WO2023019986A1

WO2023019986A1 - Science and technology service quality evaluation method and device based on combination weighting and fuzzy grey clustering

Info

Publication number: WO2023019986A1
Application number: PCT/CN2022/087222
Authority: WO
Inventors: 欧中洪; 范丽娜; 宋美娜
Original assignee: 北京邮电大学
Priority date: 2021-08-19
Filing date: 2022-04-15
Publication date: 2023-02-23
Also published as: CN113792982A

Abstract

The present invention provides a science and technology service quality evaluation method and device based on combination weighting and fuzzy grey clustering. The method comprises: analyzing the service quality of a science and technology resource service platform, obtaining an evaluation result of the service quality according to a constructed index set, and testing index data; calculating the subjective weight and the objective weight of the index data according to a majority aggregation weighted analytic hierarchy process and a weighting method; using a combination weighting method to combine the subjective weight and the objective weight; using a grey clustering theory to substitute an evaluation object into a whitening weight function, calculating whitening values comprised in different clustering indexes, and obtaining a secondary index service quality evaluation result; and performing primary index evaluation according to an evaluation index system and the secondary index service quality evaluation result to complete evaluation of the service quality of the science and technology resource service platform.

Description

基于组合赋权与模糊灰色聚类的科技服务质量评估方法和装置Evaluation method and device for scientific and technological service quality based on combination weighting and fuzzy gray clustering

相关申请的交叉引用Cross References to Related Applications

本申请基于申请号为202110956883.3、申请日为2021年08月19日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。This application is based on a Chinese patent application with application number 202110956883.3 and a filing date of August 19, 2021, and claims the priority of this Chinese patent application. The entire content of this Chinese patent application is hereby incorporated by reference into this application.

技术领域technical field

本公开涉及质量评估技术领域，特别涉及一种基于组合赋权与模糊灰色聚类的科技服务质量评估方法和装置。The present disclosure relates to the technical field of quality assessment, in particular to a method and device for evaluating the quality of scientific and technological services based on combined weighting and fuzzy gray clustering.

背景技术Background technique

科技资源服务平台是科技创新体系的重要组成部分，通过有效汇聚整合多元异构科技资源、激活创新需求，实现科技资源优化配置和开放共享，为推动科技创新发展提供了数字化、智能化、网络化的支撑体系。因此，构建科学合理的科技资源服务平台服务质量评估模型，客观全面地评估平台服务质量，使平台能及时有效地掌握自身发展状况和服务质量，对于提升我国科技资源服务平台的服务水平、创新能力、运行效率以及促进科技资源开放共享和高效利用具有重要的现实意义与深远价值。目前对科技资源服务平台服务质量进行评估的方法主要包括以下两种方法：The scientific and technological resource service platform is an important part of the scientific and technological innovation system. By effectively converging and integrating multiple heterogeneous scientific and technological resources, activating innovation needs, realizing the optimal allocation and open sharing of scientific and technological resources, it provides a digital, intelligent and networked platform for promoting the development of scientific and technological innovation. support system. Therefore, constructing a scientific and reasonable service quality evaluation model of the scientific and technological resource service platform, objectively and comprehensively evaluating the service quality of the platform, and enabling the platform to grasp its own development status and service quality in a timely and effective manner are crucial to improving the service level and innovation ability of my country's scientific and technological resource service platform. , operating efficiency and promoting the open sharing and efficient utilization of scientific and technological resources have important practical significance and far-reaching value. At present, the methods for evaluating the service quality of the science and technology resource service platform mainly include the following two methods:

(1)用传统的层次分析法构建服务质量评估模型。层次分析法是一种将定性和定量分析相结合的多指标评估方法。通过将复杂问题层次化，根据问题和需要达到的目标，将问题分解为不同的组成因素，并按照因素的相互关联及隶属关系将因素按不同层次聚集组合，形成一个多层次分析结构模型。根据***特点和基本原则，对各层因素进行对比分析，构造出判断矩阵，用求解判断矩阵最大特征根及其特征向量的方法得到各因素的相对权重，据此建立服务质量评估模型。(1) Use the traditional AHP to construct the service quality evaluation model. Analytic Hierarchy Process is a multi-index evaluation method that combines qualitative and quantitative analysis. By stratifying complex problems, decomposing the problem into different components according to the problem and the goal to be achieved, and combining the factors at different levels according to the interrelationship and affiliation of the factors, a multi-level analysis structure model is formed. According to the characteristics and basic principles of the system, the factors of each layer are compared and analyzed, and the judgment matrix is constructed. The relative weight of each factor is obtained by solving the largest eigenvalue of the judgment matrix and its eigenvector, and the service quality evaluation model is established accordingly.

(2)基于BP人工神经网络方法进行服务质量评估。BP人工神经网络是一种按照误差逆向传播算法训练的多层前馈神经网络，其能通过训练学习某种规则，在给定输入值时得到最接近期望输出值的结果。在预训练时，输入为各项指标的评分值，输出为综合评估结果，经过多轮训练后，即能自动拟合各项评估指标的实际权重，从而得到逼近真实的评估结果。(2) Service quality evaluation based on BP artificial neural network method. BP artificial neural network is a multilayer feedforward neural network trained according to the error backpropagation algorithm, which can learn certain rules through training, and get the result closest to the expected output value when the input value is given. During pre-training, the input is the score value of each index, and the output is the comprehensive evaluation result. After multiple rounds of training, it can automatically fit the actual weight of each evaluation index, so as to obtain an evaluation result close to the real one.

如上所述，目前针对科技服务平台服务质量评估的方法主要有：1)使用传统的层析分析法设计其评价指标体系并构造出判别矩阵完成指标权重计算；2)使用基于BP人工神经网络的方法进行指标权重的拟合，从而得到一个黑盒评估模型。方式1)使用层次分析法赋予权重，将复杂问题层次化，但该权重通过领域专家的主观判断得出，因此带有较强的主观性，受评估者的知识结构及偏好等影响较大。同时，在进行矩阵一致性检验时，如未能得到合理的一致性，还需对其进行调整，当分析的指标数量过多时，计算量大，运算周期长；方式2)虽然具有较好的泛化能力、非线性映射能力以及高度并行性，但其需要大量的数据来优化模型，且在基于数值计算分析时，评估过程信息容易丢失。As mentioned above, the current methods for evaluating the service quality of science and technology service platforms mainly include: 1) using the traditional tomographic analysis method to design its evaluation index system and constructing a discriminant matrix to complete the index weight calculation; 2) using the BP artificial neural network The method fits the index weights to obtain a black-box evaluation model. Method 1) Use the AHP to assign weights to stratify complex issues, but the weights are obtained through the subjective judgment of domain experts, so they are highly subjective and are greatly affected by the knowledge structure and preferences of the evaluators. At the same time, if a reasonable consistency cannot be obtained during the matrix consistency test, it needs to be adjusted. When the number of analyzed indicators is too large, the calculation amount is large and the operation cycle is long; although method 2) has a better Generalization ability, non-linear mapping ability and high parallelism, but it requires a large amount of data to optimize the model, and when it is based on numerical calculation analysis, the evaluation process information is easily lost.

现有科技服务平台服务质量评估模型主要是通过定性描述来展开分析论证，从而进行评估指标体系的设计，但缺乏对平台整体运行状态、服务提供质量等的综合考量以及评估方法选取与应用的分析。The existing technology service platform service quality evaluation model is mainly to carry out analysis and demonstration through qualitative description, so as to design the evaluation index system, but it lacks the comprehensive consideration of the overall operation status of the platform, the quality of service provision, and the analysis of the selection and application of evaluation methods .

现有的科技服务平台评价指标体系的构建中大多都使用传统的层次分析法来赋予指标权重，其权重通过领域专家的主观判断得出，因此带有较强的主观性，受评估者的知识结构及偏好等影响较大。此外，传统层次分析法在得到判断矩阵后需进行一致性检验，若不符合一致性检验则需重新进行标度并构造判断矩阵，过程繁琐。Most of the existing science and technology service platform evaluation index systems use the traditional AHP to assign index weights. The weights are obtained through the subjective judgments of domain experts, so they are highly subjective, and the knowledge of the evaluators Structure and preferences have a greater impact. In addition, the traditional analytic hierarchy process needs to perform a consistency check after the judgment matrix is obtained. If it does not meet the consistency check, it needs to re-scale and construct the judgment matrix, which is a cumbersome process.

现有的科技服务质量指标评估方法主要有人工神经网络法和基于规则的方法，基于人工神经网络的评估方法需要大量数据来优化模型，并且在基于数值计算分析时，评估过程信息容易丢失。Existing evaluation methods of scientific and technological service quality indicators mainly include artificial neural network method and rule-based method. The evaluation method based on artificial neural network requires a large amount of data to optimize the model, and when it is based on numerical calculation and analysis, the evaluation process information is easily lost.

发明内容Contents of the invention

本公开旨在至少在一定程度上解决相关技术中的技术问题之一。The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

为此，本公开的一个目的在于提出一种基于组合赋权与模糊灰色聚类的科技服务质量评估方法。Therefore, an object of the present disclosure is to propose a method for evaluating the quality of scientific and technological services based on combined weighting and fuzzy gray clustering.

本公开的另一个目的在于提出一种基于组合赋权与模糊灰色聚类的科技服务质量评估装置。Another object of the present disclosure is to propose a device for evaluating the quality of scientific and technological services based on combined weighting and fuzzy gray clustering.

