类企业视为应用研究的创新载体,集中探究高校与科研院所研发创新对企业创新产出的影响机制㊂如梁俊伟和黄德成[15]发现,高校通过知识溢出促进企业研发投入提高,进而激励企业进行发明专利申请;李柏洲和周森[16]发现,科研院所的组织内部创新与组织外部创新均能促进企业新产品绩效提升㊂还有研究围绕企业创新功能展开深入探讨,发现企业研发行为存在异质性㊂部分研究关注不同所有制企业的异质性研发行为,如H a l l&M a f f i o l i[17]发现,墨西哥㊁巴西㊁阿根廷等国家存在专门深耕基础研究的国有企业;张杰等[18]发现,在我国高新技术产业中,国有企业平均发明专利申请数高于民营企业,且自十二五规划提出国有经济的战略性调整政策以来,国有资本加速向战略性新兴产业集中㊂还有学者利用上市公司面板数据,探讨内部财务状况与外部经济环境对企业异质性研发行为的影响,如唐清泉和肖海莲[7]将涉及研究阶段的研发投入定义为探索式创新投资,将只关注开发阶段的研发投入定义为常规式创新投资,研究发现,探索式创新投资的现金流敏感度高于常规式创新投资,政府如果在进行研发补贴时适当向探索式创新投资倾斜,就能取得更好的政策效果;顾群等[8]将研究阶段的研发投入定义为探索式创新,将开发阶段的研发投入定义为开发式创新,研究发现,经济政策不确定性会促使企业开展探索式创新,但对开发式创新行为不存在显著影响㊂1.2知识溢出与企业创新产出近年来,国内外学者围绕知识溢出渠道㊁测度方法及创新效应进行了大量研究㊂曲如晓和李雪[19]将知识溢出渠道区分为物化型与非物化型两种㊂其中,物化型知识溢出渠道包括中间品及资本品的市场交易[20]㊁资本与人才等研发要素流动[21]等㊂非物化型知识溢出渠道主要是指以学术论文㊁专利为载体的知识扩散[22];㊃18㊃第24期蒋欣娟,吴福象,丛海彬:异质性研发㊁知识溢出与企业创新产出段会娟[23]总结了知识溢出的4种测度方法,即技术流量法㊁生产函数法㊁成本函数法和文献追踪法,并发现采用技术流量法测度知识溢出时,需要选择合理的权重度量知识受体内部化外溢知识的能力㊂在研究区域间知识溢出的创新效应时,可选择的权重矩阵包括地理距离权重矩阵㊁经济距离权重矩阵和技术距离权重矩阵[24];王庆喜等[25]发现,随着以通讯技术为代表的空间可压缩技术进步,专利可编码性与公共属性增强,知识溢出的地理局限大为削弱,由技术距离决定的知识搜寻能力成为知识溢出的主要影响因素;周敏等(2019)利用技术距离权重矩阵构造企业研发支出的溢出池,发现由于技术机会效应的存在,其它企业的研发支出会抑制本企业专利申请;高山行等[26]发现,跨国公司的技术溢出会抑制我国企业基础创新产出,而且内外资企业技术差距越大,抑制效应越显著㊂通过文献回顾发现,目前聚焦于企业异质性研发行为的研究大多局限于组织双元创新理论范畴,虽然部分文献探讨了不同所有制企业研发行为差异,但鲜有研究基于创新链角度,总结不同类型企业承担的创新功能差异㊂此外,考虑到创新链上各环节所承载的创新功能不同,且各环节间存在知识反馈机制[27],以往将从事异质性研发的企业视为同质知识源,据此分析知识溢出创新效应的做法可能无法全面反映企业间技术经济联系㊂从我国现实情况看,相比于民营企业,我国国有企业普遍成立时间更长㊁创新要素累积优势更显著,而且一向受到研发补贴政策的优待[28]㊂那么,我国国有企业是否也同拉丁美洲国家的国有企业一样,更多地承担了基础研究的创新功能?相应地,民营企业是否更多地承担了应用研究的创新功能如果国有企业与民营企业确实在创新链上存在明显的环节分布与功能定位差异,那么两类企业异质性研发所形成的知识溢出,对整个经济系统的创新产出是否发挥了不同作用?为回答上述问题,本文基于统计分析,从创新链视角揭示不同所有制企业存在异质性研发行为的特征事实㊂在此基础上,测度异质性研发的知识溢出,并探究其对不同类型创新产出的影响㊂最后,根据上述研究内容,围绕如何合理部署创新链提出针对性政策建议㊂2制造企业异质性研发的特征事实本文首先从专利授权情况㊁专利前向索引情况以及专利通用性指数3个角度,揭示我国制造企业存在异质性研发行为这一特征事实,即基于创新链视角,国有企业的研发活动更多地承担了基础研究的创新功能,民营企业的研发活动更多地承担了应用研究的创新功能㊂本文采用的2012 2018年制造业上市公司专利数据,依据公司年报披露的公司名称经谷歌专利检索系统(刘雯,2018)查询㊁汇总得到,企业产权属性信息由C C E R数据库得到㊂据此,将上市公司分为国有企业与民营企业进行统计分析㊂2.1基于专利授权情况的特征事实分析发明专利只有通过新颖性㊁实用性和非显而易见性方面的实质审查才能被授权,往往被视为基础研究的研发成果(李柏洲㊁苏屹,2010)㊂根据发明专利与非发明专利的授权情况,能够初步判断不同所有制企业是否存在异质性研发行为㊂表1揭示了如下3个特征事实:①尽管上市公司中国有企业数量少于民营企业,但历年国有企业发明专利授权数始终高于民营企业,且两者间的差距没有缩小趋势;②民营企业实用新型与外观设计专利授权总数高于国有企业,且两者间的差距逐年扩大;③从专利授权结构看,2012 2018年国有企业发明㊁实用新型㊁外观设计授权专利在专利授权总数中的占比分别为28.9%㊁58.6%㊁12.4%,民营企业上述3类专利的占比分别为21.1%㊁60.0%㊁18.9%㊂上述特征事实初步揭示,我国国有企业倾向于开展基础研究领域的研发活动,民营企业倾向于开展应用研究领域的研发活动㊂表1国有企业与民营企业专利授权情况专利授权年份公司总数国有企业民营企业发明授权数国有企业民营企业差值实用新型授权数国有企业民营企业差值外观设计授权数国有企业民营企业差值2012509883549845169821472611232349438774697-820 2013508913553849985402229917037526244195906-1487 2014515990700764975102477423311146345516878-2327 2015525110911666947621902299130745-775450729263-4191 20165321298151671324219252399529933-5938491610153-5237 20175411571184901711713732532240304-14982597713891-7914 20185451628199731784721263469757523-22826695015472-8522注: 差值列,由国有企业发明(实用新型/外观设计)专利授权数减民营企业发明(实用新型/外观设计)授权数得到2.2基于专利前向索引的特征事实分析根据刘林青等(2020)的研究成果,前向索引频次较高的专利在相关技术领域具备基础性特征㊂鉴于此,本文统计了不同所有制企业授权专利的前向索引情况㊂表2揭示了如下3个特征事实:①国有企业被授权的发明专利与实用新型专利中,至少被其它专利引用过一次㊃28㊃科技进步与对策2020年的专利占比分别为64.4%㊁70.7%,而民营企业的这一占比分别为59.6%㊁8.4%;②历年国有企业被授权专利的前向索引总频次都高于民营企业;③国有企业被授权专利平均前向索引频次,在绝大多数年份都高于民营企业㊂上述特征事实进一步揭示,相比于民营企业,国有企业开展了更多基础研究领域的研发活动㊂表2 授权专利前向索引情况专利授权年份至少被引用一次的专利数(项)国有企业民营企业至少被引用一次的专利占比(%)国有企业民营企业前向索引总频次(次)国有企业民营企业平均前向索引频次(次)国有企业民营企业差值发明专利20124339352878.9278.1223538203744.2814.512-0.23020134273391477.1678.3120823196863.7603.939-0.17920145011422671.5165.0521932187433.1302.8850.24520158314609371.2764.3035001236803.0002.4990.50120169412691262.0652.2030812217932.0321.6460.38620178829727447.7542.5029234211191.5811.2340.34720188377657341.9436.8320964164381.0500.9210.129实用新型专利201212630140785.7712.533940629692.6760.2642.412201319575178687.7810.484462136662.0010.2151.786201421759242787.8310.414552649771.8380.2141.624201520941312491.0810.163953753181.7200.1731.547201617232263271.818.792840338641.1840.1291.0552********196550.674.881847924630.7300.0610.66920186840108319.711.88871212380.2510.0220.230注: 至少被引用一次的专利占比列,统计的是至少被引用一次的发明(实用新型)专利在当年被授权的发明(实用新型)专利总数中的占比㊂前向索引总频次列统计的是专利自申请日起㊁至2019年底的被引用总次数㊂平均前向索引频次列由前向索引总频次除以当年专利授权数得到㊂差值列由国有企业平均前向索引频次减民营企业相应指标得到2.3 基于专利通用性指数的特征事实分析已有研究表明,引用某一项专利的技术领域分布越广,该专利的通用性越高,基础性特征越显著[29]㊂因此,根据专利前向索引情况计算的专利通用性指数能够判断不同类型企业研发行为是更接近于基础研究抑或是应用研究领域㊂专利通用性指数计算公式为:G p =1-ð650k =1(C R p k/C R p )2㊂其中,k 表示技术领域,以专利I P C 分类号主分类号的前4位加以区分[30],共计650类;C R p k 表示专利p 被技术领域k 的专利引用次数;C R p 表示专利p 的前向索引总频次㊂通用性指数的取值范围为[0,1],该指数越接近1,意味着专利被越多技术领域引用,专利越接近基础研究范畴;该指数越接近0,意味着专利越接近应用研究范畴㊂表3所整理的专利通用性指数揭示了如下3个特征事实:①国有企业被授权发明专利的通用性指数始终高于民营企业;②国有企业被授权实用新型专利的通用性指数始终高于民营企业;③无论是国有企业还是民营企业,各年度授权发明专利的通用性指数均高于实用新型专利的通用性指数㊂上述特征事实充分揭示,基于创新链视角不同所有制企业的研发行为存在异质性,国有企业的研发活动更多地承担了基础研究的创新功能,而民营企业的研发活动更多地承担了应用研究的创新功能㊂表3 