Power supply service risk identification method and system based on grey evaluation

Abstract

The invention discloses a power supply service risk identification method and system based on grey evaluation, relates to the technical field of power supply services, and is used for solving the problems that existing risk evaluation indexes are single, index importance degrees cannot be distinguished, and potential risks are difficult to pre-judge. The method specifically comprises the steps of performing standardized processing on power supply service risk monitoring data, establishing a multi-level risk index system, determining index weights by using an analytic hierarchy process, constructing an evaluation matrix in combination with risk level quantized values, calculating a comprehensive evaluation value through a gray evaluation model to judge risks, and performing risk dynamic monitoring through risk early warning. Through the method, multi-dimensional risk monitoring and accurate evaluation are realized, potential risks are positioned in advance, closed-loop management and control are formed, and the reliability of power supply service and the effectiveness of risk management and control are improved.

Description

Technical Field

[0001] This invention belongs to the field of power supply service technology, specifically relating to a method and system for identifying power supply service risks based on grey evaluation. Background Technology

[0002] With economic and social development and the growth of residential electricity demand, the demand for electricity is not only focused on the reliability of basic power supply, but also places higher demands on response time and personalized services. Power supply services cover core aspects such as power outage management, voltage guarantee, business expansion and installation, electricity bill collection, and fault repair. The service process is complex and affected by multiple intertwined factors. Power supply companies mainly rely on work order requests, customer feedback, and basic operation and maintenance data to conduct risk identification through manual screening and analysis and simple statistical summarization. Traditional power supply service risk identification data has insufficient integration, is scattered across different business systems, and data processing is mainly based on basic statistics, with an incomplete risk assessment indicator system.

[0003] The invention patent with publication number CN119809319A discloses a power supply service risk identification system based on big data analysis. Its features include: a data acquisition module for collecting multi-source data related to power supply services, including work order data, feedback data, and public opinion data; a data processing module for preprocessing the multi-source data to obtain standardized data; a data fusion module for fusing the standardized data to obtain fused data; a risk identification module for analyzing the fused data, identifying risk types in power supply services by establishing a set of power supply service risk assessment indicators, and determining the risk level; and an early warning output module for generating risk early warning information based on the risk identification results, including risk type, risk level, scope of impact, and handling suggestions. This existing technology has the following shortcomings: the risk assessment indicators are singular, unable to distinguish the degree of impact of different indicators on service risks, unable to predict potential risk points in advance, and lacking a complete closed-loop risk management mechanism. These are the deficiencies of the existing technology.

[0004] In view of this, it is very necessary to provide a power supply service risk identification method and system based on grey evaluation to solve the above-mentioned defects in the prior art. Summary of the Invention

[0005] To address the technical problems of existing technologies, such as the reliance on a single risk assessment indicator, the inability to differentiate the impact of different indicators on service risks, the inability to predict potential risk points in advance, and the lack of a sound closed-loop risk management mechanism, this invention provides a power supply service risk identification method and system based on grey evaluation to solve the aforementioned technical problems.

[0006] In a first aspect, the present invention provides a method for identifying power supply service risks based on grey evaluation, comprising: Step S1: The steps for standardizing risk assessment data, which involves classifying the dimensions of power supply service risk monitoring and standardizing the different dimensions; The dimensions for monitoring power supply service risks include positive dimensions, negative dimensions, and optimal dimensions. The normalization process for the positive dimension is expressed mathematically as follows:

[0007] in, Let x represent the set of all original data in this dimension, and max(x) and min(x) represent the maximum and minimum values ​​of the data in this dimension, respectively. Indicates the original data The normalized value obtained after normalization processing; Normalization is performed on the inverse dimension, and the mathematical expression is:

[0008] in, Let x represent the set of all original data in this dimension, and max(x) and min(x) represent the maximum and minimum values ​​of the data in this dimension, respectively. Indicates the original data The normalized value obtained after normalization processing; Normalize the optimal dimension when hour, ;when hour, ;when hour, ; in, Here, x represents the set of all the original data for that dimension. Indicates the original data The normalized values ​​obtained after normalization are: a is the lower limit of the optimal interval for the data in this dimension, and b is the upper limit of the optimal interval for the data in this dimension.