为达到上述目的，本公开一方面实施例提出了一种基于组合赋权与模糊灰色聚类的科技服务质量评估方法，所述方法包括：In order to achieve the above purpose, an embodiment of the present disclosure proposes a method for evaluating the quality of scientific and technological services based on combined weighting and fuzzy gray clustering. The method includes:

基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到所述服务质量的评估结果，基于所述评估结果得到所述服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据；Analyze the service quality of the scientific and technological resource service platform based on the service quality evaluation index system, obtain the evaluation result of the service quality according to the constructed index set, obtain the evaluation set of the service quality based on the evaluation result, and test the index data to obtain a prediction Indicator data with thresholds;

基于所述预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算所述预设阈值的指标数据的主观权重和客观权重；Based on the index data of the preset threshold, the subjective weight and the objective weight of the index data of the preset threshold are calculated according to the analytic hierarchy process and the weighting method of the majority build-up weight;

采用组合赋权法将所述主观权重和所述客观权重结合，得到满足条件的评估对象；Combining the subjective weight and the objective weight by using a combination weighting method to obtain an evaluation object that meets the conditions;

利用灰色聚类理论将所述评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照所述白化值不同的灰类进行归纳，得到二级指标服务质量评估结果；Substituting the evaluation object into the whitening weight function by using the gray clustering theory, calculating the whitening values contained in different clustering indexes, and summarizing according to the gray classes with different whitening values to obtain the service quality evaluation results of the secondary indexes;

根据所述评估指标体系和所述二级指标服务质量评价结果，进行一级指标评估，以完成对所述科技资源服务平台服务质量的评估。According to the evaluation index system and the service quality evaluation results of the second-level indicators, the first-level index evaluation is performed to complete the evaluation of the service quality of the scientific and technological resource service platform.

进一步地，在本公开的一个实施例中，基于科技资源服务平台的资源集成、服务供给与发展运行全视角，分析研究所述科技资源服务平台服务质量的考核范围与评估内容，采用定性和定量相结合的方式，分别从资源集成、创新能力、服务成效与平台运行四个维度构造所述服务质量评估指标体系。Furthermore, in one embodiment of the present disclosure, based on the full perspective of resource integration, service supply, and development and operation of the scientific and technological resource service platform, the scope and evaluation content of the service quality of the scientific and technological resource service platform are analyzed and studied, using qualitative and quantitative methods. In a combined way, the service quality evaluation index system is constructed from the four dimensions of resource integration, innovation ability, service effectiveness and platform operation.

进一步地，在本公开的一个实施例中，所述基于多数集结加权的层次分析法计算主观权重，包括：Further, in one embodiment of the present disclosure, the subjective weights calculated by the AHP based on majority assembly weighting include:

基于所述预设阈值的指标数据接收反馈信息，将所有专家的打分按照大小进行升序排序，并根据分值不同进行分类；其中，每个分类中分值大小都相同；Receiving feedback information based on the index data of the preset threshold, sorting the scores of all experts in ascending order according to size, and classifying according to different scores; wherein, the scores of each category are the same;

重复执行，从所述每个分类中各取出一个数得到第一序列，计算所述第一序列的平均值；以及，Repeat the execution, take out a number from each classification to obtain the first sequence, and calculate the average value of the first sequence; and,

减去所述每个分类中的一个数，并去掉个数为零的分类；Subtract a number from each of the categories, and remove the zero category;

直至剩下仅有一个元素的分类，以得到所述专家意见值。Until the classification with only one element is left to obtain the expert opinion value.

进一步地，在本公开的一个实施例中，各指标数据的主观权重为：Further, in an embodiment of the present disclosure, the subjective weight of each index data is:

其中，a _i表示由基于多数集结加权的层次化指标权重分析方法确定的第i个指标的主观权重值，

表示判断矩阵R第i行中所有元素的乘积。 Among them, a _i represents the subjective weight value of the i-th index determined by the hierarchical index weight analysis method based on majority assembly weighting,

Indicates the product of all elements in the i-th row of the judgment matrix R.

进一步地，在本公开的一个实施例中，在所述得到所述专家意见值后，对需要评估的n个指标数据(x ₁,x ₂,…,x _n)进行降序排列。 Further, in an embodiment of the present disclosure, after the expert opinion value is obtained, the n index data (x ₁ , x ₂ , . . . , x _n ) to be evaluated are sorted in descending order.

进一步地，在本公开的一个实施例中，所述采用组合赋权法将所述主观权重和所述客观权重结合，得到满足条件的评估对象，包括：Further, in one embodiment of the present disclosure, the combined weighting method is used to combine the subjective weight and the objective weight to obtain evaluation objects that meet the conditions, including:

定义μ _j为第j个评价指标的组合赋权值，用w _j和v _j和的线性组合表μ _j(j＝1,2,…,m)，所述第j个评价指标的组合赋权值计算公式为： Define μ _j as the combined weighted value of the jth evaluation index, using the linear combination table μ _j (j=1,2,...,m) of w _j and v _j , the combined weighted value of the jth evaluation index The weight calculation formula is:

μ _j＝(1-λ)w _j+λv _j μ _j ＝(1-λ)w _j +λv _j

变异系数法赋值计算公式为：The calculation formula of the coefficient of variation method assignment is:

其中，w _j为层次分析法确定的指标权重，v _j为熵权法确定的指标权重，λ为熵权法权重在组合赋权中所占的比例，w ₁、w ₂、…、w _q为基于多数集结加权的层次分析法确定的指标权值从小到大的重新排列，q为评价指标数。 Among them, w _j is the weight of the index determined by the analytic hierarchy process, v _j is the weight of the index determined by the entropy weight method, λ is the proportion of the weight of the entropy weight method in the combined weighting, w ₁ , w ₂ ,..., w _q It is the rearrangement of index weights determined by the majority-weighted AHP from small to large, and q is the number of evaluation indexes.

进一步地，在本公开的一个实施例中，所述利用灰色聚类理论将所述评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照所述白化值不同的灰类值进行归纳，得到二级指标的服务质量评估结果，包括：Further, in one embodiment of the present disclosure, the gray clustering theory is used to substitute the evaluation object into the whitening weight function, calculate the whitening values contained in different clustering indexes, and calculate the gray value according to the different graying values Induction is carried out to obtain the service quality evaluation results of the secondary indicators, including:

评估所述指标数据存在量纲不同的问题，对原始数据进行预处理；Assess the problem of different dimensions in the indicator data, and preprocess the original data;

根据所述科技资源服务平台的服务质量确定所述灰类值及采用依赖转折点的分段线性函数确定所述白化权函数；determining the gray class value according to the service quality of the scientific and technological resource service platform and determining the whitening weight function by using a piecewise linear function dependent on a turning point;

根据所述指标数据的主观权重和客观权重，分析所述指标数据的主观权重和客观权重的比值得到所述二级指标的聚类权值；According to the subjective weight and objective weight of described index data, analyze the ratio of the subjective weight of described index data and objective weight to obtain the clustering weight of described secondary index;

确定所述二级指标的聚类系数，得到所述二级指标的服务质量评估结果。The clustering coefficient of the secondary index is determined to obtain the service quality evaluation result of the secondary index.

进一步地，在本公开的一个实施例中，所述确定所述二级指标的聚类系数，得到所述二级指标的服务质量评估结果，包括：Further, in an embodiment of the present disclosure, the determining the clustering coefficient of the secondary index to obtain the service quality evaluation result of the secondary index includes:

所述二级指标的聚类系数由下面公式计算：The clustering coefficient of the secondary index is calculated by the following formula:

其中，u _j为二级指标组合权重，

为第i个二级指标的第k个灰类的聚类系数。 Among them, u _j is the combination weight of the secondary index,

is the clustering coefficient of the k-th gray class of the i-th secondary index.

进一步地，在本公开的一个实施例中，所述根据所述评估指标体系和所述二级指标服务质量评价结果，进行一级指标评估，以完成对所述科技资源服务平台服务质量的评估，包括：Further, in one embodiment of the present disclosure, the first-level indicator evaluation is performed according to the evaluation index system and the service quality evaluation results of the second-level indicators, so as to complete the evaluation of the service quality of the scientific and technological resources service platform ,include:

基于所述组合赋权法所述一级指标的模糊评判结果向量，利用模糊综合评判式计算科技资源服务平台一级指标评估结果，将所述一级指标评估结果进行转化，以完成对所述科技资源服务平台服务质量的评估，Based on the fuzzy evaluation result vector of the first-level index of the combined weighting method, the evaluation result of the first-level index of the scientific and technological resource service platform is calculated by using fuzzy comprehensive evaluation, and the evaluation result of the first-level index is converted to complete the evaluation of the above-mentioned Evaluation of the service quality of the science and technology resource service platform,

所述模糊综合评判式为：The fuzzy comprehensive evaluation formula is:

H＝R·WH=R·W

转化公式为：The conversion formula is:

F＝HL ^T F＝HL ^T

其中，H为模糊评判结果向量，W为权向量，·为模糊评判算子，F为平台服务质量的评价量化分值，L为平台服务质量各个等级的量化分值矩阵。Among them, H is the fuzzy evaluation result vector, W is the weight vector, · is the fuzzy evaluation operator, F is the evaluation quantitative score of platform service quality, and L is the quantitative score matrix of each level of platform service quality.

