Power supply enterprise classification evaluation method based on power supply reliability benchmarking and related device

Abstract

The invention discloses a power supply enterprise classification evaluation method based on power supply reliability benchmarking and a related device, and the method comprises the steps: obtaining the corresponding power sale quantity, power supply area, distribution network looping rate, population density and total production value of a target power supply enterprise in a preset time period; respectively determining a first reliability index, a second reliability index, a third reliability index, a fourth reliability index and a fifth reliability index of the target power supply enterprise based on the power sale quantity, the power supply area, the distribution network looping rate, the population density and the total production value, and determining a target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index and the fifth reliability index, determining a reliability benchmarking level of the target power supply enterprise based on the target reliability index, and obtaining a target reliability benchmarking level. According to the embodiment of the invention, the accuracy of reliability classification benchmarking evaluation of the power supply enterprise is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply reliability assessment, and in particular to a classification and evaluation method for power supply enterprises based on power supply reliability benchmarking and related devices. Background Art

[0002] With the rapid development of society and the economy, electricity, as the core energy source for modern society, has become increasingly important for the reliability of its supply, impacting residents' lives, industrial production, and social stability. As the primary provider of electricity, the reliability of power supply companies directly impacts the quality of electricity consumption and the level of economic development across society. Currently, there are many problems with the reliability evaluation of power supply companies. Traditional reliability evaluation methods often focus on single indicators, such as power supply equipment failure rate and outage duration, which fail to fully reflect the overall reliability of power supply companies. Therefore, improving the accuracy of reliability evaluations for power supply companies is an urgent issue that needs to be addressed. Summary of the Invention

[0003] The embodiment of the present application provides a classification and evaluation method for power supply enterprises based on power supply reliability benchmarking, which improves the accuracy of reliability classification and benchmarking evaluation of power supply enterprises.

[0004] In a first aspect, an embodiment of the present application provides a method for classifying and evaluating power supply enterprises based on power supply reliability benchmarking. The method for classifying and evaluating power supply enterprises based on power supply reliability benchmarking includes:

[0005] Obtaining the target power supply enterprise's electricity sales, power supply area, and distribution network ring rate within a preset time period, as well as the population density and gross domestic product of the target area corresponding to the target power supply enterprise within the preset time period;

[0006] Determine a first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, determine a second reliability index of the target power supply enterprise based on the power supply area and the preset power supply area, determine a third reliability index of the target power supply enterprise based on the distribution network ring network rate and the preset distribution network ring network rate, determine a fourth reliability index of the target power supply enterprise based on the population density and the preset population density, and determine a fifth reliability index of the target power supply enterprise based on the gross domestic product and the preset gross domestic product;

[0007] Determine a target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index;

[0008] The reliability benchmarking level of the target power supply enterprise is determined based on the target reliability index to obtain a target reliability benchmarking level.

[0009] In a second aspect, an embodiment of the present application provides a power supply enterprise classification evaluation device based on power supply reliability benchmarking, the device comprising: an acquisition unit and a processing unit;

[0010] The acquisition unit is used to obtain the power sales volume, power supply area, and distribution network ring rate of the target power supply enterprise within a preset time period, as well as the population density and gross domestic product of the target area corresponding to the target power supply enterprise within the preset time period;

[0011] The processing unit is configured to determine a first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, determine a second reliability index of the target power supply enterprise based on the power supply area and the preset power supply area, determine a third reliability index of the target power supply enterprise based on the distribution network ring network rate and the preset distribution network ring network rate, determine a fourth reliability index of the target power supply enterprise based on the population density and the preset population density, and determine a fifth reliability index of the target power supply enterprise based on the gross domestic product and the preset gross domestic product;

[0012] Determine a target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index;

[0013] The reliability benchmarking level of the target power supply enterprise is determined based on the target reliability index to obtain a target reliability benchmarking level.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor so that the electronic device performs the method of the first aspect.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0016] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, so that a computer executes the method of the first aspect.

[0017] The implementation of the present invention has the following beneficial effects:

[0018] It can be seen that the power supply enterprise classification and evaluation method based on power supply reliability benchmarking described in the embodiment of the present invention includes: first, obtaining the power sales volume, power supply area, and distribution network ring network rate of the target power supply enterprise within a preset time period, as well as the population density and gross domestic product of the target area corresponding to the target power supply enterprise within the preset time period; then, determining the first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, determining the second reliability index of the target power supply enterprise based on the power supply area and the preset power supply area, determining the third reliability index of the target power supply enterprise based on the distribution network ring network rate and the preset distribution network ring network rate, determining the fourth reliability index of the target power supply enterprise based on the population density and the preset population density, and determining the fifth reliability index of the target power supply enterprise based on the gross domestic product and the preset gross domestic product; then, determining the target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index and the fifth reliability index; finally, determining the reliability benchmarking level of the target power supply enterprise based on the target reliability index to obtain the target reliability benchmarking level. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the implementation methods or background technologies of the present application, the drawings required for use in the implementation methods or background technologies of the present application will be described below.

[0020] Figure 1 This is a flow chart of a classification and evaluation method for power supply enterprises based on power supply reliability benchmarking provided in an embodiment of the present application;

[0021] Figure 2 This is a flow chart for determining a first reliability indicator provided by an embodiment of the present application;

[0022] Figure 3 This is a flow chart for determining a target reliability index provided by an embodiment of the present application;

[0023] Figure 4 is a flow chart for determining power supply stability parameters provided by an embodiment of the present application;

[0024] Figure 5 This is a flow chart of determining a device failure rate corresponding to a first power supply device provided by an embodiment of the present application;

[0025] Figure 6 This is a flow chart for determining a target reliability benchmarking level provided by an embodiment of the present application;

[0026] Figure 7 This is a schematic diagram of the structure of a power supply enterprise classification evaluation device based on power supply reliability benchmarking provided in an embodiment of the present application;

[0027] Figure 8 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the present invention, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0029] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0030] Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] See also Figure 1 , Figure 1 This is a flow chart of a classification evaluation method for power supply enterprises based on power supply reliability benchmarking provided by an embodiment of the present application, including but not limited to the following steps:

[0032] S101: Obtain the power sales volume, power supply area, and distribution network ring rate of a target power supply enterprise within a preset time period, as well as the population density and gross domestic product of a target area corresponding to the target power supply enterprise within the preset time period.

[0033] In this embodiment, the target power supply enterprise refers to the power supply enterprise selected as the object in a specific study or assessment. It can be a specific power company in a certain area, or an organizational entity with power supply responsibilities within a specific scope.

[0034] The preset time period is a specific time interval determined in advance, which is used to limit the scope of data collection and analysis. This time period can be set according to the research purpose and actual situation. For example, it can be one year to analyze the annual power supply situation, or it can be a quarter, a month or even a shorter time to observe the short-term power supply characteristics in more detail. Common preset time period selections will take into account factors such as cyclical changes in electricity demand, policy adjustment cycles or corporate financial reporting cycles.

