Intelligent electric meter supplier comprehensive evaluation method and device

Abstract

The invention provides an intelligent electric meter supplier comprehensive evaluation method and device, and belongs to the field of intelligent electric meter supplier comprehensive evaluation. The method comprises the following steps: constructing a comprehensive evaluation index system according to basic characteristic information and performance evaluation information of suppliers, and constructing secondary indexes for each comprehensive evaluation item in the comprehensive evaluation index system; wherein comprehensive evaluation items in the comprehensive evaluation index system comprise pre-supply quality, arrival quality, operation quality, personnel service and performance conditions; determining the weight of each secondary index based on a binary correlation theory and ANP; and calculating to obtain the quality evaluation of each supplier by using a good and inferior solution distance method TOPSIS. According to the invention, the operation quality of the intelligent electric meter and the related service information of the electric meter supplier are comprehensively considered, and accurate evaluation is carried out based on a diversified algorithm system, so that the comprehensive evaluation of the intelligent electric meter is realized, and the objectivity and reliability of the evaluation result are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment evaluation, and in particular to a comprehensive evaluation method and device for smart meter suppliers. Background Art

[0002] In actual application scenarios, the environmental conditions of smart meters are complex and changeable. Long-term operation under extreme conditions such as severe cold, high altitude, high humidity and heat, high salt fog, and high dry heat often exposes product problems. However, it is difficult to fully reveal potential quality defects under typical environmental conditions by conducting meter type tests in accordance with current industry technical standards, because the fault induction process involves many complex factors. Therefore, we urgently need to establish a smart meter reliability evaluation system tailored to regional differences, deeply explore the comprehensive evaluation method of smart meters under typical environments, and systematically analyze the impact of various environmental indicator parameters on the reliability of smart meters.

[0003] In addition, the existing patent CN109492893A proposes a smart meter supplier evaluation method, which describes the verification error of the smart meter under inspection from four aspects: average level, fluctuation, concentration, and symmetry, and uses the expectation and standard deviation to quantify the average level and fluctuation of the supplier's meter verification results. The Analytic Hierarchy Process (AHP) is used to transform the above multiple influencing factors into a comprehensive indicator to achieve a comprehensive evaluation of smart meters and guide the bidding company in the procurement and bidding of smart meters. It is worth noting that the core idea of ​​the analytic hierarchy process as a decision-making method is to decompose the elements related to the decision into multiple levels, such as the target level, the criterion level, the solution level, etc., and conduct qualitative and quantitative analysis on this basis.

[0004] In the process of implementing the embodiments of the present application, it is found that the related technology has at least the following problems:

[0005] Relying on a single analytic hierarchy process for evaluation makes it difficult for the evaluation results of smart meters to be comprehensive and objective, and their reliability is insufficient. Summary of the invention

[0006] The embodiment of the present invention provides a smart meter supplier comprehensive evaluation method and device to solve the problem that the smart meter evaluation results are not comprehensive enough and have low reliability when relying on a single hierarchical analysis method for evaluation.

[0007] In a first aspect, an embodiment of the present invention provides a comprehensive evaluation method for smart meter suppliers, including:

[0008] According to the basic characteristic information and performance evaluation information of the supplier, a comprehensive evaluation index system is constructed, and secondary indicators are constructed for each comprehensive evaluation item in the comprehensive evaluation index system; wherein the comprehensive evaluation items in the comprehensive evaluation index system include: pre-delivery quality, arrival quality, operation quality, personnel service and contract performance;

[0009] Determine the secondary index correlation matrix based on binary connection number theory and network analysis method (Analytic Network Process, ANP);

[0010] Based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the quality evaluation results of each supplier are determined according to the secondary indicator correlation matrix.

[0011] In a possible implementation, the determining of the secondary index association matrix based on the binary connection number theory and ANP includes:

[0012] Each comprehensive evaluation item is used as an element of the ANP criterion layer, and each secondary indicator is used as an element of the network layer;

[0013] Based on the binary connection number theory, determining element grouping according to the criterion layer and the network layer element distribution, and determining the weight expression of each element group;

[0014] According to ANP and the weight expression of each element group, the secondary index correlation matrix is ​​determined.

[0015] In a possible implementation, determining the secondary index association matrix according to the ANP and the weight expressions of each element group includes:

[0016] Taking an element group as a basic comparison object, determining the relevance of each remaining element group with the element group corresponding to the basic comparison object, and sorting them based on the relevance;

[0017] The above-mentioned relevance sorting operation is performed on each element group traversal;

[0018] When the relevance ranking operation of each element group is completed, based on the ranking results of each relevance, a weightless super matrix based on the criterion layer is determined;

[0019] The unweighted supermatrix is ​​normalized to determine a weighted matrix as a secondary indicator correlation matrix.

[0020] In a possible implementation, the TOPSIS-based method determines the quality evaluation results of each supplier according to the secondary indicator correlation matrix, including:

[0021] Based on TOPSIS, the Euclidean distance positive solution and the Euclidean distance negative solution of the evaluation index are calculated according to the secondary index association matrix;

[0022] Determine the comparison information of each supplier according to the positive solution of the Euclidean distance and the negative solution of the Euclidean distance;

[0023] The quality evaluation results of each supplier are determined based on the comparative information of each supplier.

