The Fuzzy Evaluation of Environmental Performance in Green Supply Chain
物流治理外文文献外文翻译英文文献逆向物流运作渠道的决策方式
外文出处：Senthil, S., Srirangacharyulu, B., & Ramesh, A. (2021). A decision making methodology for the selection of reverse logistics operating channels. Procedia Engineering, 38, 4, 418–428.附件1：外文资料翻译译文逆向物流运作渠道的决策方式摘要：产品退货的有效治理是一项战略性的问题。
现在，客户希望厂商能够进展逆向物流系统，为了是返还的产品能够被回收。
随着逆向物流实践的不断进展和进步，逆向物流渠道的选择就显得愈来愈重要。
此刻有三种大体的逆向物流运作渠道：制造商自营，第三方运营和联合运营模式。
本文基于层次分析法（AHP）和技术模糊环境下逼近理想解排序法（TOPSIS）相结合的混合方式，提出了逆向物流运作渠道的选择和评判。
本文利用一个算例验证了该方式。
这种方式帮忙决策者更有效的选择能够知足客户要求的最正确渠道。
关键字：逆向物流多目标决策层次分析法1 引言由于有关环境的法律不断的出台，逆向物流慢慢引发了企业的关注。
逆向物流（RL）是一个计划、实施和操纵原材料能够高效、低本钱的流动的进程，也是为了达到取得更多的价值，关于在制品库存、产成品和相关的从消费者手中回到原生产商的信息进行适当的处置。
关于逆向物流的研究仍然处于探讨时期。
逆向物流使得企业降低本钱成为可能。
逆向物流概念了供给链被设计为有效的治理产品和零部件的流动，使得它们能够进行再制造、循环利用和流程的改良，以便加倍有效的利用这些资源。
逆向物流活动的执行包括各类功能部份：产品质量的把关，紧缩配置循环周期，产品的再制造与翻新，资产回收，谈判，外包和客户效劳。
除产品的存储和运输，增值效劳的价值如：JIT，快速反映和问题方案的解决也都是逆向物流的重要组成部份。
关于有缺点的产品进行再制造，维修和回收能够制造庞大利润的商业机遇。
Fuzzy
320COGNITION AND EMOTION,2000,14(3),313±Fuzzy logical model of bimodal emotion perception: Comment on``The perception of emotions by ear and by eye’’by de Gelder and VroomenDominic W.Massaro and Michael M.CohenUniversity of California,Santa Cruz,CA,USADe Gelder and Vroomen(this issue)studied how emotions are perceived from informationgiven by the face and the voice.Emotions in the face and the voice were presented under both unimodal and bimodal conditions.When partici-pants identi®ed the affect,they were in¯uenced by information from both modalities.This result occurred even when they were instructed to base their judgement on just one of the modalities.These experiments and results provide an independent replication of similar studies published in Massaro (1998).Given this opportunityfor a new set of tests,the fuzzy logical model of perception(FLMP)was t to the new results provided by de Gelder and Vroomen.Of central interest is the nature of the bimodal performance as a function of the unimodal performance.The FLMP gave a good t of perfor-mance.The description reveals that,although information differences exist across different instruction conditions,the information processing involved in pattern recognition appears to be the same and well-described by the FLMP.The in¯uence of facial information on speech perception has gained prominence in cognitive psychology beginning with the classic study of McGurk and MacDonald(1976).De Gelder and Vroomen(this issue)used these ndings as a model for their studies of emotion perception.They asked participants to identify an emotion(e.g.,happy or sad)given a photograph and/or an auditory spoken sentence.They found that theiridenti®cation judgements were in¯uenced by both sources of information,2000Psychology Press Ltd/journals/pp/02699931.html314MASSARO AND COHENeven when they were instructed to base their judgement on just one of the sources.These results are particularly valuable to us because they represent an independent test of our theoretical framework by researchers other than ourselves.We use their results to test the fuzzy logical model of perception (FLMP),which to date has survived a number of empirical tests(Massaro, 1998;Massaro&Stork,1998).The FLMP provides an account of perception and pattern recognition in a wide variety of domains.Within the FLMP,perceptual recognition is viewed as having available multiple sources of information supporting the identi®cation and interpretation of the environmental input.The assump-tions central to the model are:(1)each source of information is evaluated to give the continuous degree to which that source speci®es various alter-natives;(2)the sources of information are evaluated independently of one another;(3)the sources are integrated to provide an overall degree of support for each alternative;and(4)perceptual identi®cation and inter-pretation follows the relative degree of support among the alternatives. Figure1illustrates the stages of processing in the model.The paradigm that we have developed permits us to determine how one source of information is processed and integrated with other sources of information.The results also inform us about which of the many poten-tially functional cues are actually used by human observers(Campbell& Massaro,1997;Massaro,1987,chapter1;Massaro&Friedman,1990). The systematic variation of properties of the signal combined with the quantitative test of models of speech perception enables the investigator to test the psychological validity of different cues.This paradigm has alreadyFigure1.Schematic representation of the three processes involved in perceptual recognition. The three processes are shown to proceed left to right in time to illustrate their necessarily successive but overlapping processing.These processes make use of prototypes stored in long-term memory.The sources of information are represented by upper-case letters.Auditory information is represented by A i and visual information by V j.The evaluation process trans-forms these sources of information into psychological(or fuzzy truth,Zadeh,1965)values (indicated by lower-case letters a i and v j).These sources are then integrated to give an overall degree of support,s k,for each speech alternative,k.The decision operation maps the outputs of integration into some response alternative,R k.The response can take the form of a discretedecision or a rating of the degree to which the alternative is likely.BIMODAL EMOTION PERCEPTION:COMMENT315 proven to be effective in the study of audible,visible,and bimodal speech perception(Massaro,1987,1998).Thus,our research strategy not only addresses how different sources of information are evaluated and integrated,but can uncover what sources of information are actually used. We believe that the research paradigm confronts both the important psy-chophysical question of the nature of information and the process question of how the information is transformed and mapped into behaviour.Many independent tests point to the viability of the FLMP as a general description of pattern recognition.The FLMP is centred around a universal law of how people integrate multiple sources of information.This law and its relation-ship to other laws are presented in detail in Massaro(1998).The assumptions of the FLMP are testable because they are expressed in quantitative form.One is the idea that sources of information are evaluated independently of one another.Independence of sources is motivated by the principle of category-conditional independence(Massaro&Stork,1998): It is not possible to predict the evaluation of one source on the basis of the evaluation of another,so the independent evaluation of both sources is necessary to make an optimal category judgement.Sources are thus kept separate at evaluation,they are then integrated to achieve perception and interpretation.Multiplicative integration yields a measure of total support for a given category identi®cation.This operation,implemented in the model,allows the combination of two imperfect sources of information to yield better performance than would be possible using either source by itself.However, the output of integration is an absolute measure of support;it must be relativised,due to the observed factor of relative in¯uence(the in¯uence of one source increases as other sources become less in¯uential,i.e.,more ambiguous).Relativisation is effected through a decision stage,which divides the support for one category by the summed support for all other categories.An important empirical claim about this algorithm is that although information may vary from one perceptual situation to the next,the manner of combining this informationÐinformation proces-singÐis invariant.With our algorithm,we thus propose an invariant law of pattern recognition describing how continuously perceived(fuzzy) information is processed to achieve perception of a category.Given this framework,one emerging feature of the FLMP is the division of perception into the twin levels of information and information proces-sing.The sources of information from the auditory and visual channels make contact with the perceiver at the evaluation stage of processing.The reduction in uncertainty effected by each source is de®ned as information. In the t of the FLMP,for example,the parameter values indicating the degree of support from each modality correspond to information.These parameter values represent how informative each source of information is.316MASSARO AND COHENInformation processing refers to how the sources of information are processed.In the FLMP,this processing is described by the evaluation, integration,and decision stages.Within this framework,we can ask what information differences exist among individuals and across different pattern-recognition situations.Similarly,we can ask whether differences in information processing occur.For example,we can look for differences in both information and information processing when participants are given different instructions in a pattern-recognition task.In their rst experiment,de Gelder and Vroomen asked participants to identify the person as happy or sad.The stimuli were manipulated in an expanded factorial design with an11-step visual continuum between happy and sad and an auditory sentence that was read in either a happy or sad voice.Thus,there were112bimodal conditions,11visual-alone condi-tions,and2auditory-alone conditions,for a total of35unique stimulus conditions.The participants were instructed to watch the screen and to listen to the voice on each trial.The FLMP was t to the average results by estimating free parameters for the11levels of visual information and2levels of auditory information. Figure2gives the observed and predicted results.As can be seen in theFigure2.The points give the observed proportion of sad identi®cations in the auditory-alone,the factorial auditory-visual,and the visual-alone conditions as a function of theauditory and visual stimuli.The lines are the predictions of the FLMP.gure,the FLMP gives a good description of the average results with a root mean square deviation (RMSD)of.022.Table 1gives the parameter values.The same design was used in the second experiment except that the two auditory-alone trials were omitted.Observers were told to