[0035] Electricity sales refers to the total amount of electricity actually sold by the target power supply enterprise to users within a preset time period, usually measured in kilowatt-hours. Electricity sales reflects the enterprise's electricity sales performance over a certain period of time, and also indirectly reflects the consumption demand and dependence of users in the region on electricity. It is one of the important indicators for measuring the operating scale and economic benefits of power supply enterprises.

[0036] The power supply area refers to the geographical area that the target power supply enterprise is responsible for supplying power, generally measured in square kilometers. The size of the power supply area reflects the service coverage of the power supply enterprise. It is closely related to factors such as the layout of power supply facilities, the extension of power supply lines, and the distribution of electricity demand in different regions. A larger power supply area may mean that the enterprise needs to manage a more complex power supply network and face more power supply challenges, such as long-distance transmission losses and differences in electricity load in different regions.

[0037] The distribution network ringing rate is a key indicator for measuring the rationality and reliability of the distribution network structure. The distribution network is a network system that distributes electricity from substations to various user terminals. Ringing refers to the existence of a ring-shaped power supply line structure in the distribution network, which allows electricity to reach users through multiple paths, improving the flexibility and reliability of power supply. The distribution network ringing rate refers to the percentage of line length or number of nodes in the distribution network that implements the ring network structure to the total line length or total number of nodes. The higher the ratio, the better the redundancy and flexibility of the distribution network. When a line fails, electricity can continue to be supplied through other paths, reducing the scope and duration of power outages, thereby improving power supply reliability.

[0038] The target area corresponding to the target power supply enterprise refers to the power supply service coverage area corresponding to the target power supply enterprise. It clarifies the geographical scope involved in the study. This area can be a city, a county, a specific industrial park or other areas with clear boundaries. The determination of the target area is crucial for accurately analyzing the service objects and service environment of the power supply enterprise, because factors such as the geographical characteristics, population distribution, and economic development level of different regions will have a significant impact on power supply demand and power supply reliability.

[0039] Population density refers to the average number of people per unit area in the target area during a preset time period, usually measured in people per square kilometer. Population density reflects the concentration of the population in the area and is closely related to electricity demand. Generally speaking, areas with high population density, such as urban center areas, have relatively large and concentrated electricity demand, and higher requirements for power supply reliability and stability, because power outages may have a serious impact on the lives of a large number of residents and numerous commercial activities. In contrast, areas with low population density, such as remote rural or mountainous areas, have relatively small and scattered electricity demand. The power supply challenges faced by power supply companies in these areas may focus more on aspects such as the construction and maintenance costs of power supply facilities.

[0040] Gross domestic product refers to the final result of the production activities of all permanent units in the target area within a preset time period. It is usually measured in monetary form. Gross domestic product reflects the economic development level and the overall scale of economic activities in the region. It is closely related to electricity consumption. Regions with higher economic development levels and larger gross domestic products usually have a higher degree of dependence on electricity for industrial production, commercial activities and residents' lives, and have more stringent requirements for power supply reliability and quality, because interruptions or instability in power supply may cause greater losses to local economic development. Conversely, regions with relatively backward economic development and low gross domestic product have relatively small electricity demand, but with the development of the economy and the improvement of people's living standards, their requirements for power supply reliability will gradually increase.

[0041] S102: Determine the first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, determine the second reliability index of the target power supply enterprise based on the power supply area and the preset power supply area, determine the third reliability index of the target power supply enterprise based on the distribution network ring network rate and the preset distribution network ring network rate, determine the fourth reliability index of the target power supply enterprise based on the population density and the preset population density, and determine the fifth reliability index of the target power supply enterprise based on the gross domestic product and the preset gross domestic product.

[0042] In this embodiment, for example, the first reliability index of the target power supply enterprise is determined based on the power sales volume and the preset power sales volume. Figure 2 , Figure 2 This is a flowchart of determining a first reliability indicator provided by an embodiment of the present application, including but not limited to the following steps:

[0043] S201: Determine a ratio between the power sales amount and the preset power sales amount to obtain a first reference reliability index.

[0044] In this embodiment, the electricity sales amount is the total amount of electricity actually sold to users by the target power supply enterprise within a preset time period, and the preset electricity sales amount is a power value set in advance based on factors such as past experience, market forecasts, enterprise planning or industry standards. It represents the electricity sales level that the power supply enterprise is expected to achieve under ideal conditions or specific standards. It should be noted that, in this embodiment, the preset electricity sales amount is the benchmark value of electricity sales in the target area where the target power supply enterprise is located.

[0045] Compare the actual electricity sales with the preset electricity sales and calculate the ratio between them. This ratio can reflect the degree of closeness of the power supply company to the expected electricity sales target in actual operation. If the ratio is close to 1, it means that the actual electricity sales are close to the preset electricity sales, that is, the electricity sales benchmark value, and the power supply company's electricity sales are good. To a certain extent, it may also imply that its power supply reliability is high, because stable power supply is an important guarantee for achieving the expected electricity sales. If the ratio is less than 1, it means that there is a difference between the actual electricity sales and expectations, and further analysis of the reasons may be needed to evaluate the impact on power supply reliability. This ratio is the first reference reliability indicator, which provides a preliminary reference basis for further evaluation of the reliability of the power supply company.

[0046] S202: Obtain the average electricity price of the target power supply enterprise within the preset time period.

[0047] In this embodiment, the average electricity price refers to the average value obtained by dividing the sum of electricity charges collected by the target power supply enterprise through various sales channels and different types of users by the total electricity sales during a preset time period. It reflects the average price level of electricity sold by the power supply enterprise during the time period. The calculation of the average electricity price involves the electricity prices of different user categories (such as residential users, industrial users, commercial users, etc.) and their respective electricity consumption weights. Since different user categories may implement different electricity price policies, the average electricity price can comprehensively reflect the electricity price structure and income level of the power supply enterprise. It is also closely related to factors such as power supply costs and market supply and demand relationships. The average electricity price can also reflect factors such as the power supply cost and power supply quality of the power supply enterprise to a certain extent. A higher average electricity price may mean that the power supply enterprise has invested more in equipment maintenance, technology upgrades, etc., which may improve the reliability of power supply. Obtaining the average electricity price is of great significance for analyzing the economic benefits of the power supply enterprise and subsequently determining its impact on power supply reliability.

[0048] S203: Determine a first optimization factor corresponding to the average electricity price.

[0049] In this embodiment, the first optimization factor is a coefficient determined based on the average electricity price, which is used to adjust and optimize the first reference reliability index. Generally, the average electricity price is related to factors such as the operating costs, profit level, and power supply quality of the power supply enterprise. If the average electricity price is high, it may mean that the power supply enterprise has invested more costs in the process of power production, transmission, and distribution to ensure the stability and reliability of power supply. At this time, the corresponding first optimization factor may adjust the first reference reliability index in a direction that is conducive to improving reliability. Conversely, if the average electricity price is low, it may reflect that the power supply enterprise is more stringent in cost control, or that the power supply quality may be affected to a certain extent. In this case, the first optimization factor may make corresponding corrections to the first reference reliability index to more accurately reflect the actual reliability level of power supply. The determination of the first optimization factor usually requires a large amount of historical data, industry experience, and in-depth analysis of the operating conditions of the power supply enterprise. It is achieved by establishing a corresponding mathematical model or empirical formula. It can be a mapping relationship between a preset average electricity price and the optimization factor. Based on this mapping relationship, the first optimization factor corresponding to the average electricity price can be determined.