[0024] In a possible implementation, determining the comparison information of each supplier according to the positive solution of the Euclidean distance and the negative solution of the Euclidean distance includes:

[0025] sorting the suppliers based on the positive solution of the Euclidean distance and the negative solution of the Euclidean distance respectively;

[0026] Calculate the relative closeness to the target supplier according to the positive solution of the Euclidean distance and the negative solution of the Euclidean distance; wherein the target supplier is a supplier that the user follows or a supplier with the same Euclidean distance;

[0027] Determine the comparison information of each supplier according to the ranking results of each supplier and the calculation results of the relative closeness; wherein the ranking results of each supplier include the ranking results of the positive solution of the Euclidean distance and the ranking results of the negative solution of the Euclidean distance;

[0028] Among them, the smaller the positive solution value of the Euclidean distance is, the closer the distance between the supplier and the optimal positive ideal solution is, and the better the supplier is; the larger the negative solution value of the Euclidean distance is, the closer the distance between the supplier and the optimal negative ideal solution is, and the better the supplier is.

[0029] In a possible implementation, corresponding to the pre-delivery quality, the secondary indicators include: accuracy, performance, environment, electrical and specific requirements;

[0030] Corresponding to the said arrival quality, the secondary indicators include: performance, function and specific requirements;

[0031] Corresponding to the said operation quality, the secondary indicators include: performance and function;

[0032] Corresponding to the personnel services, the secondary indicators include: service response, service quality and personnel quality;

[0033] Corresponding to the performance status, the secondary indicators include: on-time delivery rate, qualified delivery rate and performance percentage.

[0034] In a possible implementation, the method further includes:

[0035] For the personnel service and its corresponding secondary index, a fuzzy number is calculated for the secondary index of the personnel service using a ternary connection number method;

[0036] According to the personnel service information corresponding to each supplier, the personnel service evaluation coefficient of each supplier is determined respectively.

[0037] In a possible implementation, determining the personnel service evaluation coefficient of each supplier according to the personnel service information corresponding to each supplier includes:

[0038] The fuzzy expression for determining personnel services is:

[0039] f=a+bi+cj

[0040] Where a, b, and c are the proportion of suppliers with the same status as the target, the proportion of uncertain suppliers, and the proportion of suppliers with the opposite status to the target; a+b+c=1; a and c are the proportions of suppliers with certain status; b represents the proportion of suppliers with uncertain status;

[0041] Standardize f = (a, b, c) to determine the positive ideal solution f+ and the negative ideal solution f- of the evaluated supplier;

[0042] The personnel service evaluation coefficient of each supplier is determined according to the positive ideal solution f+ and the negative ideal solution f- of each supplier; wherein the personnel service evaluation coefficient calculation formula is:

[0043] p=f- / (f-+f+).

[0044] In a second aspect, an embodiment of the present invention provides a smart meter supplier comprehensive evaluation device, including:

[0045] An evaluation model building module is used to build a comprehensive evaluation index system based on the basic characteristic information and performance evaluation information of the supplier, and to build secondary indicators for each comprehensive evaluation item in the comprehensive evaluation index system; wherein the comprehensive evaluation items in the comprehensive evaluation index system include: pre-delivery quality, arrival quality, operation quality, personnel service and contract performance;

[0046] A correlation matrix determination module is used to determine the secondary index correlation matrix based on the binary connection number theory and ANP;

[0047] The evaluation module is used to determine the quality evaluation results of each supplier based on TOPSIS and the secondary indicator correlation matrix.

[0048] In a possible implementation, the association matrix determination module is specifically used to:

[0049] Each comprehensive evaluation item is used as an element of the ANP criterion layer, and each secondary indicator is used as an element of the network layer;

[0050] Based on the binary connection number theory, determining element grouping according to the criterion layer and the network layer element distribution, and determining the weight expression of each element group;

[0051] According to ANP and the weight expression of each element group, the secondary index correlation matrix is ​​determined.

[0052] The embodiment of the present invention provides a comprehensive evaluation method and device for smart meter suppliers, which constructs a comprehensive and objective comprehensive evaluation index system based on the basic characteristic information and performance evaluation information of the supplier. The system includes multiple dimensions such as pre-delivery quality, arrival quality, operation quality, personnel service and contract performance, covering the whole process information of the meter from detection, operation to maintenance, so as to ensure the accuracy and reliability of the evaluation. In order to further refine the evaluation content, a secondary index is constructed for each comprehensive evaluation item in the comprehensive evaluation index system, and by further classifying the pre-delivery quality, arrival quality, operation quality, personnel service and contract performance, multi-angle information affecting the quality evaluation of the meter is obtained, thereby comprehensively reflecting the reliability of the meter. In the evaluation process, the secondary index correlation matrix is ​​determined based on the binary connection number theory and ANP, and the quality evaluation results of each supplier are determined based on the superior and inferior solution distance method TOPSIS. By comprehensively using multiple methods, the subjectivity that may be brought by a single hierarchical analysis method is avoided, and the objectivity and accuracy of the evaluation results are ensured. The present application scheme realizes a comprehensive evaluation of smart meters and improves the reliability of the evaluation results by integrating multiple evaluation methods. At the same time, the purchase and installation of electric meters based on the evaluation results can effectively ensure the stability of system operation. Especially in typical environments, this solution has significant advantages in the evaluation of smart meters and provides a strong guarantee for the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 is a flowchart of a comprehensive evaluation method for smart meter suppliers provided by an embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of the mutual influence relationship of various comprehensive evaluation items provided by an embodiment of the present invention;