为达到上述目的，本公开另一方面实施例提出了基于组合赋权与模糊灰色聚类的科技服务质量评估装置，包括：In order to achieve the above purpose, another embodiment of the present disclosure proposes a technology service quality evaluation device based on combined weighting and fuzzy gray clustering, including:

指标体系构建模块，用于基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到所述服务质量的评估结果，基于所述评估结果得到所述服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据；An index system construction module, configured to analyze the service quality of the scientific and technological resource service platform based on the service quality evaluation index system, obtain the evaluation result of the service quality according to the constructed index set, obtain the evaluation set of the service quality based on the evaluation result, and The indicator data is tested to obtain the indicator data of the preset threshold;

主观和客观权重计算模块，用于基于所述预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算所述预设阈值的指标数据的主观权重和客观权重；The subjective and objective weight calculation module is used to calculate the subjective weight and objective weight of the preset threshold index data based on the index data of the preset threshold according to the AHP and the weighting method of majority build-up weighting;

综合权重计算模块，用于采用组合赋权法将所述主观权重和所述客观权重结合，得到满足条件的评估对象；The comprehensive weight calculation module is used to combine the subjective weight and the objective weight by using a combined weighting method to obtain an evaluation object that satisfies the conditions;

二级指标服务质量评估模块，用于利用灰色聚类理论将所述评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照所述白化值不同的灰类进行归纳，得到二级指标服务质量评估结果；The secondary index service quality evaluation module is used to use the gray clustering theory to substitute the evaluation object into the whitening weight function, calculate the whitening values contained in different clustering indexes, and perform induction according to the gray classes with different whitening values to obtain two Level indicator service quality assessment results;

一级指标综合评估模块，用于根据所述评估指标体系和所述二级指标服务质量评价结果，进行一级指标评估，以完成对所述科技资源服务平台服务质量的评估。The first-level index comprehensive evaluation module is used to evaluate the first-level index according to the evaluation index system and the service quality evaluation results of the second-level index, so as to complete the evaluation of the service quality of the scientific and technological resource service platform.

本公开附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本公开的实践了解到。Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.

附图说明Description of drawings

本公开上述的和/或附加的方面和优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：The above and/or additional aspects and advantages of the present disclosure will become apparent and understandable from the following description of the embodiments in conjunction with the accompanying drawings, wherein:

图1为根据本公开一个实施例的基于组合赋权与模糊灰色聚类的科技服务质量评估方法处理流程图；Fig. 1 is a processing flow chart of a method for evaluating the quality of scientific and technological services based on combined weighting and fuzzy gray clustering according to an embodiment of the present disclosure;

图2为根据本公开一个实施例的基于组合赋权与模糊灰色聚类的科技服务质量评估方法的流程图；FIG. 2 is a flow chart of a method for evaluating the quality of scientific and technological services based on combined weighting and fuzzy gray clustering according to an embodiment of the present disclosure;

图3为根据本公开一个实施例的基于组合赋权与模糊灰色聚类的科技服务质量评估装置结构示意图。Fig. 3 is a schematic structural diagram of a technology service quality evaluation device based on combined weighting and fuzzy gray clustering according to an embodiment of the present disclosure.

具体实施方式Detailed ways

下面详细描述本公开的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本公开，而不能理解为对本公开的限制。Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present disclosure and should not be construed as limiting the present disclosure.

下面参照附图描述根据本公开实施例提出的基于组合赋权与模糊灰色聚类的科技服务质量评估方法和装置，首先将参照附图描述根据本公开实施例提出的基于组合赋权与模糊灰色聚类的科技服务质量评估方法。The scientific and technological service quality evaluation method and device based on combined weighting and fuzzy gray clustering proposed according to the embodiments of the present disclosure will be described below with reference to the accompanying drawings. First, the combination weighting and fuzzy gray clustering based on combined weighting and fuzzy gray Clustering technology service quality assessment method.

本公开综合考虑科技资源服务平台的科技支撑、服务供给和发展运行状况，选择从资源集成、创新能力、服务成效与平台运行四个维度出发构建科技资源服务平台服务质量评估指标体系；通过基于多数集结加权的层次分析法和CRITIC赋权法分别确定主客观指标数据的权重，再利用组合赋权方法将其结合，使其既能考虑层次分析法赋权的一致性和主观不确定性，又减小了客观数据的本身偏差带来的权重准确性问题；然后基于模糊灰色聚类方法构建了分级评估模型，按照分层思想分别对一、二级指标分别进行评估，建立一个***、全面的服务质量评估模型，有助于管理者对平台进行全面了解，具有较强的可操作性和推广价值，如图1所示。This disclosure comprehensively considers the scientific and technological support, service supply, and development and operation status of the scientific and technological resource service platform, and chooses to construct the service quality evaluation index system of the scientific and technological resource service platform from the four dimensions of resource integration, innovation ability, service effectiveness, and platform operation; The weighted AHP and the CRITIC weighting method respectively determine the weights of the subjective and objective index data, and then use the combined weighting method to combine them, so that it can not only consider the consistency and subjective uncertainty of the AHP weighting, but also The weight accuracy problem caused by the deviation of objective data is reduced; then, a hierarchical evaluation model is constructed based on the fuzzy gray clustering method, and the first and second-level indicators are evaluated separately according to the hierarchical idea, and a systematic and comprehensive The service quality evaluation model is helpful for managers to have a comprehensive understanding of the platform, and has strong operability and promotion value, as shown in Figure 1.

图2为根据本公开实施例的基于组合赋权与模糊灰色聚类的科技服务质量评估方法的流程图。Fig. 2 is a flowchart of a method for evaluating the quality of scientific and technological services based on combined weighting and fuzzy gray clustering according to an embodiment of the present disclosure.

本公开实施例的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，结合现有科技资源服务平台的服务特点和运行状况，参考经典服务质量评估标准，建立了科技服务质量评估指标体系；提出了基于多数集结加权的层次化分析方法，结合多数集结算法利用标度扩展构造判断矩阵，简化计算过程的同时避免了集结结果受少数专家不同意见的影响；通过采用组合赋权的思想，将基于多数集结加权的主观层次化指标权重分析方法和客观赋权法所计算的权重通过变异系数法进行组合形成综合权重；最后利用灰色聚类理论和模糊判别方法构建分级评估模型，分别对各层级指标进行评估量化，使模型能根据有限样本数据得到准确、综合的服务质量评估结果。该模型对提升科技服务水平、促进科技资源开放共享具有指导意义。The scientific and technological service quality assessment method based on combined weighting and fuzzy gray clustering in the embodiment of the present disclosure combines the service characteristics and operating conditions of the existing scientific and technological resource service platform, and refers to the classic service quality assessment standard to establish a scientific and technological service quality assessment index system ; A hierarchical analysis method based on majority build-up weighting is proposed, combined with the majority build-up algorithm, the scale expansion is used to construct a judgment matrix, which simplifies the calculation process and avoids the influence of the different opinions of a few experts on the build-up results; by adopting the idea of combination weighting, Combine the weights calculated by the subjective hierarchical index weight analysis method based on majority weighted weighting and the objective weighting method through the variation coefficient method to form a comprehensive weight; finally, use the gray clustering theory and fuzzy discriminant method to construct a classification evaluation model, respectively for each The hierarchical indicators are evaluated and quantified, so that the model can obtain accurate and comprehensive service quality evaluation results based on limited sample data. This model has guiding significance for improving the level of scientific and technological services and promoting the open sharing of scientific and technological resources.

如图2所示，该基于组合赋权与模糊灰色聚类的科技服务质量评估方法包括步骤S1至步骤S5。As shown in Fig. 2, the scientific and technological service quality assessment method based on combined weighting and fuzzy gray clustering includes steps S1 to S5.

S1，基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到服务质量的评估结果，基于评估结果得到服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据。S1. Analyze the service quality of the science and technology resource service platform based on the service quality evaluation index system, obtain the evaluation result of the service quality according to the constructed index set, obtain the evaluation set of the service quality based on the evaluation result, and test the index data to obtain the index of the preset threshold data.

具体的，服务质量评估指标体系构造分为指标集构造和评估集构造。鉴于科技资源服务平台的服务质量受平台自身的发展情况、运行管理水平以及科技创新能力等多维因素影响，本公开基于科技资源服务平台的资源集成、服务供给与发展运行全视角，***分析研究科技资源服务平台服务质量的考核范围、评估内容，采用定性和定量相结合的方法，分别从资源集成、创新能力、服务成效与平台运行四个维度出发作为第一层评价准则，制定了11个第二层评价指标如表1所示：Specifically, the construction of the service quality evaluation index system is divided into index set construction and evaluation set construction. In view of the fact that the service quality of the science and technology resource service platform is affected by multidimensional factors such as the development of the platform itself, the level of operation management, and the ability of technological innovation, this disclosure is based on the full perspective of resource integration, service supply, and development and operation of the science and technology resource service platform. The assessment scope and content of the service quality of the resource service platform adopt a combination of qualitative and quantitative methods, starting from the four dimensions of resource integration, innovation ability, service effectiveness and platform operation as the first-level evaluation criteria, and formulating 11 first-level evaluation criteria. The evaluation indicators of the second layer are shown in Table 1:

表1Table 1

科技资源服务平台服务质量评估指标体系Service quality evaluation index system of science and technology resource service platform

评估集是对指标集进行定性和定量分析后得出的对评估结果的直接描述。针对科技资源服务平台的服务质量状况，本方法提出的服务质量的评估集为G＝{A,B,C,D}，利用隶属度来表征评估结果。各等级具体含义如表2所示。The evaluation set is a direct description of the evaluation results obtained after qualitative and quantitative analysis of the indicator set. Aiming at the service quality status of the science and technology resource service platform, the evaluation set of service quality proposed by this method is G={A, B, C, D}, and the membership degree is used to represent the evaluation results. The specific meaning of each grade is shown in Table 2.