授权发明专利与实用新型专利通用性指数专利授权年份发明专利国有企业民营企业差值实用新型专利国有企业民营企业差值两类专利对比国有企业民营企业20120.26670.26230.00440.15630.10800.04830.11040.154320130.25460.23680.01780.13740.09690.04050.11720.139920140.23220.21330.01890.11610.08770.02840.11610.125620150.21680.20770.00910.09560.06420.03140.12120.143520160.18900.17810.01090.07430.04420.03020.11470.133920170.15680.15150.00530.05190.02360.02840.10490.127920180.14740.13580.01160.02470.01250.01210.12270.1233注: 差值列,由国有企业发明(实用新型)专利的通用性指数减民营企业发明(实用新型)专利的通用性指数得到㊂两类专利对比列,由国有企业(民营企业)发明专利的通用性指数减国有企业(民营企业)实用新型专利通用性指数得到㊃38㊃第24期蒋欣娟,吴福象,丛海彬:异质性研发㊁知识溢出与企业创新产出3模型设定与数据上述特征事实分析发现不同所有制企业存在异质性研发行为㊂在此基础上,本文进一步通过实证分析考察国有企业与民营企业异质性研发所形成的知识溢出,对企业发明专利与非发明专利这两类创新产出的影响㊂3.1模型设定根据G r i l i c h e s-J a f f e知识生产函数[31,32]的基本思想,企业研发支出及其接受的知识溢出都是自身在创新过程中所投入的资源㊂参考已有研究[19,33],本文设定如下计量模型考察异质性研发知识溢出对企业创新产出的影响㊂I n n o v a t i o n i t=α0+α1s p i l l s o e i t-1+α2s p i l l p o e i t-1+α3y f t r i t-1+α4X i t-1+ηy e a r+εi t(1)其中,i表示企业,t表示年份㊂考虑到创新活动并非一蹴而就,同时为了缓解内生性问题,所有解释变量和控制变量均滞后被解释变量一期[34]㊂I n n o v a t i o n i t 表示企业i在t年的创新产出;s p i l l s o e i t-1表示国有企业对企业i的知识溢出;s p i l l p o e i t-1表示民营企业对企业i的知识溢出,y f t r i t-1表示企业i在第t-1年的研发投入;X i t-1包括除研发投入外,其它可能影响企业创新产出的控制变量;εi t为误差项㊂由于各年度实施的创新激励政策也会影响企业创新产出(龙小宁㊁王俊,2015),所以在设定模型时加入企业所处年份的虚拟变量ηy e a r以控制时间层面的外部冲击㊂3.2变量定义3.2.1被解释变量参考曲如晓和李雪[19]的研究成果,本文以专利申请数量衡量企业创新产出,实证研究采用企业当年专利申请量加1后取自然对数的方法㊂根据我国专利法的定义,相比实用新型与外观设计专利,发明专利更能直接推动技术创新突破㊂因此,为更准确地揭示异质性研发知识溢出对企业创新产出的影响,本文从发明专利申请数量(f m)与非发明专利申请数量(f f m)两个角度衡量企业创新产出㊂3.2.2核心解释变量本文采用技术流量法测度异质性研发知识溢出,并以J a f f e指数度量的企业间技术邻近程度作为技术距离权重矩阵[35]㊂J a f f e指数计算公式为ωi j=ð650k=1p i k p j kð650k=1p2i kð650k=1p2j k,其中,p i k表示企业i在样本期内第k类专利授权量在该企业全部专利授权总量所占份额㊂国有企业与民营企业知识溢出的计算公式分别为:s p i l l s o e i t=l o g1+ðjʂi,jɪs o eωi j R D j t,s p i l l p o e i t= l o g1+ðjʂi,jɪp o eωi j R D j t,其中,R D j t为企业j在第t年的研发投入㊂3.2.3控制变量参考以往研究,选取企业研发投入㊁企业年龄㊁企业规模㊁固定资产占比㊁资产流动性㊁薪酬激励㊁市场势力和市场集中度作为控制变量,主要变量定义及计算公式如表4所示㊂3.3样本选择与数据来源本文数据主要来源于C S MA R数据库与谷歌专利检索系统(G o o g l e P a t e n t)㊂谷歌专利检索系统提供了1790年至今的专利授权信息,以及2001年至今的专利申请信息㊂通过检索申请人名称可以得到相应专利文本,获知专利的法律状态及引用情况㊂C S MA R数据库提供了沪深A股制造业上市公司的基本信息㊁研发投入以及财务数据㊂研发投入数据是本文计算知识溢出的必要数据,但这一数据在可得性与数据质量方面存在两个问题:一是在2006年底财政部公布‘企业会计准则“前,披露研发投入信息的上市公司比例很低,导致企业研发投入数据在2007年前存在大量缺失值;二是2007 2011年,制造业上市公司研发投入和实际专利申请行为间存在投入与产出倒挂现象[36],这一期间披露研发投入数据的企业比例在11%~35%之间震荡,但进行专利申请的企业比例从42%上升至65%,有相当数量进行了专利申请的企业在当年以及此前年份都没有报告研发投入㊂考虑到研发投入数据可得性以及数据质量对研究结果的影响,本文以2012 2018年作为实证研究样本期,剔除在观测期内被S T㊁*S T 等特殊处理以及财务数据缺失的上市公司后,最终样本涉及2068家企业㊂表4主要变量定义及计算公式变量名称变量符号计算公式发明申请专利数f m l n(1+发明专利申请数)非发明申请专利数f f m l n(1+实用新型专利申请数+外观设计专利申请数)国有企业知识溢出s p i l l s o e计算公式详见前文,单位为十亿元民营企业知识溢出s p i l l p o e计算公式详见前文,单位为十亿元研发投入l n y f t r l n(研发投入),单位为百万元企业年龄l n a g e l n(公司自成立年份起的年数)企业规模l n s i z e l n(总资产),单位为亿元固定资产占比f a s s e t固定资产净额/总资产资产流动性l i q u i d i t y(流动资产-流动负债)/总资产薪酬激励l n b s m l n(董事㊁监事及高管年薪总额),单位为百万元市场势力l n m a r k e t l n(1+营业收入/营业成本)市场集中度h h i营业收入HH I指数㊃48㊃科技进步与对策2020年3.4描述性统计表5为变量描述性统计分析结果㊂从核心解释变量看,国有企业知识溢出的平均值高于民营企业㊂从被解释变量看,发明专利申请数与非发明专利申请数均呈现出明显的左归并(l e f t-c e n s o r e d)特征㊂具体而言,在8708个样本中,发明专利申请数为0的样本有1263个,非发明专利申请数为0的样本有1594个㊂当被解释变量的概率分布呈零值堆积与正值连续共存的混合分布时,O L S方法无法得到一致估计㊂因此,后文采用面板T o b i t模型进行回归估计㊂表5描述性统计分析结果变量样本数平均值标准差最小值最大值f m87082.1451.4640.0002.079 f f m87082.3111.6100.0002.398 s p i l l s o e87082.0940.6930.0032.160 s p i l l p o e87081.6920.5710.0171.717 l n y f t r87083.9831.2970.0003.912 l n ag e87082.8220.3201.6092.833 l n s i z e87083.5161.1080.4323.359 f a s s e t87080.2320.1350.0000.207 l i q u i d i t y87080.2660.249-1.6990.269 l n b s m87081.6130.5280.0001.547 l n m a r k e t87080.9140.2240.5160.855h h i87080.1220.1100.0220.082 4实证结果及分析本文首先从核心解释变量与控制变量两个方面,对基准回归模型估计结果进行分析㊂为更准确地揭示异质性研发知识溢出对企业创新产出的影响,本文根据企业所有制性质与所处生命周期阶段,在进行样本分类后作进一步探讨,最后进行稳健性检验㊂4.1基准回归模型的估计结果表6为实证方程式(1)的估计结果,前两列采用的估计方法为混合最小二乘回归(P O L S),后两列采用的估计方法为面板T o b i t模型㊂对比发现,当采用两种不同的方法进行回归时,各变量系数大小有所变化,但正负没有发生改变㊂下文在分析回归估计结果时,以面板T o b i t模型估计结果为准㊂首先,考察异质性研发知识溢出对企业创新产出的影响㊂估计结果显示,国有企业知识溢出与民营企业知识溢出均能促进企业发明专利申请,且国有企业知识溢出的正向促进作用大于民营企业㊂但在非发明专利申请方面,国有企业知识溢出表现为促进作用,而民营企业知识溢出表现为抑制作用㊂上述结果说明,对于发明专利这类层次较高的创新产出而言,无论是基础研究领域的知识溢出,还是应用研究领域的知识溢出,均能起到扩展创新可能性边界的作用㊂对于实用新型与外观设计专利而言,一方面,基础研究领域的知识溢出通过为企业提供快速㊁低价掌握前沿理论的途径,促进企业应用创新;另一方面,由于这两类专利与企业核心产品迭代及市场推广结合更为紧密,随着其它企业被授权专利数量增多,企业通过突破现有技术创造市场竞争优势的潜在利益空间不断收窄㊂因此,应用研究领域的知识溢出反而会削弱企业创新动力,进而抑制企业创新产出㊂其次,考察控制变量对企业创新产出的影响㊂企业研发投入增加能够显著促进企业进行各类专利申请㊂企业规模越大㊁薪酬激励越高,申请的专利项目越多,说明创新资源获取以及激励政策实施都能够促进企业创新产出;企业年龄越大,申请的专利项目越少,说明初创企业在创新方面表现更为积极;企业市场势力越大,申请的专利项目越少,说明具有更高加价能力的企业缺乏创新动力;企业固定资产占比越高,资产流动性越低,申请的专利项目越少,意味着重资产企业可能缺乏创新精神;企业市场集中度越高,发明专利申请数量越少,非发明专利申请数量越多,说明企业在面临更激烈的市场竞争时,会减少在基础研究领域的投入,着力于进行难度相对较低的实用新型与外观设计专利申请㊂4.2分样本回归估计结果4.2.1基于企业所有制的分样本分析鉴于国有企业与民营企业在创新链上所承担的创新功能存在区别,对两类企业进行分样本回归,以进一步揭示创新知识沿创新链的流动情况以及知识溢出效应㊂表7回归结果表明,国有企业知识溢出对企业非发明专利申请起促进作用,民营企业知识溢出对企业非发明专利申请起抑制作用㊂从发明专利申请看,国有企业知识溢出对国有企业㊁民营企业的发明专利申请起显著促进作用,而民营企业知识溢出只对民营企业的发明专利申请起显著促进作用㊂鉴于国有企业从事了更多的基础研究,而民营企业从事了更多的应用研究,从创新链视角看,这一回归结果说明,在我国制造业领域,创新知识从基础研究过渡到应用研究的环节衔接较为顺畅,但根据应用研究的创新需求倒逼基础研究领域实现创新突破的信息反馈机制尚未成熟㊂㊃58㊃第24期蒋欣娟,吴福象,丛海彬:异质性研发㊁知识溢出与企业创新产出。