[0056] Figure 3 is a hierarchical schematic diagram of an evaluation model provided by an embodiment of the present invention;

[0057] Figure 4 It is a structural schematic diagram of a smart meter supplier comprehensive evaluation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0059] The analytic hierarchy process (AHP) is a decision-making method that decomposes decision-related elements into target layer, criterion layer, scheme layer, etc., and conducts qualitative and quantitative analysis on this basis. AHP is a commonly used evaluation method in the field of smart meter evaluation. It constructs a comprehensive evaluation index system for the reliability of smart meters in a multi-level and multi-objective typical environment. Since a single AHP is too subjective, there are deficiencies and defects in evaluating the reliability of smart meters in a typical environment. The evaluation results are relatively one-sided and cannot fully evaluate the reliability of smart meters in a typical environment.

[0060] In the embodiment of the present application, the main purpose of the comprehensive evaluation of smart meter suppliers is to evaluate the operational reliability of smart meters. Whether the meter can operate reliably is not a simple matter, but a complex process closely related to many factors. It is not only closely related to the operating quality of the meter itself, but also closely related to the professional quality and service quality of the meter supplier. Therefore, the comprehensive evaluation results of smart meter suppliers not only reflect the evaluation results of smart meter suppliers, but are more related to the operational reliability of the meter.

[0061] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0062] Figure 1 The implementation flow chart of the comprehensive evaluation method for smart meter suppliers provided by one embodiment of the present invention is described in detail as follows:

[0063] S101, construct a comprehensive evaluation index system based on the basic characteristic information and performance evaluation information of the supplier, and construct secondary indicators for each comprehensive evaluation item in the comprehensive evaluation index system; among them, the comprehensive evaluation items in the comprehensive evaluation index system include: pre-delivery quality, arrival quality, operation quality, personnel service and performance.

[0064] The execution subjects of each embodiment of the present application can be servers, processors, microprocessors and other devices with data processing functions. In the actual implementation process, the specific implementation method of the execution subject can be selected according to actual needs. This embodiment does not impose any special restrictions on this, as long as it is a device with data processing functions.

[0065] In the specific implementation process, whether the meter can operate reliably is not a simple matter, but a complex process closely related to many factors. It is not only closely related to the operation quality of the meter itself, but also closely related to the professional quality and service quality of the meter supplier.

[0066] As a device for measuring electric energy, the operation quality of the electric meter directly determines the accuracy and reliability of electric energy measurement. The electric meter supplier plays a vital role in the entire electric energy measurement system. On the one hand, suppliers need to provide electric meter products with reliable quality to ensure the accuracy of electric energy measurement at the source. On the other hand, suppliers also need to provide comprehensive after-sales service and technical support to promptly solve problems encountered by customers during use and ensure the normal operation of the electric meter. Therefore, based on the basic characteristic information and performance evaluation information of the supplier, a comprehensive evaluation index system is constructed to improve the comprehensiveness of the electric meter evaluation based on the comprehensive evaluation index system.

[0067] In addition, the reliable operation of the meter is closely related to the installation, commissioning, and maintenance. During the installation process, it is necessary to strictly follow the specifications to ensure that the meter is correctly connected to the power system; during the commissioning process, the meter needs to be accurately calibrated to ensure its accurate measurement; during the maintenance process, the meter needs to be regularly inspected and maintained to promptly discover and deal with potential problems. Therefore, after constructing a comprehensive evaluation index system based on the basic characteristic information and performance evaluation information of the supplier, further, a secondary indicator is constructed for each comprehensive evaluation item in the comprehensive evaluation index system, thereby effectively improving the objectivity and reliability of the evaluation results.

[0068] like Figure 2 As shown in the figure, it shows the schematic diagram of the mutual influence relationship between the comprehensive evaluation items in the comprehensive evaluation index system. Figure 2 It can be seen that pre-delivery quality, arrival quality, operation quality, personnel service and contract performance are not isolated links, but an intertwined and mutually influential whole. In every link of supply chain management, these factors play a vital role, and the synergy between them directly affects the efficiency and stability of the entire supply chain.

[0069] Among them, pre-delivery quality is the cornerstone of supply chain management. This stage mainly involves the selection of suppliers, quality control of raw materials and standardization of production processes. Excellent suppliers can provide stable and reliable products, ensuring the quality of the entire supply chain from the source. Arrival quality is a key link in testing the quality results before delivery. Through comprehensive testing of the appearance, performance, safety and other aspects of the product, potential quality problems can be discovered and solved in a timely manner to ensure that the product can smoothly enter the next stage. Operational quality refers to the performance and quality level of the product during use. This link usually involves multiple aspects such as product reliability, stability, and ease of use. Personnel service also plays a vital role in the entire supply chain. Excellent personnel service can provide customers with timely, accurate and professional technical support and after-sales service, help customers solve problems during use, and improve customer satisfaction. Finally, contract performance is an important indicator to measure the effectiveness of supply chain management.