表2Table 2

科技资源服务平台服务质量评估等级Sci-Tech Resource Service Platform Service Quality Evaluation Grade

S2，基于预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算预设阈值的指标数据的主观权重和客观权重。S2, based on the index data of the preset threshold, the subjective weight and the objective weight of the index data of the preset threshold are calculated according to the analytic hierarchy process and the weighting method of the majority build-up weight.

具体的，首先基于多数集结加权的层次分析法来计算主观权重，算法流程如下：Specifically, firstly, the subjective weight is calculated based on the majority-weighted AHP, and the algorithm flow is as follows:

收到反馈信息后，将所有专家的打分按照大小进行升序排序，并根据分值不同进行分类，每一个分类中分值大小都相同；After receiving the feedback information, sort the scores of all experts in ascending order according to the size, and classify according to the different scores, and the scores in each category are the same;

从每个分类中各取出一个数得到一个序列，计算其平均值，并把这个平均数定义为一个新分类；Take a number from each category to get a sequence, calculate its average, and define this average as a new category;

减去每个分类中的一个数(不包括第2步刚生成的分类)，并去掉个数为零的分类；Subtract a number in each category (excluding the category just generated in step 2), and remove the category with zero number;

返回第二步后不断循环2)、3)两个步骤,直至剩下仅有一个元素的分类，这个元素即为最终集结了多数专家意见的值。After returning to the second step, the two steps 2) and 3) are continuously cycled until the classification with only one element is left, and this element is the value that finally gathers the opinions of most experts.

接着根据多数集结算法得到的值对需要评估的n个指标(x ₁,x ₂,…,x _n)进行降序排列。假设x ₁＞x ₂＞…＞x _n，将x _j与x _j+1重要程度进行比较，得到标度值t _j。 Then, according to the values obtained by the majority aggregation algorithm, the n indicators (x ₁ , x ₂ ,...,x _n ) to be evaluated are sorted in descending order. Assuming x ₁ >x ₂ >...>x _n , compare the importance of x _j with x _j+1 to obtain the scale value t _j .

标度定义如表3所示：The scale definition is shown in Table 3:

表3table 3

通过各指标相互比较,得到由标度值构成的判断矩阵R＝[r _kj]。判断矩阵R满足如下性质: By comparing each index, a judgment matrix R=[r _kj ] composed of scale values is obtained. The judgment matrix R satisfies the following properties:

(1)r _kj＞0，r _kj为第k个指标和第j个指标相比得到的对应标度值，其中k,j＝1,2,…,n； (1) r _kj > 0, r _kj is the corresponding scale value obtained by comparing the k-th index with the j-th index, where k,j=1,2,...,n;

(2)r _kj＝1/r _kj； (2) r _kj = 1/r _kj ;

(3)r _jj＝1； (3) _rjj = 1;

(4)r _kj＝r _kt·r _tj,其中k＝1,2,…,n； (4) r _kj =r _kt r _tj , where k=1,2,...,n;

由式(1)可知，判断矩阵R满足一致性，无需再进行一致性检验即可得到合理的主观权重，各指标的主观权重为：It can be seen from formula (1) that the judgment matrix R satisfies consistency, and a reasonable subjective weight can be obtained without further consistency check. The subjective weight of each index is:

Indicates the product of all elements in the i-th row of the judgment matrix R.

其次，通过使用CRITIC赋权法来计算客观权重。Second, objective weights are calculated by using the CRITIC weighting method.

可以理解的是，CRITIC(Criteria Importance Through Intercriteria Correlation)赋权法是一种基于指标数据的客观赋权法，主要以数据的对比强度和评价指标间的冲突性为基础。It is understandable that the CRITIC (Criteria Importance Through Intercriteria Correlation) weighting method is an objective weighting method based on index data, which is mainly based on the comparative strength of data and the conflict between evaluation indicators.

其中，数据的对比强度通过计算标准差来反映同一个指标各评价方案间取值的差异程度，标准差越大各方案间取值差距越大，提供信息量越多，所占权重也应该越大；而评价指标的冲突性则以指标间的相关系数来体现，如果两个指标成较强的正相关，则说明指标间的冲突性较弱，提供的信息量较少，所占的权重也较小。Among them, the contrast strength of the data reflects the degree of difference in the values of the same index among the evaluation schemes by calculating the standard deviation. The larger the standard deviation is, the greater the value gap between the schemes is, and the more information is provided, the greater the weight should be. and the conflict of evaluation indicators is reflected by the correlation coefficient between indicators. If the two indicators form a strong positive correlation, it means that the conflict between the indicators is weak, and the amount of information provided is less, and the weight of Also smaller.

因此，基于以上观点对于一评价体系而言，设其共有n个指标，m个待测方案，可以构造基于两种特性的指标信息量。设C _j表示第j个评价指标所包含的信息量，则C _j可以表示为： Therefore, based on the above point of view, for an evaluation system, assuming that there are n indicators and m programs to be tested, the information content of indicators based on two characteristics can be constructed. Let C _j represent the amount of information contained in the jth evaluation index, then C _j can be expressed as:

式(3)中σ _j为第j个评价指标标准差，其计算方法为 In formula (3), σ _j is the standard deviation of the jth evaluation index, and its calculation method is

r _tj为指标t和指标j之间的相关系数，其计算方法为 r _tj is the correlation coefficient between index t and index j, and its calculation method is

对于CRITIC赋权法而言，C _j越大，则第j个指标包含的信息量越大，其相对重要性也越大。所以第j个指标的客观权重可表示为： For the CRITIC weighting method, the greater C _j is, the greater the amount of information contained in the jth index, and the greater its relative importance. Therefore, the objective weight of the jth index can be expressed as:

CRITIC赋权法不仅考虑了指标变异大小对权重的影响，还考虑了各指标间的冲突性，是一种比熵权法和标准离差法更好的客观赋权法，其得到的权重也更加符合数据的客观实际。The CRITIC weighting method not only considers the impact of index variation on the weight, but also considers the conflict between the indicators. It is a better objective weighting method than the entropy weight method and standard deviation method, and the weight obtained by it is also It is more in line with the objective reality of the data.

S3，采用组合赋权法将主观权重和客观权重结合，得到满足条件的评估对象。S3, using the combination weighting method to combine subjective weights and objective weights to obtain evaluation objects that meet the conditions.

可以理解的是采用组合赋权的方法将层次分析法确定的主观权重和熵权法求出的客观权重有机结合，使得到的权重能反映主客观的影响，可兼顾两种方法优点的同时避免了两种方法的不足，使评价指标的权重更加科学合理。It is understandable that the combined weighting method is used to organically combine the subjective weights determined by the AHP and the objective weights obtained by the entropy weight method, so that the obtained weights can reflect the subjective and objective influences, and the advantages of the two methods can be taken into account while avoiding The deficiencies of the two methods are eliminated, and the weight of the evaluation index is more scientific and reasonable.

设μ _j为第j个评价指标的组合赋权值，用w _j和v _j和的线性组合表示μ _j(j＝1,2,…,m)，其计算公式为： Let μ _j be the combined weighted value of the jth evaluation index, and use the linear combination of w _j and v _j to express μ _j (j=1,2,...,m), and its calculation formula is:

μ _j＝(1-λ)w _j+λv _j (8) μ _j ＝(1-λ)w _j +λv _j (8)

式(8)中：w _j为层次分析法确定的指标权重，v _j为熵权法确定的指标权重，λ为熵权法权重在组合赋权中所占的比例。通过采用变异系数法赋值，可以减少人为主观因素的干扰，计算公式为： In formula (8): w _j is the index weight determined by the AHP, v _j is the index weight determined by the entropy weight method, and λ is the proportion of the entropy weight method weight in the combined weighting. By assigning values using the coefficient of variation method, the interference of human subjective factors can be reduced, and the calculation formula is:

式(9)中：w ₁、w ₂、…、w _q为基于多数集结加权的层次分析法确定的指标权值从小到大的重新排列，q为其评价指标数。 In formula (9): w ₁ , w ₂ , ..., w _q are the rearrangement of index weights determined based on the majority-weighted AHP from small to large, and q is the number of evaluation indexes.

S4，利用灰色聚类理论将评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照白化值不同的灰类进行归纳，得到二级指标服务质量评估结果。S4, use the gray clustering theory to substitute the evaluation object into the whitening weight function, calculate the whitening value contained in different clustering indicators, and summarize according to the gray categories with different whitening values, and obtain the service quality evaluation results of the secondary indicators.

具体的，利用灰色聚类理论将评估对象代入白化权函数，计算不同聚类指标所拥有的白化值，并按照不同的灰类进行归纳，判断隶属于哪一个灰类。Specifically, the gray clustering theory is used to substitute the evaluation object into the whitening weight function, calculate the whitening value of different clustering indicators, and summarize according to different gray classes to determine which gray class it belongs to.

评估的关键在于如何确定灰类的白化权函数。常用的白化权函数为具有转折点的分段线性函数，可根据领域专家经验进行确定。The key to the evaluation is how to determine the whitening weight function of the gray class. The commonly used whitening weight function is a piecewise linear function with turning points, which can be determined according to the experience of domain experts.