科技期刊专注最新研究成果，聚焦重大工程进展的探索

113达3458篇，总被引频次达12336次，总下载次数达489678次，国家级基金论文数715篇。

在2010年至2020年这段时间，发表论文最多并且位列前30的作者都在核动力学科中有着深厚的研究，包括但不限于热工水力、反应堆物理、核安全、结构力学、系统设计、核燃料与材料、核化学，以及退役后处理等多个热门方向。

总体上论文发表数目排在前30位的群体，主要源自在核动力行业有着突出地位的大学或研发机构，如：中国核动力研究设计院、中广核工程有限公司、清华大学、西安交通大学、哈尔滨工程大学、上海交通大学、上海核工程研究设计院和中国工程物理研究院等。

编辑部通过分析现有资源，认为期刊应该利用研究院所、高校专家学者的影响力，通过向国家自然科学基金获得者约稿、拜访编委、邀请编委推荐撰稿人、参加学术会议、调研科研机构等渠道，组织出版专栏、专刊，将邀约的高水平稿件组稿，形成“特约稿”栏目，以实现传播最新研究成果，聚焦重大工程进展的办刊宗旨。

图1 主办单位现有作者群学科分布2.期刊办刊的探索2.1 特约稿期刊的生命力在于它的质量。

为了提高期刊的质量，各个期刊编辑部都在不断地探索，努力创造自己的特色栏目。

编辑部经过集体讨论认为，科技期刊即使在稿源充足的情况下，每期通过联系国家自然科学基金获得者、拜访编委约稿、参加学术会议等方式向院士、教授等专家邀约高水平的稿件，可扩大期刊的读者群，提高期刊的质量。

邀请专家投稿会有助于收到高水平的论文，因为他们是各自领域的杰出人士，拥有扎实的专业知识和强大的科研实力，深厚的学术造诣和严谨的学术品格。

他们同时也在科研和工程领域奋斗，在从事的领域有深入的了解，包括历史发展、现状与未来发展动向，他们所撰写的论文能够真实反映出该领域的研究前沿。

同时为保证约请稿件的质量，编辑部在约请中一般遵循以下4个基本原则：（1）受邀人在其研究领域需做出过一定的杰出工作；（2）邀请撰稿人需提前征得编辑部主任的同意；（3）编辑部主任和学科编辑，了解受邀人所在研究领域的热点及其团队的研究进展，结合期刊的需求，确定约稿方向；（4）确定受邀人及约稿方向后，学科编辑须向受邀人发送正式约稿函。

基于大数据环境的高校数据治理平台设计

数据库与信息管理本栏目责任编辑：王力基于大数据环境的高校数据治理平台设计潘银芳（浙江工贸职业技术学院，浙江温州325003）摘要：随着高校大数据技术的应用与推广，数据治理的问题逐步凸显：很多高校没有整体数据标准，缺乏数据校验，问题数据不断沉积，造成大数据分析对领导决策的支持功能失灵甚至错误。

同时，在智慧校园环境下应用系统微服务化、移动化增多，数据共享交换平台中数据交换的压力呈指数增长，数据管理部门对数据交换管理的难度和工作量迅速上升，利用传统的数据交换共享平台进行数据交换管理已经越来越不适应新的业务需求。

该文作者对高校现有业务系统大数据进行分析，通过构建恰当的数据治理模型，制定高校数据标准和工作规范，提出了高校数据治理委员会等机构的设立和功能建设，采用可视化设计方案设计数据治理平台，提出全生命周期数据治理概念，覆盖了数据对象动态发展的全过程，进而建立数据治理体系，在此过程中高校中信息化涵盖的边界得到重塑，信息化与高校核心业务实现进一步融合。