[0070] In a possible implementation, the following relationship is established between the five comprehensive evaluation indicators of pre-delivery quality, arrival quality, operation quality, personnel service and contract performance:

[0071]

[0072]

[0073] S102, determining the secondary index correlation matrix based on the binary connection number theory and ANP.

[0074] The binary connection number theory is an effective mathematical tool for describing the binary unity of opposites between things. In the embodiment of the present application, the binary connection number theory is used to conduct an in-depth analysis of the correlation between the secondary indicators. By quantifying the mutual relationship between the indicators, a more accurate and comprehensive correlation matrix is ​​constructed.

[0075] In the actual implementation process, ANP is used to determine the correlation matrix. ANP is a decision analysis method based on a network structure, which can fully consider the mutual influence and feedback relationship between indicators. In the embodiment of the present application, ANP can not only determine the direct correlation between the indicators, but also reveal the indirect correlation between them and the overall impact on the electric meter.

[0076] In the process of constructing the correlation matrix, the characteristics and interactions of each secondary indicator are fully considered, and through ANP analysis, these complex correlations are accurately captured and quantified into specific values, thereby determining the secondary indicator correlation matrix.

[0077] S103, based on TOPSIS, determine the quality evaluation results of each supplier according to the secondary indicator correlation matrix.

[0078] The TOPSIS method is a multi-attribute decision analysis method. It ranks the solutions by calculating the relative proximity of each solution to the ideal solution and the negative ideal solution, and then selects the best solution. The secondary indicator correlation matrix can reflect the quality of the supplier. After the correlation matrix is ​​constructed, the TOPSIS method is used for evaluation to determine the ideal solution and the negative ideal solution. The Euclidean distance between each supplier and the ideal solution and the negative ideal solution is calculated, which respectively represents the degree of proximity between the supplier and the ideal solution and the degree of distance from the negative ideal solution. Through this evaluation process, the quality of each supplier can be effectively determined, providing strong support for the enterprise's meter purchasing decision.

[0079] In this embodiment, a comprehensive and objective comprehensive evaluation index system is constructed based on the basic characteristic information and performance evaluation information of the supplier. The system includes multiple dimensions such as pre-delivery quality, arrival quality, operation quality, personnel service and contract performance, covering the whole process information of the meter from detection, operation to maintenance, so as to ensure the accuracy and reliability of the evaluation. In order to further refine the evaluation content, a secondary index is constructed for each comprehensive evaluation item in the comprehensive evaluation index system. By further classifying the pre-delivery quality, arrival quality, operation quality, personnel service and contract performance, multi-angle information affecting the quality evaluation of the meter is obtained, thereby comprehensively reflecting the reliability of the meter. In the evaluation process, the secondary index correlation matrix is ​​determined based on the binary connection number theory and ANP, and the quality evaluation results of each supplier are determined based on the superior and inferior solution distance method TOPSIS. By comprehensively using multiple methods, the subjectivity that may be brought about by the single hierarchical analysis method is avoided, and the objectivity and accuracy of the evaluation results are ensured. This application scheme realizes a comprehensive evaluation of smart meters by combining multiple evaluation methods, and improves the reliability of the evaluation results. At the same time, the purchase and installation of electric meters based on the evaluation results can effectively ensure the stability of system operation. Especially in typical environments, this solution has significant advantages in the evaluation of smart meters and provides a strong guarantee for the stable operation of the power system.

[0080] In a possible implementation, the secondary index correlation matrix is ​​determined based on the binary connection number theory and ANP, including:

[0081] Each comprehensive evaluation item is used as an element of the ANP criterion layer, and each secondary indicator is used as an element of the network layer;

[0082] Based on the binary connection number theory, the element grouping is determined according to the element distribution of the criterion layer and the network layer, and the weight expression of each element group is determined;

[0083] According to ANP and the weight expression of each element group, the secondary index correlation matrix is ​​determined.

[0084] In the comprehensive evaluation process, each comprehensive evaluation item is regarded as an element of the ANP criterion layer. At the same time, each secondary indicator is regarded as an element of the network layer, which helps to build a complete and hierarchical evaluation system that is suitable for handling complex evaluation problems including comprehensive electricity meters and supplier information.

[0085] The binary connection number theory is a method used to describe the relationship between things. By analyzing the distribution of elements at the criterion layer and the network layer, the internal connections and laws between elements are revealed. Based on the binary connection number theory, elements with similar attributes or mutual influences are divided into the same group, thus forming an evaluation system composed of multiple element groups.

[0086] After the element grouping is determined, the weight expression of each element group is further determined. The weight reflects the importance of different elements or element groups in the evaluation system. By using the ANP method, the mutual influence and dependency between the element groups are comprehensively considered to obtain a more accurate and reasonable weight distribution.