主要步骤如下：The main steps are as follows:

(1)原始指标数据预处理(1) Raw indicator data preprocessing

评估指标数据存在量纲不同的问题，需对原始数据进行预处理。根据式(10)、式(11)可得无量纲指标数据。The evaluation index data has different dimensions, so the original data needs to be preprocessed. According to formula (10) and formula (11), the dimensionless index data can be obtained.

定义1：x _ij为第i个二级指标中第j个一级指标的数据值，x′ _ij为无量纲化处理后的指标。数值越大评估结果越优的指标利用式(10)进行无量纲处理。 Definition 1: x _ij is the data value of the jth first-level indicator in the i-th second-level indicator, and x′ _ij is the index after dimensionless processing. Indexes with larger values and better evaluation results are dimensionless processed using formula (10).

数值越小评估结果越优的指标利用式(11)进行无量纲处理。Indexes with smaller values and better evaluation results are dimensionless processed using formula (11).

分别是第i个一级指标中第j个二级指标的最大值和最小值。

are the maximum and minimum values of the jth secondary index in the i-th primary index, respectively.

(2)确定灰类值及白化权函数(2) Determine gray value and whitening weight function

在灰色***理论中，白化权函数常用来描述某个待评估样本值隶属于某个灰类程度。目前，白化权函数普遍采用依赖转折点的分段线性函数来表示，形式简单且计算方便，转折点可由领域专家根据经验来确定。在4.2节，已将科技资源服务平台的服务质量划分为4个等级(即灰类)，将4个灰类定义在0～1之间，并将4个灰类的白化权函数分别定义为：In the gray system theory, the whitening weight function is often used to describe the degree to which a sample value to be evaluated belongs to a certain gray class. At present, the whitening weight function is generally represented by a piecewise linear function dependent on the turning point, which is simple in form and easy to calculate, and the turning point can be determined by domain experts based on experience. In Section 4.2, the service quality of the scientific and technological resource service platform has been divided into four levels (ie, gray classes), and the four gray classes are defined between 0 and 1, and the whitening weight functions of the four gray classes are defined as :

表示A级的白化权函数；

Represents the whitening weight function of level A;

表示B级的白化权函数；

Indicates the whitening weight function of class B;

表示C级的白化权函数；

Represents the whitening weight function of C level;

表示D级的白化权函数。

Indicates the whitening weight function of D level.

(3)确定二级指标的聚类权重(3) Determine the clustering weight of the secondary index

根据组合赋权理论，对主客观指标数据进行分析，利用熵权法和改进的层次分析法分别确定主客观权值，综合分析主客观权重比值得到二级指标的聚类权值。According to the combined weighting theory, the subjective and objective index data are analyzed, the subjective and objective weights are respectively determined by using the entropy weight method and the improved analytic hierarchy process, and the clustering weights of the secondary indicators are obtained by comprehensive analysis of the ratio of subjective and objective weights.

(4)确定二级指标的聚类系数(4) Determine the clustering coefficient of the secondary index

其中，二级指标的聚类系数可计算如下：Among them, the clustering coefficient of the secondary index can be calculated as follows:

其中，u _j为二级指标组合权重，

为第i个二级指标的第k个灰类的聚类系数。二级指标中聚类系数最大值对应的灰类即为平台目前的服务质量状况。 Among them, u _j is the combination weight of the secondary index,

is the clustering coefficient of the k-th gray class of the i-th secondary index. The gray class corresponding to the maximum value of the clustering coefficient in the secondary index is the current service quality status of the platform.

S5，根据评估指标体系和二级指标服务质量评价结果，进行一级指标评估，以完成对科技资源服务平台服务质量的评估。S5. According to the evaluation index system and the service quality evaluation results of the second-level indicators, perform the first-level index evaluation to complete the evaluation of the service quality of the scientific and technological resource service platform.

具体的，一级指标综合评估是依据科技资源服务平台服务质量评估指标体系和二级指标评价结果，构建一级指标模糊评判矩阵R。基于组合赋权法计算一级指标的模糊评判权向量，利用模糊综合评判式(13)计算科技资源服务平台一级指标综合评估结果，完成对科技资源服务平台的服务质量评估。Specifically, the comprehensive evaluation of the first-level indicators is based on the service quality evaluation index system of the science and technology resource service platform and the evaluation results of the second-level indicators, and the fuzzy evaluation matrix R of the first-level indicators is constructed. Based on the combined weighting method, the fuzzy evaluation weight vector of the first-level index is calculated, and the comprehensive evaluation result of the first-level index of the science and technology resource service platform is calculated by using the fuzzy comprehensive evaluation formula (13), and the service quality evaluation of the science and technology resource service platform is completed.

H＝R·W (13)H＝R·W (13)

其中，H为模糊评判结果向量，W为权向量，·为模糊评判算子。Among them, H is the fuzzy evaluation result vector, W is the weight vector, · is the fuzzy evaluation operator.

最后将评价结果H转化为表2中的评价分值即得到科技资源服务平台的服务质量评估结果。转化公式为：Finally, transform the evaluation result H into the evaluation score in Table 2 to obtain the service quality evaluation result of the science and technology resource service platform. The conversion formula is:

F＝HL ^T (14) F＝HL ^T (14)

其中，F为平台服务质量的评价量化分值；L为平台服务质量各个等级的量化分值矩阵。Among them, F is the evaluation quantitative score of platform service quality; L is the quantitative score matrix of each level of platform service quality.

综上，综合考虑科技资源服务平台的科技支撑、服务供给和发展运行状况，选择从资源集成、创新能力、服务成效与平台运行四个维度出发构建科技资源服务平台服务质量评估指标体系；通过基于多数集结加权的层次分析法和CRITIC赋权法分别确定主客观指标数据的权重，再利用组合赋权方法将其结合，使其既能考虑层次分析法赋权的一致性和主观不确定性，又减小了客观数据的本身偏差带来的权重准确性问题；然后基于模糊灰色聚类方法构建了分级评估模型，按照分层思想分别对一、二级指标分别进行评估，建立一个***、全面的服务质量评估模型，有助于管理者对平台进行全面了解，具有较强的可操作性和推广价值。In summary, considering the scientific and technological support, service supply, and development and operation status of the scientific and technological resource service platform, we choose to construct the service quality evaluation index system of the scientific and technological resource service platform from the four dimensions of resource integration, innovation ability, service effectiveness and platform operation; The majority weighted AHP and CRITIC weighting method respectively determine the weight of subjective and objective index data, and then use the combined weighting method to combine them, so that it can not only consider the consistency and subjective uncertainty of AHP weighting, It also reduces the weight accuracy problem caused by the deviation of the objective data itself; then builds a hierarchical evaluation model based on the fuzzy gray clustering method, and evaluates the first and second-level indicators respectively according to the hierarchical thinking, and establishes a systematic and comprehensive The service quality evaluation model is helpful for managers to have a comprehensive understanding of the platform, and has strong operability and promotion value.

根据本公开实施例提出的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，通过基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到服务质量的评估结果，基于评估结果得到服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据，基于预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算预设阈值的指标数据的主观权重和客观权重，采用组合赋权法将主观权重和客观权重结合，得到满足条件的评估对象，利用灰色聚类理论将评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照白化值不同的灰类进行归纳，得到二级指标服务质量评估结果，根据评估指标体系和二级指标服务质量评价结果，进行一级指标评估，以完成对科技资源服务平台服务质量的评估。该模型对提升科技服务水平、促进科技资源开放共享具有指导意义，结合多数集结算法利用标度扩展构造判断矩阵，无需再进行复杂的一致性检验，并通过对意见相近专家的评分赋以较大权重来避免集结结果受少数不同意见专家打分的影响。同时采用组合赋权方法将主观赋权法和客观赋权法相结合，弥补单一赋权带来的权重不准确的不足，实现主客观权重统一以及信息量和价值量统一。本公开提出采用模糊灰色聚类方法，结合灰色聚类理论和模糊判决方法的优势，按照分层评估的思想对科技资源服务平台的服务质量开展评估，可兼顾两者的优点，得到更准确合理的评估结果。According to the scientific and technological service quality evaluation method based on combined weighting and fuzzy gray clustering proposed by the embodiments of the present disclosure, the service quality of the scientific and technological resource service platform is analyzed based on the service quality evaluation index system, and the evaluation result of the service quality is obtained according to the constructed index set. Based on the evaluation results, the evaluation set of service quality is obtained, and the index data is tested to obtain the index data of the preset threshold. Based on the index data of the preset threshold, the index of the preset threshold is calculated according to the majority assembly weighted AHP and the weighting method. Subjective weight and objective weight of the data, using the combination weighting method to combine the subjective weight and objective weight to obtain the evaluation object that satisfies the conditions, use the gray clustering theory to substitute the evaluation object into the whitening weight function, and calculate the whitening value contained in different clustering indicators , and inducted according to the gray categories with different whitening values to obtain the evaluation results of the service quality of the second-level indicators. According to the evaluation index system and the evaluation results of the service quality of the second-level indicators, the first-level indicator evaluation is carried out to complete the evaluation of the service quality of the scientific and technological resources service platform. Evaluate. This model has guiding significance for improving the level of scientific and technological services and promoting the opening and sharing of scientific and technological resources. Combining with the majority of aggregation algorithms and using scale expansion to construct a judgment matrix, there is no need for complex consistency checks, and by assigning higher scores to experts with similar opinions. Weights are used to avoid the impact of the aggregation results from the scores of a small number of experts with different opinions. At the same time, the combined weighting method is used to combine the subjective weighting method and the objective weighting method to make up for the inaccurate weight caused by a single weighting method, and to achieve the unity of subjective and objective weights and the unity of information and value. This disclosure proposes to use the fuzzy gray clustering method, combined with the advantages of gray clustering theory and fuzzy judgment method, to evaluate the service quality of the scientific and technological resource service platform according to the idea of hierarchical evaluation, which can take into account the advantages of both, and obtain more accurate and reasonable results. evaluation results.

