Zone area health state fuzzy evaluation method based on edge calculation

Abstract

The invention provides a district health state fuzzy evaluation method based on edge calculation, and the method comprises the steps: S1, deploying a district edge calculation device, collecting power data, carrying out the edge calculation of the collected power data, and obtaining a district operation evaluation quantitative index; s2, constructing a transformer area operation evaluation index system comprising a transformer area operation evaluation quantitative index and a transformer area service type index; s3, calculating an operation index weight corresponding to the transformer area according to the transformer area operation evaluation index system, and determining a transformer area operation health comment discourse domain; s4, calculating a health index deterioration degree corresponding to the transformer area according to the operation index weight corresponding to the transformer area, and determining a corresponding index deterioration degree membership degree function to form a transformer area health membership degree matrix; and S5, based on the health index deterioration degree corresponding to the transformer area and the transformer area health membership degree matrix, calculating a transformer area operation health comprehensive evaluation vector, and completing transformer area operation evaluation. According to the invention, accurate evaluation of the power health state operation of the transformer area is realized.

Description

Technical Field

[0001] The present invention belongs to the field of power grid evaluation, and in particular relates to a fuzzy evaluation method for the health status of a substation based on edge computing. Background Art

[0002] With the rapid development of the power system, the health management of the substation (referring to a specific power supply area) has become the key to ensure the stable operation of the power grid. Traditional substation and equipment evaluation methods often rely on regular manual inspections and empirical judgments, which is not only inefficient, but also difficult to reflect the operating status of the equipment in real time and accurately. The specific problems include the following: First, the existing substation monitoring system has not fully utilized the advantages of edge computing, and the data collection and processing capabilities are limited. It is impossible to achieve comprehensive and efficient collection of substation electrical characteristic parameters, and the lack of real-time data processing and analysis makes it difficult to meet the power system's demand for rapid response; second, the existing evaluation system often lacks unified standards and scientificity, and the selection and weight allocation of evaluation indicators mostly rely on expert experience, lacking objectivity and systematicity; third, most of the existing evaluation methods require manual participation and lack intelligent and automated processing capabilities, which not only reduces work efficiency, but also increases the risk of human errors; fourth, due to the limitations of the evaluation method, the application scope of the evaluation results is limited, and it is difficult to provide effective data support for the optimization and decision-making of the power system. Summary of the invention

[0003] The purpose of this invention is to provide a fuzzy evaluation method for the health status of a substation based on edge computing to improve the efficiency and accuracy of health management of power equipment and realize the intelligence and automation of the power system.

[0004] To achieve the above-mentioned purpose, the present invention provides a fuzzy evaluation method for the health status of a substation based on edge computing, which includes: S1, deploying an edge computing device in the substation, collecting power data at different times in any time period of each substation, and performing edge computing processing on the collected power data to obtain quantitative indicators for substation operation evaluation; S2, constructing a substation operation evaluation index system including the substation operation evaluation quantitative indicators and substation business type indicators; S3, calculating the operating indicator weights corresponding to each substation according to the substation operation evaluation index system, and determining the substation operation health comment domain; S4, calculating the health indicator degradation degree corresponding to the substation according to the operating indicator weights corresponding to each substation, and determining the corresponding indicator degradation degree membership function based on the health indicator degradation degree to form a substation health membership matrix; S5, calculating the substation operation health comprehensive evaluation vector based on the health indicator degradation degree corresponding to the substation and the substation health membership matrix, and completing the substation operation evaluation.