关键词：数据治理；高校；全生命周期中图分类号：TP311.13文献标识码：A文章编号：1009-3044(2020)36-0029-03开放科学（资源服务）标识码（OSID ）：The Design and Implementation of Data Governance in Big Data Environment PAN Yin-fang(Zhejiang Industry&Trade Vocational College,Wenzhou 325000,China)Abstract:With the application and promotion of big data technology in colleges and universities,the problem of data governance has gradually emerged:many colleges lack a school-wide overall plan for data standards and implement them in accordance with the plan,lack a data verification mechanism,and continue to deposit problematic data,resulting in big data analysis for supporting leadership decision-making malfunctioned or even wrong.At the same time,in the smart campus environment,application systems have become more micro-services and mobile,and the pressure of data exchange in the data sharing and exchange platform has in⁃creased exponentially.The difficulty and workload of data exchange management by the data management department has in⁃creased rapidly,using traditional data exchange.The traditional sharing platform for data exchange management has become in⁃creasingly unsuitable for new business needs.The author of this article analyzes the big data of the existing business systems in col⁃leges,and by constructing an appropriate data governance model,formulating university data standards and work specifications,proposing the establishment and functional construction of institutions such as the university data governance committee,and adopting a visual design plan to design data governance.The platform puts forward the concept of full life cycle data governance,covering the entire process of the dynamic development of data objects,and then establishing a data governance system.In this pro⁃cess,the boundaries covered by informatization in colleges and universities are reshaped,and informatization is further integrated with the core business of colleges and universities.Key words:data governance;colleges and universities;full life cycle1引言近年来，随着大数据技术的推广应用，高校信息化建设进一步发展，在原有业务系统信息化的基础上，利用其产生的海量数据以及其他外部数据，进行挖掘和分析，通过建立分析模型，开发出了很多诸如行为画像、與情监控预警、就业指导建设、消费分析等大数据应用。

X8711A 物联网 (IoT) 设备功能测试

商标Bluetooth®和Bluetooth®徽标是美国Bluetooth® SIG, Inc. 所有并授权于Keysight Technologies, Inc. 的商标。

手册部件号X8711-90010版本第 2 版，2018 年 6 月印刷地区：马来西亚印刷发布者：Keysight TechnologiesBayan Lepas Free Industrial Zone, 11900 Penang, Malaysia技术许可本文档中描述的硬件和/或软件仅在得到许可的情况下提供并且只能根据此类许可的条款进行使用或复制。

符合性声明可以从网上下载本产品以及其他Keysight 产品的符合性声明。

有关详细信息，请访问/go/ conformity 。

然后，可以按产品编号进行搜索，以找到最新的符合性声明。

美国政府权利本软件属于联邦采购法规（“FAR”）2.101 定义的“商用计算机软件”。

按照 FAR 12.212 和 27.405-3 以及国防部FAR 补充条款（“DFARS”）227.7202，美国政府根据按惯例向公众提供商用计算机软件的相同条款采购本软件。

同样，Keysight根据其标准商业许可向美国政府客户提供本软件，该许可包含在其最终用户许可协议 (EULA) 中，可以在以下位置找到该许可协议的副本：/find/sweula。

EULA 中规定的使用许可为独占使用许可，根据该使用许可，美国政府可以使用、修改、分发或披露本软件。

除了其他事项之外，EULA 及其规定的许可不要求或不允许Keysight：(1)提供通常不会向公众提供的与商用计算机软件或商用计算机软件文档相关的技术信息；或者 (2) 让与或以其他方式提供的政府权利超过通常向公众提供的有关使用、修改、复制、发布、执行、显示或披露商用计算机软件或商用计算机软件文档方面的权利。

06ms201-3

06ms201-3IntroductionIn this document, we will explore the topic of 06ms201-3. This is a subject that encompasses various aspects and has importance in different domains. We will delve into its significance, applications, and potential future developments.Significance of 06ms201-306ms201-3 plays a crucial role in many fields and industries. It has become an essential element in modern technology, research, and development. The significance of 06ms201-3 lies in its ability to solve complex problems, increase efficiency, and improve overall performance. As a result, it has gained attention and recognition from professionals worldwide.Applications of 06ms201-3There are several applications of 06ms201-3. Let’s take a look at some of its key uses in different domains:1.Technology: 06ms201-3 is widely used in the technology sector,particularly in software development and programming. It helps developersimprove code efficiency, optimize algorithms, and enhance overall systemperformance.2.Data Analysis: With the increasing volume of data being generated,06ms201-3 is essential for analyzing this data and extracting valuable insights.It helps organizations make data-driven decisions and improve their operations.3.Artificial Intelligence: 06ms201-3 is a fundamental aspect ofdeveloping artificial intelligence systems. It enables machines to learn, reason, and make intelligent decisions based on various data inputs.4.Finance: 06ms201-3 is used extensively in the finance industry,especially in high-frequency trading and risk management. It helps analyzemarket patterns, predict stock prices, and optimize investment strategies.5.Healthcare: 06ms201-3 plays a vital role in healthcare, fromanalyzing patient data to drug discovery and personalized medicine. It has the potential to revolutionize healthcare by providing more accurate diagnostics and improved treatment options.Future DevelopmentsThe field of 06ms201-3 is continuously evolving, and we can expect several future developments in this area. Some important advancements include:1.Increased Automation: With advancements in machine learning andartificial intelligence, we can expect increased automation in various industries.This will lead to improved efficiency, reduced costs, and more personalizedexperiences for end-users.2.Enhanced Security: As technology continues to advance, so does theneed for better cybersecurity measures. 06ms201-3 will play a crucial role in developing advanced security algorithms, detecting threats, and mitigatingrisks.3.IoT Integration: The Internet of Things (IoT) is gaining popularity,and 06ms201-3 will become increasingly important in handling and analyzing the massive amounts of data generated by IoT devices. This integration willenable smarter decision-making and efficient management of IoT ecosystems.4.Advancements in Healthcare: 06ms201-3 will continue to havesignificant impacts on healthcare. With improved data analysis, personalized medicine will become more accessible, and new treatment options will emerge.5.Optimized Resource Allocation: With the help of 06ms201-3,organizations will be able to optimize resource allocation, reduce wastage, and make better decisions by analyzing data patterns and trends.ConclusionIn conclusion, 06ms201-3 is a significant field that finds applications in various domains. Its importance lies in its ability to solve complex problems, improve efficiency, and facilitate data analysis. As technology continues to advance, we can expect further developments in this field, particularly in the areas of automation, cybersecurity, and healthcare. The future of 06ms201-3 is promising, and it will continue to revolutionize industries and impact our daily lives.。