其次参照附图描述根据本公开实施例提出的基于组合赋权与模糊灰色聚类的科技服务质量评估装置。Next, with reference to the accompanying drawings, the scientific and technological service quality evaluation device based on combined weighting and fuzzy gray clustering proposed according to an embodiment of the present disclosure will be described.

图3是本公开一个实施例的基于组合赋权与模糊灰色聚类的科技服务质量评估装置的结构示意图。Fig. 3 is a schematic structural diagram of a technology service quality evaluation device based on combined weighting and fuzzy gray clustering according to an embodiment of the present disclosure.

本公开实施例的基于组合赋权与模糊灰色聚类的科技服务质量评估装置，结合现有科技资源服务平台的服务特点和运行状况，参考经典服务质量评估标准，建立了科技服务质量评估指标体系；提出了基于多数集结加权的层次化分析方法，结合多数集结算法利用标度扩展构造判断矩阵，简化计算过程的同时避免了集结结果受少数专家不同意见的影响；通过采用组合赋权的思想，将基于多数集结加权的主观层次化指标权重分析方法和客观赋权法所计算的权重通过变异系数法进行组合形成综合权重；最后利用灰色聚类理论和模糊判别方法构建分级评估模型，分别对各层级指标进行评估量化，使模型能根据有限样本数据得到准确、综合的服务质量评估结果。该模型对提升科技服务水平、促进科技资源开放共享具有指导意义。The scientific and technological service quality assessment device based on combined weighting and fuzzy gray clustering in the embodiment of the present disclosure combines the service characteristics and operating conditions of the existing scientific and technological resource service platform, and refers to the classic service quality assessment standards to establish a scientific and technological service quality assessment index system ; A hierarchical analysis method based on majority build-up weighting is proposed, combined with the majority build-up algorithm, the scale expansion is used to construct a judgment matrix, which simplifies the calculation process and avoids the influence of the different opinions of a few experts on the build-up results; by adopting the idea of combination weighting, Combine the weights calculated by the subjective hierarchical index weight analysis method based on majority weighted weighting and the objective weighting method through the variation coefficient method to form a comprehensive weight; finally, use the gray clustering theory and fuzzy discriminant method to construct a classification evaluation model, respectively for each The hierarchical indicators are evaluated and quantified, so that the model can obtain accurate and comprehensive service quality evaluation results based on limited sample data. This model has guiding significance for improving the level of scientific and technological services and promoting the open sharing of scientific and technological resources.

如图3所示，该基于组合赋权与模糊灰色聚类的科技服务质量评估装置10包括：As shown in Figure 3, the technology service quality evaluation device 10 based on combination weighting and fuzzy gray clustering includes:

指标体系构建模块100，用于基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到服务质量的评估结果，基于评估结果得到服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据；The index system construction module 100 is used to analyze the service quality of the scientific and technological resource service platform based on the service quality evaluation index system, obtain the evaluation result of the service quality according to the constructed index set, obtain the evaluation set of the service quality based on the evaluation result, and test the index data Get indicator data with preset thresholds;

主观和客观权重计算模块200，用于基于预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算预设阈值的指标数据的主观权重和客观权重；The subjective and objective weight calculation module 200 is used to calculate the subjective weight and objective weight of the indicator data with the preset threshold according to the AHP and the weighting method based on the majority build-up weighted index data based on the preset threshold;

综合权重计算模块300，用于采用组合赋权法将主观权重和客观权重结合，得到满足条件的评估对象；The comprehensive weight calculation module 300 is used to combine the subjective weight and the objective weight by using the combined weighting method to obtain the evaluation object satisfying the conditions;

二级指标服务质量评估模块400，用于利用灰色聚类理论将评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照白化值不同的灰类进行归纳，得到二级指标服务质量评估结果；The secondary index service quality evaluation module 400 is used to substitute the evaluation object into the whitening weight function by using the gray clustering theory, calculate the whitening values contained in different clustering indexes, and perform induction according to the gray classes with different whitening values to obtain the secondary index service quality assessment results;

一级指标综合评估模块500，用于根据评估指标体系和二级指标服务质量评价结果，进行一级指标评估，以完成对科技资源服务平台服务质量的评估。The first-level index comprehensive evaluation module 500 is used to evaluate the first-level index according to the evaluation index system and the service quality evaluation results of the second-level index, so as to complete the evaluation of the service quality of the scientific and technological resource service platform.

需要说明的是，前述对于组合赋权与模糊灰色聚类的科技服务质量评估方法实施例的解释说明也适用于该实施例的基于组合赋权与模糊灰色聚类的科技服务质量评估装置，此处不再赘述。It should be noted that the aforementioned explanations for the embodiment of the method for evaluating the quality of scientific and technological services based on combined weighting and fuzzy gray clustering are also applicable to the device for evaluating the quality of scientific and technological services based on combined weighting and fuzzy gray clustering in this embodiment. I won't repeat them here.

根据本公开实施例提出的基于组合赋权与模糊灰色聚类的科技服务质量评估装置，通过基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到服务质量的评估结果，基于评估结果得到服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据，基于预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算预设阈值的指标数据的主观权重和客观权重，采用组合赋权法将主观权重和客观权重结合，得到满足条件的评估对象，利用灰色聚类理论将评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照白化值不同的灰类进行归纳，得到二级指标服务质量评估结果，根据评估指标体系和二级指标服务质量评价结果，进行一级指标评估，以完成对科技资源服务平台服务质量的评估。该模型对提升科技服务水平、促进科技资源开放共享具有指导意义，结合多数集结算法利用标度扩展构造判断矩阵，无需再进行复杂的一致性检验，并通过对意见相近专家的评分赋以较大权重来避免集结结果受少数不同意见专家打分的影响。同时采用组合赋权方法将主观赋权法和客观赋权法相结合，弥补单一赋权带来的权重不准确的不足，实现主客观权重统一以及信息量和价值量统一。本公开提出采用模糊灰色聚类方法，结合灰色聚类理论和模糊判决方法的优势，按照分层评估的思想对科技资源服务平台的服务质量开展评估，可兼顾两者的优点，得到更准确合理的评估结果。According to the scientific and technological service quality evaluation device based on combined weighting and fuzzy gray clustering proposed by the embodiments of the present disclosure, the service quality of the scientific and technological resource service platform is analyzed based on the service quality evaluation index system, and the evaluation result of the service quality is obtained according to the constructed index set. Based on the evaluation results, the evaluation set of service quality is obtained, and the index data is tested to obtain the index data of the preset threshold. Based on the index data of the preset threshold, the index of the preset threshold is calculated according to the majority assembly weighted AHP and the weighting method. Subjective weight and objective weight of the data, using the combination weighting method to combine the subjective weight and objective weight to obtain the evaluation object that satisfies the conditions, use the gray clustering theory to substitute the evaluation object into the whitening weight function, and calculate the whitening value contained in different clustering indicators , and inducted according to the gray categories with different whitening values to obtain the evaluation results of the service quality of the second-level indicators. According to the evaluation index system and the evaluation results of the service quality of the second-level indicators, the first-level indicator evaluation is carried out to complete the evaluation of the service quality of the scientific and technological resources service platform. Evaluate. This model has guiding significance for improving the level of scientific and technological services and promoting the opening and sharing of scientific and technological resources. Combining with the majority of aggregation algorithms and using scale expansion to construct a judgment matrix, there is no need for complex consistency checks, and by assigning higher scores to experts with similar opinions. Weights are used to avoid the impact of the aggregation results from the scores of a small number of experts with different opinions. At the same time, the combined weighting method is used to combine the subjective weighting method and the objective weighting method to make up for the inaccurate weight caused by a single weighting method, and to achieve the unity of subjective and objective weights and the unity of information and value. This disclosure proposes to use the fuzzy gray clustering method, combined with the advantages of gray clustering theory and fuzzy judgment method, to evaluate the service quality of the scientific and technological resource service platform according to the idea of hierarchical evaluation, which can take into account the advantages of both, and obtain more accurate and reasonable results. evaluation results.

本公开的有益效果为：The beneficial effects of the disclosure are:

1)基于组合赋权与模糊灰色聚类的科技服务质量评估模型，不仅结合现有科技资源服务平台的服务特点和运行状况，建立了科技服务质量评估指标体系，还将组合赋权与模糊灰色聚类方法结合起来，完成了指标权重的计算和各层指标的评估量化，使得模型能根据有限样本数据得到准确、综合的服务质量评估结果。该模型对提升科技服务水平、促进科技资源开放共享具有指导意义；1) The scientific and technological service quality evaluation model based on combined weighting and fuzzy gray clustering not only combines the service characteristics and operating conditions of the existing scientific and technological resource service platform, establishes a scientific and technological service quality evaluation index system, but also combines the combined weighting and fuzzy gray clustering The combination of clustering methods completes the calculation of index weights and the evaluation and quantification of indicators at each layer, so that the model can obtain accurate and comprehensive service quality evaluation results based on limited sample data. This model has guiding significance for improving the level of scientific and technological services and promoting the open sharing of scientific and technological resources;

2)本公开提出了一种基于多数集结加权的层次化指标权重分析方法，结合多数集结算法利用标度扩展构造判断矩阵，无需再进行复杂的一致性检验，并通过对意见相近专家的评分赋以较大权重来避免集结结果受少数不同意见专家打分的影响。同时采用组合赋权方法将主观赋权法和客观赋权法相结合，弥补单一赋权带来的权重不准确的不足，实现主客观权重统一以及信息量和价值量统一。2) This disclosure proposes a hierarchical index weight analysis method based on majority aggregation weighting, which is combined with majority aggregation algorithm and uses scale expansion to construct a judgment matrix, which eliminates the need for complex consistency checks, and assigns scores to experts with similar opinions. Use a larger weight to avoid the impact of the assembly results from the scores of a small number of experts with different opinions. At the same time, the combined weighting method is used to combine the subjective weighting method and the objective weighting method to make up for the inaccurate weight caused by a single weighting method, and to achieve the unity of subjective and objective weights and the unity of information and value.