[0005] Preferably, the power data includes phase voltage and phase current; the quantitative indicators of substation operation evaluation obtained through edge computing processing include load rate, line loss rate, three-phase imbalance, and voltage change rate;

[0006] The load rate is the ratio of the actual power load of the substation to its rated capacity within a certain period of time, which measures the load utilization of the substation. The calculation formula of the load rate is as follows:

[0007]

[0008] In the formula, P is the accumulated active power of all users in the substation per unit time, and S is the rated capacity of the transformer in the substation;

[0009] The line loss rate is the ratio of the power loss on the power supply wire to the power supply in the process of the distribution transformer supplying energy to the user. The calculation formula of the line loss rate is as follows:

[0010]

[0011] Where W i Supply power to the low-voltage outgoing line of the distribution transformer in the substation, W o It is the amount of electricity supplied by users in the area;

[0012] The three-phase unbalance degree reflects the three-phase unbalance condition of the transformer area by calculating the current condition in the circuit. The calculation formula of the three-phase unbalance degree is as follows:

[0013]

[0014] In the formula, I max I is the current value of the maximum current phase of the low-voltage busbar in the substation area; min is the current value of the minimum current phase of the low-voltage busbar in the substation;

[0015] The voltage change rate reflects the change of the user voltage at two adjacent acquisition moments, and reflects whether the voltage at the user end of the station area is stable. When the voltage effective value change amplitude of the user voltage at a certain moment is greater than σ, it is judged as a voltage sudden change. The judgment formula is as follows:

[0016]

[0017] In the formula, num(F) is the number of statistically qualified ones; F is the set of time periods of voltage sudden change; U(t) is the voltage measurement value at time t, and σ is the threshold value;

[0018] The voltage change rate formula is as follows:

[0019]

[0020] Where N is the acquisition period.

[0021] Preferably, the area business type indicators include the timeliness rate of business expansion work order completion, the timeliness rate of fault repair, and the timeliness rate of appeal response;

[0022] The business expansion work order completion timeliness rate is used to reflect whether the time taken from the receipt to the completion of the business expansion work order meets the predetermined service standard or the promised time. The calculation formula of the business expansion work order completion timeliness rate is as follows: business expansion work order completion timeliness rate = number of business expansion work orders completed on time / total number of business expansion work orders;

[0023] The fault repair timeliness rate is used to reflect the ability of the power company to complete fault repair within the specified time. The calculation formula of the fault repair timeliness rate is as follows: fault repair timeliness rate = number of faults repaired on time / total number of faults;

[0024] The timeliness rate of responding to requests is used to measure the response speed to customer or user requests. The calculation formula of the timeliness rate of responding to requests is as follows: timeliness rate of responding to requests = number of requests responded to on time / total number of requests.

[0025] Preferably, the step S2 of constructing the substation operation evaluation index system further includes: normalizing the substation operation evaluation quantitative index and the substation business type index to form a standardized value θ, and the standardized normalization processing formula includes:

[0026]

[0027] In the formula, γ max and γ min are respectively the maximum and minimum values ​​of the quantitative index for the operation evaluation of the substation or the service type index j of the substation; γ op is the most suitable attribute value of the quantitative index for evaluating the operation of the substation or the service type index j of the substation; γ high and γ low They are respectively the upper and lower limits of the acceptance interval of the quantitative index of the substation operation evaluation or the value of the substation business type index j.

[0028] Preferably, the calculation of the operating index weights corresponding to each substation in step S3 specifically includes the following steps:

[0029] S31. Determine random weight factor

[0030] S32, based on the random weight factor Calculate the random combination weight w i As the weight of the operation index corresponding to the substation;

[0031] Among them, the random combination weight w i The calculation formula is as follows:

[0032]

[0033] In the formula, w i is the random combination weight, The weights calculated using the fuzzy analytic hierarchy process (FAHP) are i 'The weight calculated by the entropy weight method.

[0034] Optionally, the random weights determined in step S31 are The specific steps include:

[0035] S311, searching the said area operation evaluation quantitative index and the said area business type index based on the best priority search algorithm, searching for n optimal features

[0036] S312, for n optimal features Standardize to obtain the random weight factor

[0037] The random weight factor The calculation formula is as follows:

[0038]

[0039] In the formula, is the random weight factor, which obeys the uniform distribution of U[0,1]. The value range is (0, 1), and satisfy:

[0040]

[0041] Preferably, the domain of determining the health evaluation of the substation operation includes excellent (r 1 ), good(r 2 ), general (r 3 )、Attention(r 4 ) and warning (r 5 ).