[0087] In this embodiment, each comprehensive evaluation item is used as an element of the ANP criterion layer, and each secondary indicator is used as an element of the network layer. The element grouping and weight expression are determined based on the binary connection number theory and the ANP method, and then the secondary indicator correlation matrix is ​​constructed to improve the accuracy and scientificity of the evaluation results and provide a more reliable basis for supplier evaluation.

[0088] In a possible implementation, the secondary index correlation matrix is ​​determined according to the ANP and the weight expression of each element group, including:

[0089] Taking an element group as a basic comparison object, determining the relevance between the remaining element groups and the element group corresponding to the basic comparison object, and sorting them based on the relevance;

[0090] The above-mentioned relevance sorting operation is performed on each element group traversal;

[0091] When the relevance ranking operation of each element group is completed, based on the ranking results of each relevance, a weightless super matrix based on the criterion layer is determined;

[0092] The unweighted supermatrix is ​​normalized and the weighted matrix is ​​determined as the secondary indicator correlation matrix.

[0093] The purpose of sorting is to clarify the relative position of each element group in terms of relevance, which helps to more intuitively understand the relationship between the elements. The above relevance sorting operation is performed on each element group to ensure that all element groups are fully analyzed and compared.

[0094] After the correlation ranking operation of all element groups is completed, a weightless supermatrix based on the criterion layer is constructed based on these ranking results. This supermatrix contains the correlation information between each element group and can clearly show the mutual influence and correlation of each element group in different dimensions.

[0095] The unweighted supermatrix only reflects the relative correlation between element groups, without considering their weights in the overall evaluation of suppliers. Therefore, the unweighted supermatrix is ​​further normalized to determine the weighted matrix so that the contribution of each element group matches its weight.

[0096] In a specific embodiment, five first-level indicators, namely, pre-delivery quality, arrival quality, operation quality, personnel service and contract performance, are used as the algorithm criterion layer, and 16 second-level indicators are used as the network layer;

[0097] The element P in the criterion layer j (j=1~5), the element T in the network layer ij (j=1~16), the mutual constraints and influence relationships of each element are compared, and the comparison results are sorted according to the importance. According to the mutual influence results of the elements, the comparison results are normalized according to a certain calculation formula. According to this method, each element group in the network element layer is a basic comparison object, and then each element in the remaining element group is compared and sorted with it, and this is repeated until the elements in each element group are compared. A weightless super matrix S based on the criterion layer can be obtained:

[0098]

[0099] Among them, S ij is the impact factor of the i-th first-level indicator on the j-th second-level indicator.

[0100] Each element group in the criterion layer is a basic comparison object, and the remaining element groups in the criterion layer are set as basic comparison element groups, and are compared, analyzed and sorted. Finally, the matrix is ​​normalized according to the theoretical calculation formula, and the weighted matrix W for the element group can be obtained as follows:

[0101]

[0102] Among them, W mn It is the weighted impact factor of the mth first-level indicator on the nth second-level indicator.

[0103] In a possible implementation, based on TOPSIS, the quality evaluation results of each supplier are determined according to the secondary indicator correlation matrix, including:

[0104] Based on TOPSIS, the positive and negative solutions of the Euclidean distance of the evaluation index are calculated according to the secondary index correlation matrix;

[0105] According to the positive solution of Euclidean distance and the negative solution of Euclidean distance, the comparison information of each supplier is determined;

[0106] The quality evaluation results of each supplier are determined based on the comparative information of each supplier.

[0107] In a specific embodiment, after obtaining the weighted matrix W of the relationship between elements, the following formula is used to calculate the positive and negative solutions of the Euclidean distance and the relative closeness of the evaluation index:

[0108]

[0109]

[0110] in, is the Euclidean distance solution; It is the negative solution of Euclidean distance; For relative closeness.

[0111] The positive solution of the Euclidean distance represents the distance between each supplier and the optimal positive ideal solution. The closer the distance is, the better the supplier is. The negative solution of the Euclidean distance represents the distance between each supplier and the negative ideal solution. The farther the distance is, the better the supplier is.

[0112] In a possible implementation, the comparison information of each supplier is determined according to the positive solution of the Euclidean distance and the negative solution of the Euclidean distance, including:

[0113] Rank the suppliers based on the positive and negative solutions of Euclidean distance respectively;

[0114] Calculate the relative closeness to the target supplier based on the positive solution of Euclidean distance and the negative solution of Euclidean distance; the target supplier is the supplier that the user follows or the supplier with the same Euclidean distance;

[0115] Determine the comparison information of each supplier according to the ranking results of each supplier and the calculation results of the relative closeness; wherein the ranking results of each supplier include the ranking results of the positive solution of the Euclidean distance and the ranking results of the negative solution of the Euclidean distance;

[0116] Among them, the smaller the positive solution value of the Euclidean distance, the closer the distance between the supplier and the optimal positive ideal solution, and the better the supplier; the larger the negative solution value of the Euclidean distance, the closer the distance between the supplier and the optimal negative ideal solution, and the better the supplier.