3)本公开提出采用模糊灰色聚类方法，结合灰色聚类理论和模糊判决方法的优势，按照分层评估的思想对科技资源服务平台的服务质量开展评估，可兼顾两者的优点，得到更准确合理的评估结果。3) This disclosure proposes to use the fuzzy gray clustering method, combined with the advantages of gray clustering theory and fuzzy judgment method, to evaluate the service quality of the scientific and technological resource service platform according to the idea of hierarchical evaluation, which can take into account the advantages of both, and obtain more Accurate and reasonable assessment results.

此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本公开的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本公开的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the described specific features, structures, materials or characteristics may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples and features of different embodiments or examples described in this specification without conflicting with each other.

尽管上面已经示出和描述了本公开的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本公开的限制，本领域的普通技术人员在本公开的范围内可以对上述实施例进行变化、修改、替换和变型。Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations on the present disclosure, and those skilled in the art can understand the above-mentioned embodiments within the scope of the present disclosure. The embodiments are subject to changes, modifications, substitutions and variations.

Claims

一种基于组合赋权与模糊灰色聚类的科技服务质量评估方法，其特征在于，包括：基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到所述服务质量的评估结果，基于所述评估结果得到所述服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据；A scientific and technological service quality evaluation method based on combined weighting and fuzzy gray clustering, characterized in that it includes: analyzing the service quality of the scientific and technological resource service platform based on the service quality evaluation index system, and obtaining the evaluation of the service quality according to the constructed index set As a result, an evaluation set of the service quality is obtained based on the evaluation result, and the index data is checked to obtain the index data of a preset threshold;

基于所述预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算所述预设阈值的指标数据的主观权重和客观权重；Based on the index data of the preset threshold, the subjective weight and the objective weight of the index data of the preset threshold are calculated according to the analytic hierarchy process and the weighting method of the majority build-up weight;

采用组合赋权法将所述主观权重和所述客观权重结合，得到满足条件的评估对象；Combining the subjective weight and the objective weight by using a combination weighting method to obtain an evaluation object that meets the conditions;

利用灰色聚类理论将所述评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照所述白化值不同的灰类进行归纳，得到二级指标服务质量评估结果；Substituting the evaluation object into the whitening weight function by using the gray clustering theory, calculating the whitening values contained in different clustering indexes, and summarizing according to the gray classes with different whitening values to obtain the service quality evaluation results of the secondary indexes;

根据所述评估指标体系和所述二级指标服务质量评价结果，进行一级指标评估，以完成对所述科技资源服务平台服务质量的评估。According to the evaluation index system and the service quality evaluation results of the second-level indicators, the first-level index evaluation is performed to complete the evaluation of the service quality of the scientific and technological resource service platform.
根据权利要求1所述的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，其特征在于，基于科技资源服务平台的资源集成、服务供给与发展运行全视角，分析研究所述科技资源服务平台服务质量的考核范围与评估内容，采用定性和定量相结合的方式，分别从资源集成、创新能力、服务成效与平台运行四个维度构造所述服务质量评估指标体系。The scientific and technological service quality assessment method based on combined weighting and fuzzy gray clustering according to claim 1, characterized in that, based on the full perspective of resource integration, service supply, and development and operation of the scientific and technological resource service platform, the scientific and technological resources are analyzed and studied The assessment scope and assessment content of the service quality of the service platform adopts a combination of qualitative and quantitative methods, and constructs the service quality assessment index system from four dimensions of resource integration, innovation ability, service effectiveness and platform operation.
根据权利要求1所述的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，其特征在于，所述基于多数集结加权的层次分析法计算主观权重，包括：The scientific and technological service quality assessment method based on combined weighting and fuzzy gray clustering according to claim 1, wherein the subjective weight is calculated by the AHP based on majority assembly weighting, including:

基于所述预设阈值的指标数据接收反馈信息，将所有专家的打分按照大小进行升序排序，并根据分值不同进行分类；其中，每个分类中分值大小都相同；Receiving feedback information based on the index data of the preset threshold, sorting the scores of all experts in ascending order according to size, and classifying according to different scores; wherein, the scores of each category are the same;

重复执行，从所述每个分类中各取出一个数得到第一序列，计算所述第一序列的平均值；以及，Repeat the execution, take out a number from each classification to obtain the first sequence, and calculate the average value of the first sequence; and,

减去所述每个分类中的一个数，并去掉个数为零的分类；Subtract a number from each of the categories, and remove the zero category;

直至剩下仅有一个元素的分类，以得到所述专家意见值。Until the classification with only one element is left to obtain the expert opinion value.
根据权利要求3所述的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，其特征在于，各指标数据的主观权重为：According to the scientific and technological service quality evaluation method based on combined weighting and fuzzy gray clustering according to claim 3, it is characterized in that the subjective weight of each index data is:

其中，a _i表示由基于多数集结加权的层次化指标权重分析方法确定的第i个指标的主观权重值，
表示判断矩阵R第i行中所有元素的乘积。 Among them, a _i represents the subjective weight value of the i-th index determined by the hierarchical index weight analysis method based on majority assembly weighting,
Indicates the product of all elements in the i-th row of the judgment matrix R.
根据权利要求3所述的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，其特征在于，在所述得到所述专家意见值后，对需要评估的n个指标数据(x ₁,x ₂,…,x _n)进行降序排列。 The scientific and technological service quality evaluation method based on combination weighting and fuzzy gray clustering according to claim 3, characterized in that, after obtaining said expert opinion value, n index data (x ₁ , x ₂ ,…,x _n ) in descending order.
根据权利要求1所述的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，其特征在于，所述采用组合赋权法将所述主观权重和所述客观权重结合，得到满足条件的评估对象，包括：The scientific and technological service quality evaluation method based on combined weighting and fuzzy gray clustering according to claim 1, wherein the combined weighting method is used to combine the subjective weight and the objective weight to obtain a satisfactory Assessment objects, including:

定义μ _j为第j个评价指标的组合赋权值，用w _j和v _j和的线性组合表示μ _j(j＝1,2,…,m)，所述第j个评价指标的组合赋权值计算公式为： Define μ _j as the combined weighted value of the jth evaluation index, expressed by the linear combination of w _j and v _j and μ _j (j=1,2,...,m), the combined weighted value of the jth evaluation index The weight calculation formula is:

μ _j＝(1-λ)w _j+λv _j μ _j ＝(1-λ)w _j +λv _j

变异系数法赋值计算公式为：The calculation formula of the coefficient of variation method assignment is:

其中，w _j为层次分析法确定的指标权重，v _j为熵权法确定的指标权重，λ为熵权法权重在组合赋权中所占的比例，w ₁、w ₂、…、w _q为基于多数集结加权的层次分析法确定的指标权值从小到大的重新排列，q为评价指标数。 Among them, w _j is the weight of the index determined by the analytic hierarchy process, v _j is the weight of the index determined by the entropy weight method, λ is the proportion of the weight of the entropy weight method in the combined weighting, w ₁ , w ₂ ,..., w _q It is the rearrangement of index weights determined by the majority-weighted AHP from small to large, and q is the number of evaluation indexes.
根据权利要求1所述的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，其特征在于，其特征在于，所述利用灰色聚类理论将所述评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照所述白化值不同的灰类值进行归纳，得到二级指标的服务质量评估结果，包括：The scientific and technological service quality evaluation method based on combined weighting and fuzzy gray clustering according to claim 1, characterized in that, the gray clustering theory is used to substitute the evaluation object into the whitening weight function to calculate different The whitening value contained in the clustering index is summarized according to the different gray values of the whitening value to obtain the service quality evaluation results of the secondary index, including:

评估所述指标数据存在量纲不同的问题，对原始数据进行预处理；Assess the problem of different dimensions in the indicator data, and preprocess the original data;

根据所述科技资源服务平台的服务质量确定所述灰类值及采用依赖转折点的分段线性函数确定所述白化权函数；determining the gray class value according to the service quality of the scientific and technological resource service platform and determining the whitening weight function by using a piecewise linear function dependent on a turning point;

根据所述指标数据的主观权重和客观权重，分析所述指标数据的主观权重和客观权重的比值得到所述二级指标的聚类权值；According to the subjective weight and the objective weight of the index data, analyze the ratio of the subjective weight and the objective weight of the index data to obtain the clustering weight of the secondary index;