[0009] Step S2: The steps for constructing a risk identification model include: establishing a power supply service risk indicator system; using the analytic hierarchy process (AHP) to determine the weights of risk indicators; classifying risk levels and assigning quantitative values; constructing an evaluation matrix based on risk indicators and risk level values; setting evaluation gray class and whitening weight functions; calculating gray evaluation coefficients to construct a gray evaluation weight vector set matrix; and calculating the comprehensive evaluation value to determine the risk value of the risk indicators. The power supply service risk indicator system includes a primary evaluation indicator set, a secondary evaluation indicator set, a tertiary evaluation indicator set, and a quaternary evaluation indicator set. The primary evaluation index set U is represented as: ,in, For power supply capacity, For service quality; Secondary evaluation indicator set Represented as Where i is the number of primary evaluation indicators and n is the number of secondary evaluation indicators under the primary evaluation indicators; Three-level evaluation indicator set Represented as Where i is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under the primary evaluation indicators, and k is the number of tertiary evaluation indicators under the secondary evaluation indicators. Level 4 Evaluation Indicator Set Represented as Where i is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under the primary evaluation indicators, k is the number of tertiary evaluation indicators under the secondary evaluation indicators, and l is the number of quaternary evaluation indicators under the tertiary evaluation indicators. The method of determining the weights of risk indicators using the analytic hierarchy process includes constructing a judgment matrix, calculating the weight vector and eigenvalues ​​based on the judgment matrix, performing a consistency check on the judgment matrix, and defining the weight set. A judgment matrix is ​​constructed, and through expert consultation, indicators within the same level are compared pairwise. The judgment matrix for each level of indicators is then built using the 1-9 scaling method. ; Calculating the weight vector and eigenvalues ​​based on the judgment matrix A includes normalizing each column of the judgment matrix using the root mean square, and summing the results row-wise to obtain the weight vector components of the i-th index. The mathematical expression is: ; Intermediate variables for calculating weight vectors The mathematical expression is: ; right After normalization, the weight vector w is obtained, and its mathematical expression is: ; The maximum eigenvalue is calculated based on the judgment matrix A and the weight vector w. The mathematical expression is:

[0010] The largest eigenvalue is represented as: ; Perform a consistency check on the judgment matrix A, using a consistency index. Consistency ratio test The mathematical expression for the consistency index is:

[0011] The mathematical expression for the consistency ratio test is:

[0012] in, The average random consistency index; if If the consistency check of the judgment matrix is ​​passed, the weights are valid. The constraints for each level of weight set are determined. The constraints for the first-level weight set are expressed as follows:

[0013] in, The weight of the i-th indicator in the first-level indicators; The second-order weight set constraint is expressed as follows: , ; The constraint condition for the third-level weight set is expressed as follows: , ; The fourth-level weight set constraint is expressed as follows: , ; The risk level classification and quantification process includes using qualitative evaluation indicators and quantifying these indicators by specifying risk level evaluation standards, thus classifying the risk level into three levels. The risk assessment criteria set is represented as follows: The corresponding risk levels are {low, medium, high}, where the risk level is assigned a value of -3 for low, -2 for medium, and -1 for high. When the evaluation index is between (0,1), a value of 0.5 is assigned; When the evaluation index is between (1,2), assign a value of 1.5; When the evaluation index is between (2,3), assign a value of 2.5; The evaluation matrix is ​​constructed based on risk indicators and risk level assignments. This involves inviting experts to score each of the four levels of evaluation indicators according to the scoring criteria based on historical data, resulting in the scoring matrix, denoted as: (i=1,2;j=1,2,....,n;k=1,2,....,54), of which 54 experts were invited; Based on the evaluation data of all evaluation indicators from the experts, an evaluation matrix is ​​obtained, the mathematical expression of which is: ; The evaluation gray class is defined as having 3 classes, with h = 1, 2, and 3. For gray numbers, the evaluation gray categories are set as excellent, average, and poor; The first gray category is excellent, and the score is [high]. The white weighting function is The mathematical expression is: ; The second gray class is medium, with a score of (1,3), and the white weighting function is: The mathematical expression is: ; The third gray category is medium, and the score is... The white weighting function is The mathematical expression is: ; Calculate the grey rating coefficient for the evaluation indicators. (i=1,2, corresponding to the first-level evaluation index; j is the second-level evaluation index), the gray evaluation coefficient belonging to the h-th gray category is denoted as The mathematical expression is = ; The gray evaluation coefficients belonging to each gray evaluation category are denoted as follows: Then there is = ; Constructing the grey evaluation weight vector set weight matrix includes considering all evaluation experts' evaluation indicators. The gray evaluation weight for the h-th gray class is denoted as The mathematical expression is: = / ; For the three identified gray categories, the evaluation indicators are... Evaluation weight vectors for each gray class ={ , , }; By combining the grey evaluation weight vectors of the evaluation indicators, the i-th primary indicator is obtained. The gray evaluation weight matrix of the corresponding indicators for each gray category. The mathematical expression is (i=1,2;j=1,2,...,n); The mathematical expression for the comprehensive evaluation of the secondary evaluation indicators is as follows: = ={ , },in, The comprehensive evaluation result of the secondary evaluation indicators. This indicates the weight of the secondary evaluation indicators. It is the comprehensive evaluation weight coefficient of the first-level evaluation index for the h-th gray class; Then, a comprehensive evaluation is performed on the primary evaluation indicators, and the mathematical expression is: B = a ={ , }, where B is the comprehensive evaluation result, and a represents the weight of the primary evaluation indicator. It is the overall evaluation weight coefficient of the power supply service for the h-th gray category; Its characteristic is that each evaluation gray category level is assigned a value according to the "gray level", and the value vector of each evaluation gray category level is represented as C=(3,2,1); The overall evaluation value is R=B C; The risk levels of units or regions at the same level are ranked and analyzed by comparing the size of R. When assessing the risk of a single area, the degree of risk in that area can be evaluated by the magnitude of the comprehensive evaluation value R.