[0042] Preferably, the health index degradation degree f corresponding to the station area calculated in step S4 is calculated by randomly combining the weights w i It is obtained by multiplying and accumulating the standardized value θ corresponding to each substation operation evaluation quantitative index and substation business type index.

[0043] Preferably, the index degradation degree membership function that meets the degradation of the health status index of the substation area is obtained through the ridge distribution function:

[0044]

[0045] Among them, the optimal value interval of the health index degradation degree f and the sine function period obtained by calculation are used to determine the index degradation degree membership function corresponding to the substation area to form the substation area health membership matrix, which at least includes the domain of excellent health evaluation of each substation area (r 1 ), good(r 2 ), general (r 3 )、Attention(r 4 ) and warning (r 5 ) corresponding to the indicator degradation degree membership function; the health indicator degradation degree f is mapped to the indicator degradation degree membership function corresponding to the health comment domain of each substation, and the comprehensive evaluation vector of the operation health of each substation is obtained.

[0046] The present invention also provides an electronic device, characterized in that it includes a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the fuzzy evaluation method for the health status of a substation based on edge computing as described above is implemented.

[0047] Compared with the prior art, the present invention has at least the following beneficial effects: by deploying the edge computing device in the substation, the power data at different times in any time period of each substation is collected, and the edge computing model is used to perform edge analysis and processing on the data collected by the monitoring terminal; then, a substation health status evaluation system is constructed from the substation operation evaluation quantitative indicators and the substation business type indicators, and the substation operation indicator weights containing random weight factors are proposed to evaluate the substation health status. By determining the indicator degradation degree membership function, the comprehensive evaluation vector of the operation health of each substation is finally calculated, so as to realize the accurate evaluation of the operation health status of the substation power. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the fuzzy evaluation method of the health status of the substation based on edge computing of the present invention;

[0049] Figure 2 The substation index membership diagram used in the embodiment of the present invention;

[0050] Figure 3 It is the membership value of the area operation health review domain in the embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the attached embodiment of the present invention Figure 1 ~Attached Figure 3 , the technical solutions, structural features, objectives achieved and effects in the embodiments of the present invention are described in detail.

[0052] It should be noted that the drawings are in a very simplified form and use non-precise proportions. They are only used to conveniently and clearly assist in explaining the embodiments of the present invention, and are not used to limit the conditions for the implementation of the present invention. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical content disclosed by the present invention.

[0053] It should be noted that, in the present invention, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only the elements explicitly listed, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0054] In order to achieve accurate evaluation of the health status of power distribution area, the present invention provides a fuzzy evaluation method of the health status of distribution area based on edge computing, such as Figure 1 As shown, the evaluation method includes: S1, deploying an edge computing device in a substation, collecting power data at different times in any time period of each substation, and performing edge computing processing on the collected power data to obtain quantitative indicators for substation operation evaluation; S2, constructing a substation operation evaluation index system including the substation operation evaluation quantitative indicators and substation business type indicators; S3, calculating the operating indicator weights corresponding to each substation according to the substation operation evaluation index system, and determining the substation operation health comment domain; S4, calculating the health indicator degradation degree corresponding to the substation according to the operating indicator weights corresponding to each substation, and determining the corresponding indicator degradation degree membership function based on the health indicator degradation degree to form a substation health membership matrix; S5, calculating the substation operation health comprehensive evaluation vector based on the health indicator degradation degree corresponding to the substation and the substation health membership matrix, and completing the substation operation evaluation.

[0055] In order to solve the problem that offline analysis after uploading the substation data to the metering system cannot reflect the actual operation status of the substation, step S1 of the present invention can reflect the actual operation status of the substation in real time by deploying the substation edge computing device locally in the substation, thereby supporting the collection, processing and calculation and analysis of local power data. Specifically, step S1 uses the substation as an edge computing node to directly collect power data such as phase voltage and phase current, realize the detection and metering of distribution transformers, and the quantitative indicators of substation operation evaluation obtained by edge computing processing include load rate, line loss rate, three-phase imbalance, and voltage change rate, as shown in Table 1.