有压力才有动力挑战性压力源对个体创新行为的影响

有压力才有动力:挑战性压力源对个体创新行为的影响陈春花1,2,廖琳1,李语嫣1,王甜1,3(1.华南理工大学工商管理学院,广东广州510000;2.北京大学国家发展研究院,北京100029;3.卡迪夫大学商学院,威尔士卡迪夫999020)收稿日期:2020-05-20 修回日期:2020-06-15基金项目:国家社会科学基金项目(18B G L 126)作者简介:陈春花(1964-),女,广东湛江人,博士,博士后,华南理工大学工商管理学院教授㊁博士生导师,北京大学国家发展研究院教授,研究方向为企业文化管理㊁中国企业成长模式㊁中国领先企业等;廖琳(1996-),女,湖南娄底人,华南理工大学工商管理学院硕士研究生,研究方向为组织行为与人力资源管理;李语嫣(1996-),女,湖南郴州人,华南理工大学工商管理学院硕士研究生,研究方向为组织行为与人力资源管理;王甜(1991-),女,山东聊城人,华南理工大学工商管理学院博士研究生,卡迪夫大学商学院联合培养博士研究生,研究方向为组织行为与人力资源管理㊂摘要:围绕挑战性压力源如何对创新行为产生影响这一问题,基于自我决定理论,假设任务重塑是连接挑战性压力源与创新行为的中介机制,进一步剖析政治技能的调节作用㊂通过分析两时点收集的244名大学生数据,研究发现:挑战性压力源正向影响个体创新行为,任务重塑在此过程中发挥部分中介作用;政治技能正向调节挑战性压力源与创新行为之间的关系,同时也正向调节任务重塑与创新行为之间的关系㊂关键词:挑战性压力源;任务重塑;政治技能;创新行为D O I :10.6049/k j j b yd c .2020040483 开放科学(资源服务)标识码(O S I D ):中图分类号:F 272.92 文献标识码:A 文章编号:1001-7348(2021)11-0135-08M o t i v a t i o n C o me sf r o m P r e s s u r e :I m p a c t o f C h a l l e n ge S t r e s s o r s o n I n d i v i d u a l I n n o v a t i o n B e h a v i o rC h e n C h u n h u a 1,2,L i a o L i n 1,L i Y u y a n 1,W a n g Ti a n 1,3(1.S c h o o l o f B u s i n e s s A d m i n i s t r a t i o n ,S o u t h C h i n a U n i v e r s i t y o f T e c h n o l o g y ,G u a n gz h o u 510000,C h i n a ;2.S c h o o l o f N a t i o n a l D e v e l o p m e n t ,P e k i n g U n i v e r s i t y ,B e i j i n g 100029,C h i n a ;3.B u s i n e s s S c h o o l ,C a r d i f f U n i v e r s i t y ,C a r d i f f 999020,E n gl a n d )A b s t r a c t :H o w d o e s c h a l l e n g e s t r e s s o r s a f f e c t i n n o v a t i o n b e h a v i o r ?A i m i n g a t t h i s p r o b l e m ,t h e s t u d ys t a r t e d f r o m t h e p e r -s p e c t i v e o f s e l f -d e t e r m i n a t i o n t h e o r y a n d h y p o t h e s i z e d t h a t t a s k c r a f t i n g p l a y s a n i n t e r m e d i a r y me c h a n i s m t h a t c o n n e c t s t h e c h a l l e n g e s t r e s s o r s w i t h i n n o v a t i o n b e h a v i o r ,a n df u r t h e r a n a l y z e d t h e m o d e r a t i ng r o l e o f po l i t i c a l s k i l l s .B a s e d o n t h e c o l l e c t e d 244c o l l e g e s t u d e n t s d a t a a t t w o t i m e p o i n t s ,o u r r e s e a r c h i n d i c a t e d t h a t t h e c h a l l e n g e s t r e s s o r s h a d a s i gn i f i c a n t -l y p o s i t i v e e f f e c t o n t h e i n n o v a t i o n b e h a v i o r ,t a s k c r a f t i n g p l a y e d a p a r t i a l l y m e d i a t i n gr o l e b e t w e e n t h e t w o .P o l i t i c a l s k i l l s p o s i t i v e l y m o d e r a t e d t h e r e l a t i o n s h i p b e t w e e n c h a l l e n g i n g s t r e s s o r s a n d i n n o v a t i o n b e h a v i o r ,a s w e l l a s t h e r e l a t i o n s h i p be -t w e e n t a s k c r af t i ng an d i n n o v a t i o n b e h a v i o r .K e y Wo r d s :C h a l l e n g e S t r e s s o r s ;T a s k C r a f t i n g ;P o l i t i c a l S k i l l s ;I n n o v a t i o n B e h a v i o r 0 引言商业环境的不确定性及激烈的竞争环境使得组织中个体不得不应对更重的工作负荷㊁更高的时间要求以及更严重的角色冲突[1],而这些不断提高的工作要求往往会给个体带来沉重的工作压力,且个体需要在压力下持续创新[2]㊂已有研究表明,压力与个体创新之间有着密切关系,但对两者之间具体呈现的影响关系却得出了不一致的结论㊂阻碍性-挑战性压力源是当前被普遍采纳的压力源二维分类法[3]㊂其中,阻碍性压力源负向影响创新已经达成共识[4],挑战性压力源与创新的关系则尚未得到一致结论[4-7]㊂特别地,有关挑战性压力源影响创新的理论归因㊁内在机制也没有取得一致结论[8]㊂现有挑战性压力源积极影响创新的研究主要从以下几个理论视角展开:首先,从个体出发,早期的理论视角多基于期望理论[2]和压力鼓励理论[4],认为挑战性压力源本身能促使个体积极应对问题,进而提高创新绩效㊂近些年有研究从社会认知㊁资源保存㊁激活理论等视角出发,发现挑战性压力源能够提升个体自我效能感[9,10],积极影响工作繁荣[11]状态,进而促进个体表现出积极创新行为;其次,从工作情境出发,孙健敏等[12]基于工作要求-资源模型,探索了挑战性压力源㊁领导-成员交换以及辱虐管理三维交互作用下,挑战性压力源对个体创新的影响机制㊂此外,从人工作情境交互视角出发,有研究发现挑战性压力源积极影响组织支持感[13],提升组织承诺[14],从而激发个体表现出积极创新行为㊂R e n[15]基于人工作契合理论认为,创新行为是个体适应挑战性压力源所采取的应对策略㊂可见,从不同理论视角出发,挑战性压力源对个体创新行为的影响路径各异㊂然而,这些理论视角中,较少从个体内部决定因素出发探讨挑战性压力源对个体创新行为的影响路径㊂B u n c e&W e s t[16]指出,面对压力时,与被动适应环境相比,个体更倾向于主动作出改变,压力能够唤醒个体改变的动机与需求㊂为积极响应B y r o n等[8]关于亟需新的理论视角及相应实证研究以更好地揭示压力与创新行为内在机制的呼吁,本研究从自我决定理论视角出发,探讨挑战性压力源如何通过满足个体基本需求激发相应主动行为,进而促进创新行为,这有助于厘清挑战性压力源如何对个体行为产生影响以及在何种情况下产生此种影响㊂作为一种好的压力源,挑战性压力源可以为个体成长提供机会,激发个体主动采取创新行为[13]㊂在此过程中,对现有工作方式㊁内容等进行重新审视和调整,即工作重塑,是个体得以在压力环境下进行持续创新的关键一环㊂其中,任务重塑是工作重塑三维度中最基础和最重要的部分[17],聚焦于个体对有关任务的行为改变㊂L e p i n