[0050] S204: Optimize the first reference reliability index based on the first optimization factor to obtain the first reliability index.

[0051] In this embodiment, the first reliability index is calculated specifically according to the following formula:

[0052] First reliability index = first reference reliability index × (1 + first optimization factor);

[0053] According to the above formula, the first reference reliability index can be optimized based on the first optimization factor to obtain the first reliability index.

[0054] It can be seen that calculating the ratio of electricity sales to the preset electricity sales to obtain the first reference reliability index can directly reflect the degree of compliance between the power supply enterprise's actual electricity sales and the expected target, reflecting the power supply enterprise's ability to meet market electricity demand. A high ratio indicates that the power supply enterprise can better guarantee power supply and meet user electricity demand, which indirectly reflects the high reliability of power supply. Obtaining the average electricity price of the target power supply enterprise during the preset time period and determining the corresponding first optimization factor can link the economic benefits of power supply with reliability. The average electricity price reflects factors such as power supply cost, power quality, and service level to a certain extent. For example, a higher average electricity price may mean that the enterprise has invested more in grid construction, equipment maintenance, and other aspects, thus being more likely to ensure the stability and reliability of power supply. In this case, optimizing the first reference reliability index through the first optimization factor can more comprehensively reflect the reliability of power supply. By optimizing the first reference reliability index based on the first optimization factor, a more accurate first reliability index is obtained. This optimization method comprehensively considers both electricity sales and average electricity prices, avoiding the one-sidedness of relying on a single indicator to assess power supply reliability. The first optimization factor adjusts the first reference reliability index according to different average electricity prices, so that the final first reliability index can more accurately reflect the actual reliability level of the power supply enterprise. This method encourages power supply enterprises to pay attention not only to the economic indicator of electricity sales, but also to factors such as cost and quality reflected by the average electricity price. To improve reliability indicators, power supply enterprises will ensure the completion of electricity sales tasks while reasonably controlling costs, optimizing electricity price structures, increasing investment in grid construction and maintenance, and improving power supply quality and service levels, thereby achieving sustainable development and operational optimization.

[0055] Exemplarily, the second reliability index of the target power supply enterprise is determined based on the power supply area and the preset power supply area. Specifically, the ratio of the power supply area to the preset power supply area is calculated to obtain a second reference reliability index. This ratio reflects the relationship between the actual power supply coverage of the target power supply enterprise and the expected power supply coverage. If the ratio is high, it means that the power supply enterprise has achieved or exceeded the preset target in terms of power supply area, and has a high reliability basis from the perspective of the breadth of power supply coverage. Factors related to the power supply area are considered, such as the power supply cost per unit area and the power supply difficulty coefficient in different areas. The corresponding second optimization factor is determined based on the average power supply cost per unit area. Generally speaking, a higher power supply cost per unit area may mean that the enterprise has invested more in the construction and maintenance of power supply facilities, thereby being more likely to ensure the stability and reliability of power supply. Accordingly, the second optimization factor will adjust the second reference reliability index in a higher direction. Conversely, if the power supply cost per unit area is low, it may reflect insufficient investment or inadequate maintenance of power supply facilities. The second optimization factor will appropriately adjust the second reference reliability index downward. The second reference reliability index is optimized based on the second optimization factor to obtain the second reliability index. The second reliability index obtained in this way comprehensively considers the completion status of the power supply area and related cost factors, and more comprehensively reflects the actual reliability level of the power supply enterprise in terms of power supply coverage.

[0056] Exemplarily, a third reliability index of the target power supply enterprise is determined based on the distribution network ringing rate and a preset distribution network ringing rate. Specifically, the ratio of the distribution network ringing rate to the preset distribution network ringing rate is calculated to obtain a third reference reliability index. The distribution network ringing rate is an important indicator for measuring the rationality of the distribution network structure and the reliability of power supply. This ratio reflects the comparison between the actual and expected ringing levels of the target power supply enterprise's distribution network. A higher ratio indicates a more complete ring structure of the distribution network. When a line fails, load can be transferred through the ring network, reducing the scope and duration of power outages and improving power supply reliability. Factors affecting the ringing of the distribution network are considered, such as the aging of the ring network equipment and the maintenance level of the ring network. A corresponding third optimization factor is determined based on the average aging rate of the ring network equipment. The lower the aging rate of the ring network equipment, the better the equipment condition and the greater the ability to ensure the normal operation and power supply reliability of the ring network. In this case, the third optimization factor will adjust the third reference reliability index upward. Conversely, the higher the aging rate, the greater the risk of equipment failure. The third optimization factor will adjust the third reference reliability index downward to reflect the actual reliability status. The third reference reliability index is optimized using the third optimization factor to obtain the third reliability index. This approach comprehensively considers actual operating factors such as the distribution network ring ratio and equipment aging, allowing the third reliability index to more accurately reflect the true reliability level of the distribution network.

[0057] For example, the fourth reliability index of the target power supply enterprise is determined based on the population density and the preset population density. Specifically, the ratio of the population density to the preset population density is calculated to obtain the fourth reference reliability index. The population density reflects the concentration of electricity demand in the power supply area. The ratio can illustrate the difference between the actual population density and the expected population density in the area served by the target power supply enterprise. A higher ratio may mean that the power supply enterprise is facing greater power supply pressure. However, if the enterprise can ensure the quality and reliability of power supply in this situation, it means that it has strong power supply capacity and the ability to cope with complex power consumption environments. A lower ratio may indicate that the power supply enterprise has relatively abundant power supply resources, but there may also be problems with low utilization efficiency of power supply facilities. The corresponding fourth optimization factor is determined based on the average peak-to-valley difference rate of residential electricity consumption. The larger the peak-to-valley difference rate, the greater the fluctuation of electricity load and the higher the stability and reliability requirements of the power supply system. If the power supply enterprise can still ensure power supply under the condition of a large peak-to-valley difference rate, the fourth optimization factor will appropriately increase the fourth reference reliability index to reflect the enterprise's ability to cope with load fluctuations. Conversely, a smaller peak-to-valley difference rate indicates that the electricity load is relatively stable and the power supply pressure is small. The fourth optimization factor may cause the fourth reference reliability index to remain unchanged or slightly decrease. The fourth reference reliability index is optimized based on the fourth optimization factor to obtain the fourth reliability index. Such a fourth reliability index comprehensively considers factors such as population density and electricity load fluctuation, and more comprehensively reflects the reliability level of power supply enterprises in meeting the electricity demand in areas with different population densities.