[0014] Step S3: Risk warning step, used for maintaining the risk level of power supply services and conducting dynamic risk monitoring; The maintenance of power supply service risk levels includes adding, deleting, modifying, and querying power supply service levels, dynamically monitoring risk levels, and performing visual operations.

[0015] Secondly, the technical solution of the present invention also provides a power supply service risk identification system based on grey evaluation, including a risk evaluation data standardization processing module, a risk identification model construction module, and a risk early warning module; The risk assessment data standardization processing module classifies the dimensions of power supply service risk monitoring and standardizes the processing of different dimensions. The risk identification model construction module establishes a power supply service risk indicator system, uses the analytic hierarchy process to determine the risk indicator weights, divides risk levels and assigns quantitative values, constructs an evaluation matrix based on risk indicators and risk level values, sets evaluation gray class and whitening weight functions, calculates gray evaluation coefficients to construct a gray evaluation weight vector set matrix, and calculates the comprehensive evaluation value to determine the risk value of the risk indicators. The power supply service risk indicator system includes a primary evaluation indicator set, a secondary evaluation indicator set, a tertiary evaluation indicator set, and a quaternary evaluation indicator set. The primary evaluation index set U is represented as: ,in, For power supply capacity, For service quality; Secondary evaluation indicator set Represented as Where m is the number of primary evaluation indicators, and n is the number of secondary indicators under the primary indicators. The secondary evaluation indicators include power outages, voltage quality, business expansion and installation, electricity metering, electricity bill collection, channel services, emergency repair services, and power grid services. Three-level evaluation indicator set Represented as Where m is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under the primary evaluation indicators, and k is the number of tertiary evaluation indicators under the secondary evaluation indicators. The tertiary evaluation indicators include the number of power outages, the duration of power outages, the scope of power outages, the situation of repeated power outages, poor perception, the number of low voltage occurrences, the intensity of demands, sensitive events, and business processing. Level 4 Evaluation Indicator Set Represented as Where m is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under primary evaluation indicators, k is the number of tertiary evaluation indicators under secondary evaluation indicators, and l is the number of quaternary evaluation indicators under tertiary evaluation indicators. The quaternary evaluation indicators include the number of fault reports per 10,000 households, the number of power outage hours per 10,000 households, the power outage user coverage rate, the number of frequent power outage customer requests per 10,000 households, the number of low voltage repair reports per 10,000 households, the number of complaints, the number of opinions, the number of service applications, the number of issues under supervision, the number of issues identified through overt and covert investigations, the number of repeated requests, the satisfaction rate of follow-up visits, and the timeliness rate of business processing.

[0016] The risk warning module is used to maintain the risk level of power supply services and to conduct dynamic risk monitoring.