[0056] The load rate is the ratio of the actual power load of the substation to its rated capacity within a certain period of time, which measures the load utilization of the substation. The calculation formula of the load rate is as follows:

[0057]

[0058] In the formula, P is the accumulated active power of all users in the substation per unit time, and S is the rated capacity of the transformer in the substation;

[0059] The line loss rate is the ratio of the power loss on the power supply wire to the power supply in the process of the distribution transformer supplying energy to the user. The calculation formula of the line loss rate is as follows:

[0060]

[0061] Where W i Supply power to the low-voltage outgoing line of the distribution transformer in the substation, W o It is the amount of electricity supplied by users in the area;

[0062] The three-phase unbalance degree reflects the three-phase unbalance condition of the transformer area by calculating the current condition in the circuit. The calculation formula of the three-phase unbalance degree is as follows:

[0063]

[0064] In the formula, I max I is the current value of the maximum current phase of the low-voltage busbar in the substation area; min is the current value of the minimum current phase of the low-voltage busbar in the substation;

[0065] The voltage change rate reflects the change of the user voltage at two adjacent acquisition moments, and reflects whether the voltage at the user end of the station area is stable. When the voltage effective value change amplitude of the user voltage at a certain moment is greater than σ, it is judged as a voltage sudden change. The judgment formula is as follows:

[0066]

[0067] In the formula, num(F) is the number of statistically qualified ones; F is the set of time periods of voltage sudden change; U(t) is the voltage measurement value at time t, and σ is the threshold value;

[0068] The voltage change rate formula is as follows:

[0069]

[0070] Wherein, N is the collection period, and the collection period in this embodiment is 96 points at intervals of 15 minutes.

[0071] Further, the substation business type indicators in the substation operation evaluation index system constructed in step S2 are shown in Table 1, including: the timeliness rate of business expansion work order completion, the timeliness rate of fault repair, and the timeliness rate of appeal response;

[0072] The business expansion work order completion timeliness rate is used to reflect whether the time taken from the receipt to the completion of the business expansion work order meets the predetermined service standard or the promised time. The calculation formula of the business expansion work order completion timeliness rate is as follows: business expansion work order completion timeliness rate = number of business expansion work orders completed on time / total number of business expansion work orders;

[0073] The fault repair timeliness rate is used to reflect the ability of the power company to complete fault repair within the specified time. This indicator reflects the reliability of power supply. The calculation formula of the fault repair timeliness rate is as follows: fault repair timeliness rate = number of faults repaired on time / total number of faults;

[0074] The timeliness rate of response to requests is used to measure the response speed to customer or user requests. In the power industry, timely response to users' electricity demands is crucial to ensuring service quality and user satisfaction. The calculation formula for the timeliness rate of response to requests is as follows: Timeliness rate of response to requests = number of requests responded to on time / total number of requests.

[0075] It should be noted that the above-mentioned related calculation indicators are the ratio of the number completed within the specified time to the total number. The greater the timeliness rate, the higher the completion efficiency and the better the evaluation.

[0076]

[0077] Table 1

[0078] In order to unify the standards of the evaluation indicators, the step S2 of constructing the substation operation evaluation indicator system further includes: normalizing the substation operation evaluation quantitative indicators and the substation business type indicators to form a standardized value θ, and the processing formula of the standardized normalization includes:

[0079]

[0080] In the formula, γ max and γ min are respectively the maximum and minimum values ​​of the quantitative index for the operation evaluation of the substation or the service type index j of the substation; γ op is the most suitable attribute value of the quantitative index for evaluating the operation of the substation or the service type index j of the substation; γ high and γ loware the upper and lower limits of the acceptance interval of the quantitative index of the operation evaluation of the substation or the value of the substation business type index j. In practical applications, any of the above-mentioned standardized processing formulas is selected for standardization according to the types of the quantitative index of the operation evaluation of the substation and the type of the substation business type index.