[0058] Exemplarily, the fifth reliability index of the target power supply enterprise is determined based on the gross domestic product and the preset gross domestic product. Specifically, the ratio of the gross domestic product to the preset gross domestic product is calculated to obtain the fifth reference reliability index. The gross domestic product reflects, to a certain extent, the level of economic activity and the degree of dependence on electricity in the power supply area. The ratio reflects the comparison between the actual economic development situation of the area served by the target power supply enterprise and the expected economic development situation in terms of electricity supply requirements. A higher ratio indicates that the power supply enterprise has played a better role in supporting regional economic development. It also means that the enterprise needs to provide a more reliable power supply to meet the needs of economic activities. A lower ratio may indicate that the power supply enterprise still has room for improvement in promoting economic development, or that the demand for electricity for economic development has not yet been fully released. The corresponding fifth optimization factor is determined based on the output value share of high-energy-consuming industries. The higher the proportion of high-energy-consuming industries, the higher the requirements for the stability and reliability of power supply, because power outages in such industries can cause significant economic losses. If power supply companies can meet the power demand in areas with a high proportion of high-energy-consuming industries, the fifth optimization factor will increase the fifth reference reliability index to reflect the company's ability to ensure power supply for key industries. Conversely, if the proportion of high-energy-consuming industries is low and overall power demand is relatively stable, the fifth optimization factor may cause the fifth reference reliability index to be adjusted accordingly. The fifth reliability index is obtained by optimizing the fifth reference reliability index through the fifth optimization factor. The fifth reliability index determined in this way comprehensively considers factors such as gross domestic product and industrial structure, and more accurately reflects the power supply reliability level of power supply companies in supporting regional economic development.

[0059] S103: Determine a target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index.

[0060] In this implementation, see Figure 3 , Figure 3 This is a flowchart of determining a target reliability index provided by an embodiment of the present application, including but not limited to the following steps:

[0061] S301: Determine a first weight corresponding to the first reliability indicator, a second weight corresponding to the second reliability indicator, a third weight corresponding to the third reliability indicator, a fourth weight corresponding to the fourth reliability indicator, and a fifth weight corresponding to the fifth reliability indicator.

[0062] In this embodiment, the sum of the first weight, the second weight, the third weight, the fourth weight, and the fifth weight is 1. In order to comprehensively evaluate the reliability of the target power supply enterprise, it is necessary to determine the relative importance of each reliability indicator, that is, the weight. The first reliability indicator, the second reliability indicator, the third reliability indicator, the fourth reliability indicator, and the fifth reliability indicator correspond to the first weight, the second weight, the third weight, the fourth weight, and the fifth weight, respectively. The sum of these weights is 1 to ensure that the influence of all indicators can be reasonably distributed and balanced during the comprehensive calculation, avoiding the situation where the sum of weights is too large or too small, resulting in inaccurate evaluation results.

[0063] It should be explained that weights can be assigned using the Analytic Hierarchy Process (AHP). Specifically, the objectives of power supply enterprise reliability assessment can be broken down into different levels, such as the target level (power supply enterprise reliability assessment), the criterion level (reliability indicators related to electricity sales, power supply area, distribution network ring ratio, population density, and gross domestic product), and the scenario level (specific assessment indicators and data). By establishing a hierarchical model, constructing a judgment matrix, calculating the weight vectors for each indicator, and performing a consistency test, the weights can be determined. Principal component analysis can also be used to assign weights. Specifically, by processing the raw data, multiple highly correlated indicators can be converted into a few independent principal components. The weights of each indicator are then determined based on the variance contribution of the principal components. If the analysis reveals that the variance contribution of indicators related to electricity sales is large, this indicates a greater impact of electricity sales on power supply reliability, and the corresponding first weight will be higher. Conversely, if the variance contribution of indicators related to population density is small, the fourth weight will be lower.

[0064] In this implementation, electricity sales and the distribution network ring rate are relatively important and have higher weights. Electricity sales directly reflect the business volume of the power supply enterprise and its power supply capacity to society. The distribution network ring rate reflects the structural rationality of the power supply network and the stability and flexibility of the power supply, which is crucial to ensuring the quality of power supply. The power supply area reflects the service scope of the power supply enterprise, but may have a relatively weaker direct impact on reliability. Population density and gross domestic product have a certain correlation with electricity demand, but their impact on power supply reliability is indirect, so the weights may be relatively low. However, the specific weight distribution needs to be adjusted according to the actual situation of the target power supply enterprise, such as the enterprise's development stage, grid structure, and the economic characteristics of the region where it is located.

[0065] S302: Calculate based on the first weight, the second weight, the third weight, the fourth weight, the fifth weight, the first reliability index, the second reliability index, the third reliability index, the fourth reliability index and the fifth reliability index to obtain a reference reliability index.

[0066] In this embodiment, the reference reliability index is calculated specifically according to the following formula:

[0067] Reference reliability index = first reliability index × first weight + second reliability index × second weight + third reliability index × third weight + fourth reliability index × fourth weight + fifth reliability index × fifth weight;

[0068] According to the above formula, calculation can be performed based on the first weight, the second weight, the third weight, the fourth weight, the fifth weight, the first reliability index, the second reliability index, the third reliability index, the fourth reliability index and the fifth reliability index to obtain a reference reliability index.

[0069] S303: Determine the power supply stability parameters of the target power supply enterprise.

[0070] In this implementation, see Figure 4 , Figure 4 This is a flow chart of determining a power supply stability parameter provided by an embodiment of the present application, including but not limited to the following steps:

[0071] S401: Obtain n power supply devices corresponding to the target power supply enterprise.

[0072] In this embodiment, n is an integer greater than 1. The n power supply equipment corresponding to the target power supply enterprise may include power generation equipment, power transformation equipment, power distribution equipment, reactive power compensation equipment, automation equipment and other equipment, which are not limited here.

[0073] S402: Determine a device failure rate corresponding to each of the n power supply devices to obtain n device failure rates.

[0074] In this implementation, see Figure 5 , Figure 5 This is a flowchart of determining a device failure rate corresponding to a first power supply device provided by an embodiment of the present application, including but not limited to the following steps:

[0075] S501: Obtaining a fault downtime duration and an operating time duration of a first power supply device within the preset time period.

[0076] In this embodiment, the first power supply device is any one of the n power supply devices. The downtime and operation time of the first power supply device in the preset time period can be obtained from a monitoring and data acquisition system, fault records or operation and maintenance logs.

[0077] S502: Determine a reference failure rate of the first power supply device based on the fault downtime and the operating time.

[0078] In this embodiment, the reference failure rate of the first power supply device is calculated specifically according to the following formula:

[0079]

[0080] Among them, F is the reference failure rate of the first power supply device, A is the failure downtime of the first power supply device, and B is the operating time of the first power supply device.

[0081] According to the above formula, the reference failure rate of the first power supply device can be determined based on the fault downtime and the operating time.

[0082] S503: Obtain the number of failures of the first power supply device within the preset time period.

[0083] In this embodiment, the number of failures directly reflects the operational stability of the power supply equipment. A smaller number of failures usually means higher equipment reliability and relatively better power supply stability. Conversely, a larger number of failures indicates that there may be more problems with the equipment and the power supply stability may be poor. The more failures there are, the more likely and longer the power supply interruption will be, which will lead to a decrease in power supply continuity and thus affect the power supply stability parameters. Multiple failures of the equipment may also trigger a chain reaction, affecting the operation of other equipment and even causing a decrease in the stability of the local or entire power system. A large number of failures will increase the maintenance cost of the equipment and also increase the difficulty and complexity of maintenance. If maintenance is not timely or in place, the performance of the equipment will gradually decline, further affecting the power supply stability parameters. Therefore, it is necessary to obtain the number of failures of the first power supply equipment within the preset time period.