judge the face and to ignore the voice.The FLMP was t to these new results by estimating a new set of free parameters for the 11levels of visual information and 2levels of auditory information.Figure 3gives the observed and predicted BIMODAL EMOTION PERCEPTION:COMMENT317TABLE 1Parameter values for the 11levels of the face and 2levels of the voice forExperiment 1(use both modalities)and Experiment 2(ignore the voice)Figure 3.The points give the observed proportion of sad identi®cations in the factorial auditory-visual and the visual-alone conditions as a function of the auditory and visual stimuli.The lines are the predictions of the FLMP.results.As can be seen in the gure,the FLMP gives a good description of the average results with a RMSD of.027.Table 1gives the parameter values.Comparison of the parameter values across the two experiments in Table 1allows us to test the hypothesis that there are information differences in the two different instruction conditions.As can be seen in the table,the parameter values for the happy and sad voice are made much more neutral (closer to .5)in the situation in which participants were instructed to ignore the voice than in the situation in which they were told to use both modalities.The parameter values for the face were mostly similar across the two conditions.Thus,the FLMP is capable of describing the results by simply assuming that the information from the voice was attenuated when participants were instructed to ignore it.The good t of the FLMP in both instruction conditions,however,indicates that the two sources are integrated in the same manner regardless of instructions.In the third experiment aimed at having observers judge the voice and to ignore the face,a 7-step auditory sentence continuum was made between happy and afraid.The visual stimuli were happy and fearful photographs of the speaker of the sentences.For some reason the auditory-alone stimuli were not presented.Thus,there were only 7214experimental conditions.The FLMP was t to the average results by estimating free parameters for the 7levels of auditory infor-mation and 2levels of visual information.Figure 4gives the observed and predicted results.As can be seen in the gure,the FLMP gives a good description of the average results with an RMSD of .023.Table 2gives the parameter values.Although a direct comparison between this experiment and Experiment 1is not justi®ed because of the different stimuli that were used,we can observe that the in¯uence of the face was much smaller when participants were instructed to ignore it.Tables 1and 2show that the parameter values for the prototypical emotions were much attenuated in Experiment 3relative to Experiment 1.318MASSARO ANDCOHENTABLE 2Parameter values for the 7levels of the voice and2levels of the face for Experiment 3In conclusion,we were successful in testing the FLMP against a new set of data from a new set of investigators.The framework and model provide a parsimonious account of several experimental manipulations.The dis-tinction between information and information processing is a powerful concept and reveals how instructional differences can modulate perfor-mance in the task.This outcome replicates the ndings in Massaro (1998,chapter 8)and adds to the body of results supporting a universal principle for pattern recognition.Manuscript received 11March 1999REFERENCESCampbell,C.S.,&Massaro,D.W .(1997).Visible speech perception:In¯uence of spatialquantization.Perception ,26,627±644.Massaro,D.W .(1987).Speech perception by ear and eye:A paradigm for psychological inquiry .Hillsdale,NJ:Erlbaum.Massaro,D.W .(1998).Perceiving talking faces:From speech perception to a behavioral prin-ciple .Cambridge,MA:MIT Press.BIMODAL EMOTION PERCEPTION:COMMENT 319Figure 4.The points give the observed proportion of afraid identi®cations in factorial auditory-visual conditions as a function of the auditory and visual stimuli.The lines are the predictions of the FLMP.320MASSARO AND COHENMassaro,D.W.,&Friedman,D.(1990).Models of integration given multiple sources of information.Psychological Review,97,225±252.Massaro,D.W.,&Stork,D.G.(1998).Speech recognition and sensory integration.American Scientist,86,236±244.McGurk,H.,&MacDonald,J.(1976).Hearing lips and seeing voices.Nature,264,746±748. Zadeh,L.A.(1965).Fuzzy rmation and Control,8,338±353.。
绿色环境作文英语
绿色环境作文英语Green environments are essential for our wellbeing and the health of our planet. Here are some points to consider when writing an essay on a green environment1. Introduction to Green EnvironmentsBegin with a brief introduction to what constitutes a green environment emphasizing the importance of clean air water and sustainable practices.2. Importance of Green EnvironmentsDiscuss the significance of green environments for human health biodiversity and the overall balance of ecosystems.3. Effects of PollutionHighlight the negative impacts of pollution on the environment including air and water pollution and their effects on human health and wildlife.4. Sustainable PracticesDescribe sustainable practices such as recycling composting and using renewable energy sources that contribute to a green environment.5. Urban Green SpacesExplain the role of urban green spaces like parks and community gardens in improving air quality reducing heat island effects and providing habitats for urban wildlife.6. Individual ActionsSuggest ways individuals can contribute to a green environment such as reducing waste conserving water and supporting ecofriendly products.7. Government and Corporate InitiativesMention government policies and corporate initiatives that promote a green environment such as carbon tax renewable energy subsidies and green building standards.8. Challenges and SolutionsAddress the challenges faced in creating and maintaining green environments and propose solutions to overcome these obstacles.9. Future OutlookProvide an optimistic view of the future with the potential for technological advancements and increased awareness leading to a more sustainable and greener world.10. ConclusionSummarize the main points and emphasize the collective responsibility of individuals communities and governments to protect and promote green environments. Remember to use relevant examples and data to support your arguments and to write in a clear and engaging manner that will resonate with your readers.。
耕地质量评价
耕地质量评价简介耕地质量评价是一种综合考虑农田土壤肥力、排水和土地利用状况的评估方法。
它是农业生产规划和土地资源利用的重要依据，可为农民合理选择种植作物、合理施肥、科学排水等提供科学依据。
本文将介绍耕地质量评价的定义和意义，以及评价方法和指标的概述。
定义和意义耕地质量评价是通过对土壤肥力、排水和土地利用状况的评估，确定耕地的质量水平，对土地资源进行合理开发和管理的一种方法。
耕地质量评价可帮助农民了解土地资源的优势和不足，从而合理规划农作物种植结构和施肥措施，提高土地的产出和效益。
耕地质量评价在农业生产规划和土地资源利用中具有重要意义。
首先，它可为农民提供科学的建议和指导，帮助他们选择适合当地土壤条件的农作物品种和施肥方式。
其次，耕地质量评价可为土地利用规划提供科学依据，有助于合理规划农田布局和土地利用结构，充分发挥土地资源的潜力。
此外，耕地质量评价也是保护和维护生态系统平衡的重要手段，有助于减少土地的退化和污染。
评价方法和指标耕地质量评价方法根据评价目的和数据的可获取性不同，可划分为定性评价和定量评价两类。
定性评价主要依靠专家经验和判断，通过观察和分析土壤的性质和现象来评价土地质量。
定量评价则依赖于详细的土壤调查和采样分析数据，采用数学模型和统计方法进行评价。
在定量评价中，常用的耕地质量评价指标包括土壤肥力指数、土壤物理性质指标和土地利用指标。
土壤肥力指数是评价土壤肥力程度的重要指标，常用的指标包括土壤有机质含量、全氮、全磷和全钾等。
土壤物理性质指标包括土壤质地、土壤容重和土壤水分保持性等，这些指标反映了土壤的保水性和透气性。
土地利用指标包括土地利用类型和农田排水状况等，这些指标能够反映土地的利用效益和可持续性。
耕地质量评价的步骤耕地质量评价的基本步骤包括数据收集、评价指标确定、指标权重确定和评价结果分析等。
第一步是数据收集，包括对土壤肥力、土壤物理性质和土地利用等数据的收集和整理。
这些数据可以来自土壤调查和采样分析、农田实地观察和农民的农作物生产记录等。
模糊综合评价法英语
模糊综合评价法英语English:The fuzzy comprehensive evaluation method is a decision-making technique used to deal with uncertainties and subjective judgments in complex systems. It combines fuzzy mathematics and expert knowledge to assess the performance or suitability of alternatives. The process involves several steps: defining evaluation criteria, establishing the fuzzy evaluation matrix, determining the weight of each criterion, fuzzifying the data to represent linguistic terms, applying fuzzy operators to compute the comprehensive evaluation value, and finally ranking the alternatives based on their evaluation scores. This method allows decision-makers to incorporate both quantitative and qualitative factors into the evaluation process, providing a more comprehensive and flexible approach compared to traditional methods. Moreover, it can handle imprecise and ambiguous information effectively, making it suitable for various fields such as engineering, economics, and environmental studies. However, the success of fuzzy comprehensive evaluation depends heavily on the accuracy of fuzzy sets, the selection of appropriatemembership functions, and the expertise of the evaluators in defining linguistic variables.中文翻译:模糊综合评价法是一种决策技术，用于处理复杂系统中的不确定性和主观判断。
中期答辩外文翻译
模糊系统与数学131（2002）101-106一个基于方案模糊信息的多属性决策方法Zhi-Ping Fan a,b , Jian Ma b,∗ , Quan Zhang b信息与决策部门，工商管理学院，东北大学，沈阳110006，中国部门的信息系统，香港大学，83Tat中港大道，九龙，香港摘要本文研究了对多属性决策方案的问题。
本文提出了一种新的方法来解决决策问题，也就是决策者对于他/她的偏好方案的一个模糊的关系。
对re.ect决策者的偏好信息，构建一个最优化模型来评估属性权重然后选择最理想的替代品。
最后，用一个例子来说明所提出的问题。
2001由Elsevier Science公司出版。
关键词：多属性决策；模糊偏好信息；方案排序1 引言在多属性决策（MADM）问题里，决策者往往需要选择或者排序非均匀和相互冲突的属性[3,14]。
MADM是在现实世界中许多的情况下出现的一个决策性问题[3,6,14,19,20]，例如，在生产计划的问题上，生产速度，质量和运营成本都要考虑选择满意的方案。
大量的研究工作一直在进行一个多属性决策问题，其中之一的研究热点就是模糊集理论的应用解决不精确信息的多属性决策问题以[3,4,24]为代表的模糊条款。
通讯作者。
电话: +852-2788-8514 传真: +852-2784-4198电子邮件地址: isjian@.hk (J. Ma).0165-0114/01/$-见前页c 2001 由Elsevier科学有限公司出版PII: S0165-0114(01)00258-5在多属性决策问题里，决策者的偏好信息经常被用来排列替代品，然而，决策者的判断因形式和深度而异。
决策者可能不代表他/她所有喜欢的，或者可能代表他/她的属性或替代偏好的形式。
决策者的判断能力也各种各样。
解决多属性决策问题可以分成三类，根据决策者给出diEerent形式的偏好信息[3,14]：（1）无偏好信息[2,8,12]的方法，（2）与属性[1,5,7,9,22]信息;（3）对替代品[3,14,16,17,20,23]信息的方法。
层次分析结合模糊评价法在苹果原料加工NFC_果汁适应性评价中的应用
中国果菜China Fruit &Vegetable第43卷,第3期2023年3月精深加工Deep Processing 层次分析结合模糊评价法在苹果原料加工NFC 果汁适应性评价中的应用张莹2，路遥1，李根1，宋烨1，马寅斐1，丁辰1，朱风涛1，张鑫1，赵岩1，初乐1*（1.中华全国供销合作总社济南果品研究所，山东济南250014；2.山东农业工程学院，山东济南250100）摘要:非浓缩复原汁（not from concentrate ，NFC ）果汁产品品质对原料的要求较高。
为评价不同品种苹果加工NFC 果汁的适应性，以10个苹果品种为原料，对其固酸比、色值、总酚、维生素C 、稳定性等12个指标进行测定，并以测定结果为依据，采用层次分析法构建模型。
结果表明，NFC 苹果汁品质评价模型为=0.0866×可溶性固形物＋0.1841×固酸比＋0.2385×总酚＋0.0408×*-0.4500×稳定性，依据此模型计算得出‘富士’苹果的得分最高，这说明‘富士’苹果较适宜加工NFC 果汁。
采用模糊评价法对构建的模型进行验证，建模得分与模糊评价法得分呈线性相关，因此该模型可用于不同品种苹果加工NFC 果汁适应性的判定。