[0117] In a possible implementation, when the relative closeness of a target supplier is calculated based on the positive solution of Euclidean distance and the negative solution of Euclidean distance, the target supplier is a supplier that the user follows, so as to satisfy the user's focused understanding of the supplier and determine further cooperation plans based on a cooperative relationship, or promote cooperation without a cooperative relationship.

[0118] In other possible implementations, when the relative closeness of the target supplier is calculated based on the positive solution of the Euclidean distance and the negative solution of the Euclidean distance, the target supplier is the supplier with the same Euclidean distance, so as to achieve differentiation and comparison of suppliers with the same ranking.

[0119] In different embodiments, the secondary index is constructed in different ways. Figure 3 It is a hierarchical schematic diagram of an evaluation model provided by an embodiment of the present invention.

[0120] In one possible implementation, corresponding to pre-supply quality, the secondary indicators include: accuracy, performance, environment, electrical, and specific requirements;

[0121] Corresponding to the arrival quality, the secondary indicators include: performance, function and specific requirements;

[0122] Corresponding to operational quality, the secondary indicators include: performance and functionality;

[0123] For personnel services, the secondary indicators include: service response, service quality and personnel quality;

[0124] Corresponding to the performance status, the secondary indicators include: on-time delivery rate, qualified delivery rate and performance percentage.

[0125] In other embodiments, the secondary indicators corresponding to each comprehensive evaluation indicator include: Figure 3 One or more of the secondary indicators shown, or Figure 3 Expand other secondary indicators based on the secondary indicators shown.

[0126] In a possible implementation, the method further includes:

[0127] For personnel services and their corresponding secondary indicators, the fuzzy numbers of the secondary indicators of personnel services are calculated using the ternary connection number method;

[0128] According to the personnel service information corresponding to each supplier, the personnel service evaluation coefficient of each supplier is determined respectively.

[0129] In a possible implementation, the personnel service evaluation coefficient of each supplier is determined according to the personnel service information corresponding to each supplier, including:

[0130] The fuzzy expression for determining personnel services is:

[0131] f=a+bi+cj

[0132] Where a, b, and c are the proportion of suppliers with the same status as the target, the proportion of uncertain suppliers, and the proportion of suppliers with the opposite status to the target; a+b+c=1; a and c are the proportions of suppliers with certain status; b represents the proportion of suppliers with uncertain status;

[0133] Standardize f = (a, b, c) to determine the positive ideal solution f+ and the negative ideal solution f- of the evaluated supplier;

[0134] The personnel service evaluation coefficient of each supplier is determined according to the positive ideal solution f+ and the negative ideal solution f- of each supplier; the personnel service evaluation coefficient calculation formula is:

[0135] p=f- / (f-+f+).

[0136] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0137] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0138] Figure 4 The structure diagram of the smart meter supplier comprehensive evaluation device provided by the embodiment of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows: The smart meter supplier comprehensive evaluation device includes:

[0139] Evaluation model building module 401 is used to build a comprehensive evaluation index system based on the basic characteristic information and performance evaluation information of the supplier, and to build secondary indicators for each comprehensive evaluation item in the comprehensive evaluation index system; wherein the comprehensive evaluation items in the comprehensive evaluation index system include: pre-delivery quality, arrival quality, operation quality, personnel service and contract performance;

[0140] The association matrix determination module 402 is used to determine the secondary index association matrix based on the binary connection number theory and ANP;

[0141] Evaluation module 403 is used to determine the quality evaluation results of each supplier based on TOPSIS and the secondary indicator correlation matrix.

[0142] In a possible implementation, the correlation matrix determination module 402 is specifically configured to:

[0143] Each comprehensive evaluation item is used as an element of the ANP criterion layer, and each secondary indicator is used as an element of the network layer;

[0144] Based on the binary connection number theory, the element grouping is determined according to the element distribution of the criterion layer and the network layer, and the weight expression of each element group is determined;

[0145] According to ANP and the weight expression of each element group, the secondary index correlation matrix is ​​determined.

[0146] In a possible implementation, the correlation matrix determination module 402 is specifically configured to:

[0147] Taking an element group as a basic comparison object, determining the relevance of each remaining element group with the element group corresponding to the basic comparison object, and sorting them based on the relevance;

[0148] The above-mentioned relevance sorting operation is performed on each element group traversal;

[0149] When the relevance ranking operation of each element group is completed, based on the ranking results of each relevance, a weightless super matrix based on the criterion layer is determined;

[0150] The unweighted supermatrix is ​​normalized to determine a weighted matrix as a secondary indicator correlation matrix.

[0151] In a possible implementation, the evaluation module 403 is specifically configured to:

[0152] Based on TOPSIS, the Euclidean distance positive solution and the Euclidean distance negative solution of the evaluation index are calculated according to the secondary index association matrix;

[0153] Determine the comparison information of each supplier according to the positive solution of the Euclidean distance and the negative solution of the Euclidean distance;

[0154] The quality evaluation results of each supplier are determined based on the comparative information of each supplier.