e[2]提出个体会选择主动或解决问题的策略积极应对挑战性压力源㊂自我决定理论指出,当个体的3种基本心理需求(自主㊁胜任与关系需求)得到满足后,能够激发出个体主动行为,且自主性是个体内部与外部动机的连续体,高自主性表明个体在工作中主要受内部动机的影响[18],而高水平的个人自主性又与创造性思想的产生有关[19]㊂也就是说,面对挑战性压力源,奖励性的工作要求能够满足个体的3种基本心理需求,激发其对工作任务进行再认识和再思考,主动采取行动积极应对压力,从而表现出任务重塑行为㊂个体通过任务重塑,使工作任务与自身能力㊁兴趣等更加匹配,这不仅能够促进个体主观能动性的充分发挥,还能通过任务重塑提升其创造力[20],表现出更高的工作创造性[21]㊂以往的许多研究已经证实,工作情境因素对个体的影响往往是不同的[22],压力作为一种常见的情境因素,是个体和环境交互作用的结果[23]㊂政治技能作为一种具备社交效能的个体能力,可以帮助个体结合工作情境调整自身行为和态度[24]㊂因而,政治技能水平高低会对个体行为表现产生差异化影响㊂政治技能高的个体其人际交往能力以及情境控制感更强[25],对外界非常灵敏,往往容易发现创新的关键信息或创新灵感[26],更容易表现出创新行为,从而促进挑战性压力源对创新行为的影响作用;此外,高政治技能的个体更易获取创新所必需的资源,更能够将获得的新知识和新资源创造性地运用到工作任务中[27],有助于促进任务重塑对创新行为的影响作用㊂鉴于此,本研究引入政治技能这一个体能力,探讨不同水平政治技能会对挑战性压力源与创新行为以及任务重塑与创新行为之间的关系产生何种影响㊂综上所述,本研究基于自我决定理论,从个体内部决定因素出发,聚焦于任务重塑在连接挑战性压力源与创新行为二者关系中所发挥的中介机制,探讨挑战性压力源对个体创新行为的具体影响路径及政治技能在此过程中的调节效应,进一步丰富对挑战性压力源与创新行为之间关系的理解,为从新的理论视角剖析挑战性压力源对创新行为的影响路径进行有益补充,并为工作场所中合理管理压力㊁优化管理工作压力提供参考㊂1理论基础与研究假设1.1挑战性压力源与创新行为当前,个体需要在充满压力的环境下进行创新,工作中的压力也可以激发个体创新[28]㊂挑战性压力源是指那些被个体认为具有奖励性工作体验的工作要求,如工作负荷㊁工作复杂性等[3,29],这些工作要求可以提供个体成长机会,带来未来收益[30],通常被认为是一种好的压力源㊂以往实证研究从社会认知㊁资源保存㊁社会交换㊁激活理论等视角出发,发现挑战性压力源能够通过提升个体自我效能感[9,10]㊁组织支持感[13]㊁组织承诺[14]㊁工作繁荣[11],促进个体表现出积极创新行为㊂创新行为是指工作中产生㊁传播和执行新想法[31]㊂相关文献指出,那些可以激励个体关注新想法的产生并将资源投入到新想法落地中的工作环境,有助于个体创新水平提升[31]㊂压力源是个体感触频繁,且对个体影响较大的情境因素[32],个体创新行为通常需要在充满压力的环境中产生[33],压力源亦具有激励个体投入创新的功能[4]㊂那些被个体视为奖励性工作体验的工作要求,即挑战性压力源,为个体提供成长机会㊂基于自我决定理论的解释,在满足个体基本心理需求后,能够激发个体积极组织行为㊂也就是说,在个体面对挑战性压力源时,奖励性工作体验的工作要求使个体感受到自己被信任,认为自己能够胜任工作,并能够满足自我需求,这能够激发个体表现出积极组织行为,如创造性思考以解决问题,由此产生创新行为㊂因此,本文提出以下假设:㊃631㊃科技进步与对策2021年H1:挑战性压力源会促进个体的创新行为㊂1.2任务重塑的中介作用工作重塑是指个体在工作中重新定义和塑造工作内容㊁工作方式以及与他人的关系所采取的积极行动[17]㊂更进一步地,W r z e s n i e w s k i&D u t t o n[17]将工作重塑划分为任务㊁关系和认知重塑三维度㊂特别地,工作重塑并不是一个高阶概念[34],不同维度的重塑其影响效果存在较大差别[35]㊂根据研究问题及目的,可以选择个别维度展开讨论,如L i n等[20]探讨了任务重塑对创造力的影响㊂作为最为基础和核心的子维度,任务重塑聚焦于任务,意指行为上的改变㊂在任务重塑过程中,个体能够自主开展工作㊂L e p i n e[2]提出个体会选择主动或解决问题的策略来积极应对挑战性压力源,H a r j u[21]也发现挑战性工作要求可以减少个体工作倦怠,增加工作重塑㊂此外, T i m s&B a k k e r[36]在探讨如何使个体对那些感觉到有压力的工作内容作出积极改变问题时指出,通过为个体提供与其能力和需求有关的定制化工作要求及资源,使个体意识到潜在的问题,可以激发出个体的工作重塑动机㊂从自我决定理论视角出发,挑战性压力源能够满足个体的3种基本心理需求:自主㊁胜任以及关系需求,进而激发个体主动动机,作出行为改变㊂首先,个体在应对挑战性压力源时,更能体验到参与决策及完成任务的自主性[37],这能够满足个体自主需求;其次,挑战性压力源会给个体带来一种对未来收益的期许:个体积极应对挑战后,能够得到更好的工作绩效[2,4,37]㊂同时,克服压力产生的成就感[3]也有利于个体体验到较高水平的控制感,进而满足个体胜任需求;最后,当个体接收到更多任务,表明其被信任㊁期待和授权[38,39],这不仅能够满足个体的自主和胜任需求,而且个体会认为自己被重视,从而获得一种归属感,满足其关系需求㊂因此,面对挑战性压力源,当个体的3种基本心理需求得到满足后,能够激发其对工作任务进行再认识和再思考,主动采取行动积极应对压力,促进任务重塑行为㊂通过任务重塑,个体能够表现出更多的工作主动性及创造性[21]㊂具体而言,通过任务重塑,使工作更加匹配个体自身能力,能够充分发挥个体主动性㊂此外,个体进行任务重塑的过程中,会尝试运用新方法执行任务,充分发挥现有资源的作用并灵活应对工作中的不确定性,增加创新行为[20]㊂因此,在面对挑战性压力源时,通过任务重塑这一主动行为,个体能够表现出更多工作创造性[21],有助于其在工作实践过程中产生新颖可行的想法进行创新[40]㊂因此,本文提出以下假设:H2:任务重塑在挑战性压力源和个体创新行为之间发挥中介作用㊂1.3政治技能的调节作用F e r r i s等[24]指出,政治技能是指个体在工作中充分理解㊁积极影响并获得他人支持,以实现个人及组织目标的能力㊂政治技能被认为是个体获得成功的重要因素[41]㊂以往许多研究已经证实,工作情境因素对个体的影响效果常常具有差异性[22]㊂伴随环境不确定性的加剧,工作量大等挑战性压力源在工作场所中普遍存在,而压力又是环境与个体共同作用的结果[23]㊂作为一种具备互动风格及社交效能的个体能力,政治技能有助于个体在不同的工作情境下适时调整其行为和态度[24],在促进个体适应和塑造工作环境方面起着重要作用[42]㊂因而,政治技能水平的高低,会对个体行为表现产生差异化影响㊂当个体具有高水平的政治技能时,能够强化挑战性压力源对创新行为的正向影响㊂原因在于:一方面,拥有高水平政治技能的个体有更高的外显真诚性,能够给他人带来良好印象,激发他人理解和信任,在面对挑战性压力源时能够获得同伴支持与认可并建立高质量工作关系[33,36],增加个体获得外界资源和帮助的可能性,进而增加其资源存量,促进创新行为的产生;另一方面,在面对挑战性压力源时,高水平政治技能个体的人际交往能力较强,具备较多其他人不具有的人际资源,从而产生一种自豪感,这种自豪感让其主动参与工作,并坚信通过对过程施加控制能够预测结果[43],对创新任务也更有信心和把握,有助于促进创新行为的产生㊂当个体具有高水平的政治技能时,能够强化任务重塑对创新行为的正向影响㊂原因在于:一方面,在任务重塑过程中,个体较高水平的政治技能能够促进人际网络构建,这些人际网络中包括对个人和组织成功至关重要与必不可少的资源[44],从而个体能够通过信息告知㊁理性的逻辑推演㊁基于友情的呼吁等方式向他人传递工作任务,影响他人的意见和看法,请求他人提供工作相关资源和帮助来更好地开展工作[45]㊂通过人际网络,个体掌握了创新关键且不可或缺的知识和技术,有助于促进创新行为的产生;另一方面,较高水平政治技能的个体具有较强的社会机敏性[26],对外界的人和事十分敏感,关注能够获得新知识的各种渠道[42],能够运用敏锐的洞察力识别和利用新奇知识,从而在任务重塑时能够在工作和不经意交谈中产生创新想法或收集到对创新至关重要的信息,促进任务重塑对创新行为的影响㊂因此,本文提出以下假设:H3:政治技能正向调节挑战性压力源与个体创新行为之间的关系,即当个体具备高水平政治技能时,挑战性压力源与创新行为之间的正向关系更加显著㊂H4:政治技能正向调节任务重塑与个体创新行为之间的关系,即当个体具备高水平政治技能时,任务重塑与创新行为之间的正向关系更加显著㊂综上所述,基于自我决定理论,本研究认为挑战性压力源带来的工作要求为个人成长提供了机会,能够㊃731㊃第11期陈春花,廖琳,李语嫣,等:有压力才有动力:挑战性压力源对个体创新行为的影响满足个体的基本心理需求,并激发个体表现出积极组织行为,即有助于创新行为的产生㊂具体而言,在挑战性压力源的影响下,个体的心理需求得到满足后,会激发其对工作任务的再认识和再思考,主动采取行为积极应对压力,促进任务重塑行为㊂个体通过任务重塑,使自身与工作的匹配程度更高,进一步提高其工作积极性,且在任务重塑过程中,个体尝试采用新方法来执行任务,灵活运用资源,更巧妙地应对环境不确定性进行创新㊂此外,考虑到压力是环境与个体共同作用的结果,个体对压力的感知亦具备差异性,因而有必要对作为个体社会能力的政治技能在挑战性压力源对创新行为的影响机制中所发挥的作用展开讨论㊂作为一种具备社交效能的个体能力,政治技能可以帮助个体结合工作情境调整自身行为和态度[24]㊂具有高水平政治技能的个体对外界非常灵敏,在面对挑战性压力源时,容易寻得创新关键信息,更能表现出创新行为,这有利于促进挑战性压力源对创新行为的影响关系;同时,具有高水平政治技能的个体更容易获取创新所必需的资源,更能够将所获得的新知识和新资源创造性地运用到工作任务中,从而促进任务重塑对创新行为的影响作用㊂鉴于此,本研究以任务重塑为中介变量,关注政治技能对个体行为的影响,构建挑战性压力源对创新行为的影响路径模型,如图1所示㊂图1 