[0017] The beneficial effects of this invention lie in the fact that it provides a power supply service risk identification method and system based on grey evaluation. By collecting risk evaluation data and building a power supply service risk indicator system, it achieves multi-dimensional risk monitoring. The analytic hierarchy process (AHP) is used to assign weights to each risk indicator, solving the problem that traditional methods cannot distinguish the importance of indicators. A grey evaluation model is employed to conduct in-depth analysis of standardized risk data. By constructing an evaluation matrix and setting a whitening weight function to calculate a comprehensive evaluation value, risks can be predicted in advance. This changes the traditional model's passive response to already occurring risks, enabling early prediction and precise location of potential risk points. Simultaneously, the steps for constructing a risk early warning system form a complete closed-loop mechanism, further improving the systematicness and effectiveness of risk management.

[0018] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a power supply service risk identification method based on grey evaluation provided by the present invention.

[0021] Figure 2 This is a schematic diagram of a power supply service risk identification system based on grey evaluation provided by the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0024] Example 1: like Figure 1 As shown in the figure, this embodiment of the invention provides a method for identifying power supply service risks based on grey evaluation, including the following steps: Step S1: The steps for standardizing risk assessment data, which involves classifying the dimensions of power supply service risk monitoring and standardizing the different dimensions; The dimensions for monitoring power supply service risks include positive dimensions, negative dimensions, and optimal dimensions. The normalization process for the positive dimension is expressed mathematically as follows:

[0025] in, Let x represent the set of all original data in this dimension, and max(x) and min(x) represent the maximum and minimum values ​​of the data in this dimension, respectively. Indicates the original data The normalized value obtained after normalization processing; Normalization is performed on the inverse dimension, and the mathematical expression is:

[0026] in, Let x represent the set of all original data in this dimension, and max(x) and min(x) represent the maximum and minimum values ​​of the data in this dimension, respectively. Indicates the original data The normalized value obtained after normalization processing; Normalize the optimal dimension when hour, ;when hour, ;when hour, ; in, Here, x represents the set of all the original data for that dimension. Indicates the original data The normalized values ​​obtained after normalization are: a is the lower limit of the optimal interval for the data in this dimension, and b is the upper limit of the optimal interval for the data in this dimension.

[0027] Step S2: The steps for constructing a risk identification model include: establishing a power supply service risk indicator system; using the analytic hierarchy process (AHP) to determine the weights of risk indicators; classifying risk levels and assigning quantitative values; constructing an evaluation matrix based on risk indicators and risk level values; setting evaluation gray class and whitening weight functions; calculating gray evaluation coefficients to construct a gray evaluation weight vector set matrix; and calculating the comprehensive evaluation value to determine the risk value of the risk indicators. The power supply service risk indicator system includes a primary evaluation indicator set, a secondary evaluation indicator set, a tertiary evaluation indicator set, and a quaternary evaluation indicator set. The primary evaluation index set U is represented as: ,in, For power supply capacity, For service quality; Secondary evaluation indicator set Represented as Where i represents the number of primary evaluation indicators, and n represents the number of secondary evaluation indicators under the primary evaluation indicators. The secondary evaluation indicators include power outages, voltage quality, business expansion and installation, electricity metering, electricity bill collection, channel services, emergency repair services, and power grid services. Three-level evaluation indicator set Represented as Where i is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under the primary evaluation indicators, and k is the number of tertiary evaluation indicators under the secondary evaluation indicators. The tertiary evaluation indicators include the number of power outages, the duration of power outages, the scope of power outages, the situation of repeated power outages, poor perception, the number of low voltage occurrences, the intensity of demands, sensitive events, and business processing. Level 4 Evaluation Indicator Set Represented as Where i represents the number of primary evaluation indicators, n represents the number of secondary evaluation indicators under the primary evaluation indicators, k represents the number of tertiary evaluation indicators under the secondary evaluation indicators, and l represents the number of quaternary evaluation indicators under the tertiary evaluation indicators. The quaternary evaluation indicators include the number of fault reports per 10,000 households, the number of power outage hours per 10,000 households, the power outage user coverage rate, the number of frequent power outage customer requests per 10,000 households, the number of low voltage repair reports per 10,000 households, the number of complaints, the number of opinions, the number of service applications, the number of issues under supervision, the number of issues identified through overt and covert investigations, the number of repeated requests, the satisfaction rate of follow-up visits, and the timeliness rate of business processing. The method of determining the weights of risk indicators using the analytic hierarchy process includes constructing a judgment matrix, calculating the weight vector and eigenvalues ​​based on the judgment matrix, performing a consistency check on the judgment matrix, and defining the weight set. A judgment matrix is ​​constructed, and through expert consultation, indicators within the same level are compared pairwise. The judgment matrix for each level of indicators is then built using the 1-9 scaling method. ; Calculating the weight vector and eigenvalues ​​based on the judgment matrix A includes normalizing each column of the judgment matrix using the root mean square, and summing the results row-wise to obtain the weight vector components of the i-th index. The mathematical expression is: ; Intermediate variables for calculating weight vectors The mathematical expression is: ; right After normalization, the weight vector w is obtained, and its mathematical expression is: ; The maximum eigenvalue is calculated based on the judgment matrix A and the weight vector w. The mathematical expression is:

[0028] The largest eigenvalue is represented as: ; Perform a consistency check on the judgment matrix A, using a consistency index. Consistency ratio test The mathematical expression for the consistency index is:

[0029] The mathematical expression for the consistency ratio test is:

[0030] in, The average random consistency index; if If the consistency check of the judgment matrix is ​​passed, the weights are valid. The constraints for each level of weight set are determined. The constraints for the first-level weight set are expressed as follows:

[0031] in, The weight of the i-th indicator in the first-level indicators; The second-order weight set constraint is expressed as follows: , ; The constraint condition for the third-level weight set is expressed as follows: , ; The fourth-level weight set constraint is expressed as follows: , ; The risk level classification and quantification process includes using qualitative evaluation indicators and quantifying these indicators by specifying risk level evaluation standards, thus classifying the risk level into three levels. The risk assessment criteria set is represented as follows: The corresponding risk levels are {low, medium, high}, where the risk level is assigned a value of -3 for low, -2 for medium, and -1 for high. When the evaluation index is between (0,1), a value of 0.5 is assigned; When the evaluation index is between (1,2), assign a value of 1.5; When the evaluation index is between (2,3), assign a value of 2.5; The evaluation matrix is ​​constructed based on risk indicators and risk level assignments. This involves inviting experts to score each of the four levels of evaluation indicators according to the scoring criteria based on historical data, resulting in the scoring matrix, denoted as: (i=1,2;j=1,2,....,n;k=1,2,....,54), of which 54 experts were invited; Based on the evaluation data of all evaluation indicators from the experts, an evaluation matrix is ​​obtained, the mathematical expression of which is: ; The evaluation gray class is defined as having 3 classes, with h = 1, 2, and 3. For gray numbers, the evaluation gray categories are set as excellent, average, and poor; The first gray category is excellent, and the score is [high]. The white weighting function is The mathematical expression is: ; The second gray class is medium, with a score of (1,3), and the white weighting function is: The mathematical expression is: ; The third gray category is medium, and the score is... The white weighting function is The mathematical expression is: ; Calculate the grey rating coefficient for the evaluation indicators. (i=1,2, corresponding to the first-level evaluation index; j is the second-level evaluation index), the gray evaluation coefficient belonging to the h-th gray category is denoted as The mathematical expression is = ; The gray evaluation coefficients belonging to each gray evaluation category are denoted as follows: Then there is = ; Constructing the grey evaluation weight vector set weight matrix includes considering all evaluation experts' evaluation indicators. The gray evaluation weight for the h-th gray class is denoted as The mathematical expression is: = / ; For the three identified gray categories, the evaluation indicators are... Evaluation weight vectors for each gray class ={ , , }; By combining the grey evaluation weight vectors of the evaluation indicators, the i-th primary indicator is obtained. The gray evaluation weight matrix of the corresponding indicators for each gray category. The mathematical expression is (i=1,2;j=1,2,...,n) The mathematical expression for the comprehensive evaluation of the secondary evaluation indicators is as follows: = ={ , },in, The comprehensive evaluation result of the secondary evaluation indicators. This indicates the weight of the secondary evaluation indicators. It is the comprehensive evaluation weight coefficient of the first-level evaluation index for the h-th gray class; Then, a comprehensive evaluation is performed on the primary evaluation indicators, and the mathematical expression is: B = a ={ , }, where B is the comprehensive evaluation result, and a represents the weight of the primary evaluation indicator. It is the overall evaluation weight coefficient of the power supply service for the h-th gray category; Its characteristic is that each evaluation gray category level is assigned a value according to the "gray level", and the value vector of each evaluation gray category level is represented as C=(3,2,1); The overall evaluation value is R=B C; The risk levels of units or regions at the same level are ranked and analyzed by comparing the size of R. When assessing the risk of a single area, the degree of risk in that area can be evaluated by the magnitude of the comprehensive evaluation value R.