[0155] In a possible implementation, the evaluation module 403 is specifically configured to:

[0156] sorting the suppliers based on the positive solution of the Euclidean distance and the negative solution of the Euclidean distance respectively;

[0157] Calculate the relative closeness to the target supplier according to the positive solution of the Euclidean distance and the negative solution of the Euclidean distance; wherein the target supplier is a supplier that the user follows or a supplier with the same Euclidean distance;

[0158] Determine the comparison information of each supplier according to the ranking results of each supplier and the calculation results of the relative closeness; wherein the ranking results of each supplier include the ranking results of the positive solution of the Euclidean distance and the ranking results of the negative solution of the Euclidean distance;

[0159] Among them, the smaller the positive solution value of the Euclidean distance is, the closer the distance between the supplier and the optimal positive ideal solution is, and the better the supplier is; the larger the negative solution value of the Euclidean distance is, the closer the distance between the supplier and the optimal negative ideal solution is, and the better the supplier is.

[0160] In a possible implementation, corresponding to the pre-delivery quality, the secondary indicators include: accuracy, performance, environment, electrical and specific requirements;

[0161] Corresponding to the said arrival quality, the secondary indicators include: performance, function and specific requirements;

[0162] Corresponding to the said operation quality, the secondary indicators include: performance and function;

[0163] Corresponding to the personnel services, the secondary indicators include: service response, service quality and personnel quality;

[0164] Corresponding to the performance status, the secondary indicators include: on-time delivery rate, qualified delivery rate and performance percentage.

[0165] In a possible implementation, the system further includes: a personnel service evaluation coefficient evaluation module, which is used to:

[0166] For the personnel service and its corresponding secondary index, a fuzzy number is calculated for the secondary index of the personnel service using a ternary connection number method;

[0167] According to the personnel service information corresponding to each supplier, the personnel service evaluation coefficient of each supplier is determined respectively.

[0168] In a possible implementation, determining the personnel service evaluation coefficient of each supplier according to the personnel service information corresponding to each supplier includes:

[0169] The fuzzy expression for determining personnel services is:

[0170] f=a+bi+cj

[0171] Where a, b, and c are the proportion of suppliers with the same status as the target, the proportion of uncertain suppliers, and the proportion of suppliers with the opposite status to the target; a+b+c=1; a and c are the proportions of suppliers with certain status; b represents the proportion of suppliers with uncertain status;

[0172] Standardize f = (a, b, c) to determine the positive ideal solution f+ and the negative ideal solution f- of the evaluated supplier;

[0173] The personnel service evaluation coefficient of each supplier is determined according to the positive ideal solution f+ and the negative ideal solution f- of each supplier; wherein the personnel service evaluation coefficient calculation formula is:

[0174] p=f- / (f-+f+).

[0175] In this embodiment, a comprehensive and objective comprehensive evaluation index system is constructed based on the basic characteristic information and performance evaluation information of the supplier. The system includes multiple dimensions such as pre-delivery quality, arrival quality, operation quality, personnel service and contract performance, covering the whole process information of the meter from detection, operation to maintenance, so as to ensure the accuracy and reliability of the evaluation. In order to further refine the evaluation content, a secondary index is constructed for each comprehensive evaluation item in the comprehensive evaluation index system. By further classifying the pre-delivery quality, arrival quality, operation quality, personnel service and contract performance, multi-angle information affecting the quality evaluation of the meter is obtained, thereby comprehensively reflecting the reliability of the meter. In the evaluation process, the secondary index correlation matrix is ​​determined based on the binary connection number theory and ANP, and the quality evaluation results of each supplier are determined based on the superior and inferior solution distance method TOPSIS. By comprehensively using multiple methods, the subjectivity that may be brought about by the single hierarchical analysis method is avoided, and the objectivity and accuracy of the evaluation results are ensured. This application scheme realizes a comprehensive evaluation of smart meters by combining multiple evaluation methods, and improves the reliability of the evaluation results. At the same time, the purchase and installation of electric meters based on the evaluation results can effectively ensure the stability of system operation. Especially in typical environments, this solution has significant advantages in the evaluation of smart meters and provides a strong guarantee for the stable operation of the power system.

[0176] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0177] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0178] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned smart meter supplier comprehensive evaluation method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0179] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for smart meter suppliers, characterized in that: include: According to the basic characteristic information and performance evaluation information of the supplier, a comprehensive evaluation index system is constructed, and secondary indicators are constructed for each comprehensive evaluation item in the comprehensive evaluation index system; wherein the comprehensive evaluation items in the comprehensive evaluation index system include: pre-delivery quality, arrival quality, operation quality, personnel service and contract performance; Determine the secondary index correlation matrix based on binary connection number theory and network analysis method ANP; Based on the superior-inferior solution distance method TOPSIS, the quality evaluation results of each supplier are determined according to the secondary indicator correlation matrix.

2. The comprehensive evaluation method for smart meter suppliers according to claim 1 is characterized in that: The method of determining the secondary index correlation matrix based on the binary connection number theory and ANP includes: Each comprehensive evaluation item is used as an element of the ANP criterion layer, and each secondary indicator is used as an element of the network layer; Based on the binary connection number theory, determining element grouping according to the criterion layer and the network layer element distribution, and determining the weight expression of each element group; According to ANP and the weight expression of each element group, the secondary index correlation matrix is ​​determined.