[0084] S504: Determine a target adjustment parameter corresponding to the number of failures.

[0085] In this embodiment, a greater number of failures indicates a potentially lower reliability of the device. Based on a pre-established correspondence or empirical data, a corresponding target adjustment parameter is determined for each number of failures. This parameter is used to optimize and adjust the reference failure rate to more accurately reflect the actual failure rate of the device. Specifically, in this embodiment, a preset mapping relationship between the number of failures and the adjustment parameter can be used, and based on this mapping relationship, the target adjustment parameter corresponding to the number of failures can be determined.

[0086] S505: Optimize the reference failure rate based on the target adjustment parameter to obtain a device failure rate corresponding to the first power supply device.

[0087] In this embodiment, the device failure rate corresponding to the first power supply device is calculated specifically according to the following formula:

[0088] The equipment failure rate corresponding to the first power supply equipment = reference failure rate × (1 + target adjustment parameter);

[0089] According to the above formula, the reference failure rate can be optimized based on the target adjustment parameter to obtain the device failure rate corresponding to the first power supply device.

[0090] As can be seen, determining a reference failure rate by combining downtime and operating time can reflect the impact of actual equipment downtime caused by faults on overall operation. Optimizing the reference failure rate by combining the number of faults and the corresponding target adjustment parameters avoids the one-sidedness of assessing equipment failures based solely on a single factor, and more comprehensively and accurately reflects the equipment's true operating status and failure probability. Downtime reflects the direct impact of faults on equipment operating time, while the number of faults reflects the frequency of faults. Some equipment may have a short total downtime but a high number of faults, indicating recurring minor issues that affect its stability. Other equipment may experience occasional, extended downtime. These two scenarios have different impacts on the equipment failure rate. Accurate equipment failure rates facilitate the development of more effective maintenance plans. For equipment with a high failure rate, increased maintenance frequency, pre-stocked spare parts, or even equipment upgrades or replacements can be considered to minimize the impact of faults on power supply. For equipment with a low failure rate, extended maintenance cycles can be appropriately implemented to avoid excessive maintenance and resource waste. By calculating the equipment failure rate of each power supply device, we can further evaluate the stability of the entire power supply system, understand the impact of each device in the system on the overall stability, identify possible weak links, and provide a basis for system optimization and improvement.

[0091] It needs to be explained that the method for determining the equipment failure rate corresponding to each of the n power supply devices is the same as the method for determining the equipment failure rate corresponding to the first power supply device. Therefore, according to the method for determining the equipment failure rate corresponding to the first power supply device, the equipment failure rate corresponding to each of the n power supply devices can be determined to obtain n equipment failure rates.

[0092] S403: Determine the power supply stability parameter of the target power supply enterprise based on the n device failure rates.

[0093] In this embodiment, illustratively, the average value of the failure rates of the n devices is determined to obtain the average device failure rate. Specifically, the failure rates of the n devices are added together and then divided by the total number of devices n. The result is the average device failure rate, which reflects the overall average failure level of this group of power supply equipment. It is a general indicator that can help us understand the failure conditions of these n devices as a whole.

[0094] Exemplarily, a mapping relationship between equipment failure rate and power supply stability parameters is obtained. Specifically, there is a certain correlation between equipment failure rate and power supply stability parameters. Through historical data, experimental research or theoretical analysis, the corresponding relationship between them can be found. This relationship can usually be expressed in the form of mathematical models, graphs or empirical formulas. For example, it may be found that the higher the equipment failure rate, the lower the power supply stability parameter, and there is a certain functional relationship between the two, such as a linear relationship or an exponential relationship. This mapping relationship is the key basis for the subsequent calculation of the power supply stability parameters.

[0095] Exemplarily, the power supply stability parameter corresponding to the average device failure rate is determined based on the mapping relationship to obtain a reference power supply stability parameter. Specifically, after obtaining the average device failure rate, the average device failure rate is substituted into the mapping relationship obtained previously to calculate the corresponding power supply stability parameter. This parameter is called the reference power supply stability parameter, which is a power supply stability index preliminarily estimated based on the overall failure situation of the n power supply devices, providing a basic value for subsequent precise adjustment.

[0096] Exemplarily, the standard deviation of the failure rates of the n devices is determined. Specifically, the standard deviation is a statistic used to measure the degree of dispersion of a set of data. For these n device failure rates, calculating their standard deviation can reflect the degree of difference between the failure rates of these devices. If the standard deviation is large, it means that the failure rates of various devices vary greatly. Some device failure rates may be much higher than the average, while others are much lower than the average. If the standard deviation is small, it indicates that the failure rates of these devices are relatively concentrated and are close to the average device failure rate. By calculating the standard deviation, the distribution of device failure rates can be understood, providing a reference for further adjusting the power supply stability parameters.

[0097] Exemplarily, a target fine-tuning parameter corresponding to the standard deviation is determined. Specifically, a larger standard deviation indicates a greater dispersion in device failure rates and a more complex impact on power supply stability. A larger adjustment to the reference power supply stability parameter may be required, and thus a larger target fine-tuning parameter may be required. Conversely, a smaller standard deviation indicates a smaller target fine-tuning parameter. A mapping relationship between the standard deviation and the fine-tuning parameter can be preset, and the target fine-tuning parameter corresponding to the standard deviation can be determined based on this mapping relationship.

[0098] Exemplarily, the reference power supply stability parameter is adjusted based on the target fine-tuning parameter to obtain the power supply stability parameter of the target power supply enterprise. Specifically, the power supply stability parameter is calculated according to the following formula:

[0099] Power supply stability parameter = reference power supply stability parameter × (1 + target fine-tuning parameter);

[0100] According to the above formula, the reference power supply stability parameter can be adjusted based on the target fine-tuning parameter to obtain the power supply stability parameter of the target power supply enterprise.

[0101] As can be seen, calculating the average failure rate of n devices, to obtain the average device failure rate, provides a macroscopic view of the overall failure level of power supply equipment, providing a basic reference indicator for evaluating power supply stability and helping to understand the general operating status of the power supply system. Determining the standard deviation of the n device failure rates and using it to determine the target fine-tuning parameter reflects the dispersion of each device's failure rate, i.e., the differences between individual devices. This allows the assessment to consider not only the overall average level but also the varying performance of individual devices, avoiding bias in the overall stability assessment due to excessively high or low failure rates of individual devices. By obtaining a mapping relationship between device failure rates and power supply stability parameters, the device failure status can be directly linked to the stability of the power supply, making the assessment more targeted and accurate. Based on this mapping relationship, a reference power supply stability parameter is determined based on the average device failure rate, providing a preliminary quantitative indicator for stability assessment. Using the target fine-tuning parameter corresponding to the standard deviation to adjust the reference power supply stability parameter further considers the impact of the distribution characteristics of device failure rates on power supply stability, thereby more accurately reflecting the actual power supply stability status and ensuring that the resulting power supply stability parameter better reflects the actual operating conditions of the power supply system.