关键词:苹果；NFC 果汁；层次分析法；模糊评价法；模型构建中图分类号：TS255文献标志码：A文章编号：1008-1038(2023)03-0006-08DOI：10.19590/ki.1008-1038.2023.03.002Evaluation of the Adaptability of Different Varieties of Apple ProcessingNFC Juice Based on the Analytic Hierarchy Process and FuzzyEvaluation MethodZHANG Ying 2,LU Yao 1,LI Gen 1,SONG Ye 1,MA Yinfei 1,DING Chen 1,ZHU Fengtao 1,ZHANG Xin 1,ZHAO Yan 1,CHU Le 1*(1.Jinan Fruit Research Institute,All China Federation of Supply &Marketing Cooperatives,Jinan 250014,China;2.Shandong Agriculture and Engineering University,Jinan 250100,China)Abstract:The quality of NFC fruit juice requires high raw materials.In order to evaluate the adaptability of differentvarieties of apples for processing NFC apple juice,10varieties of apples were used as raw materials,12indicators such as sugar-acid ratio,color value,polyphenols,vitamin C and stability were measured,and then the analytic hierarchy process was used to build the model.Results showed that the quality characteristic model of NFC apple收稿日期:2022-11-30基金项目:山东省重点研发计划泰山产业领军人才项目（LJNY202001）；山东省重点研发计划（乡村振兴科技创新提振行动计划）（2022TZXD007）第一作者简介:张莹（1980—），女，副教授，本科，主要从事农产品加工与工厂设计等工作*通信作者简介:初乐（1987—），女，副研究员，硕士，主要从事果蔬精深加工及产品开发工作苹果（Mill.）是蔷薇科苹果属植物的果实[1]。
用户需求导向的产品设计方案质量评价模型
小型微型计算机系统Journal of Chinese Computer Systems 2021年1月第1期 Vol.42 No. 12021用户需求导向的产品设计方案质量评价模型彭定洪w，黄子航彭勃3、昆明理工大学管理与经济学院，昆明650093)2 (昆明理工大学质量发展研究院管理，昆明650093)3(南昌大学管理学院，南昌330031)E-mail ：************************摘要：针对企业产品设计方案质量评价问题，提出一种以满足用户期望为核心的产品设计方案质量评估方法.首先，利用模 糊K A N O模型对收集的指标进行分析归类，建立用户导向的评价指标体系.其次，基于产品设计方案评价模型及指标体系的前 期研究成果，构建一套适用于以用户期望为主导的产品设计方案评价模型.由于用户期望具有一定的模糊性以及难以全部达成 的特点，将以几何均解做为参考解的多属性边界逼近区域比较法拓展到犹豫模糊领域，并进一步优化了适用于该方法的标准化 技术，系统的阐述了产品设计方案的评价流程.最后，通过算例对该评价模型进行了验证，通过对比分析证明该模型具有一定的 优越性和适应性.关键词：产品设计方案质量;综合评价;模糊K A N O模型;多属性边界逼近区域比较法（M A B A C);犹豫模糊集（HFS)中图分类号：TB115 文献标识码:A 文章编号：1000-1220(2021)014218>07User Demand-oriented Product Design Quality Evaluation ModelPENG Ding-hong1,2,HUANG Zi-hang1*2,PENG Bo31( School of Management and Economics, Kunming University of Science and Technology .Kunming 650093, China)2 (Institute of Quality Development,Kunming University of Science and Technology,Kunming 650093 .China)3 ( School of Management,Nanchang University,Nanchang 330031 .China)A b s t r a c t： Aiming at the problem of quality evaluation of enterprise product design schemes,this paper proposes a quality evaluation method of product design schemes that meets user expectations. Firstly,the fuzzy KANO model is used to analyze and classify the collected indicators, establishing a user-oriented evaluation index system. Secondly, based on the previous research results of the product design plan evaluation model and index system, a set of product design plan evaluation models suitable for user expectations is the key. Because users expect a certain degree of ambiguity and it is difficult to achieve all of them,the multi-attribute boundary approximation area comparison method using the geometric mean solution as the reference solution is extended to the hesitant fuzzy field, and the standardization technology applicable to this method is further optimized. The evaluation prcx:ess of the product design scheme is expatiated systematically. Finally,the evaluation mcxlel is verified by an example,and the comparative analysis proves that the mcxlel has certain advantages and adaptability.Key w o r d s：prcxluct design scheme quality ；comprehensive evaluation ；fuzzy KANO model ；M A BA C; hesitation fuzzy set ( HFS)i引言随着全球化进程的稳步推进与技术水平的快速提升，我 国企业所处的竞争环境将日益严峻，企业必须迅速通过产品 创新这一方式取得竞争优势，以响应日益多元化的用户及市 场需求m.因此，新产品的研发与创新设计为企业在复杂多面的竞争环境中提供了重要机遇.在创新产品投入生产前，对 其设计方案进行事前评估是产品生产过程中一个重要阶段，它有助于评估设计方案对设计目标的总体效用|2].且设计方 案的不良选择在设计过程后期很难进行弥补，很可能导致高 昂的二次设计成本.但由于国内企业的起步较晚且存在多方 面经验欠缺不足的问题，致使其缺乏相应的设计管理制度，更缺乏产品设计方案质量评价的意识及方法.因此，为弥补我国 企业在创新产品设计评估上的不足，本文拟构建一种适用于 产品设计方案质量的评价模型，以提升企业创新产品质量及 完善设计方案评估制度.另一方面，全球消费市场正逐渐由企业主导型向顾客主导 型转变,而企业在复杂的竞争环境中，不断的对产品进行创新 设计,也同样是为了获得顾客青睐进而提升顾客满意度.如何 更好地满足顾客多样化的需求，成为了产品设计方案质量评估 过程中关键的出发点.针对顾客需求分析以及需求与满意度间 的相互关系，日本学者狩野纪昭提出了对影响顾客满意度因 素进行划分的_〇模型，并且随着_〇模型的不断完善 与推广，目前该模型被广泛应用在以满足客户需求为核心的诸收稿H期:202(M)2^05收修改稿日期:2〇2(H)3-30基金项目：国家自然科学基金项目（71861018,71761027,61364016)资助；云南省哲学社 会科学规划项目（Y B2019067)资助.作者简介：彭定洪（通讯作者），男，1982年生，博士，教授,研究方向为系统工程、模糊决策；黄子航，女，1994年生，硕士研究生，研究方向为质量评价、多准则决策；彭勃，男，1982年生.博士，副教授，研究方向为决策理论与方法研究.彭定洪等:用户需求导向的产品设计方案质量评价模型219期多领域.耿秀丽[4]针对传统质量功能展开中考虑功能需求间自 相关关系不足的问题，采用模糊K A N O问卷进行分析并建立 了产品功能需求优化模型;唐中君[5]根据_〇模型的内涵，提出一种可以对用户个性化需求进行获取的方法，有效的解决 了用户满意期望与产品成本之间存在的冲突性;孟庆良W构建 了能够对分析型K A N O模型进行设计的方法，解决了 KANO 模型在分类准则时可能出现的主观局限性问题,实现质量因素 的客观化分类;采用卡诺二维质量模型，通过卡诺调査问 卷分析消费者对不同服务质量要素的满意度，确定其中必不可 少的质量要素.通过上述研究成果可知，KAN〇模型在分析以 用户需求为中心的评价问题时，是一种较为准确高效的研究工 具,但大部分学者都利用K A N O模型进行简单的用户调查、需 求分析或指标分类等研究,却没能就取得的分析结果进行深入 的研究利用.因而,本文受的思想启发，通过K A N O模型 固有的五种分类标准，对目前在产品设计方案质量评估领域 内，被广泛选用的指标进行筛选、分类、整合，进而构建以用户 需求为主导，质量保证为目的的评估指标体系_为对产品设计方案质量进行客观评价，除了需要合理的指 标体系外，同样需要严谨的评价方法.李付星[8]以工业设计作 为产品设计评价的出发点，将开发过程中涉及到的评价方法进 行了分析和汇总;王海伟[9]引人信息熵对指标权重的不确定性 进行描述，并根据极大熵原理建立设计方案评价模型;刘征 宏[1°]使用TOPSIS法对备选方案与用户需求的各感性维度匹 配度进行分析;V a r u n[l l]通过改进以区间数为数据基础的VIKOR方法，开发了用于设计方案评估的M R-VIKOR评价模 型.对文献进行分析发现，目前针对于产品设计方案的评价方 法大都集中在几种典型的多准则评价方法上.然而，上述方法 在解决以用户需求为核心的产品设计评价问题上虽有一定的 适用性,但也同样存在以下两点问题:l)TOPSIS与VIKOR均 为以极端解作为参考解多准则决策方法,虽然可以选择出无限 靠近最理想解的备选方案,但对于以用户需求为核心的产品设 计方案质量评估，是以期找到在成本可接受范围内，最能满足 用户偏好与期望的方案.而如果选择用户期望做为决策目标，那就势必存在用户主观的最理想期望会由于现实条件制约而 无法达到的问题;2)在产品设计过程中，不仅涉及到技术、材料 及成本，而且在进行评价的过程中还要考虑用户的摇摆不定. 这些因素不但多而且很难量化，实际上是一个模糊概念•再加 上“评价”自身就是带有较强主观性的判断活动，这样评价结 果自然会带有较大的模糊性[12].为解决上述存在的问题，本文 拟拓展一种以犹豫模糊集为数据基础的多属性边界逼近区域 比较法(HF-M AB AC)，并对其必要的标准化一步进行改进，增 强其适用性.一则，_A C是一种以均解作为参考解的评价 方法，可以弥补上述方法中最优解无法达到的缺陷;二则，采用 犹豫模糊集作为数据基础,可以涵盖评价过程中专家给出的带 有犹豫不定性的数据，在充分体现数据模糊性的基础上给出具 有代表性的决策信息.2以用户需求为导向的评价指标体系2.1 KANO模型自20世纪80年代提出以来，K A N O二维模型已成为各行业管理从业者和研究人员中最受欢迎的质量模型之一，其 旨在说明和确定研究目标的质量属性._〇模型放弃了产 品对客户满意度影响的严格线性视图，允许分类可能影响客 户满意度的特定属性，且认识到客户需求履行与客户满意度 之间的关系是非线性的.其对用户关于产品的需求类型划分 以下几种:1)魅力质量（A: Attractive):若产品中存在充足的 此类要素，用户满意度则会因为该要素的存在而会得到较大 幅度的提升;如果缺失或者此类要素不足时，用户也不会因此 而产生对产品的不满;2) —维质量（0:One-dimensional):此 类质量要素的充足或缺失都会影响用户对产品的满意度，作 为必备的质量要素，其充足程度的增加，能够使得顾客满意度 也得到增长，反之将导致顾客满意度呈线形下降的状态;3) 必备质量（M:M U S t-b e):此类质量要素是顾客认为产品中必 须具有的关键要素，若该类要素不能充足的体现在产品中，顾 客满意度会因为必备性能的缺失而急速下降.相反，无论该类 要素的充足性进行怎样的提升,对用户满意度产生的影响都 相对较小;4)无差异质量（IJndifferent):用户满意度并不会 针对此类要素的变化而发生改变，换言之此类要素对用户而 言并不重要;5)逆向质量（^R e v e r s e):若此类型质量要素表 现充足时将导致用户满意度的下降，不充足时将导致用户满 意度的上升[13],客户对产品或服务的特定质量的满意度可能 会因其对质量属性的偏好而异，函数变化关系见图1.满意图1K A N O质量因素关系图F i g.1KANO q u a l i t y f a c t o r r e l a t i o n s h i p d i a g r a m2.2构建产品设计方案质置评价指标体系通过文献回顾可知，K A N O模型在用户导向的质量评价 领域应用较为广泛，本节所提出的指标体系构建方法主要有 以下部分组成:产品设计方案质量评价的相关指标收集、引人 模糊K A N O问卷进行准则要素调查分析、筛选准则指标并进 行类型划分最后构建指标体系.首先，收集整理近年来关于产品设计方案质量的准则指 标，其中原思聪[将灰关联分析方法应用到机械设计综合评 价体系各模块，构建了机械产品设计的评价指标体系;Qi u:l5]根据不断变化的业务策略，在不同生命周期阶段对复杂产品 分别构建了指标体系;杨东[16]应用Q F D和案例推理的思想，利用通过Q F D方法形成的质量特性及其权重来构建指标体 系，整理后的指标见表1.其次，引入Ch en[〜8]等学者提出的兼顾不确定思想的模 糊K A N O模型（F K M)，使用更灵活的方式允许用户使用个性化标准来回答问题，用户还可以用更详细的数据表示来表220小型微型计算机系统2021 年达用户的真实想法.目前应用较为广泛的模糊K A N O问卷的主要形式如表2所示，相关问卷问题的表达形式如表3所示.表1分析整理后的准则指标T a b l e 1A n a l y s i s o f t h e r e v i s e d i n d i c a t o r i n d i c a t o r s~评价准则总产品质量产品效率产品尺寸可靠性—方案修改成本目保障性使用寿命疲劳寿命材料生产工艺标加工繁杂程度制造成本使用成本维修成本回收利用率复杂性 操作性结构 市场需求盈利率表2模糊K A N O问卷T a b l e2 F u z z y KANO q u e s t i o n n a i r e功能需求能够实现不能实现________________模糊K A N O问卷/%_________________比较满意不可缺少^~~能够忍受相对不满表3模糊K A N O问卷的问题形式（以产品质量为例）T a b l e 3 P r o b l e m f o r m o f t h e f u z z y KANO q u e s t i o n n a i r e (t a k i n g p r o d u c t q u a l i t y a s a n e x a m p l e)问题:所购买产品质量优良满足必须这样 中立 可以忍受不满远项（％)()() () ()()3基于用户期望的产品设计方案质量评价方法以K A N O质量因素划分为框架的指标体系，是产品设计 方案质量评价的基本蓝图，而构建既能适应其指标体系又能 满足其以用户需求为决策目标的评价方法，是完善综合评价 体系至关重要的一步.通过对各类研究成果的深入分析发现，产品设计方案质量评价的多目标、多准则特性决定该问题为 典型的多准则决策问题;再者，用户期望的模糊性与决策专家 的局限性也同样为其奠定了不定性的准则基调.为解决上述 问题，本文拓展了一种对该问题具有适应性的HF-MABAC 评价模型.3.1 犹豫模糊集（H e s i t a n t f u z z y s e t,HFS)自1965年,著名管理学家Z a d e h[l9]先生提出了模糊集的 概念以来，其为在不确定环境下进行研究的众多学者,提供了 模糊决策这一全新的研究方向.但伴随着日益繁杂的科学研 究，简单的模糊集已不足以支撑各种复杂多维的决策模型.为 此，一批杰出的学者提出了模糊集的多种拓展形式，其中包 括:犹豫模糊集[2〇]、2-17156型模糊集、区间模糊集[~以及直觉 模糊集等.通过对上述多种拓展形式的研究，笔者发现犹豫模 糊集对于企业软质量的评价问题上，具有相对较大的优势，主 要有以下几点原因：1)用户期望本身就是一种无法进行精确 度量的概念，且将其作为决策目标则更会增加其选择的难度.因此与传统的精确数值相比，犹豫模糊集既可以表达出更为最后，通过收回的调査问卷，整理出用户期望的准则指标 分类,剔除不在用户期望内的指标，形成以用户期望为导向的 产品设计方案质量评估指标体系（见表4).由于用户对待不 同类型质量因素的态度及重视程度的不同，本文根据KANO 模型中质量因素划分类型确定准则权重与指标权重.但不同 产品的产品特性不同，因此不能给出统一的标准，应根据实际 产品及消费者态度进行调整.不确定的目标内涵，又能够表达决策者主观的犹豫模糊性，更 具备实际应用的价值;2)与模糊集的其他形式相较，犹豫模 糊集既不需要如t y p e-2模糊集般归纳隶属函数，又不似直觉 模糊集与区间模糊集受到元素个数的限制，可以更加全面自 由的表达出用户对产品多方面的期望.下面给出犹豫模糊集 的定义及运算法则.定义1[20]•令X为一给定的集合,M= ，…，《…丨为表4产品设计方案质量评价指标体系T a b l e4 P r o d u c t d e s i g n s c h e m e q u a l i t y e v a l u a t i o n i n d e x s y s t e mK A N O需求类型指标指标描述效率产品设计方案是否能够高效的投人生产魅力质量因素方案修改成本产品设计方案的二次修改创新成本保障性产品设计方案能否保证实施使用寿命产品设计方案是否能保持长时间的创新性材料产品设计方案所需的材料是否易得—维质量因素复杂性产品设计方案是否简单易行操作性按照方案设计出的产品时候易于操作质量按照设计方案生产的产品是否有较高的质量必备质量因素可靠性按照设计方案生产的产品是否有较强的可靠性使用成本产品设计方案的实施成本是否合理工艺按照设计方案生产的产品是否有精良的工艺无差异质量因素结构产品设计方案的结构是否合理回收利用率按照设计方案生产的产品是否有回收利用的价值逆向质量因素维修成本按照设计方案生产的产品是否维修成本过高给定集合的W个隶属函数，则有关隶属函数w的犹豫模糊 集，即定义为：H/n = { <x,hM(x)> I jce X} (1)其中，心U) = 是值域位于[0,1]上的一个集合，表示集合中X的元素;c属于集合的若干种可能隶属度为表述方便，把有限论域X上的全体犹豫模糊集记为,称为A的犹豫模糊元，简写为定义2[20].对于任意的3个犹豫模糊元和\，它们的运算法则如下（其中0为一个常数>:1)*, n/i2=W|min('yl ,y2) ly, e A, ,y2 e A j | ;彭定洪等:用户需求导向的产品设计方案质量评价模型221 1期2) /i,\J h2 =//|max(y, ,y2)l y,e/i,,y2e/i21 ;3) 6>/z= //|l -(1 ,(9>0；A)he = H\y e \ y E.h\,0>〇;5) hc =H\ \ -y\y &h \；=//|(y i +y2U2)丨7丨e/i丨，y2;l)h x®h2=H\y x y2\yx g/i, ,y2g/i21.定义3[22].设乂 ,/i,是任意两个犹豫模糊元，则犹豫模糊 元圮，心之间的距离公式计算为：伙a)=i -73^^) +其中#<表示犹豫模糊元中元素的数量圮，#&表示犹豫模糊元中元素的数量圮.定义4[23].定义函数©:[0,丨广—[0,1],在参考集合中 犹豫模糊集X由i V个犹豫模糊元组成(W= |,/«2，…,乂丨是在集合X上的一个犹豫模糊集），在集合中的一个扩展函数 0在犹豫模糊集W中对每一个;c都有：^h(x) = U y e|A j(X)x...x a^j c)! {^(y)} (3)定义5[M].设A U)为犹豫模糊元，则称八/!(；〇) =为的得分函数，其中m u)表示中包含的元素个数.给定两个犹豫模糊元圮和\，如果4圮）>4/>4)，则 >圮;如果5(0 =■*(&),则 *« =*»•3.2改进的犹豫模糊M A B A C评价法在如TOPSIS[23]及VIKOTR这一类以参考解为导向的多 准则决策方法中，大都以极端的正负理想解作为参考的期望 标准.但诸如此类的期望解却无法适应用户导向的方案质量 评价问题，这就需要构建一种既可以保留用户期望为核心的 标准,又要做到不违背多准则评价的方向的方法.因而，本文 以PamU6a r[261于2015年提出的多属性边界逼近区域比较(M A B A C)法作为评价框架，该方法不仅是一种满足多准则 决策要求的评价方法，而且其作为参考的期望解是体现平均 思想的几何均解.多属性边界逼近区域比较法以全部可行解 作为评价选择区域，以均解作为基本衡量期望，但也同样包含 超出满意度的区域范围以契合K A N O质量模型中的魅力质 量因素;而低于用户期望的区域则体现了必备质量因素的思 想.再者，以均解作为衡量标准,一方面可以避免极端的用户 期望无法实现的问题；另一方面也能够借M A B A C法区域划 分的优势体现多种方案的不同特性.该方法自提出以来得到 了众多学者的广泛研究,Sun[27]利用B o n f e r r o n i均值对其进 行改进，减少标准之间固有的相互依赖性的影响，并应用在中 国医患研究的领域内；L i a n g[28]提出了一种与TFNs相对应的 扩展多属性边界区域比较法，对岩爆风险进行评估；彭定 洪[29]利用拓展情景模糊M A B A C法对我国可能再生能源进 行了选择.上述研究虽然都对该方法进行了研究，但一则没能 提出一种适用于犹豫模糊领域的拓展形式，二则针对于该模 型中的标准化形式，也没能改进出可以避免极端数值影响的 形式.接下来就本文所构建的方法进行介绍：首先，建立初始决策矩阵X.设有备选项人.〇_= 1,2,…，m),评价指标q+(y_= 1,2,…,n).\是第；个备选项的第j个 指标值，其中,\ = 丨，关于备选项的初始决策矩阵即为：C\C2C n4又丨1X12•••a2x =X2l X22i2nL…x mn_C,C2c…'U r.inly|u T6i|2lrt…u T6ilJ r l'u r Ei2i Irl u T ti22l y l…u r“J y丨-U r…其中,m是指备选项总数，《是指评价指标总数.