研究模型2 研究方法2.1 研究样本与数据来源本研究数据来自于广东省两所高校参加2019年企业管理模拟运营比赛(G l o b a l M a n a ge m e n t C h a l -l e n ge ,简称GM C )的大学生参赛人员,其在完成比赛任务的过程中所表现出的任务互动和对成果的追求非常接近实际工作中的个体㊂在比赛过程中,每支队伍的5名成员共同经营一家虚拟公司㊂在每个虚拟季度,成员们需要对公司经营作出决策,与其它同在该市场上的公司竞争㊂其中,有关生产㊁研发㊁营销㊁财务等多个方面及其对应的多个参数共同组成公司经营决策,最大限度地还原了企业真实经营状态㊂因此,针对该大学生群体样本研究结果,对真实企业场景下个体创新行为具有一定指导意义㊂本研究采用线下分两时点实地调研的方式收集数据㊂在初赛中期(时间点1),课题组进行第一阶段数据收集工作,测量控制变量及挑战性压力源㊁任务重塑㊁政治技能,共发出330份问卷,收回297份问卷㊂在初赛后期(时间点2,即时间点1的后两周),向第一阶段完成有效问卷的调研对象进行第二阶段数据收集工作,测量个体创新行为㊂通过比对剔除未匹配的问卷和数据缺失的问卷,得到有效问卷244份,有效率为82.2%㊂被调查者中,女性占比62.3%;年龄分布在17~35岁;学历均在本科及以上㊂为减少共同方法偏差的影响,本研究采取以下措施:分两个时间点对变量进行测量;平衡测量题项顺序;向愿意参与调研的选手说明研究目的是为了学术研究,考虑比赛过程中个人感受和行动,尽可能将被调研选手在心理上分离;向所有被调研选手阐明本研究采用匿名填答方式,所收集答卷皆会采取严格保密措施,以保护被调研者隐私;明确表明该调查与比赛最终成绩无关,以减少被调研者对测量目的的猜疑㊂2.2 变量测量为保证问卷具有良好内容效度,本研究选用国内外相关研究中较为成熟的量表,并通过翻译-回译的形式最大限度保证英文量表准确性㊂问卷采用李克特5点计分法度量㊂(1)挑战性压力源㊂采用C a v a n a u gh [3]的量表进行测量,共6个条目,题项如我在决策中所承担任务的数量等㊂该量表C r o n b a c h 's α值为0.880㊂(2)任务重塑㊂采用S l e m p [46]的量表进行测量,共5个条目,题项如我会更改决策完成过程中任务的范围或类型等㊂该量表C r o n b a c h 's α值为0.771㊂(3)创新行为㊂采用S c o t t [31]的量表进行测量,共6个条目,题项如我能产生创新的想法等㊂该量表C r o n b a c h 's α值为0.851㊂(4)政治技能㊂采用F e r r i s [24]的量表进行测量,共6个条目,题项如我很会设身处地为别人着想等㊂该量表C r o n b a c h 's α值为0.871㊂(5)控制变量㊂本研究对个体性别㊁年龄㊁学历等人口统计学变量及先前参加类似比赛的经验进行控制㊂已有研究指出,上述背景变量会对个体创新行为产生影响[9,12]㊂3 实证分析与结果3.1 共同方法偏差和验证性因子分析通过H a r m a n 单因素检验法,本研究对共同方法偏差的检验结果显示,未经旋转的首因子解释变异量为28.28%,小于40%,表明不存在严重的共同方法偏差㊂更进一步地,本研究对变量进行单因素C F A 分析㊂由于假设模型中变量存在多个维度,每个维度又包含多个项目,为减少误差,对数据进行打包处理㊂结果显示,单因素C F A 的拟合结果很差(χ2=780.401;d f =51;χ2/d f =15.3;G F I =0.624;I F I=0.569;T L I=㊃831㊃科技进步与对策 2021年0.438;C F I =0.565;R M S E A=0.243),说明本研究共同方法偏差得到较好的控制,问卷质量可靠㊂本研究使用AMO S 对变量进行模型拟合,进一步检验各变量之间的区分效度㊂从表1结果可知,4因素模型的拟合效果优于其它模型,表明本研究变量具有较好的区分效度㊂表1 验证性因子分析结果模型χ2d fχ2/d f G F II F IT L IC F IR M S E A单因素模型780.4015115.30.6240.5690.4380.5650.2432因素模型567.1955011.340.7110.6950.5930.6920.2063因素模型141.805482.950.9190.9450.9230.9440.0904因素模型73.883461.610.9540.9840.9760.9830.050注:单因素模型:挑战性压力源+任务重塑+政治技能+创新行为;2因素模型:挑战性压力源+任务重塑+政治技能㊁创新行为;3因素模型:挑战性压力源㊁任务重塑+政治技能㊁创新行为;4因素模型:挑战性压力源㊁任务重塑㊁政治技能㊁创新行为3.2 描述性统计分析表2列出了各变量的均值㊁标准差以及相关系数㊂从表2结果可知,挑战性压力源与创新行为(r =0.438,p <0.01)㊁任务重塑(r =0.388,p <0.01)均呈现正相关㊂任务重塑与创新行为(r =0.478,p <0.01)也呈现正相关㊂表2 变量描述性统计分析变量均值标准差1234567性别1.620.49年龄2.320.82-0.141*比赛经验2.051.41-0.039-0.010学历1.270.65-0.1110.732**0.040挑战性压力源3.320.63-0.126*-0.064-0.0180.025任务重塑3.470.60-0.0850.0450.0420.0300.388**创新行为3.420.58-0.135*-0.0540.047-0.0070.438**0.478**政治技能2.660.62-0.144*-0.0380.081-0.0170.204**0.142*0.486**注:N=244;*表示p <0.05;**表示p <0.01;***表示p <0.001;性别:1表示男,2表示女;年龄:1表示17岁及以下,2表示18~22岁,3表示23~25岁,4表示26~30岁,5表示31岁及以上;比赛经历:1表示之前未参加过,2表示1次,3表示2次,4表示3次,5表示4次及以上;学历:1表示本科,2表示硕士,3表示M B A ,4表示博士及以上;时间压力:1表示强烈不同意,2表示不同意,3表示中立,4表示同意,5表示强烈同意;下同3.3 假设检验本研究通过B a r o n 层级回归检验步骤与P R O C E S S 程序对任务重塑的中介效应进行检验,结果见表3㊂为了保证结果可靠性,回归前对数据进行标准化处理㊂(1)检验自变量对因变量的影响,发现挑战性压力源对创新行为有显著正向影响(β=0.426,p <0.001,M 4),假设H 1得到支持;其次,检验自变量对中介变量的影响,结果显示挑战性压力源对任务重塑有显著正向影响(β=0.395,p <0.001,M 2),支持进行下一步分析;最后,将挑战性压力源和任务重塑加入创新行为回归方程,结果显示任务重塑对创新行为的影响显著(β=0.363,p<0.001,M 5),挑战性压力源对创新行为的影响显著(β=0.283,p <0.001,M 5),但系数明显下降(0.283<0.426),说明任务重塑部分中介挑战性压力源对创新行为的影响,假设H 2得到支持㊂表3 回归分析结果变量任务重塑M 1M 2创新行为M 3M 4M 5M 6M 7M 8M 9性别-0.079-0.024-0.143*-0.085-0.076-0.034-0.032-0.048-0.052年龄0.0430.120-0.120-0.037-0.081-0.024-0.031-0.110-0.117比赛经验0.0400.0520.0370.0510.0320.0180.023-0.010-0.010学历-0.012-0.0730.063-0.0020.0240.0040.0140.0630.069挑战性压力源0.395***0.426***0.283***0.350***0.311***任务重塑0.363***0.418***0.407***政治技能0.408***0.318***0.418***0.378***任务重塑ˑ政治技能0.135*挑战性压力源ˑ政治技能0.230***R20.0100.1610.0270.2030.3140.3580.4000.4140.431ΔR 2-0.0070.1430.0110.1860.2960.3420.3820.4000.414F 0.6009.116***1.66712.107***18.040***22.071***22.500***27.949***25.515***通过层级回归可知,任务重塑在挑战性压力源和创新行为的关系中承担中介作用,为了保证结论可靠性,本研究采用P R O C E S S 程序进一步验证任务重塑在挑战性压力源与创新行为间的中介作用㊂结果见表4,中介效应模型拟合较好(R 2=0.3135,F=18.0403,d f 1=6,d f 2=237,p =0.0000),挑战性压力源对创新行为的总效应显著(β=0.4264,p =0.0000),直接效应显著(β=0.2829,p =0.0000),任务重塑在挑战性压力源与创新行为间的间接作用显著(β=0.1435,置信区间为[0.0748,0.2210]),说明任务重塑在连接挑战性压力源㊃931㊃第11期陈春花,廖琳,李语嫣,等:有压力才有动力:挑战性压力源对个体创新行为的影响。