[0102] S304: Determine a target adjustment factor corresponding to the power supply stability parameter.

[0103] In this embodiment, a mapping relationship between a preset power supply stability parameter and an adjustment factor may be used, and a target adjustment factor corresponding to the power supply stability parameter may be determined based on the mapping relationship.

[0104] S305: Adjust the reference reliability index based on the target adjustment factor to obtain the target reliability index.

[0105] In this embodiment, the target reliability index is calculated specifically according to the following formula:

[0106] Target reliability index = reference reliability index × (1 + target adjustment factor);

[0107] According to the above formula, the reference reliability index can be adjusted based on the target adjustment factor to obtain the target reliability index.

[0108] It can be seen that by determining multiple reliability indicators, the reliability of the power supply system can be measured from different perspectives, covering various aspects that may affect power supply reliability, such as equipment performance, line stability, power supply continuity, etc., avoiding the one-sidedness of relying on a single indicator for evaluation, and being able to more comprehensively and accurately reflect the true reliability level of the power supply system. By assigning corresponding weights to each reliability indicator, differentiated considerations can be made based on the importance of each indicator to power supply reliability. The setting of weights reflects the relative importance of different indicators in the overall reliability assessment, making the assessment results more consistent with the actual situation and highlighting the impact of key factors on power supply reliability. Determining the power supply stability parameters of the target power supply enterprise and finding the corresponding target adjustment factors can incorporate the important factor of power supply stability into the adjustment of the reliability indicator. Power supply stability is directly related to the quality and reliability of power supply. In this way, the reference reliability indicator can be dynamically adjusted according to the actual power supply situation, making the assessment results more realistic and targeted. Adjusting the reference reliability index based on the target adjustment factor to obtain the target reliability index can further optimize the evaluation results and improve the accuracy and reliability of the evaluation. This adjustment mechanism can correct the reference reliability index that comprehensively considers multi-dimensional factors according to different power supply stability conditions, better reflect the actual reliability level of the power supply system in actual operation, and provide a more accurate basis for the decision-making of power supply enterprises.

[0109] S104: Determine the reliability benchmarking level of the target power supply enterprise based on the target reliability index to obtain a target reliability benchmarking level.

[0110] In this implementation, see Figure 6 , Figure 6 This is a flowchart for determining a target reliability benchmarking level provided by an embodiment of the present application, including but not limited to the following steps:

[0111] S601: When the target reliability index is greater than or equal to a first threshold, determining that the target reliability benchmarking level is level one.

[0112] In this embodiment, if the target reliability index of the target power supply enterprise obtained by the previous calculation reaches or exceeds the set first threshold, then the reliability benchmarking level of the power supply enterprise is judged to be the highest level, that is, level one, which means that the power supply enterprise performs very well in terms of power supply reliability. The combination of various reliability indicators shows that its power supply system has high stability and reliability and can provide users with high-quality power supply.

[0113] Although the power supply company is at the first-level reliability benchmark level and has excellent overall performance, it can still continue to monitor and maintain the power supply system, continuously optimize various indicators, and pursue a higher level of reliability.

[0114] S602: When the target reliability index is less than the first threshold and greater than or equal to a second threshold, determine that the target reliability benchmarking level is level two.

[0115] In this embodiment, when the target reliability index is less than the first threshold but not less than the second threshold, the reliability benchmarking level of the power supply enterprise is judged to be level two, indicating that the reliability level of the power supply enterprise is relatively high. Although it is not as good as level one, it can still better ensure the stability and continuity of power supply. However, there may be some small room for improvement in some aspects.

[0116] When a target power supply enterprise reaches Level 2 reliability benchmarking, it needs to conduct further refined management, identify potential problems, and implement targeted optimization and improvements to move toward Level 1 reliability benchmarking. For example, this can include conducting preventive maintenance on key equipment and strengthening inspections of power supply lines.

[0117] S603: When the target reliability index is less than the second threshold and greater than or equal to a third threshold, determine that the target reliability benchmarking level is level three.

[0118] In this embodiment, if the target reliability index is between the second threshold and the third threshold, that is, it is less than the second threshold but greater than or equal to the third threshold, then the reliability benchmarking level of the power supply enterprise is level three, which means that the reliability of the power supply enterprise is at a medium level and can basically meet the electricity needs of users, but there may be some areas that need attention and improvement to further improve the power supply quality.

[0119] When the target reliability benchmarking level of the target power supply enterprise is level three, the target power supply enterprise needs to conduct a comprehensive assessment of the power supply system, identify the main factors affecting reliability, and formulate and implement improvement plans, which may include increasing the intensity of equipment renewal and transformation, optimizing the grid structure, and improving the technical level and emergency response capabilities of personnel, so as to improve power supply reliability and strive to reach level two or higher.

[0120] S604: When the target reliability index is less than the third threshold and greater than or equal to a fourth threshold, determine that the target reliability benchmarking level is level four.

[0121] In this embodiment, when the target reliability index is less than the third threshold and greater than or equal to the fourth threshold, the reliability benchmarking level of the power supply enterprise is determined to be level four, which indicates that the reliability level of the power supply enterprise is relatively low, and some more obvious problems or failures may occur during the power supply process, which will have a certain impact on the user's power consumption experience, and measures need to be taken to improve it.

[0122] When the target reliability benchmark level of the target power supply enterprise is level four, the target power supply enterprise should immediately take effective corrective measures, such as focusing on repairing or replacing equipment that frequently fails, strengthening real-time monitoring of power supply, and increasing the deployment of emergency power generation equipment, etc., so as to improve reliability indicators as soon as possible, improve power supply quality, and avoid greater impact on users.

[0123] S605: When the target reliability index is less than the fourth threshold, determine that the target reliability benchmarking level is level five.

[0124] In this embodiment, if the target reliability index is less than the fourth threshold, then the reliability benchmarking level of the power supply enterprise is level five, which is the lowest level, indicating that the power supply enterprise has major problems in power supply reliability and may frequently experience power outages or other power supply failures, which seriously affect the normal power consumption of users. It is necessary to take large-scale improvement measures immediately to improve power supply reliability.

[0125] When the target reliability benchmarking level of the target power supply enterprise is level five, the target power supply enterprise needs to formulate a comprehensive and systematic rectification plan, which may involve large-scale equipment upgrades, technical transformation, management system optimization, etc. At the same time, it is necessary to strengthen communication with users, promptly inform users of the rectification status and power supply restoration plan, strive for users' understanding and support, and regularly evaluate and adjust the rectification effects to ensure that power supply reliability can be rapidly improved.