[0032] Step S3: Risk warning step, used for maintaining the risk level of power supply services and conducting dynamic risk monitoring; The maintenance of power supply service risk levels includes adding, deleting, modifying, and querying power supply service levels, dynamically monitoring risk levels, and performing visual operations.

[0033] Example 2: like Figure 2 As shown, this embodiment also provides a power supply service risk identification system based on grey evaluation, including a risk evaluation data standardization processing module 1, a risk identification model construction module 2, and a risk early warning module 3; Risk assessment data standardization processing module 1 classifies the dimensions of power supply service risk monitoring and standardizes the different dimensions; Risk identification model construction module 2: build a power supply service risk indicator system, use the analytic hierarchy process to determine the risk indicator weights, divide risk levels and quantify them, construct an evaluation matrix based on risk indicators and risk level values, set evaluation gray class and whitening weight functions, calculate gray evaluation coefficients to construct a gray evaluation weight vector set matrix, and calculate the comprehensive evaluation value to judge the risk value of the risk indicators. The power supply service risk indicator system includes a primary evaluation indicator set, a secondary evaluation indicator set, a tertiary evaluation indicator set, and a quaternary evaluation indicator set. The primary evaluation index set U is represented as: ,in, For power supply capacity, For service quality; Secondary evaluation indicator set Represented as Where m is the number of primary evaluation indicators, and n is the number of secondary indicators under the primary indicators. The secondary evaluation indicators include power outages, voltage quality, business expansion and installation, electricity metering, electricity bill collection, channel services, emergency repair services, and power grid services. Three-level evaluation indicator set Represented as Where m is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under the primary evaluation indicators, and k is the number of tertiary evaluation indicators under the secondary evaluation indicators. The tertiary evaluation indicators include the number of power outages, the duration of power outages, the scope of power outages, the situation of repeated power outages, poor perception, the number of low voltage occurrences, the intensity of demands, sensitive events, and business processing. Level 4 Evaluation Indicator Set Represented as Where m is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under primary evaluation indicators, k is the number of tertiary evaluation indicators under secondary evaluation indicators, and l is the number of quaternary evaluation indicators under tertiary evaluation indicators. The quaternary evaluation indicators include the number of fault reports per 10,000 households, the number of power outage hours per 10,000 households, the power outage user coverage rate, the number of frequent power outage customer requests per 10,000 households, the number of low voltage repair reports per 10,000 households, the number of complaints, the number of opinions, the number of service applications, the number of issues under supervision, the number of issues identified through overt and covert investigations, the number of repeated requests, the satisfaction rate of follow-up visits, and the timeliness rate of business processing.

[0034] Risk warning module 3 is used for maintaining the risk level of power supply services and for dynamic risk monitoring.

[0035] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0036] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0037] In the embodiments provided by this invention, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0038] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0039] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0040] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0041] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0042] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0043] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. A method for identifying power supply service risks based on grey evaluation, characterized in that, Includes the following steps: Step S1: The steps for standardizing risk assessment data, which involves classifying the dimensions of power supply service risk monitoring and standardizing the different dimensions; Step S2: The steps for constructing a risk identification model include: establishing a power supply service risk indicator system; using the analytic hierarchy process (AHP) to determine the weights of risk indicators; classifying risk levels and assigning quantitative values; constructing an evaluation matrix based on risk indicators and risk level values; setting evaluation gray class and whitening weight functions; calculating gray evaluation coefficients to construct a gray evaluation weight vector set matrix; and calculating the comprehensive evaluation value to determine the risk value of the risk indicators. Step S3: Risk warning step, used for maintaining the risk level of power supply services and conducting dynamic risk monitoring.

2. The power supply service risk identification method based on grey evaluation according to claim 1, characterized in that, The dimensions for monitoring power supply service risks include positive dimensions, negative dimensions, and optimal dimensions. The normalization process for the positive dimension is expressed mathematically as follows: in, Let x represent the set of all original data in this dimension, and max(x) and min(x) represent the maximum and minimum values ​​of the data in this dimension, respectively. Indicates the original data The normalized value obtained after normalization processing; Normalization is performed on the inverse dimension, and the mathematical expression is: in, Let x represent the set of all original data in this dimension, and max(x) and min(x) represent the maximum and minimum values ​​of the data in this dimension, respectively. Indicates the original data The normalized value obtained after normalization processing; Normalize the optimal dimension when hour, ;when hour, ;when hour, ; in, Here, x represents the set of all the original data for that dimension. Indicates the original data The normalized values ​​obtained after normalization are: a is the lower limit of the optimal interval for the data in this dimension, and b is the upper limit of the optimal interval for the data in this dimension.