[0081] Furthermore, in order to accurately calculate the weight of the evaluation index, a random weight factor is introduced The combined weight is optimized to reduce the influence of uncertain factors on the index weight. The calculation of the operating index weight corresponding to each substation in step S3 specifically includes the following steps:

[0082] S31. Determine random weight factor

[0083] S32, based on the random weight factor Calculate the random combination weight w i As the weight of the operation index corresponding to the substation;

[0084] Among them, the random combination weight w i The calculation formula is as follows:

[0085]

[0086] In the formula, w i is the random combination weight, The weights calculated using the fuzzy analytic hierarchy process (FAHP) are i 'The weights calculated using the entropy weight method.

[0087] Wherein, the random weight determination described in step S31 The specific steps include:

[0088] S311, searching the said area operation evaluation quantitative index and the said area business type index based on the best priority search algorithm, searching for n optimal features

[0089] S312, for n optimal features Standardize to obtain the random weight factor

[0090] The random weight factor The calculation formula is as follows:

[0091]

[0092] In the formula, is the random weight factor, which obeys the uniform distribution of U[0,1]. The value range is (0, 1), and satisfy:

[0093]

[0094] Furthermore, since the operation status of the power supply area is a relative and gradually fuzzy process, it is impossible to achieve quantitative evaluation of the operation status of the area. Therefore, the present invention evaluates its operation status through the operation health evaluation domain. Specifically, the determination of the operation health evaluation domain of the area includes excellent (r 1 ), good(r 2 ), general (r 3 )、Attention(r 4 ) and warning (r 5 ), as shown in Table 2.

[0095] Rating Status description excellent The operation of the substation is within the normal range and close to the optimal value good The operation of the substation is within the normal range and there is no risk trend. generally The operation of the substation is basically within the normal range, with a certain deterioration trend Notice The operation of the substation is close to the warning value, and some indicators do not meet the requirements warn The operation of the station area is in an abnormal stage

[0096] Table 2

[0097] After calculating the operating index weights corresponding to each substation, the health index degradation degree f corresponding to the substation is calculated. The health index degradation degree f corresponding to the substation is calculated by combining the random combination weights w i It is obtained by multiplying and accumulating the standardized value θ corresponding to each substation operation evaluation quantitative index and substation business type index.

[0098] Furthermore, in order to determine the index membership function of the substation, this embodiment obtains the index degradation degree membership function that meets the index degradation of the substation health status through the ridge distribution function:

[0099]

[0100] Among them, the optimal value interval of the health index degradation degree f and the sine function period obtained by calculation are used to determine the index degradation degree membership function corresponding to the substation area to form the substation area health membership matrix, which at least includes the domain of excellent health evaluation of each substation area (r 1 ), good(r 2 ), general (r 3 )、Attention(r 4 ) and warning (r 5 ) corresponding to the indicator degradation degree membership function; the health indicator degradation degree f is mapped to the indicator degradation degree membership function corresponding to the health comment domain of each substation, and the comprehensive evaluation vector of the operation health of each substation is obtained.

[0101] In a specific embodiment, taking the operation data of Zhangjiang Central Station in May as an example, by randomly combining the weights w i The calculation formula is used to calculate the quantitative index w of the substation operation evaluation. 1And the area business type index w 2 , respectively:

[0102] w 1 =(0.343, 0.254, 0.272, 0.131) correspond to load factor, line loss rate, three-phase unbalance, and voltage change rate respectively;

[0103] w 2 =(0.324, 0.401, 0.275) correspond to the timely completion rate of business expansion work orders, the timely repair rate, and the timely response rate of demands respectively.

[0104] Based on the power data of Zhangjiang Central Substation in May, the comprehensive evaluation vector of the operation health of each substation in the operation health comment domain (i.e., the five levels of operation status) was calculated by normalizing the quantitative indicators of the substation operation evaluation and the business type indicators of the substation and determining the indicator degradation degree membership function. The degradation degree membership function diagram of each indicator is shown in the figure below: Figure 2 shown.