[0126] It can be seen that by subdividing reliability benchmarking levels and corresponding different improvement measures to different levels, power supply companies can clearly understand their own position and the areas where they need to work. Taking appropriate measures for different levels of reliability issues can gradually resolve various problems in the power supply system. From optimized management and equipment maintenance to large-scale technical transformation, all of these will help improve the stability and continuity of power supply, reduce the number of power outages and the incidence of failures, thereby ensuring that users can obtain high-quality power supply, meet their electricity needs, and improve user satisfaction. Improving power supply reliability can establish a good image for power supply companies and enhance user trust in the company. This will help the company gain a competitive advantage in the market, attract more users and investment, and promote the company's long-term and stable development. At the same time, through continuous improvement and optimization of the power supply system, the company's own technical level and management capabilities can also be improved, enhancing its core competitiveness. Different reliability benchmarking levels correspond to different degrees of improvement measures, avoiding excessive waste or insufficiency of resources. For enterprises with higher reliability benchmarking levels, such as first- and second-level enterprises, the main focus is on continuous optimization and refined management, without the need for large-scale resource investment. For enterprises with lower reliability benchmarking levels, such as fourth- and fifth-level enterprises, resources need to be concentrated on key rectification and comprehensive improvement. This allows for the rational allocation of human, material, and financial resources based on the actual situation of the enterprise, so that resources are used most effectively and benefits are maximized. The power industry has certain regulations and regulatory requirements for power supply reliability. By classifying power supply enterprises according to reliability benchmarking levels and taking corresponding measures, it helps enterprises comply with industry standards and regulatory provisions, avoid penalties and risks due to non-compliance, and ensure the compliance of enterprises. At the same time, it also facilitates regulatory authorities to classify and supervise different enterprises, thereby improving the overall management level of the industry.

[0127] In summary, the implementation of the present invention has the following beneficial effects:

[0128] It can be seen that the power supply enterprise reliability evaluation method described in the embodiment of the present invention includes: first obtaining the power sales volume, power supply area, distribution network ring network rate of the target power supply enterprise within a preset time period, and the population density and gross domestic product of the target area corresponding to the target power supply enterprise within the preset time period; then determining the first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, determining the second reliability index of the target power supply enterprise based on the power supply area and the preset power supply area, determining the third reliability index of the target power supply enterprise based on the distribution network ring network rate and the preset distribution network ring network rate, determining the fourth reliability index of the target power supply enterprise based on the population density and the preset population density, determining the fifth reliability index of the target power supply enterprise based on the gross domestic product and the preset gross domestic product, and then determining the target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index and the fifth reliability index; finally, determining the reliability benchmarking level of the target power supply enterprise based on the target reliability index to obtain the target reliability benchmarking level.

[0129] See also Figure 7 , Figure 7 700 is a schematic structural diagram of a power supply enterprise classification and evaluation device based on power supply reliability benchmarking provided in an embodiment of the present application. The power supply enterprise classification and evaluation device based on power supply reliability benchmarking 700 includes: an acquisition unit 701 and a processing unit 702;

[0130] The acquisition unit 701 is used to obtain the power sales volume, power supply area, and distribution network ring rate of the target power supply enterprise within a preset time period, as well as the population density and gross domestic product of the target area corresponding to the target power supply enterprise within the preset time period;

[0131] The processing unit 702 is configured to determine a first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, determine a second reliability index of the target power supply enterprise based on the power supply area and the preset power supply area, determine a third reliability index of the target power supply enterprise based on the distribution network ring network rate and the preset distribution network ring network rate, determine a fourth reliability index of the target power supply enterprise based on the population density and the preset population density, and determine a fifth reliability index of the target power supply enterprise based on the gross domestic product and the preset gross domestic product;

[0132] Determine a target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index;

[0133] The reliability benchmarking level of the target power supply enterprise is determined based on the target reliability index to obtain a target reliability benchmarking level.

[0134] In some possible implementations, in determining the first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, the processing unit 702 is specifically configured to:

[0135] determining a ratio between the power sales amount and the preset power sales amount to obtain a first reference reliability index;

[0136] Obtaining the average electricity price of the target power supply enterprise during the preset time period;

[0137] determining a first optimization factor corresponding to the average electricity price;

[0138] The first reference reliability index is optimized based on the first optimization factor to obtain the first reliability index.

[0139] In some possible implementations, in determining the target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index, the processing unit 702 is specifically configured to:

[0140] Determining a first weight corresponding to the first reliability index, a second weight corresponding to the second reliability index, a third weight corresponding to the third reliability index, a fourth weight corresponding to the fourth reliability index, and a fifth weight corresponding to the fifth reliability index; the sum of the first weight, the second weight, the third weight, the fourth weight, and the fifth weight is 1;

[0141] Perform calculation based on the first weight, the second weight, the third weight, the fourth weight, the fifth weight, the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index to obtain a reference reliability index;

[0142] Determining power supply stability parameters of the target power supply enterprise;

[0143] determining a target adjustment factor corresponding to the power supply stability parameter;

[0144] The reference reliability index is adjusted based on the target adjustment factor to obtain the target reliability index.

[0145] In some possible implementations, in determining the power supply stability parameter of the target power supply enterprise, the processing unit 702 is specifically configured to:

[0146] Obtain n power supply devices corresponding to the target power supply enterprise; n is an integer greater than 1;

[0147] Determine a device failure rate corresponding to each of the n power supply devices to obtain n device failure rates;

[0148] The power supply stability parameter of the target power supply enterprise is determined based on the n device failure rates.

[0149] In some possible implementations, in determining the device failure rate corresponding to each of the n power supply devices to obtain the n device failure rates, the processing unit 702 is specifically configured to:

[0150] Obtaining a fault downtime and an operating time of a first power supply device within the preset time period; the first power supply device is any one of the n power supply devices;

[0151] Determine a reference failure rate of the first power supply device based on the fault downtime and the operating time;

[0152] Obtaining the number of failures of the first power supply device within the preset time period;

[0153] determining a target adjustment parameter corresponding to the number of failures;

[0154] The reference failure rate is optimized based on the target adjustment parameter to obtain a device failure rate corresponding to the first power supply device.

[0155] In some possible implementations, in determining the power supply stability parameter of the target power supply enterprise based on the n device failure rates, the processing unit 702 is specifically configured to:

[0156] Determine an average of the n device failure rates to obtain an average device failure rate;

[0157] Obtaining the mapping relationship between equipment failure rate and power supply stability parameters;

[0158] Determine a power supply stability parameter corresponding to the average device failure rate based on the mapping relationship to obtain a reference power supply stability parameter;

[0159] Determining the standard deviation of the failure rates of the n devices;

[0160] determining a target fine-tuning parameter corresponding to the standard deviation;

[0161] The reference power supply stability parameter is adjusted based on the target fine-tuning parameter to obtain the power supply stability parameter of the target power supply enterprise.

[0162] In some possible implementations, in determining the reliability benchmarking level of the target power supply enterprise based on the target reliability index to obtain the target reliability benchmarking level, the processing unit 702 is specifically configured to:

[0163] When the target reliability index is greater than or equal to a first threshold, determining that the target reliability benchmarking level is level one;

[0164] When the target reliability index is less than the first threshold and greater than or equal to a second threshold, determining that the target reliability benchmarking level is level two;

[0165] When the target reliability index is less than the second threshold and greater than or equal to a third threshold, determining that the target reliability benchmarking level is level three;

[0166] When the target reliability index is less than the third threshold and greater than or equal to a fourth threshold, determining that the target reliability benchmarking level is level four;

[0167] When the target reliability index is less than the fourth threshold, the target reliability benchmarking level is determined to be level five.