3. The comprehensive evaluation method for smart meter suppliers according to claim 2 is characterized in that: Determining the secondary index association matrix according to the ANP and the weight expressions of each element group includes: Taking an element group as a basic comparison object, determining the relevance of each remaining element group with the element group corresponding to the basic comparison object, and sorting them based on the relevance; The above-mentioned relevance sorting operation is performed on each element group traversal; When the relevance ranking operation of each element group is completed, based on the ranking results of each relevance, a weightless super matrix based on the criterion layer is determined; The unweighted supermatrix is ​​normalized to determine a weighted matrix as a secondary indicator correlation matrix.

4. The comprehensive evaluation method for smart meter suppliers according to claim 1 is characterized in that: Based on TOPSIS, the quality evaluation results of each supplier are determined according to the secondary indicator correlation matrix, including: Based on TOPSIS, the Euclidean distance positive solution and the Euclidean distance negative solution of the evaluation index are calculated according to the secondary index association matrix; Determine the comparison information of each supplier according to the positive solution of the Euclidean distance and the negative solution of the Euclidean distance; The quality evaluation results of each supplier are determined based on the comparative information of each supplier.

5. The comprehensive evaluation method for smart meter suppliers according to claim 4 is characterized in that: Determining the comparison information of each supplier according to the positive solution of the Euclidean distance and the negative solution of the Euclidean distance includes: sorting the suppliers based on the positive solution of the Euclidean distance and the negative solution of the Euclidean distance respectively; Calculate the relative closeness to the target supplier according to the positive solution of the Euclidean distance and the negative solution of the Euclidean distance; wherein the target supplier is a supplier that the user follows or a supplier with the same Euclidean distance; Determine the comparison information of each supplier according to the ranking results of each supplier and the calculation results of the relative closeness; wherein the ranking results of each supplier include the ranking results of the positive solution of the Euclidean distance and the ranking results of the negative solution of the Euclidean distance; Among them, the smaller the positive solution value of the Euclidean distance is, the closer the distance between the supplier and the optimal positive ideal solution is, and the better the supplier is; the larger the negative solution value of the Euclidean distance is, the closer the distance between the supplier and the optimal negative ideal solution is, and the better the supplier is.

6. The comprehensive evaluation method for smart meter suppliers according to any one of claims 1 to 5, characterized in that: Corresponding to the pre-delivery quality, the secondary indicators include: accuracy, performance, environment, electrical and specific requirements; Corresponding to the said arrival quality, the secondary indicators include: performance, function and specific requirements; Corresponding to the said operation quality, the secondary indicators include: performance and function; Corresponding to the personnel services, the secondary indicators include: service response, service quality and personnel quality; Corresponding to the performance status, the secondary indicators include: on-time delivery rate, qualified delivery rate and performance percentage.

7. The comprehensive evaluation method for smart meter suppliers according to claim 6 is characterized in that: Also includes: For the personnel service and its corresponding secondary index, a fuzzy number is calculated for the secondary index of the personnel service using a ternary connection number method; According to the personnel service information corresponding to each supplier, the personnel service evaluation coefficient of each supplier is determined respectively.

8. The comprehensive evaluation method for smart meter suppliers according to claim 5, characterized in that: Determining the personnel service evaluation coefficient of each supplier according to the personnel service information corresponding to each supplier includes: The fuzzy expression for determining personnel services is: f=a+bi+cj Where a, b, and c are the proportion of suppliers with the same status as the target, the proportion of uncertain suppliers, and the proportion of suppliers with the opposite status to the target; a+b+c=1; a and c are the proportions of suppliers with certain status; b represents the proportion of suppliers with uncertain status; Perform standardization on f = (a, b, c) to determine the positive ideal solution f+ and the negative ideal solution f- of the evaluated supplier; The personnel service evaluation coefficient of each supplier is determined according to the positive ideal solution f+ and the negative ideal solution f- of each supplier; wherein the personnel service evaluation coefficient calculation formula is: p=f- / (f-+f+).

9. A comprehensive evaluation device for smart meter suppliers, characterized in that: include: An evaluation model building module is used to build a comprehensive evaluation index system based on the basic characteristic information and performance evaluation information of the supplier, and to build secondary indicators for each comprehensive evaluation item in the comprehensive evaluation index system; wherein the comprehensive evaluation items in the comprehensive evaluation index system include: pre-delivery quality, arrival quality, operation quality, personnel service and contract performance; A correlation matrix determination module is used to determine the secondary index correlation matrix based on the binary connection number theory and ANP; The evaluation module is used to determine the quality evaluation results of each supplier based on TOPSIS and the secondary indicator correlation matrix.

10. The smart meter supplier comprehensive evaluation device according to claim 9, characterized in that: The association matrix determination module is specifically used for: Each comprehensive evaluation item is used as an element of the ANP criterion layer, and each secondary indicator is used as an element of the network layer; Based on the binary connection number theory, determining element grouping according to the criterion layer and the network layer element distribution, and determining the weight expression of each element group; According to ANP and the weight expression of each element group, the secondary index correlation matrix is ​​determined.

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