第2步进行标准化处理，传统的标准化方法是通过与最 大值最小值的差距而进行的归一化计算，但是其也同样存在 极端值产生的误差影响，且M A B A C模型自身也未能发展出 完善的犹豫模糊形式.受KU mar[30]中优劣测度的启发，所提 出的标准化方式是利用均值与标准差进行计算以消除极端值 的影响，具体形式为：其中，《=i!,w[i!(w]T该算法与均值进行衡量避免了极端值的影响，与标准差 相比确保了归一化的稳定.为发展出一种适合于犹豫模糊元 的优劣测度标准化形式，现对其进行拓展改进：—⑶其中，^表示评价值^中的中值，使用中值替代均值既 能保留样本平均性，又能避免极端值对均值的影响.为简便犹 豫模糊元的标准差算法,选择用最大值最小值与中值的乘积 后开方进行改进，既可以从距离和的角度衡量其稳定性，又简 化了由于模糊元个数不定带来的繁琐过程.为确定犹豫模糊 元的中值，通过定义5进行犹豫模糊元的排序.利用式（5)得 到了标准化后的犹豫模糊矩阵Kc x c2…Cn兄2…ym~Y=>21》22 (2)Am L…y n u,-c,Q Cn'u y^n l r l U y e'y\21y1…U…J y l-u r e h i i r l•«•U rehn^lAm.u r6>m i I r t u y“m2丨7 1…JyOmn17 i-第3步计算加权决策矩阵Z.设％是指标C;(y = 1，2,…,m)的权重，则备选项A,〇= 1,2,•••,;〇的第7个指标的 指标加权值为&= % • (h+1).这一步中所给出的指标权222小型微型计算机系统2021 年域矩阵的距离矩阵备选项与边界逼近区域的距离矩阵Z)，由备选项的加权决策矩阵Z中的指标加权值‘与边界逼近区域矩阵G中相应指标下的边界逼近区域值&的差值组成.D=Z-G：C2…Cn■-8i:12_》2…Z l n- g…"a2^21 _8y;22_》2…^2n - 8nAm-Zml~8i…^mn8 n-C,c2•••Cn^11^12 ^21^22du d2…da:-^m<^m2…其中,f表示为元素全为零的犹豫模糊元，距离测度A表 示为：d C z y-g j), d(Z i j-h') >d(gj-h')〇,d(i r h X gj-h.)-d(gj - z,j),dCz,j-h")<d(gj-A* )备选项次（/= 1,2,…，/〇可能属于边界逼近区域G,上逼 近区域CT或下逼近区域G_，即A,+e |G V G+V G_丨.属于 上逼近区域C T的备选项A,是理想备选项反之，属于下 逼近区域G_的备选项A,+是非理想备选项/T.同样，该方法 中的边界区域划分也与K A N O模型具有相通之处，关系图见 图2所示.备选项的归属区域（0,0+或0_)按以下情形决定：G+if>〇G if=0G~if d,j<〇所以，为了使备选项A,.成为最优选项，则此备选项的指 标要尽可能多的属于上逼近区域，即需要尽可能多的A>0.最后，进行备选产品设计方案的排序与择优.为进一步体 现用户期望在该评价方法中的重要性,对原方法中单纯的贴 近度分量累加的形式进行了拓展，采用用户对指标的期望权 重与贴近度分量的加权计算，得出各备选方案的贴近度.贴近 系数CC,+ ( the closen ess c o e ffic ie n t)的值越大,备选项越优.用户平均$望重％是由决策专家根据第1部分K A N O模型中，质量因素 对不同消费群体的重要性程度给出的,该权重不仅可以在保留专家经验的基础上充分体现用户期望偏好,更能体现以用 户为导向的评价思想,加权决策矩阵Z为：c丨Q…c,Q… c…^12••A,'U y e(y1I + !H)i 1 -~y)W l1U re(>i2+ll|) I1 -C 1 -y)W2\…⑴）11-(1-7广|-^21in** iln_^2U ye(hi+ H I) 11__r)W，1A m-Zm2^m n-A m■u”、丨+ m)U-(1-y广M u rE(y^+m)I1 -(!-r)^!…U T e(w l l|)|l-(l-y)-l.第4步确定用户期望参考解，这一步中确定的参考解为 企业可达到的平均用户期望，边界用户期望矩阵G表示为：G -[容丨，容2，."，5n]lxuIs 1/ = 1i = 1求得边界矩阵后，接下来就是计算各备选方案与边界区贴近系数的计算公式如下：CC,= X w j d u»*= 1, •(6)A+用户最高期望用户满意度逐渐增^X/用户满意度逐渐降A•用户最低期望图2边界逼近区域关系图Fig. 2 Boundary approxim ation area relationship diagram4算例实证4.1问题背景与数据来源为验证上述模型的有效性和优越性，现根据具体算例进 行验证.随着我国电子产品行业的飞速发展与人均电子产品 持有率的逐年攀升，电子产品已经在方方面面融人我们的生 活，其中最必不可少的就是智能手机和计算机设备，本文以某 智能电子企业的高端计算机业务为背景进行产品设计方案质 量评估.高端计算机设备的消费群体主要为从事精密计算的 工作人员或研究型企业，由于其具有针对性的消费特征以及 产品特性，所以对于该产品的设计方案更加需要时刻关注消 费群体的期望与产品技术的需求，也就更适用于以用户需求 为导向的综合评价体系.现该企业组建由5名专家构成的决 策小组，其中专家A与B为产品设计部门内由于多年设计经 验的设计师和管理人员；专家C为多年从事评价领域理论研 究的专家学者;专家D与E为主要消费群体中的用户代表. 4.2评价步骤通过上文中构建的指标体系对次，A2，…,A55个设计方 案进行排序择优，得到初始决策矩阵X.决策小组根据消费群表5 K A N O质量因素准则权重表Table 5 K A N O quality factor criteria w eight table准则指标C3Q Q Q C7权重0.060.060.060.070.060.060.07准则指标c9Cl〇C n c12c13c14权重0.070. 100. 100.100.060.060.07体对不同K A N O质量因素的期望偏好，给出专家权重（见 表5).。
一种基于德尔菲法和模糊综合评价的侦察装备运用效能评估方法
2020(Sum. No 215)2020年第11期(总第215期)信息通信INFORMATION & COMMUNICATIONS一种基于德尔菲法和模糊综合评价的侦察装备运用效能评估方法张庆东，蒋晓瑜(陆军装甲兵学院，北京10072)摘要:为了辅助指挥员预先评价侦察装备在执行军事任务时的完成情况，提出一种基于德尔菲法和模糊综合评价的侦察装备运用效能评估方法。
该方法在建立侦察装备评估体系的基础上，利用德尔菲法来确定体系的各指标的权重比例，再利用模糊综合评价法计算最终的评估结果，德尔菲法和模糊综合评价法均能够充分利用专家智慧，为面向军事任务的侦 察装备效能评估的提供一种解决方案,具有较强的实用价值。
关键词:德尔菲法;模糊综合评价;侦察装备;效能评估中图分类号:TN925文献标识码:A 文章编号：1673-1131(2020)11-0161-03Evaluation of Reconnaissance Equipment Utilization Efficiency Based onDelphi Method and Fuzzy Comprehensive EvaluationZhang Qingdong,Jiang Xiaoyu(Army Academy of Annored Forces, Beying 100072, China)Abstract: In order to assist the commander in pre-evaluating the accomplishment of r econnaissance equipment in the executionof m ilitary tasks, a method of reconnaissance equipment utilization effectiveness evaluation based on Delphi method and fiizzy comprehensive evaluation is proposed. Based on the establishment of the reconnaissance equipment evaluation system, this method uses the Delphi method to determine the weight ratio of t he system ? s indicators, and then uses the fuzzy comprehensive evaluation method to calculate the final evaluation results. Both the Delphi method and the fuzzy comprehensive evaluationmethod can be fully utilized Expert wisdom provides a solution for the effectiveness evaluation of m ilitary mission-oriented re connaissance equipment, which has strong practical value.Keywords: Delphi method; fuzzy comprehensive evaluation; reconnaissance equipment; effectiveness evaluation0引言随着现代战争中情报作用的不断提高，侦察装备在作战 中的地位愈发重要叫侦察装备运用效能评估也成为了军事领 域的研究热点。
模糊综合评价法(fuzzy comprehensive evaluation method)
模糊综合评价法（fuzzy comprehensive evaluation method）1.什么是模糊综合评价法模糊综合评价法是一种基于模糊数学的综合评标方法。
该综合评价法根据模糊数学的隶属度理论把定性评价转化为定量评价，即用模糊数学对受到多种因素制约的事物或对象做出一个总体的评价。
它具有结果清晰,系统性强的特点,能较好地解决模糊的、难以量化的问题,适合各种非确定性问题的解决。
2.模糊综合评价法的术语及其定义为了便于描述，依据模糊数学的基本概念，对模糊综合评价法中的有关术语定义如下：1．评价因素（F）：系指对招标项目评议的具体内容（例如，价格、各种指标、参数、规范、性能、状况，等等）。
为便于权重分配和评议，可以按评价因素的属性将评价因素分成若干类（例如，商务、技术、价格、伴随服务，等），把每一类都视为单一评价因素，并称之为第一级评价因素（F1）。
第一级评价因素可以设置下属的第二级评价因素（例如，第一级评价因素“商务”可以有下属的第二级评价因素：交货期、付款条件和付款方式，等）。
第二级评价因素可以设置下属的第三级评价因素（F3）。
依此类推。
2．评价因素值（Fv）：系指评价因素的具体值。
例如，某投标人的某技术参数为120，那么，该投标人的该评价因素值为120。
3．评价值（E）：系指评价因素的优劣程度。
评价因素最优的评价值为1（采用百分制时为100分）；欠优的评价因素，依据欠优的程度，其评价值大于或等于零、小于或等于1（采用百分制时为100分），即0≤E≤1（采用百分制时0≤E≤100）。
4．平均评价值（Ep）：系指评标委员会成员对某评价因素评价的平均值。
平均评价值（Ep）＝全体评标委员会成员的评价值之和÷评委数5．权重（W）：系指评价因素的地位和重要程度。
第一级评价因素的权重之和为1；每一个评价因素的下一级评价因素的权重之和为1 。
6．加权平均评价值（Epw）：系指加权后的平均评价值。
Evaluation on Environment State of Breeding Area in Shuidong Bay by Using Fuzzy Comprehensive Ev
Eva lua tion on Environment State of Breeding Are a in Shuidong Bay by Using Fuzzy Compre hensive Eva lua tion MethodAbs tr a c t ：Fuzzy comprehensive evaluation method was use d to develop a standar d model to analyze and evaluate nearness degree of water environment quality at breeding area of Shuidong Bay in China.Results showed that certain environment contamination factors in some areas seriously exceeded the standard value and led to the whole water quality at the third class level.The measurements should be taken to promote the sustainable development of breeding area in Shuidong Bay.Ke y wo rd s ：fuzzy comprehensive evaluation method,nearness degree,water environment quality C LC n um b e r:F323.2Doc u m e nt c od e :AArtic le I D:1006-8104(2010)-02-0064-05IntroductionThe evaluation of environment state of water area based on some evaluation methods is an important issue of environment.The comprehensive evaluation method may be employed to transform the data mea-sured in the interested water area to the value indicated the quality of the environment states.Considering the fuzziness of environment state of water boundary and overcoming the deficiency of other evaluation methods,comprehensive evaluation method is found to be available for the evaluation of environment state of water area.In this paper,a new system of fuzzy comprehensive evaluation method was proposed to evaluate the environment state of breeding area in Shuidong Bay of China.Ba ckgroundsShuidong Bay,lied in the middle of the west of Guang-dong Province of China,is the important base of trans-portations and production of shery .The water depth of lagoon is lower than 2.5m in Shuidong Bay,and the mouth is between 5-9m with the area of 2607km 2.In this area,the annual mean temperature is between 11.3-18.1℃,and the average annual rainfall is 1558mm. There are nearly 10000cages in Shuidong Bay to breed the high-quality fish at expanding scale.How-ever,some problems,such as high sea pollution,ex-cessive water development,variable climate condi-tions,result in the decline of aquaculture ability [1].In order to promote the sustainable aquaculture develop-ment of cage in Shuidong Bay,an investigation of the environment state of cultivating water area was performed by setting up four sampling points in xed position to monitor the sea water environment in Shuidong Bay cages during May 12th to 28th,2003. Shuidong Ba y environme ntal monitoring da ta of water quality of cage cultivating area is shown in T able 1.Received 3March 2009Supported by China Postdoctoral Science Foundation (20090460873)XU Yonghua (1976-),female,Master,engaged in the research of management science and engineering teaching and research.*Correspo nding au tho r.SUN Yo ng,as sociate pro fes so r,Ph.D,eng ag ed in the research o f bio mass conv ers io n and u tilizatio n.E -mail:y 3@63XU Y onghua 1,and SUN Yong 2*1Heilongjiang Agriculture Polytechnic University,Jiamusi 154007,Heilongjiang,China 2Engin eering College,Northeast Agricultural University,Harb in 150030,Chinax @sun E-ma i l:u eb ao eng l is h nea Grades COD ≤DO >BOD 5≤Volatiephenol Hydride Inorganic nitrogen Active phosphate Oil Sulphide Level Ⅰ2610.0050.0050.200.0150.050.02Level Ⅱ3530.0050.0050.300.0300.050.05Level Ⅲ4440.0100.1000.400.0300.300.10Level Ⅳ5350.0500.2000.500.0450.500.25Station COD DO BOD 5Volatile phenol Cyan ide ion Inorganic nitrogen Active phosphate Oil Sul de A 5.35 5.250.830.02050.00860.3420.0090.1220.053B 4.01 5.230.580.02000.00570.3980.0300.1730.052C 4.56 5.280.550.01880.00870.3640.0270.1790.053D3.955.650.530.01420.00780.3700.0210.1470.057Eva lua tion of Environme nt Sta te ofWa te r Are aP rinc ip le o f fuz zy c o mp re h e ns ive e va lu a tio n m e th odBased on the result of existed study [1-2],a term of near-ness degree was named to describe the approximation intensity between the objective and standard sets.A standard set A is proposed by assumed m fuzzy subset of {A 1,A 2,…,A m }in the discourse domain U={x 1,x 2,…,x n }.An objective set B may be discriminated by using Eq (1). Where,σi (A i ,B)is the nearness degree,and the bigger the value is,the higher the approximationdegree between the A and B set is.ωkis the evaluation w eigh t fa c to r.Both "∧"an d "∨"ar e Za de h Operators,which express maximum and minimum value taken in calculation,respectively.Eva lua tio n p roc e s sSe le ction of the e va lua tion sta nda rdAccording to sea water quality standard of China (GB 3097-1997)shown in Table 2[2],the seawater is classi ed into four grades.Evaluation indexes include chemical oxygen demand (COD),dissolve oxygen (DO),biochemical oxygen demand BOD 5,volatile phenol,hydride,inorganic nitrogen,active phosphate,oil,and sulphide.Cons truction of sta nda rd a nd obje ctive se ts Based on the value shown in Table 2that was selected as the standard set,the normalizations for data mea-sured from sampling points were conducted by using Eq (2)to calculate nearness degree.Table 1 Shuidong Ba y envir onmental m onitor ing da ta of water qua lity of cage cultiva ting a rea (m g L -1)σi (A i ,B)=Δωk (A i (x k)∨B(x k ))ωk (A i (x k )∧B(x k ))∑k=1n∑k=1n(1)Ta ble 2 National sea water qua lity standa r d (m g L -1) Where,x -the kth evaluation factor,min{x }-theminimum value of the k th evaluation factor;max{x ik }-the maximum value of the k th evaluation factor. The standard sets were obtained in Table 3and theobjective sets were shown in Table 4.