[0105] Among them, the comprehensive evaluation vector of the operation health of the substation reflects the membership values ​​of the operation health comment domain of each substation in the "excellent", "good", "general", "caution" and "warning" states. The membership values ​​of the specific operation health comment domain of the substation are shown in Table 3. Furthermore, based on the correction of the random combination weight and the membership value, the overall operation level of the substation is determined based on the maximum membership principle (that is, the membership corresponding to the health comment domain is the largest, and the health comment domain is the operation status of the substation) for the membership of the operation status of each substation in the Zhangjiang Center area (that is, the health comment domain).

[0106] Specific, for example Figure 3 For the substation in the first row, the membership value of the substation health evaluation domain is "excellent" is 0.471, which is greater than the membership values ​​of other "good", "general", "caution" and "warning". Therefore, based on the maximum membership principle, the operating status of this substation is excellent.

[0107] The present invention also provides an electronic device, which includes a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the fuzzy evaluation method for the health status of a substation based on edge computing described in the above embodiment is implemented.

[0108] To summarize, the fuzzy evaluation method for the health status of a substation based on edge computing provided by the present invention deploys an edge computing device in the substation to collect power data at different times in any time period of each substation, and uses an edge computing model to perform edge analysis and processing on the data collected by the monitoring terminal; then, a substation health status evaluation system is constructed from the substation operation evaluation quantitative indicators and the substation business type indicators, and a substation operation indicator weight containing a random weight factor is proposed to evaluate the health status of the substation. By determining the indicator degradation degree membership function, the comprehensive evaluation vector of the operation health of each substation is finally calculated to achieve accurate evaluation of the health status of the power in the substation.

[0109] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. A fuzzy evaluation method for the health status of a substation based on edge computing, characterized in that: include: S1. Deploy edge computing devices in the substation area to collect power data at different times in any time period of each substation area, and perform edge computing processing on the collected power data to obtain quantitative indicators for substation operation evaluation; S2. Constructing a substation operation evaluation index system including the substation operation evaluation quantitative index and substation business type index; S3. Calculate the operation index weight corresponding to each substation according to the substation operation evaluation index system, and determine the substation operation health evaluation domain; S4, calculating the health index degradation degree corresponding to the substation according to the operating index weights corresponding to each substation, and determining the corresponding index degradation degree membership function based on the health index degradation degree to form a substation health membership matrix; S5. Based on the health indicator degradation degree corresponding to the substation and the health membership matrix of the substation, calculate the comprehensive evaluation vector of the substation operation health to complete the evaluation of the substation operation.

2. The fuzzy evaluation method for the health status of a substation based on edge computing according to claim 1 is characterized in that: The power data includes phase voltage and phase current; the quantitative indicators of substation operation evaluation obtained through edge computing processing include load rate, line loss rate, three-phase imbalance, and voltage change rate; The load rate is the ratio of the actual power load of the substation to its rated capacity within a certain period of time, which measures the load utilization of the substation. The calculation formula of the load rate is as follows: In the formula, P is the accumulated active power of all users in the substation per unit time, and S is the rated capacity of the transformer in the substation; The line loss rate is the ratio of the power loss on the power supply wire to the power supply in the process of the distribution transformer supplying energy to the user. The calculation formula of the line loss rate is as follows: Where W i Supply power to the low-voltage outgoing line of the distribution transformer in the substation, W o It is the amount of electricity supplied by users in the area; The three-phase imbalance reflects the three-phase imbalance of the substation by calculating the current in the circuit. The calculation formula of the three-phase imbalance is as follows: In the formula, I max I is the current value of the maximum current phase of the low-voltage busbar in the substation area; min is the current value of the minimum current phase of the low-voltage busbar in the substation; The voltage change rate reflects the change of the user voltage at two adjacent acquisition moments, and reflects whether the voltage at the user end of the station area is stable. When the voltage effective value change amplitude of the user voltage at a certain moment is greater than σ, it is judged as a voltage sudden change. The judgment formula is as follows: In the formula, num(F) is the number of statistically qualified ones; F is the set of time periods of voltage sudden change; U(t) is the voltage measurement value at time t, and σ is the threshold value; The voltage change rate formula is as follows: Where N is the acquisition period.