确定所述二级指标的聚类系数，得到所述二级指标的服务质量评估结果。The clustering coefficient of the secondary index is determined to obtain the service quality evaluation result of the secondary index.
根据权利要求7所述的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，其特征在于，所述确定所述二级指标的聚类系数，得到所述二级指标的服务质量评估结果，包括：The scientific and technological service quality assessment method based on combined weighting and fuzzy gray clustering according to claim 7, wherein said determining the clustering coefficient of said secondary index obtains the service quality assessment of said secondary index Results, including:

所述二级指标的聚类系数由下面公式计算：The clustering coefficient of the secondary index is calculated by the following formula:

其中，u _j为二级指标组合权重，
为第i个二级指标的第k个灰类的聚类系数。 Among them, u _j is the combination weight of the secondary index,
is the clustering coefficient of the k-th gray class of the i-th secondary index.
根据权利要求1所述的基于组合赋权与模糊灰色聚类的科技服务质量评估方法，其特征在于，所述根据所述评估指标体系和所述二级指标服务质量评价结果，进行一级指标评估，以完成对所述科技资源服务平台服务质量的评估，包括：The scientific and technological service quality evaluation method based on combined weighting and fuzzy gray clustering according to claim 1, wherein the first-level index is performed according to the evaluation index system and the service quality evaluation results of the second-level index Evaluation to complete the evaluation of the service quality of the scientific and technological resource service platform, including:

基于所述组合赋权法所述一级指标的模糊评判结果向量，利用模糊综合评判式计算科技资源服务平台一级指标评估结果，将所述一级指标评估结果进行转化，以完成对所述科技资源服务平台服务质量的评估，Based on the fuzzy evaluation result vector of the first-level index of the combined weighting method, the evaluation result of the first-level index of the scientific and technological resource service platform is calculated by using fuzzy comprehensive evaluation, and the evaluation result of the first-level index is converted to complete the evaluation of the above-mentioned Evaluation of the service quality of the science and technology resource service platform,

所述模糊综合评判式为：The fuzzy comprehensive evaluation formula is:

H＝R·WH=R·W

转化公式为：The conversion formula is:

F＝HL ^T F＝HL ^T

其中，H为模糊评判结果向量，W为权向量，·为模糊评判算子，F为平台服务质量的评价量化分值，L为平台服务质量各个等级的量化分值矩阵。Among them, H is the fuzzy evaluation result vector, W is the weight vector, · is the fuzzy evaluation operator, F is the evaluation quantitative score of platform service quality, and L is the quantitative score matrix of each level of platform service quality.
一种基于组合赋权与模糊灰色聚类的科技服务质量评估装置，其特征在于，包括：A technology service quality evaluation device based on combined weighting and fuzzy gray clustering, characterized in that it includes:

指标体系构建模块，用于基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到所述服务质量的评估结果，基于所述评估结果得到所述服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据；An index system construction module, configured to analyze the service quality of the scientific and technological resource service platform based on the service quality evaluation index system, obtain the evaluation result of the service quality according to the constructed index set, obtain the evaluation set of the service quality based on the evaluation result, and The indicator data is tested to obtain the indicator data of the preset threshold;

主观和客观权重计算模块，用于基于所述预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算所述预设阈值的指标数据的主观权重和客观权重；The subjective and objective weight calculation module is used to calculate the subjective weight and objective weight of the preset threshold index data based on the index data of the preset threshold according to the AHP and the weighting method of majority build-up weighting;

综合权重计算模块，用于采用组合赋权法将所述主观权重和所述客观权重结合，得到满足条件的评估对象；The comprehensive weight calculation module is used to combine the subjective weight and the objective weight by using a combined weighting method to obtain an evaluation object that satisfies the conditions;

二级指标服务质量评估模块，用于利用灰色聚类理论将所述评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照所述白化值不同的灰类进行归纳，得到二级指标服务质量评估结果；The secondary index service quality evaluation module is used to use the gray clustering theory to substitute the evaluation object into the whitening weight function, calculate the whitening values contained in different clustering indexes, and perform induction according to the gray classes with different whitening values to obtain two Level indicator service quality assessment results;

一级指标综合评估模块，用于根据所述评估指标体系和所述二级指标服务质量评价结果，进行一级指标评估，以完成对所述科技资源服务平台服务质量的评估。The first-level index comprehensive evaluation module is used to evaluate the first-level index according to the evaluation index system and the service quality evaluation results of the second-level index, so as to complete the evaluation of the service quality of the scientific and technological resource service platform.
一种电子设备，包括：An electronic device comprising:

至少一个处理器；以及at least one processor; and

与所述至少一个处理器通信连接的存储器；其中，a memory communicatively coupled to the at least one processor; wherein,

所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行以下步骤：The memory stores instructions executable by the at least one processor, the instructions are executed by the at least one processor to enable the at least one processor to perform the following steps:

基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到所述服务质量的评估结果，基于所述评估结果得到所述服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据；Analyze the service quality of the scientific and technological resource service platform based on the service quality evaluation index system, obtain the evaluation result of the service quality according to the constructed index set, obtain the evaluation set of the service quality based on the evaluation result, and test the index data to obtain a prediction Indicator data with thresholds;

基于所述预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算所述预设阈值的指标数据的主观权重和客观权重；Based on the index data of the preset threshold, the subjective weight and the objective weight of the index data of the preset threshold are calculated according to the analytic hierarchy process and the weighting method of the majority build-up weight;

采用组合赋权法将所述主观权重和所述客观权重结合，得到满足条件的评估对象；Combining the subjective weight and the objective weight by using a combination weighting method to obtain an evaluation object that meets the conditions;

利用灰色聚类理论将所述评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照所述白化值不同的灰类进行归纳，得到二级指标服务质量评估结果；Substituting the evaluation object into the whitening weight function by using the gray clustering theory, calculating the whitening values contained in different clustering indexes, and summarizing according to the gray classes with different whitening values to obtain the service quality evaluation results of the secondary indexes;

根据所述评估指标体系和所述二级指标服务质量评价结果，进行一级指标评估，以完成对所述科技资源服务平台服务质量的评估。According to the evaluation index system and the service quality evaluation results of the second-level indicators, the first-level index evaluation is performed to complete the evaluation of the service quality of the scientific and technological resource service platform.
一种存储有计算机指令的非瞬时计算机可读存储介质，其中，所述计算机指令用于使所述计算机执行以下步骤：A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the following steps:

基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到所述服务质量的评估结果，基于所述评估结果得到所述服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据；Analyze the service quality of the scientific and technological resource service platform based on the service quality evaluation index system, obtain the evaluation result of the service quality according to the constructed index set, obtain the evaluation set of the service quality based on the evaluation result, and test the index data to obtain a prediction Indicator data with thresholds;

基于所述预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算所述预设阈值的指标数据的主观权重和客观权重；Based on the index data of the preset threshold, the subjective weight and the objective weight of the index data of the preset threshold are calculated according to the analytic hierarchy process and the weighting method of the majority build-up weight;

采用组合赋权法将所述主观权重和所述客观权重结合，得到满足条件的评估对象；Combining the subjective weight and the objective weight by using a combination weighting method to obtain an evaluation object that meets the conditions;

利用灰色聚类理论将所述评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照所述白化值不同的灰类进行归纳，得到二级指标服务质量评估结果；Substituting the evaluation object into the whitening weight function by using the gray clustering theory, calculating the whitening values contained in different clustering indexes, and summarizing according to the gray classes with different whitening values to obtain the service quality evaluation results of the secondary indexes;

根据所述评估指标体系和所述二级指标服务质量评价结果，进行一级指标评估，以完成对所述科技资源服务平台服务质量的评估。According to the evaluation index system and the service quality evaluation results of the second-level indicators, the first-level index evaluation is performed to complete the evaluation of the service quality of the scientific and technological resource service platform.
一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现以下步骤：A computer program product comprising a computer program which, when executed by a processor, carries out the steps of:

基于服务质量评估指标体系分析科技资源服务平台服务质量，根据构造的指标集得到所述服务质量的评估结果，基于所述评估结果得到所述服务质量的评估集，并将指标数据进行检验得到预设阈值的指标数据；Analyze the service quality of the scientific and technological resource service platform based on the service quality evaluation index system, obtain the evaluation result of the service quality according to the constructed index set, obtain the evaluation set of the service quality based on the evaluation result, and test the index data to obtain a prediction Indicator data with thresholds;

基于所述预设阈值的指标数据，根据多数集结加权的层次分析法和赋权法计算所述预设阈值的指标数据的主观权重和客观权重；Based on the index data of the preset threshold, the subjective weight and the objective weight of the index data of the preset threshold are calculated according to the analytic hierarchy process and the weighting method of the majority build-up weight;

采用组合赋权法将所述主观权重和所述客观权重结合，得到满足条件的评估对象；Combining the subjective weight and the objective weight by using a combination weighting method to obtain an evaluation object that meets the conditions;

利用灰色聚类理论将所述评估对象代入白化权函数，计算不同聚类指标含有的白化值，并按照所述白化值不同的灰类进行归纳，得到二级指标服务质量评估结果；Substituting the evaluation object into the whitening weight function by using the gray clustering theory, calculating the whitening values contained in different clustering indexes, and summarizing according to the gray classes with different whitening values to obtain the service quality evaluation results of the secondary indexes;

根据所述评估指标体系和所述二级指标服务质量评价结果，进行一级指标评估，以完成对所述科技资源服务平台服务质量的评估。According to the evaluation index system and the service quality evaluation results of the second-level indicators, the first-level index evaluation is performed to complete the evaluation of the service quality of the scientific and technological resource service platform.