≤≤1≤k ≤9x'ik =max{x ik }–min{x ik }1≤k ≤91≤k ≤91≤k ≤9x ik –min{x ik }(2)COD:chemical oxygen demand,DO:dissolve oxygen ,BOD 5:biochemical oxygen demand.ik ik 1k 9h ttp ://pub l is h.ne a u.e du.c nCa lc ula tionDue to the difference of individual contribution to a special environmental unit,the weight value should be considered for every individual event index.A pollution weighting method was adopted to evaluate the level of exceeding standard.The more exceeding standard is,the larger the actual density is,which shows the gre ate r influence.Pollutant pr oduced danger occurs only when the weight reaches certain density ,which re ects the exceeding standard case [3-5].Calculation of the weight formula is as following: Where,C k is the k th concentration measured of the pollutant,S k is the kth standard value of the con-centration under certain conditions of the index. Due to the absence of the individual index weight dependence on the its grade standard under a special function,an average value of S k for the individual indexes may be taken as follow: In addition,the weight of dissolving oxygen (DO),as a non-polluter,has the positive effect on water quality.A fetched and counted backwards was taken as follow: Normalization for every individual term weight was conducted for the further fuzzy operation as following: In domain U,the weight term ωk was introduced to represent the contribution of every item in all factors.According to Eqs (3)-(6),nine weights value of evalua-tion factor were calculated as shown in Table 5.Ca lc ula tion of de gre e of ne a rne ssThe nearness degree values were calculated by using Eq (1).Listed in Table 6.ωk=C kS k(3)GradesModelsx 1x 2x 3x 4x 5x 6x 7x 8x 9COD ≤DO>BOD 5≤V olatile pheno l HydrideInorganic nitrogen Active phosphate Oil Sulphide Level ⅠA 10.3328 1.00000.1660000.03250.00170.00750.0025Level ⅡA 20.5996 1.00000.5996000.05910.00500.00900.0090Level ⅢA 3 1.0000 1.0000 1.000000.02260.09770.00500.07270.0226Level ⅣA 41.00000.59641.00000.00100.03130.09180.09180.0414Table 3 Sta ndar d setsStationsModelsx 1x 2x 3x 4x 5x 6x 7x 8x 9CODDO BOD 5Volatile phenol HydrideInorganic nitrogen Active phosphateOil Sulphide A B 1 1.00000.98130.15380.002200.062400.02120.0083B B 20.7665 1.00000.10990.002700.07510.00470.03200.0089C B 30.8634 1.00000.10270.001900.06740.00350.03230.0084DB 40.69871.00000.09260.00110.06420.00230.02470.0087Ta ble 4 Objective sets(4)S k =1i (S 1k +S 2k+…+S ik )ωDo =C DoS Do(5)(C Do ≠0)ωk=C k /S k∑k =1n(6)C k /S kx @E-ma i l:u eb ao eng l is h nea Weight COD DO BOD 5Volatile phenol Hydride Inorganic nitrogen Active phosphate Oil Sulphide sk3.50004.5000 3.25000.01750.07750.35000.03000.22500.1050ωA 1.52860.85710.2554 1.17140.11100.97710.30000.54220.5048ωB 1.14570.86040.1785 1.14290.0735 1.1371 1.00000.76890.4952ωC 1.30290.85230.1692 1.07430.1123 1.04000.90000.79560.5048ωD 1.12860.79650.16310.81140.1006 1.05710.70000.65330.5429ωA 0.24470.13720.04090.18750.01780.15640.04800.08680.0808ωB 0.16840.12650.02620.16800.01080.16720.14700.11300.0728ωC 0.19300.12620.02510.15910.01660.15400.13330.11780.0748ωD0.18960.13380.02740.13630.01690.17760.11760.10970.0912 As shown in T able 6,the water quality at sampling point A,324th fishing arrangement at the continent island eastward about 1000m,lied in the range of the third and the fourth grade level.Higher nearness to the third grade level value proved that the water quality at this sampling point can be appraised as the third grade case [6].Therefore,it is necessary to take effective mea-sures to prevent worse case of the local water quality environment.The water quality at the sampling point C (137th fishing arrangement at the center of east gulf),sampling point B (105th fishing arrangement at the right bank of east gulf)and sampling point D (area away from breeding district)matched with the second and the third grade level.The water quality level at B and D belonged to the second grade level,which had a change trend toward the third grade q y ,q y point C was at the third grade level.To sum up,the overall water quality of this water area was in the third grade level based on the national sea water quality standard.Based on the above mentioned results,some suitable measurements should be taken to promote the sustainable development of the cultivating area.Conclus ionsFuzzy comprehensive evaluation,using the nearness degree to describe the environmental state of water areas,is an effective method to evaluate environment state of interested water area.In fuzzy comprehensive evaluation method,the weight level factor depended on the difference of value measured at the sampling point exceeding the standard value,considering the f f j Calculated nearness degreeσ(A 1,B)σ(A 2,B)σ(A 3,B)σ(A 4,B)σmax(A i ,B)Water qualitygrade A 0.56870.71130.88960.77260.8896Level ⅠB 0.69140.83850.79140.63870.8385Level ⅡC 0.63760.79010.83760.69440.8376Level ⅢD0.72150.88000.74870.60190.8800Level ⅣTable 6 C alcula tion of the near ness degr ee Table 5 Weights of evaluation fa ctorwater uali t and the water ualit at the samplingcontribution rate o individual actor on the ob ectiveh ttp ://pub l is h.ne a u.e du.c nresult.The evaluation results reflected the order of wate r quality at different sampling points,which showed the development trend of the water quality state of this water area.Refe re nc e s1Zhang Y,Wang S Z,Zhu X H.Evaluation of surface water quality with fuzzy mathematics[J].J ournal of Dalian Railway Institute, 2004,25(1):7-11.2Lin D N.The evaluation of env ironment state on water area o f breeding area in Shuidong Bay[J].Res earch of Soil and Water Conservation,2005,12(4):258-260.3Xie J J,Liu C P.Fuzzy mathematical method and application[M].2nd ed.Wuhan:Huazhong University of Science and Technology Press,2000.4Song H B,Liu Z Y,Xu e B.Ap plication stu dy on cons truction project assessment by using fuzzy mathematics[J].Journal of Hebei Institute of Architectural Engineering,1999,17(4):11-14.5Li X H,J X Y,Huang T M.Fuzzy mathematics method for water environmental q uality ass essment in arid area[J].Journal of Arid Land Resources and Env ironment,2004,18(8):163-167.6Tan g Y F.Ev aluation of water quality in Shuangtaihe river by fuzzy mathematic method[J].Water Resources and Hydropower of Northeast China,2003,21(5):35-36.x@E-ma i l:u eb ao eng l is h nea 。
《环境工程技术学报》投稿须知
SLN J M,LI L L. Research on low carbon evaluation method of power grid based on combination of quantitative and qualitative analysis [ J]. Science and Technology Management Research, 2019,39(1) ：242-248. [53] WANG W Q, BI Y G, SHEN X H. Application of fuzzy comprehensive evaluation method based on AHP in product materials [ J ] . IOP Conference Series： Materials Science and Engineering,2019 ,612 ：032164. [54 ]李宏艳,董军,杨善林•企业信息化模糊综合评估指标体系与 方法研究[J].合肥工业大学学报(自然科学版),2004,27 (6) ：592-596. LI H Y , DONG J , YANG S L. Research on fuzzy comprehensive evaluation index system of enterprise informatization [J]. Journal of Hefei Lniversity of Technology ( Natural Science ) , 2004,27 (6) ：592-596. [55] 王丽丽•模糊数学法结合层次分析法用于清洁生产潜力评估 研究[D].重庆:重庆大学,2010. [56] 郭俊•基于层次分析法和模糊数学法的木薯淀粉行业清洁生 产潜力评估研究[D].南宁:广西大学,2016. [57] GORAI A K, LPADHYAY K A, et al. Design of fuzzy synthetic evaluation model for air quality ssment [ J ] . Environment Systems and Decisions,2014,34(3) ：456-469.毬
基于模糊评价的绿色供应链管理可行性评估方案
第38卷第4期2016年12月湘潭大学自然科学学报Natural Science Journal of Xiangtan UniversityVol. 38 No. 4Dec.2016基于模糊评价的绿色供应链管理可行性评估方案尹小悦、王娟、陈昭玖(1.赣南医学院人文社科学院，江西赣州341000;2.江西农业大学经济管理学院，江西南昌330045)[摘要]针对企业实施绿色供应链管理(GSCM)的可行性评估问题，提出一种基于模糊评价的评估方法.首先，根据大量文献和实地调查确定GSCM的影响因素，并组建一个专家委员会，对各因素之间的关系作出三角模糊评价.然后，利用决策与试验评价实验室方法分析各因素之间的相关性，获得各因素的重要性权重.接着，基于一个企业的实际情况，专家对该企业成功实施GSCM中各因素的贡献作出模糊评价，并利用多准则决策（MCDM)方法获得贡献评级.最后，根据重要性权重与贡献评级获得最终的成功实施GSCM的可行性概率值.关键词：绿色供应链管理；可行性评估;模糊评价；决策与试验评价实验室；多准则决策中图分类号：F124,TP391 文献标识码：A 文章编号：1000 - 5900( 2016) 04 - 0116 - 05A Feasibility Evaluation Scheme of Green Supply Chain ManagementBased on Fuzzy EvaluationYIN X ia o-yu e1 , WANG J u a n1, CHEN Z h a o-jiu2*(1. School of Humanities &- Social Sciences, Gannan. Medical University, Ganzhou 341000;2. School of Economics and Management,Jiangxi Agriculture University, Nanchang 330045 China)【Abstract】For the issue that the feasibility evaluation of implementing green supply chain management (GSCM), a new evaluation scheme based on fuzzy evaluation is proposed. First, the influence factors of GSCM are determined according to a large number of literatures and field surveys. At the same time, a committee of experts is formed, so as to make triangular fuzzy evaluations for relationship between the various factors. Then, the decision-making and testing evaluation laboratory is used to analyze the correlation among these factors, and to obtain the importance weight of each factor. After that, the experts make a fuzzy evaluation about the contribution of each factor for successfully implement GSCM, which based on the actual situation of an enterprise. So that can obtain the contribution rating by multiple criteria decision making (MCDM). Finally, the importance weights of each factor and the contribution rating are multiplied to obtain the feasible probability value of the final successful implementation of GSCM.Key words：green supply chain management; feasibility evaluation; fuzzy evaluation; decision-making and trial evaluation laboratory; multi-criteria decision making绿色供应链管理（Green Supply Chain Management,GSCM)[1]是指在传统供应链管理上更多 地关注资源利用率和对环境的影响，有助于提高企业的国际竞争力.目前，国内企业还处于GSCM 的初级阶段，随着G S C M的重要性逐渐提高，如何有效地实施G S C M成为企业管理者重视的问题.在开展G S C M的实践和研究中，确定G S C M的影响因素是首要问题[2].在确定影响因素后，企 业可以根据自身的情况来评估成功实施G S C M的可行性，以此可以发现自身的不足点，使其能够 更好地实施GSCM[3].提出了一种用于评估实施G S C M可行性的方法.利用决策与试验评价实验室（Decision-Making and T ria l Evaluation Laboratory,D E M A T E L)方法[4]，根据专家给出的各G S C M影响因素之间相互作用的模糊评价，获得各因素的重要性权重.利用多准则决策（M u lti-*收稿日期：2016-04 - 17基金项目：赣州市社联课题（14471)通信作者：陈昭玫（1969—），男，江西赣州人，博士，教授• E-mail: yinxiaoyue88@126. com第4期尹小悦，等基于模糊评价的绿色供应链管理可行性评估方案117Crkeria Decision M a kin g ，M C D M )方法歐，根据特定企业中各因素对其成功实施G S C M 的模糊责献评价，获得各因素的贡献评级.