3. The power supply service risk identification method based on grey evaluation according to claim 1, characterized in that, The power supply service risk indicator system includes a primary evaluation indicator set, a secondary evaluation indicator set, a tertiary evaluation indicator set, and a quaternary evaluation indicator set. The primary evaluation index set U is represented as: ,in, For power supply capacity, For service quality; Secondary evaluation indicator set Represented as Where i is the number of primary evaluation indicators and n is the number of secondary evaluation indicators under the primary evaluation indicators; Three-level evaluation indicator set Represented as Where i is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under the primary evaluation indicators, and k is the number of tertiary evaluation indicators under the secondary evaluation indicators. Level 4 Evaluation Indicator Set Represented as Where i is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under the primary evaluation indicators, k is the number of tertiary evaluation indicators under the secondary evaluation indicators, and l is the number of quaternary evaluation indicators under the tertiary evaluation indicators.

4. The power supply service risk identification method based on grey evaluation according to claim 3, characterized in that, The method of determining the weights of risk indicators using the analytic hierarchy process includes constructing a judgment matrix, calculating the weight vector and eigenvalues ​​based on the judgment matrix, performing a consistency check on the judgment matrix, and defining the weight set. A judgment matrix is ​​constructed, and through expert consultation, indicators within the same level are compared pairwise. The judgment matrix for each level of indicators is then built using the 1-9 scaling method. ; Calculating the weight vector and eigenvalues ​​based on the judgment matrix A includes normalizing each column of the judgment matrix using the root mean square, and summing the results row-wise to obtain the weight vector components of the i-th index. The mathematical expression is: ； Intermediate variables for calculating weight vectors The mathematical expression is: ； right After normalization, the weight vector w is obtained, and its mathematical expression is: ； The maximum eigenvalue is calculated based on the judgment matrix A and the weight vector w. The mathematical expression is: The largest eigenvalue is represented as: ; Perform a consistency check on the judgment matrix A, using a consistency index. Consistency ratio test The mathematical expression for the consistency index is: The mathematical expression for the consistency ratio test is: in, The average random consistency index; if If the consistency check of the judgment matrix is ​​passed, the weights are valid. The constraints for each level of weight set are determined. The constraints for the first-level weight set are expressed as follows: in, The weight of the i-th indicator in the first-level indicators; The second-order weight set constraint is expressed as follows: ， ; The constraint condition for the third-level weight set is expressed as follows: ， ; The fourth-level weight set constraint is expressed as follows: ， ; The risk level classification and quantification process includes using qualitative evaluation indicators and quantifying these indicators by specifying risk level evaluation standards, thus classifying the risk level into three levels. The risk assessment criteria set is represented as follows: The corresponding risk levels are {low, medium, high}, where the risk level is assigned a value of -3 for low, -2 for medium, and -1 for high. When the evaluation index is between (0,1), a value of 0.5 is assigned; When the evaluation index is between (1,2), assign a value of 1.5; When the evaluation index is between (2,3), the value is assigned to 2.

5.