3. The fuzzy evaluation method for the health status of a substation based on edge computing according to claim 2 is characterized in that: The business type indicators of the station area include the timeliness rate of business expansion work order completion, the timeliness rate of fault repair, and the timeliness rate of complaint response; The business expansion work order completion timeliness rate is used to reflect whether the time taken from the receipt to the completion of the business expansion work order meets the predetermined service standard or the promised time. The calculation formula of the business expansion work order completion timeliness rate is as follows: business expansion work order completion timeliness rate = number of business expansion work orders completed on time / total number of business expansion work orders; The fault repair timeliness rate is used to reflect the ability of the power company to complete fault repair within the specified time. The calculation formula of the fault repair timeliness rate is as follows: fault repair timeliness rate = number of faults repaired on time / total number of faults; The timeliness rate of responding to requests is used to measure the response speed to customer or user requests. The calculation formula of the timeliness rate of responding to requests is as follows: timeliness rate of responding to requests = number of requests responded to on time / total number of requests.

4. The fuzzy evaluation method for the health status of a substation based on edge computing according to claim 3 is characterized in that: The step S2 of constructing the substation operation evaluation index system further includes: normalizing the substation operation evaluation quantitative index and the substation service type index to form a standardized value θ, and the standardized normalization processing formula includes: In the formula, γ max and γ min are respectively the maximum and minimum values ​​of the quantitative index for the operation evaluation of the substation or the service type index j of the substation; γ op is the most suitable attribute value of the quantitative index for evaluating the operation of the substation or the service type index j of the substation; γ high and γ low They are respectively the upper and lower limits of the acceptance interval of the quantitative index of the substation operation evaluation or the value of the substation business type index j.

5. The fuzzy evaluation method for the health status of a substation based on edge computing according to claim 4 is characterized in that: The calculation of the operating index weights corresponding to each substation in step S3 specifically includes the following steps: S31. Determine random weight factor S32, based on the random weight factor Calculate the random combination weight w i As the weight of the operation index corresponding to the substation; Among them, the random combination weight w i The calculation formula is as follows: In the formula, w i is the random combination weight, The weights calculated using the fuzzy analytic hierarchy process (FAHP) are w i 'The weights calculated using the entropy weight method.

6. The fuzzy evaluation method for the health status of a substation based on edge computing according to claim 5 is characterized in that: Determining random weights in step S31 The specific steps include: S311, searching the said area operation evaluation quantitative index and the said area business type index based on the best priority search algorithm, searching for n optimal features S312, for n optimal features Standardize to obtain the random weight factor The random weight factor The calculation formula is as follows: In the formula, is the random weight factor, which obeys the uniform distribution of U[0,1]. The value range is (0, 1), and satisfy:

7. The fuzzy evaluation method for the health status of a substation based on edge computing according to claim 5 is characterized in that: The domain of determining the area operation health review includes excellent (r1), good (r2), general (r3), attention (r4) and warning (r5).

8. The fuzzy evaluation method for the health status of a substation based on edge computing according to claim 7 is characterized in that: The health index degradation degree f corresponding to the calculation area in step S4 is calculated by randomly combining the weights w i It is obtained by multiplying and accumulating the standardized value θ corresponding to each substation operation evaluation quantitative index and substation business type index.

9. The fuzzy evaluation method for the health status of a substation based on edge computing according to claim 8, characterized in that: The index degradation degree membership function that meets the degradation of the health status index of the substation area is obtained through the ridge distribution function: Among them, the indicator degradation degree membership function corresponding to the substation is determined by calculating the optimal value interval and the sine function period of the health indicator degradation degree f, so as to form the substation health membership matrix, which at least includes the indicator degradation degree membership function corresponding to each substation operation health review domain of excellent (r1), good (r2), general (r3), attention (r4) and warning (r5); the health indicator degradation degree f is mapped to the indicator degradation degree membership function corresponding to each of the substation health review domains, so as to obtain the comprehensive evaluation vector of the operation health of each substation.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the fuzzy evaluation method for the health status of a substation based on edge computing as described in any one of claims 1 to 9 is implemented.

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