最终获得该企业成功实施G S C M 的可行性，暴露需要改善的薄 弱因素，为企业管理者提供决策依据.1 G SC M 因素的确定为了实现G SCM ，通过查阅大量现有文献和实地调查，最终选择了 10个与实施G S C M 相关的 影响因素，如表1所迅.表 1GSCM 因素 Tab. 1Factors of GSCMG S C M 因素述G S C M 因素掛述S1员工的环保意识S 6最髙管理者的承诺与支持S2显工的参与S7绿色企业文化S3道德标准与企业社会责任S 8对环保投资的意愿S4信息技术基础设施S9组织结构SS战略规划S1Q环境教育G S C M 因素中，最高管理层的承诺及支持（S 6)、员工环保意识（S I )这2个因素非常有助于实施GSCM .另外，信息技术基础设施（S 4)和员工的参与CS 2)能够有效提高整个绿色供应链的活 性.道德标准与企业社会责任（S 3)有助于保护和促进供应链的长远利益.适当的战略规划（S 5)能 够促进成功的G S C M 实施.G S C M 的其他因素如组织结构（S 9)、绿色企业文化（S 7)、环境教育 (S 10)等可影响环境意识、管理者的态度和绿色认证的可能性.愿意为任何环保组织投资的积极态 度（S 8)将有助丁•实现最终目标.2提出的GSCM 实施可行性评估方案结合模糊评价、:D E M A T E L 和M C D M 方法来预测GSCM 实施的成功性.提出的方案可分为两个步骤:首 先，通过D E M A T E L 方法，根据专家的 模糊评价，对各影响因素之间的内部联 系进行分析，以此确定各因素对GSCM 实施的耍性权觅;然后，根据专家对 一个特定企业的考察，给出该企业中每 个因素对其成功实施G SC M 的贡献评 级，利用模糊M C D M 方法计算成功实 施GSCM 的可能性值.图1为提出方图1用来预测G S C M 实施成功性的方案框架白勺丰匡身^F ig.l The framework of prediction scheme for GSCM implementation success probability对于各种G S C M 因素，为了减少主观认知偏差的影响，使用了以三角模糊数量化的语言变f f l ，来表述各因素之间的影响程度和各圃素对成功实施G S C M 的贡献等级.如表2所示.2.1基于DEM ATEL 计算因素的重要性权重这一步骤中，通过使用D E M A T E L 来确定G S C M 中各因素之间的相互影响程度，以获得各因 素的权重，从而确定G S C M 成功实施中的关键影响因索17 .具体步骤如下：组成决策制定专家团队和制定评估标准获得各因素的模糊评价构建初始直接影响矩阵(A)获得规范化的直接影响矩阵的获得综合影响矩阵r=£(/-£)_|计算因素重要性权重Y |通过模糊MCDM 获得成功的可能性评级|预测GSCM 成功实施的概率模糊D E M A T E L'计算影响因素的重 要性权屯:达到预定的概率丨完善或补救丨[ 实施GSCM 1118湘潭大学自然科学学报2016 年步骤1:用三角模糊数（Z ，m ，r )来表示影响评 价.设定4，r y .)为第々个专家给出的因素z 对因素j 的影响程度的模糊评价， Z \= = max — min ，将专家的模糊评价进行 标准化：x l % = (/■■ — m inZ |)/Z \S ?~1«Km i n /|)M X , xr % = (r | -, (D1«JC1«JC表2影响程度或贡献等级的语言值Tab. 2 Linguistic scales of impact value or contribution level语言术语语言值非常高的影响/贡献（V H )(0. 75,1. o a . 0)高影响/贡献（H )(0. 5，0. 75，1. 0)低影响/贡献（L )(0. 25，0. 5，0. 75)非常低的影响/贡献（V L )(0,0. 25,0. 5)没有影响/贡献（N O )(0, 0, 0.25)然后，计算左和右的归一化值：= ^4/(1+^4 — 一观！），接着，计算总的归一化值：= [乂4,a —乂4,）+'r r 4，]/(i +'r r 4，一乂4,）.通过解模糊化计算评价的明 确值[S ]:P | = niinZ 丨一 4 m inA = .重复上述操作，将所有K 个专家的评价进行整合，获得综合1«JC 1«JC 影响程度得分：％ = 士，以此构建一个初始直接影响矩阵a = [^]，〜表示因素*对因々k 素_?的直接影响程度.步骤2:将矩阵A 中的所有元素规范化到<1，获得规范化直接影响矩阵£= [X ,]=v (s g E ;-1A.步骤3:通过T =£ a -£广\获得综合影响矩阵r ，反映每一对因素之间的总关系，j 为单位矩阵.步骤4:计算综合影响矩阵r 的各行元素的和r ,及各列元素的和q E~ A =.步骤5 :获得第t 个因素的最终重要性权重死=X ^+ c ,)/ X “ X ^+ c , )[9].2.2基于M CDM 评估因素的贡献值这一步骤中，使用模糊M C D M 方法，根据多因素来评估在一个企业成功实施G S C M 的可能性 评级，发现该企业中制约G S C M 实施的影响因素.具体步骤如下：步骤1:对于该企业中的各影响因素（S ，t = 1，2,…，ra )对成功实施G S C M 的贡献，各专家(£%_； = 1，2,…，T O )使用语言学术语提供一个模糊贡献评价，以此构造一个决策矩阵X :其中m 表示专家数，n 为影响因素数表示第个专家对第t 个影响因素的模糊贡献评价值.步骤2:专家评价及其经验、直觉或知识相关，故有所不同本文使用平均分数法来整合™个专家对第*个影响因素的模糊贡献评级[11]，获得综合三角模糊贡献评级心=(L g u M ?,，％,)=m L jt [J步骤3:将&解模糊化得到数字型贡献评级兑，用来表示该企业的第t 个影响因素对GSCM 成功实施的贡献级别[12]Q , = [0/心一 L ^) + (M & — L ^)]/3+L ^ .2.3计算GSCM 成功实施的概率值通过将所有影响因素的重要性权重乘以贡献评级兑，可获得在该企业成功实施G S C M 的预测概率值九u _s =• Q ,.i -1第4期尹小悦，等基于模糊评价的绿色供应链管理可行性评估方案1193案例分析选择本地一个规模较大的汽车配件公司作为案例.首先，确定如表3所示的GSCM 10个影响因素，然后，找了 8位专家组成委员会，并对10个GSCM 因素之间的相互影响给出评价.接着，计算初始直接 影响矩阵A 和归一化的综合影响矩阵T (如表3所示），以及每个因素的重要性权重死（如表4所示).表3各因素之间的综合影响矩阵Tab. 3The comprehensive effects matrix between various factorsG S C M 因素S1S2S3S4S5S 6S7S 8S9S10S100. 4280. 4750. 4900. 5950. 5220. 4660. 5120. 4620.423S20. 31100. 3850. 3880. 4600. 4170. 3840. 4370. 3980.305S30. 2530. 35300. 3440. 4080. 3930. 3150. 3900. 2820.261S40. 4280. 4050. 46700. 6000. 5470. 4680. 5350. 4720.423S50. 4730. 3560. 4850. 59400. 5680. 4650. 5520. 5480.493S 60. 4820. 4780. 5130. 6030. 65700. 5110. 6090. 5250.483S70. 3460. 3530. 4380. 4260. 4990. 45300. 4200. 3720.355S 80. 3970. 3740. 4600. 5410. 5410. 5230. 45300. 4810.386S90. 3770. 3870. 4700. 5250. 5710. 5370. 4120. 52200.421S100. 3390. 3360. 3670. 4470. 4930. 4230. 3670. 4450. 371Tab. 4表4每个因素的重要性权重值The importance weight value of each factorG S C M 因素S I S2S3S4S5S 6S7S 8S9S10rt 4. 693 3. 765 3. 158 4. 722 5. 237 5. 359 4. 006 4. 606 4. 549 3. 881ct 3. 734 3. 8024. 325 4. 7895. 3564. 8624. 156 4. 8624. 2633. 825W i0. 0960. 0860. 0850. 1080. 120. 1160. 0930. 1080. 10. 088为了获得在该企业上成功实施GSCM 的预测概率值，要求8位专家使用语言变 量对该企业中每个因素的贡献进行评价，并将其转化为相应的三角模糊数X ，如表 5所示.然后，整合专家门给出的各因素的 贡献评级，从而获得综合三角模糊贡献评级g ,.将g ,进行解模糊化为数字型贡献 评级Q ，如表6所示.最后获得最终的成 功率预测值，如表7所示.表6各因素综合贡献评级的解模糊化值Tab. 6The defuzzy value of comprehensive contribution ratingG S C M 因素专家编号qiaE lE 2E 3E 4E 5E 6E 7E 8S I (0.25,0.5,0.75)(0. 75,1. 0,1. 0)(0.5,0.75,1.0)(0,0.25,0.5)(0. 75,1.0,1. 〇)(0.25,0.5,0.75)(0.5,0.75,1.〇)(0. 75,1. 0,1. 0X 0. 47,07288)069S 2(0,0,0.25)(0.25,0.5,0.75)(0. 75,1.0,1. 0)(0.5,0.75,1.0)(0.25,0.5,0.75)(0. 75,1. 0,1. 〇)(0.5,0.75,1.〇)(0. 5,0. 75,1.0X 0. 44,06684)065S 3C 〇5,0.75,.0)(0,0,0.25)(0.25,0.5,0.75)(0.5,0.75,1.0)(0. 75,1.0,1. 〇)(0.25,0.5,0.75)(0,0.25,0.5)(0. 5,0. 75,1.0X 0. 34,05678)056S 4C 〇5,0.75,.〇)(0.5,0.75,1.〇)(0. 75,1.0,1. 0)(0.25,0.5,0.75)(0. 75,1.0,1. 〇)(0.5,0.75,1.0)(0.5,0.75,1.〇)(0.25,0.5,0.75^0.50,07594)073S 5C 〇75,1. 〇,.〇)(0.25,0.5,0.75)(0. 75,1.0,1. 0)(0.5,0.75,1.0)(0.25,0.5,0.75)(0. 75,1. 0,1. 〇)(0.5,0.75,1.〇)(0. 5,0. 75,1.0X 0. 53,07894)075S 6CO 75,1. 0,.0)(0.5,0.75,1.0)(0. 75,1.0,1. 0)(0. 75,1. 0,1. 0)(0. 75,1.0,1. 0)(0.25,0.5,0.75)(0.5,0.75,1.0)(0. 75,1. 0,1. 0X 0. 63,08897)082S 7c 〇5,0.75,.〇)(0,0,0.25)(0.25,0.5,0.75)(0. 75,1. 0,1. 〇)(0. 5,0.75,1.〇)(0. 75,1. 0,1. 〇)(0.25,0.5,0.75)(0. 5,0. 75,1.0X 0. 44,06684)065S 8c 〇75,1. 〇,.〇)(0. 75,1. 0,1. 〇)(0. 75,1.0,1. 0)(0.5,0.75,1.0)(0. 75,1.0,1. 〇)(0.5,0.75,1.0)(0. 75,1.0,1. 〇)(0.25 ,0.5,0.75^0.56,08194)077S 9CO 75,1. 0,.0)(0,0.25,0.5)(0. 75,1.0,1. 0)(0.25,0.5,0.75)(0. 75,1.0,1. 0)(0. 75,1. 0,1. 0)(0.5,0.75,1.0)(0. 75,1. 0,1. 0X 0. 56,08691)076S 10(0,0.25,05)(0.5,0.75,1.〇)(0.5,0.75,1.0)(0.25,0.5,0.75)(0. 75,1.0,1. 〇)(0.5,0.75,1.0)(0. 75,1.0,1. 〇)(0.25,0.5,0. 75X 0.44,06988)067表5各因素的三角模糊贡献值Tab. 5 The triangular fuzzy contribution value of factorsG S C M专家编号因素E l E2E3E4E5E 6E7E 8S I L V H H V L V H L H V H S2N O L V H H L V H H H S3H N O L H V H L V L H S4H H V H L V H H H L S5V H L V H H L V H H H S 6V H H H V H V H L H V H S7H N O L V H H V H L H S 8L V H V H H V H H V H L S9V H V L V H L V H V H H V H S10V LHHLV HHV HL通过表7可以看出，该企业实施G S C M 的成功率为71. 2%.其中，该企业的高层管理者的承120湘潭大学自然科学学报2016 年诺和支持（S6)、战略规划（S5)、环保投资意愿（S8)、组织结构（S9)和信息技术基础设施（S4)这5个 方面为G S C M成功实施提供了良好基础，但其他方面的贡献较低，还需进一步改善.根据实际调查，预测结果与该企业的实际情况相符合.在一个企业决定实施G S C M前，需要根据实际情况对实施成功性进行预测.为此，本文提出 了一种基于模糊D E M A T E L和M C D M的可行性评估方法.其中所有的专家评价都采用三角模糊表示，利用D E M A T E L获得各G S C M影响因素的权重，利用M C D M获得影响因素的贡献值，从 而获得可行性概率.案例分析表明，本文方案能够提供一个企业中各因素对实施G S C M的影响 值，为决策者提供有力依据.表7 每个因素的重要性权重、贡献评级和GSCM成功实施的预测概率Tab. 7 The importance weight, contribute rating of each factorand the forecast probability of GSCM implementation successG S C M因素w!Q y W,,X Qj夕s u c c e s sSI0.0960. 690. 066S20.0860. 650. 056S30.0850. 560. 048S40. 1080. 730. 079S50. 1200. 750. 0900. 712S60. 1160. 820. 096S70.0930. 650. 060S80. 1080. 770. 083S90. 1000. 760. 076S100.0880. 670. 058参考文献[1]刘晓伟，姚石顺，龚玉洁.企业绿色供应链管理绩效评价的研究[J].辽宁工业大学学报：自然科学版，2013,33(6)：407-412.[2] LIN J, DAI X. Study on green supply chain management of agricultural products processing enterprises of Jilin Province[J]. Journal of Business Administration Research, 2015，4(1) ：145-152.[3]张毕西，张明珠，韩正涛.基于模糊TOPSIS的绿色供应链绩效评价[J].天津工业大学学报，2014, 33(4):76-79.[4]刘永清，胡义润，贺竹磬，等.废旧电器回收渠道决策影响因素的DEMATEL分析[J].湘潭大学自然科学学报，2015, 37(1): 115-120.[5]袁宇，关涛，闫相斌，等.基于区间直觉模糊数相关系数的多准则决策模型[J].管理科学学报，2014, 17(4)：11-18.[6] YANG J. Integrative performance evaluation for supply chain system based on logarithm triangular fuzzy num-ber-AHP method[J]. Kybernetes, 2013, 38(10) ：1760-1770.[7] GANDHI S, MANGLA S K, KUMAR P, et al. Evaluating factors in implementation of successful greensupply chain management using DEMATEL：A case stud y[J]. International Strategic Management Review, 2015, 3(1)：96-109.[8] LIN K P, LIN R J, HUNG K C. Developing T〇>,fuzzy DEMATEL method for evaluating green supply chainmanagement practices[J]. Fuzzy Systems，2014，18(1) ：1422-1427.[9] MIRHEDAYATIAN S M, AZADI M, SAEN R F. A novel network data envelopment analysis model for evaluatinggreen supply chain management[J]. International Journal of Production Economics，2014，147(1) :544-554.[10]段茜，黄梦醒，万兵，等.云计算环境下基于马尔可夫链动态模糊评价的供应链伙伴选择研究[J].计算机应用研究，2014, 31(8): 2403-2406.[11] RAMAN A D V, RAO K N, KUMAR J S. Evaluation of performance metrics of leagile supply chain throughfuzzy MCDM[J]. Decision Science Letters, 2013，2(3) ：211-222.[12] LIN Y，TSENG M L，CHIU A S，et al. Implementation and performance evaluation of a firm’s green supply chainmanagement under uncertainty [J]. Industrial Engineeering & Management Systems, 2014(1) ：15-28.责任编辑：龙顺潮。
国家重点生态功能区县域环境监测质量评价方法及应用示范
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国家重点生态功能区县域环境监测质量评价方法及应用示范
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１３ ｅｖａｌｕａｔｉｏｎ ｅｌｅｍｅｎｔｓ ａｓ ｆｏｌｌｏｗｓ: ｐｅｒｓｏｎｎｅｌ ｏｐｅｒａｔｉｏｎꎬ ｐｏｓｔ ｈｏｌｄｉｎｇ ｃｅｒｔｉｆｉｃａｔｅꎬ ｍｏｎｉｔｏｒｉｎｇ ｑｕａｌｉｆｉｃａｔｉｏｎꎬ ｔｒａｉｎｉｎｇꎬ ｓａｍｐｌｉｎｇ
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三层:第一层为目标层ꎬ即国家重点生态功能区县域环境监测质量ꎻ第二层为准则层ꎬ包括人员及资质、现场监测、实验室
管理、报告编制及数据上报ꎻ第三层为方案层ꎬ包括人员操作、持证上岗、资质认定、人员培训、水质布点采样流转情况、空
气自动站运维情况、现场质控实施情况、实验室环境条件、样品试剂的保存与管理、仪器检定与校准、实验室质量控制实
施情况、数据填报软件运行情况、监测报告规范性等 １３ 个评价要素ꎮ 经矩阵一致性检验确定了各评价要素的权重ꎬ将该
权重与各要素得分运算后得到县域环境监测质量评价结果ꎮ 在此基础上ꎬ选取广东、山西、陕西、四川和青海等 ５ 个省份
的 １５ 个国家重点生态功能区县域作为典型区开展了实地调研ꎬ并应用评价体系对其进行了监测质量等级评价ꎮ 结果表
第一次英语高考作文押题成功朋友圈文案