5. The power supply service risk identification method based on grey evaluation according to claim 4, characterized in that, The evaluation matrix is ​​constructed based on risk indicators and risk level assignments. This involves inviting experts to score each of the four levels of evaluation indicators according to the scoring criteria based on historical data, resulting in the scoring matrix, denoted as: (i=1,2;j=1,2,....,n;k=1,2,....,54), of which 54 experts were invited; Based on the evaluation data of all evaluation indicators from the experts, an evaluation matrix is ​​obtained, the mathematical expression of which is: ； The evaluation gray class is defined as having 3 classes, with h = 1, 2, and 3. For gray numbers, the evaluation gray categories are set as excellent, average, and poor; The first gray category is excellent, and the score is [high]. The white weighting function is The mathematical expression is: ； The second gray class is medium, with a score of (1,3), and the white weighting function is: The mathematical expression is: ； The third gray category is medium, and the score is... The white weighting function is The mathematical expression is: 。 6. The power supply service risk identification method based on grey evaluation according to claim 5, characterized in that, Calculate the grey rating coefficient for the evaluation indicators. (i=1,2, corresponding to the first-level evaluation index; j is the second-level evaluation index), the gray evaluation coefficient belonging to the h-th gray category is denoted as The mathematical expression is = ; The gray evaluation coefficients belonging to each gray evaluation category are denoted as follows: Then there is = ; Constructing the grey evaluation weight vector set weight matrix includes considering all evaluation experts' evaluation indicators. The gray evaluation weight for the h-th gray class is denoted as The mathematical expression is: = / ; For the three identified gray categories, the evaluation indicators are... Evaluation weight vectors for each gray class ={ , , }; By combining the grey evaluation weight vectors of the evaluation indicators, the i-th primary indicator is obtained. The gray evaluation weight matrix of the corresponding indicators for each gray category. The mathematical expression is ,(i=1,2;j=1,2,....,n); The mathematical expression for the comprehensive evaluation of the secondary evaluation indicators is as follows: = ={ , },in, The comprehensive evaluation result of the secondary evaluation indicators. This indicates the weight of the secondary evaluation indicators. It is the comprehensive evaluation weight coefficient of the first-level evaluation index for the h-th gray class; Then, a comprehensive evaluation is performed on the primary evaluation indicators, and the mathematical expression is: B = a ={ , }, where B is the comprehensive evaluation result, and a represents the weight of the primary evaluation indicator. It is the overall evaluation weight coefficient of the power supply service for the h-th gray category.

7. The power supply service risk identification method based on grey evaluation according to claim 6, characterized in that, Each evaluation gray category level is assigned a value according to the "gray level", and the value vector of each evaluation gray category level is represented as C=(3,2,1); The overall evaluation value is R=B C.

8. The power supply service risk identification method based on grey evaluation according to claim 1, characterized in that, The maintenance of power supply service risk levels includes adding, deleting, modifying, and querying power supply service levels, dynamically monitoring risk levels, and performing visual operations.

9. A power supply service risk identification system based on grey evaluation, characterized in that, It includes a risk assessment data standardization processing module, a risk identification model construction module, and a risk early warning module; The risk assessment data standardization processing module classifies the dimensions of power supply service risk monitoring and standardizes the different dimensions. The risk identification model construction module establishes a power supply service risk indicator system, uses the analytic hierarchy process to determine the weight of risk indicators, divides risk levels and assigns quantitative values, constructs an evaluation matrix based on risk indicators and risk level values, sets evaluation gray class and whitening weight functions, calculates gray evaluation coefficients to construct a gray evaluation weight vector set weight matrix, and calculates a comprehensive evaluation value to determine the risk value of risk indicators. The risk warning module is used for maintaining the risk level of power supply services and for dynamic risk monitoring.

10. A power supply service risk identification system based on grey evaluation according to claim 9, characterized in that, The power supply service risk indicator system includes a primary evaluation indicator set, a secondary evaluation indicator set, a tertiary evaluation indicator set, and a quaternary evaluation indicator set. The primary evaluation index set U is represented as: ,in, For power supply capacity, For service quality; Secondary evaluation indicator set Represented as Where m is the number of primary evaluation indicators, and n is the number of secondary indicators under the primary indicators. The secondary evaluation indicators include power outages, voltage quality, business expansion and installation, electricity metering, electricity bill collection, channel services, emergency repair services, and power grid services. Three-level evaluation indicator set Represented as Where m is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under the primary evaluation indicators, and k is the number of tertiary evaluation indicators under the secondary evaluation indicators. The tertiary evaluation indicators include the number of power outages, the duration of power outages, the scope of power outages, the situation of repeated power outages, poor perception, the number of low voltage occurrences, the intensity of demands, sensitive events, and business processing. Level 4 Evaluation Indicator Set Represented as Where m is the number of primary evaluation indicators, n is the number of secondary evaluation indicators under primary evaluation indicators, k is the number of tertiary evaluation indicators under secondary evaluation indicators, and l is the number of quaternary evaluation indicators under tertiary evaluation indicators. The quaternary evaluation indicators include the number of fault reports per 10,000 households, the number of power outage hours per 10,000 households, the power outage user coverage rate, the number of frequent power outage customer requests per 10,000 households, the number of low voltage repair reports per 10,000 households, the number of complaints, the number of opinions, the number of service applications, the number of issues under supervision, the number of issues identified through overt and covert investigations, the number of repeated requests, the satisfaction rate of follow-up visits, and the timeliness rate of business processing.

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