《物联网技术》杂志投稿要求

2022年 / 第1期物联网技术11图2 TP4056充电电路3 软件设计本文的设计中使用并发服务器和Socket 通信[10]。

服务器监听加入的连接，并调用fork()将自身拆分为父进程和子进程。

子进程处理连接并将接收的数据存入数据库，父进程则返回监听是否有新的连接加入其中。

具体工作流程如图3所示。

4 结语本文设计了基于单片机的农业大棚数据采集以及远程监控系统。

该系统能够进行数据采集、网关数据转发以及服务[4]费祥，张梅.基于LoRa 的温湿度监测节点设计[J].物联网技术，2019，9（3）：34-36.[5]解施媛，姜重然，王烯霖，等. WiFi 在农业物联网温湿度测量的研究[J].山西电子技术，2019，47（5）：88-90.[6]吴进，赵新亮，赵隽. LoRa 物联网技术的调制解调[J].计算机工程与设计，2019，40（3）：617-622.[7]宋维，周新虹.基于LoRa 技术的智慧校园物联网数据网关的设计与实现[J].信息技术与信息化，2020，17（11）：208-212.[8]于良波. 基于LoRa 的无线传感器通信系统设计与实现[D]. 重庆：重庆邮电大学，2020.[9]刘书伦，彭高辉，贾宝华.基于LoRa 物联网的智能节水灌溉系统[J].北方园艺，2021，34（6）：167-171.[10]张晓娜，常乐冉，吴炜，等. Linux 系统下Socket 通信的实现[J].电声技术，2020，44（1）：87-89.作者简介：邓甜甜（2000—），女，贵州黔西人，研究方向为物联网工程。

《物联网技术》杂志投稿要求《物联网技术》杂志的论文格式要求如下：1. 投稿的论文稿件中应具有中文标题、作者单位和署名、摘要、关键词（6个以上），论文正文部分应具有引言和结束语，参考文献（10条以上），文后应附主要作者简介（作者简介包括：姓名、出生年月、性别、学历、职称、研究方向）；2. 稿件中的图表一般不超过5幅，并要求标注清楚、规范；3. 稿件长度在5 000字以内；4. 投稿稿件请用Word 文档编辑（编排格式不限）并通过网站在线投稿。

物联网应用论文3000字

物联网应用论文3000字篇一：物联网应用论文物联网论文姓名：汪千飞班级：国贸111班学号：2403110018摘要:近几年来物联网技术受到了人们的广泛关注。

本文介绍了物联网技术的研究背景，传感网的原理、应用、技术，无锡是首个国家传感网信息中心。

以最具代表性的基于RFID的物联网应用架构、基于传感网络的物联网应用架构、基于M2M的物联网应用架构为例,对物联网的网络体系与服务体系进行了阐述;分析了物联网研究中的关键技术,包括RFID技术、传感器网络与检测技术、智能技术和纳米技术;关键词：物联网RFID 传感网 M2M物联网的原理物联网是在计算机互联网的基础上，利用RFID、无线数据通信等技术，构造一个覆盖世界上万事万物的“Internet of Things”。

在这个网络中，物品（商品）能够彼此进行“交流”，而无需人的干预。

其实质是利用射频自动识别（RFID）技术，通过计算机互联网实现物品（商品）的自动识别和信息的互联与共享。

而RFID，正是能够让物品“开口说话”的一种技术。

在“物联网”的构想中，RFID标签中存储着规范而具有互用性的信息，通过无线数据通信网络把它们自动采集到中央信息系统，实现物品（商品）的识别，进而通过开放新的计算机网络实现信息交换和共享，实现对物品的“透明”管理。

“物联网”概念的问世，打破了之前的传统思维。

过去的思路一直是将物理基础设施和IT基础设施分开：一方面是机场、公路、建筑物，而令一方面是数据中心，个人电脑、宽带等。

而在“物联网”时代，钢筋混凝土、电缆将与芯片、宽带整合为统一的基础设施，在此意义上，基础设施更像是一块新的地球工地，世界的运转就在它上面进行，其中包括经济管理、生产运行、社会管理乃至个人生活。

应用与技术物联网可以以以电子标签和EPC（Electronic Product Code,产品电子代码）码为基础，建立在计算机互联网基础上形成实物互联网络，其宗旨是实现全球物品信息的实时共享和互通。

用户使用报告

“远程监控平台系统”、为全国各大学计算机专业、通讯专业及新开的物联网专业等学院建设相关配套的“物联网实验室”等产品，目前部分产品已跨入国际先进水平，领先于国内同行业者，并与本年七月份被国家部委授予《中国新兴产业（物联网）研究基地》的称号，是物联网行业的领跑者。

山东易博物联电力科技有限公司拥有一批国内一流的专家队伍及优秀的研发团队，并聘请光纤通信、无线局域网和计算机技术领域国际知名专家进行技术指导，已突破了光载无线交换机系统研究中的多项关键技术，实现了2000多米的光纤传输和基于该系统网络的无线控制、视频传输、传感器信号传输、RFID读写等。

拥有独立的软件开发、硬件开发场地及开发环境。

目前，公司承担供电局的“光载无线技术在汽车充电站中的应用研究”项目，并制定了“电动汽车充电桩光载无线通信系统设计规范”标准，首次将光载无线技术应用于智能电网和电动汽车充电站（桩）。

公司于2010 年12月参加中石油管道公司举行的物联网技术在长输油气管道重大安全风险解决方案研讨会，方案得到与会专家的肯定。

物联网建设在国家发展中将会发挥重要作用，山东易博物联电力科技有限公司也必将在物联网的潮流中发展壮大。

公司秉承创新、领先的企业文化，引进物联网及行业应用领域的工程师，并与中国石油大学、中国矿大、厦门大学、山东科技大学、大连理工大等高等院校建立了长期战略合作关系，拥有丰富经验的高端人才，铸就软件和硬件研发的优秀团队，我们始终坚持以客户需求为导向，以行业的专业精神为驱动，以企业的不断创新和不懈努力实现企业的宏伟蓝图。

作为中国战略性产业（物联网）研究基地，山东易博物联电力科技有限公司积极参与各领域的优秀企业和合作伙伴携手，打造完整的物联网产、学、研、开发、应用、服务和资产业链，为广大用户及合作伙伴带来效益和价值，实现共赢，为提升城市品位、生活便捷、管理创新、社会和谐提供服务。

光载无线传输与交换技术的物联网信息平台技术特点：1.基于射频信号切换的动态带宽分配技术；2.高线性、低成本的光纤、无线收发一体的远端节点技术；3.射频光纤拉远及不同拓扑结构下的传输技术；4.无线传感及组网技术；5.基于WiFi-ROF网络的室内定位技术；用户评价及意见：通过在我公司6个月的试用，我们认为山东易博物联电力科技有限公司公司的光载无线传输与交换技术的物联网信息平台产品总体来讲充分利用光纤高带宽、低损耗的特点，大大拓展微波/毫米波信号的传输距离，简化远端基站结构，降低系统传输成本并提高系统传输性能、频谱效率、覆盖区域和灵活性，实现超宽带微波/毫米波无线接入与光传输技术的融合。