第一次英语高考作文押题成功朋友圈文案全文共3篇示例，供读者参考篇1Here's a 2000-word draft in English of a social media post from the perspective of a student who successfully predicted the topic of their English exam essay:Squad, you won't believe this! I just aced my English exam and I'm still buzzing. You know how we've been stressing over that essay section for months? Well, let me tell you, all those late nights poring over past papers and trying to predict the prompts were worth it!So there I was, settled into the exam hall, bags under my eyes from cramming. The invigilators were doing their usual spiel about unauthorized materials, bathroom breaks, all that jazz. My heart was pounding like a freight train. This was it - the big one. The envelope was ripped open and...No way. No actual way.It was THE prompt. The one I'd been hunching on for weeks. My jaw must've hit the floor. All those wacky theories and prediction strategies that had my friends rolling their eyessuddenly paid off in spades. I could've started writing that essay with my eyes closed!You should've seen the look on Emma's face when I leaned over and told her. Sheer disbelief. We've been dreaming of nailing this bad boy since freshman year, putting in more hours than I care to count. All those nights bitching about the ambiguity and unpredictability of these prompts made this moment so sweet.I put pen to paper and the words just flowed. I was in the zone, fam. Totally locked in. It was like an out-of-body experience almost. I shut out all the noise, all the nervous shuffling and pencil tapping around me. Just me and the paper and the prompt I'd envisioned a thousand times.The key was being prepared for anything, you know? We went so deep into the potential topics, analyzing every angle, every controversial issue they could possibly throw at us. Social media's impact, climate change, you name it. We turned over every rock until we were convinced we had it figured out.With 15 minutes left, I finally resurfaced and looked around in a daze. Kids were frantically scribbling while I just sat there grinning like a fool. I could've kept writing for days. When theyfinally called time, I very calmly set down my pen and stretched with the biggest smile.You know that warm, fuzzy feeling of pure satisfaction? Like you genuinely couldn't have given it any more effort or preparation? That was me leaving the hall today. On cloud nine. We've been putting in work since the first essay writing workshop back in 10th grade, and today it showed.Of course, the savvy teacher in me knows one exam doesn't make or break anything. In the grand scheme of things, it's just a small slice of the big evaluation pie. But you have to celebrate the wins, am I right? All that stress and doubt melted away the second I read that beautiful, beautiful prompt.Shoutout to the real MVPs who stayed up with me going over those past papers till ungodly hours - you know who you are. The ones who kept me centered when I started spiraling about potential curveballs. We've climbed this mountain together and seeing that prompt today was the payoff for all those breakdowns and revisions.To everyone who called me a madman for my obsessive prediction methods, for the reams of notes and sources plastering my bedroom walls...who's laughing now? Haha justplaying, you know I love you really. I just had a vision and went all-in, no regrets!Don't get me wrong, there's still so much left to write in this crazy journey. No way am I resting on my laurels after one W. We've got Finals coming up thick and fast. But tonight, we celebrate this small victory. It's been a long road, filled with more false starts and rabbit holes than I care to remember. But that makes moments like this all the sweeter.It's been a huge grind, but we made it over this first huge hump together, fam. We manifested that prompt through hard work, dedication, and maybe a little bit of divine intervention. Who knows? All I know is I've never felt this sort of pure euphoria and pride in my life.I'll leave it at that before I start welling up or something, haha. Just wanted to shout out the crew and share this little personal triumph. We've got plenty more writing ahead, but tonight we revel in dodging those unpredictable essay bullets. Here's to putting in the work and seeing it pay off.More to come, but for now...your boy's just feeling accomplished. I'll catch you all for the celebratory stack of pancakes tomorrow morning. My shout, you already know. Congratulations to us - we did it!篇2The Thrill of Nailing the Exam Essay TopicYou know that incredible feeling when you study super hard for a test and your preparation pays off in a huge way? That's exactly what happened to me on the English exam last week. Months of analyzing past essay prompts, practicing timed writings, and staying up late trying to anticipate potential topics finally came to fruition when I cracked the code on the exam essay topic. Let me tell you, landing the perfect prediction was an absolute rush!It all started way back at the beginning of the semester when my English teacher emphasized how crucial the essay portion would be. She scared us all half to death with statistics on how many students bomb that section every year. However, she also offered a glimmer of hope - she said if we worked systematically to understand the patterns and trends in prior years' prompts, we could take an educated guess at what we might see on our exam. Deducing the topic ahead of time could give us a massive advantage.At first, I'll admit I was pretty skeptical of her advice. Exam topics always seemed to be drawn from such an enormous poolof concepts and angles. How could anyone possibly predict with confidence what we'd be writing about? But my teacher was relentless. She had us meticulously catalog hundreds of old prompts, categorizing them into themes like technology, the environment, education, and so on. We analyzed word choices, tones, perspectives, and approaches the prompts took for each subject area.As tedious as it was at times, I could start to see the wisdom behind her methods. Definite trends were emerging in terms of what types of issues and angles tended to be recycled year over year. Some prompts would delve into more abstract realms like the nature of success, truth, or beauty. But many seemed to orbit a core set of high-interest, high-importance societal topics - the environment, education, poverty, etc. I started to realize these "Greatest Hits" Subject Areas were where our writing prompt was most likely to originate.My dedication to understanding prior prompts kicked into an even higher gear after winter break. I pored over my spreadsheets of categorized topics from past years, looking for even the slightest pattern or insight that could hint at our exam topic. Several themes emerged as promising candidates based on how frequently and recently they had been represented. Butthere was one issue in particular that seemed to fit best with the contemporary social climate we were living in.You could probably guess what that hot-button issue was - environmental protection and sustainability. It was all anyone could talk about, from the news to social media. Extreme weather events, wildlife extinction, plastic pollution choking the oceans. Everywhere you looked, there were impassioned calls to take action on climate change and be better stewards of the planet. It seemed the perfect backdrop for our writing task - a relevant, high-stakes topic that we could take a critical look at from multiple angles. I became convinced this had to be our exam essay focus.In the weeks leading up to test day, I drafted practice essays and arguments from every conceivable position on environmental issues. I worked to anticipate potential counterpoints and craft cohesive, nuanced responses drawing in topics like environmental policy, developing sustainable technologies, biodiversity, and environmental justice. My thinking was that by establishing a comprehensive content base for this presumptive topic, I would be able to pivot seamlessly when the actual prompt was released.So when exam day arrived and the proctor read us the essay question, you can imagine my elation. There it was, clear as day - "In an era of rising climate change concerns and environmental degradation, what role do you think young people should play in protecting the planet's future? Provide specific examples to support your position." I couldn't believe my fortune! Hitting the bullseye on the topic felt like the biggest adrenaline rush.All those late nights and sacrificed weekends studying past prompts and prepping content had paid off immensely. As my peers frantically underlined key parts of the prompt, jotted down thoughts, and broke out into a cold sweat trying to formulate a response, I was the picture of calm and confidence. I had done so much prewriting and content preparation that I was able to launch straight into my intro paragraph without skipping a beat. The ideas, examples, and analysis just flowed from my brain to the paper.You've never seen someone write an essay faster or with more extreme focus. I tapped into an otherworldly writing zen, not wasting a single second as I cranked out an insightful, multi-perspective response packed with facts, statistics, and substantive examples. I tied in environmental policy discussions, the role of developing clean energy technologies, preservingbiodiversity, and uplifting marginalized communities disproportionately impacted by things like air pollution and resource depletion. My conclusion was tight and resonant, leaving the reader with a sense of urgency to be part of the solution.When I finally emerged from my writing trance and put down my pen, I felt a mix of elation, pride, and relief. My gamble of going all-in on predicting the environmental protection topic had hit the jackpot. All my hard work, determination, and content mastery had culminated in absolutely crushing this make-or-break exam essay. Even though I knew my final score was still weeks away, I celebrated that night like I had already aced the test.My English exam triumph really drove home for me the value of diligent preparation and doing your homework, no matter what curve balls get thrown your way. By taking a systematic, resourceful approach to understanding the landscape of potential essay topics, I was able to position myself optimally. Then it was just about putting in the hard work - logging the hours prewriting, studying examples, and mastering relevant content. The payoff of having all that expertise at my fingertipswhen the pivotal moment arrived on test day made any sacrifices leading up to it feel incredibly worthwhile.So for any students reading this who are working towards a major exam or academic milestone, let my story be an inspiration and proof of concept. Don't view preparing for an open-ended essay or writing篇3Title: Nailing the English Exam Essay Topic - A Shared Victory!You know that feeling when you've been studying and preparing for something huge for months, and it all comes down to one nerve-wracking moment? The English exam essay was undoubtedly that make-or-break component for so many of us. But let me tell you, fam, the sweet taste of victory is incredible when your prediction game is on point!I still remember the buildup to the big day like it was yesterday. We've all been there - trawling through past papers, analyzing every little nudge and wink the examiners seemed to give about potential topics. Tapping into those exquisite linguistic grapevines for any crumb of insider info was pretty much an obsession.And then there were the bets, oh the bets! Correct predictions would be hailed like prophecies, while being even slightly off-topic could shatter egos and crack resolves. We're all low-key adrenaline junkies, right? The high of getting it right was unmatched.Anyway, for those who hadn't heard my humble brag yet, I'm proud to declare - I smashed it! My carefully cultivated hunch was so on-point that I could practically hear the sweet sighs of the exam invigilators as they read my sterling response. My friends are still raving about it weeks later. I'm basically Babe Ruth calling my shot before knocking it out the park, but for the English classroom. Dazzling, I know.What made my prediction so elite, you ask? Well, my genius process was an artful blend of deep topical analysis, luck, and gut instinct. First up, I diligently charted out the recent prevalence and significance of various prominent social themes across past papers. This gave me a basic sense of what big-picture topics were rippling across recent zeitgeists.From there, I laser-focused on any tiny breadcrumbs or clues buried within recent reading comprehension passages and question phrasings. Any remotely meaningful quotes or angles got meticulously logged away. By this point, my brain waspractically rewired into a supercomputer cross-referencing key terms and detecting coded semantic patterns.But where I really separated myself from the pack was tapping into the underground network of uncannily wayward mystics who have seemingly developed a preternatural sense for sniffing out exam board motives. Yeah, I'm talking about those wise 'question-sages' who have foreseen the precise wording of multiple-choice problems before they ever manifested on paper. Their occult knowledge bathed my aura in an ineffable energy.So behold, my prediction for the English exam essay topic materialized from the ether: "In an era of propagating artificial intelligence across all realms of society, do you think there are still spaces where human ingenuity should reign supreme? Provide justifications for your stance." A beauty, right?My openingaragraphs buttered up the readers by acknowledging AI's remarkable impact in streamlining and optimizing many industries. I conceded how narrow AI domains could readily enhance efficiency for various data processing roles. However, the bulk of my argument centered around the notion that broad cognitive replications and uniquely human capacities like creativity, emotional intelligence, and dynamicproblem-solving must be safeguarded for humanity's sovereignty.I waxed poetic about AI's grand limitations in curiosity, ambition, and independent purpose. I stressed how ourself-determination and identities hinge upon domains like ethical reasoning, subjective artistic expression, and progressive development remaining fundamentally human-driven. While I couldn't resist cheekily footnoting that robotic markers shouldn't feel too threatened by my rhetorical prowess just yet.That's my magnum opus, baked to thematic perfection! I can still picture the visible pride spreading across my teacher's face as they relished each impeccably composed paragraph. Just sheer stratospheric quality.And let's be real, my complete prophecy obliteration of that topic prompt sparked a minor social media flurry too. You've no doubt stumbled across some of the rapturous posts and reactions from my crew? My modesty only stretched so far before I had to share the revelatory achievement far and wide.Whether it was my legendary humble-brag tweet thread meticulously recounting my cerebral chef's-kiss predictions for the ages, orrather more gaudy victory lap posts complete withdigital eagle top-gun flyover memes, I spared no indulgence in savoring my deserved laurels.Heck, I even caught myself intermittently dubbing audio clips of my multilayered analysis dissecting every semantic potentiality which led me to the Topic Truth. Did a fair few of those just naturally evolve into the voiceovers for cinematically-scored YouTube video dissertation breakdowns too? You already know the answer to that one.I soaked up every ounce of those glorious reverberations across cyberspace. My dear friends swiftly found themselves border-line merchants of my achievement's significance. "You truly love to see it" and similarly impassioned props abounded. Obligatory fire emoji reactions to my legendary content trailed as far as my social streams could reach.Was it all a bit much, you wonder? Was I being the human embodiment of thaticily arrogant lad who won't shut up about acing the LSAT or something? You could maybe argue I'd drifted into stratospheric self-parody at points there. But can you really blame a young visionary for wanting his immense clairvoyance appreciated in full? I was the schooling Nostradamus!Those euphoric victory laps couldn't last forever though, sad as that truth was to accept. My humbling poetry slammed backto Earth as the adrenaline inevitably wore off. I sheepishly returned to hitting the books, preparing to wow them all over again on the next go-round. The prophecy game respects hard work, not laurel resting.But hey, for that shimmering stretch at least, I was the bulletproof conquering hero. My carefully preempted genius unraveled before all to admire in its full glory! Even if my feats get inevitably eclipsed by someone else's sooner or later, nothing will ever erase my name from the hallowed grounds of English Wizardry.To my contemporaries still sharpening their glimpse into the future, be fortified. Keep grinding, keep sleuthing, keep attuning yourselves to every coded signal that could hint at your next meant-to-be prediction. Stay hungry for those ballsy prognosis plays that separate the non-fiction best-sellers from the fiction flops. If you're arrogant enough to will yourself to prophetic levels, perhaps you too can etch your chapter into these sacred texts one day.So yes, brag ample about your heroics while they remain evergreen! Propagate the reckoning that was your immaculate intellect far and wide across those cheerfully jealous airwaves. Every retweet and rhetorical crown emoji secured for yourlegend only enhances its lustre. Induct your supreme cognitive eminence into the pantheon for this quad's snarkiest group chat banter to referentially revel in for eons!。
基于古斯塔夫法的大型购物中心火灾风险评估_陶亦然
人员特征因子 k 用于 标示 火灾 中人 员清 醒状 态、对 疏散路径的熟悉程 度以及 特定人 群的 行动能 力, 根 据不
同建筑物及人员 特性进 行分 类。人 员危 险因 子 H 的取
值受人员多少、对建筑物疏散通道 的熟悉程度、出口位置
以及 数量 等因素 影响。 烟气 因子 F 主要 考虑 烟气 的毒
影响建筑物火灾风 险的要 素主 要有几 个方 面: 建 筑 物结构特点、可燃物 燃烧特 性及其 分布、火灾烟 气特 点、 建筑物内人员特点、消防部门灭火救援 能力、建筑物内 部
及周边的消防设施 状况 等。通过对 各要 素归纳 总结, 可 得到公共 建 筑 火灾 风 险 R, 它 是 由 建 筑 物 火 灾 危 险 度 GR, 建筑物内人员危险度 I R, 以及控制 风险的 能力 C 三 个因素共同决定的。古斯塔夫法用纵坐标 表示建筑物 火 灾危险度 GR, 用横坐标代表建筑物内 人员危险度 I R, 绘 出建筑危险度分布 图。将分 布图的 平面 分成 4 个区, 根 据具体条件选用保护方案。 1. 1 建筑物火灾危险度 GR 的分析与确定
性、烟气浓度、哪 些材 料容易 产生烟、烟的 各种 间接 腐蚀
性等。人员数量因子 ∀和以上 3 个因子亦 可通过 查表获
得具体取值。
1. 3 控制风险能力因子 C 的分析与确定
控制风险能力为消防队控制风 险能力与建 筑物自身
控制风险能力之和。消防队控制风 险能力受建 筑物周围
单个消防中队灭火救援的能力、距 离建筑物的 路程、建筑 物高度以及外部消防设施影响消防 队控制风险 能力等因 素影响。C 的计算见式( 4) :
防车行驶时 速, km/ h。 一般 来说, 地 级市 以上 的大 城 市
消防 车行驶 时速取 20 km/ h, 县级市 及乡 镇消防 车行 驶 时速取 30 km/ h。
基于贝叶斯网络的深基坑风险模糊综合评估方法_周红波
Risk Assessment of Deep Foundat ion Pit by Using Fuzzy Comprehensive Evaluation Method Based on Bayesian Networks
Z H OU Hongbo 1 , 2 ( 1. Shanghai Research Instit ute o f Building Sciences , Shang hai 200032 , China ; 2. Shanghai Jianke P roject M anagement Co . , Ltd . , Shanghai 200032 , China) Abst ract : A f uzzy comprehensive evaluat ion met ho d based on Baye sian netw o rks w as propo sed . M o reove r , a sim ple accident dat abase of deep foundati on pi ts w as established , w hich could provide the probabilit y of basic events and the di st ributi on of risk consequence . T hrough linear deduction of Bay esian netw o rks , the pro babi li ty of every risk event w as calculat ed . T hen the co nsequence of every risk event w as calculated by usi ng f uzzy analy si s method through t he stati sti cal co nsequence di st ribut ion and w eig ht coeff icient of ri sk event s fro m the dat abase . F urt herm ore , a f uzzy com prehensive evaluation m odel w as advanced , and the risk rat ing of each risk event of deep f oundat ion pit was assessed . T he result s show that the w hole risk rating of t he fo undat ion pit i s rati ng 3 , in w hich the tw o risk event s of st rut coll apse and ove rlo ads on the st rut s a re rating 4 , and t he ot her event s such as leakage of diaphrag m w all , soi l slide in the pi t , confined w ater outburst , pit bot t om upheaval and injuries by machines and so o n are ra ting 3 . T he case study verif ies the pract icabili ty and rat ionali ty of the risk assessm ent method . T herefo re , the proposed ri sk assessm ent me thod co uld be applied in the risk precauti ons f or deep foundatio n pit constructions . Key words : deep fo undat ion pi t ; risk assessment ; Bayesian netw o rks ; fuzzy com prehensive evaluat ion ; membe rship functio n
江安河武侯区段水质监测与评价
江安河武侯区段水质监测与评价摘要：合理设置断面于２０１０年１１月至２０１１年３月每月采样１次对江安河武侯区段的一些常规污染指标进行了监测，并结合单因子污染指数法对各断面水质进行了评价，然后结合评价结果进行了分析。
结果表明，以地表水环境质量标准中ⅲ类标准计算得到的硫化物、ｔｐ、ｃｏｄｃｒ等指标的污染指数均已超过１，尤其是硫化物污染指数甚至达到９９．６０，表明江安河武侯区段水体已经受到严重污染，上游带来的污染是该段水体污染的主要原因。
关键词：江安河武侯区段；单因子污染指数法；监测；评价investigation and assessment of water quality in wuhou interval of jiang’an riverｌｉｕｙａｏ－ｙｕａｎ，ｚｏｕｃｈａｎｇ－ｗｕ，ｙｉｎｊｉａｎ，ｘｉａｈｅ，ｚｈｏｕｙｕａｎ－ｙｕａｎ，ｚｈａｎｇｙｕｎ－ｐｅｎｇ（ｄｅｐａｒｔｍｅｎｔｏｆｒｅｓｏｕｒｃｅｓａｎｄｅｎｖｉｒｏｎｍｅｎｔ，ｃｈｅｎｇｄｕｕｎｉｖｅｒｓｉｔｙｏｆｉｎｆｏｒｍａｔｉｏｎｔｅｃｈｎｏｌｏｇｙ，ｃｈｅｎｇｄｕ６１０２２５，ｃｈｉｎａ）ａｂｓｔｒａｃｔ：ｓｏｍｅｒｏｕｔｉｎｅｐｏｌｌｕｔｉｏｎｉｎｄｅｘｅｓｏｆｗａｔｅｒｑｕａｌｉｔｙｉｎｗｕｈｏｕｉｎｔｅｒｖａｌｏｆｊｉａｎｇ＇ａｎｒｉｖｅｒｗａｓｍｏｎｉｔｏｒｅｄａｔｓｅｖｅｒａｌｓｅｃｔｉｏｎｓ；ａｎｄｔｈｅｗａｔｅｒｑｕａｌｉｔｙｗａｓａｓｓｅｓｓｅｄｂｙｓｉｎｇｌｅｆａｃｔｏｒｃｏｎｔａｍｉｎａｎｔｉｎｄｅｘｍｅｔｈｏｄ．ａｆｔｅｒｔｈａｔ，ｔｈｅｒｅａｓｏｎｏｆｐｏｌｌｕｔｉｏｎｉｎｔｈｉｓｉｎｔｅｒｖａｌｗａｓ analyzed．ｔｈｅｒｅｓｕｌｔｓｓｈｏｗｅｄｔｈａｔｔｈｅｃｏｎｔａｍｉｎａｎｔｉｎｄｅｘ（ｂａｓｅｄｏｎｒａｎｋⅲｏｆｗａｔｅｒｑｕａｌｉｔｙｓｔａｎｄ）ｏｆｃｏｄｃｒ，ｓｕｌｆｉｄｅｓａｎｄｔｏｔａｌｐｈｏｓｐｈｏｒｕｓ（ｔｐ）ｗｅｒｅｍｏｒｅｔｈａｎ１，ｅｓｐｅｃｉａｌｌｙｔｈａｔｏｆｓｕｌｆｉｄｅｓｒｅａｃｈｅｄ up to ９９．６０，ｗｈｉｃｈｓｈｏｗｅｄｔｈａｔｔｈｉｓｉｎｔｅｒｖａｌｗａｓｐｏｌｌｕｔｅｄ，ａｎｄｔｈｅｐｏｌｌｕｔａｎｔwas mainly brought ｂｙｗａｔｅｒｆｒｏｍｕｐ－ｆｌｏｗ．ｋｅｙｗｏｒｄｓ：ｗｕｈｏｕｉｎｔｅｒｖａｌｏｆｊｉａｎｇ’ａｎｒｉｖｅｒ；ｓｉｎｇｌｅｆａｃｔｏｒｃｏｎｔａｍｉｎａｎｔｉｎｄｅｘｍｅｔｈｏｄ；ｉｎｖｅｓｔｉｇａｔｉｏｎ；ａｓｓｅｓｓｍｅｎｔ水是生命的源泉，是人类生存和经济社会发展的基础性的自然资源，又是生态环境的控制要素［１－３］。