[0168] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided by the embodiment of this application. Figure 8 As shown, electronic device 800 includes a transceiver 801, a processor 802, and a memory 803. These are connected via a bus 804. The memory 803 is used to store computer programs and data, and the transceiver 801 can transmit the data stored in the memory 803 to the processor 802. The above program includes instructions for executing the following steps:

[0169] Obtaining the target power supply enterprise's electricity sales, power supply area, and distribution network ring rate within a preset time period, as well as the population density and gross domestic product of the target area corresponding to the target power supply enterprise within the preset time period;

[0170] Determine a first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, determine a second reliability index of the target power supply enterprise based on the power supply area and the preset power supply area, determine a third reliability index of the target power supply enterprise based on the distribution network ring network rate and the preset distribution network ring network rate, determine a fourth reliability index of the target power supply enterprise based on the population density and the preset population density, and determine a fifth reliability index of the target power supply enterprise based on the gross domestic product and the preset gross domestic product;

[0171] Determine a target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index;

[0172] The reliability benchmarking level of the target power supply enterprise is determined based on the target reliability index to obtain a target reliability benchmarking level.

[0173] In some possible implementations, in determining the first reliability index of the target power supply enterprise based on the power sales amount and the preset power sales amount, the program includes instructions for executing the following steps:

[0174] determining a ratio between the power sales amount and the preset power sales amount to obtain a first reference reliability index;

[0175] Obtaining the average electricity price of the target power supply enterprise during the preset time period;

[0176] determining a first optimization factor corresponding to the average electricity price;

[0177] The first reference reliability index is optimized based on the first optimization factor to obtain the first reliability index.

[0178] In some possible implementations, in determining the target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index, the program includes instructions for performing the following steps:

[0179] Determining a first weight corresponding to the first reliability index, a second weight corresponding to the second reliability index, a third weight corresponding to the third reliability index, a fourth weight corresponding to the fourth reliability index, and a fifth weight corresponding to the fifth reliability index; the sum of the first weight, the second weight, the third weight, the fourth weight, and the fifth weight is 1;

[0180] Perform calculation based on the first weight, the second weight, the third weight, the fourth weight, the fifth weight, the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index to obtain a reference reliability index;

[0181] Determining power supply stability parameters of the target power supply enterprise;

[0182] determining a target adjustment factor corresponding to the power supply stability parameter;

[0183] The reference reliability index is adjusted based on the target adjustment factor to obtain the target reliability index.

[0184] In some possible implementations, in determining the power supply stability parameter of the target power supply enterprise, the program includes instructions for performing the following steps:

[0185] Obtain n power supply devices corresponding to the target power supply enterprise; n is an integer greater than 1;

[0186] Determine a device failure rate corresponding to each of the n power supply devices to obtain n device failure rates;

[0187] The power supply stability parameter of the target power supply enterprise is determined based on the n device failure rates.

[0188] In some possible implementations, in determining the device failure rate corresponding to each of the n power supply devices to obtain the n device failure rates, the program includes instructions for executing the following steps:

[0189] Obtaining a fault downtime and an operating time of a first power supply device within the preset time period; the first power supply device is any one of the n power supply devices;

[0190] Determine a reference failure rate of the first power supply device based on the fault downtime and the operating time;

[0191] Obtaining the number of failures of the first power supply device within the preset time period;

[0192] determining a target adjustment parameter corresponding to the number of failures;

[0193] The reference failure rate is optimized based on the target adjustment parameter to obtain a device failure rate corresponding to the first power supply device.

[0194] In some possible implementations, in determining the power supply stability parameter of the target power supply enterprise based on the n device failure rates, the program includes instructions for performing the following steps:

[0195] Determine an average of the n device failure rates to obtain an average device failure rate;

[0196] Obtaining the mapping relationship between equipment failure rate and power supply stability parameters;

[0197] Determine a power supply stability parameter corresponding to the average device failure rate based on the mapping relationship to obtain a reference power supply stability parameter;

[0198] Determining the standard deviation of the failure rates of the n devices;

[0199] determining a target fine-tuning parameter corresponding to the standard deviation;

[0200] The reference power supply stability parameter is adjusted based on the target fine-tuning parameter to obtain the power supply stability parameter of the target power supply enterprise.

[0201] In some possible implementations, in terms of determining the reliability benchmarking level of the target power supply enterprise based on the target reliability indicator to obtain the target reliability benchmarking level, the program includes instructions for executing the following steps:

[0202] When the target reliability index is greater than or equal to a first threshold, determining that the target reliability benchmarking level is level one;

[0203] When the target reliability index is less than the first threshold and greater than or equal to a second threshold, determining that the target reliability benchmarking level is level two;

[0204] When the target reliability index is less than the second threshold and greater than or equal to a third threshold, determining that the target reliability benchmarking level is level three;

[0205] When the target reliability index is less than the third threshold and greater than or equal to a fourth threshold, determining that the target reliability benchmarking level is level four;

[0206] When the target reliability index is less than the fourth threshold, the target reliability benchmarking level is determined to be level five.

[0207] It should be understood that the electronic devices in this application may include smartphones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptops, mobile Internet devices (MIDs) or wearable devices, or servers, edge computing nodes, etc. The above electronic devices are only examples and are not exhaustive, including but not limited to the above electronic devices.

[0208] The embodiments of the present application further provide a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any one of the methods described in the above method embodiments.

[0209] The embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any one of the methods described in the above method embodiments.

[0210] It should be noted that for the aforementioned method implementations, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by this application.

[0211] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0212] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0213] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0214] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0215] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various implementation methods of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0216] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0217] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, based on the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A classification and evaluation method for power supply enterprises based on power supply reliability benchmarking, characterized in that: include: Obtaining the target power supply enterprise's electricity sales, power supply area, and distribution network ring rate within a preset time period, as well as the population density and gross domestic product of the target area corresponding to the target power supply enterprise within the preset time period; Determine a first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, determine a second reliability index of the target power supply enterprise based on the power supply area and the preset power supply area, determine a third reliability index of the target power supply enterprise based on the distribution network ring network rate and the preset distribution network ring network rate, determine a fourth reliability index of the target power supply enterprise based on the population density and the preset population density, and determine a fifth reliability index of the target power supply enterprise based on the gross domestic product and the preset gross domestic product; Determine a target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index; The reliability benchmarking level of the target power supply enterprise is determined based on the target reliability index to obtain a target reliability benchmarking level.

2. The method according to claim 1, wherein The determining of the first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume includes: determining a ratio between the power sales amount and the preset power sales amount to obtain a first reference reliability index; Obtaining the average electricity price of the target power supply enterprise during the preset time period; determining a first optimization factor corresponding to the average electricity price; The first reference reliability index is optimized based on the first optimization factor to obtain the first reliability index.

3. The method according to claim 1, wherein The determining the target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index includes: Determining a first weight corresponding to the first reliability index, a second weight corresponding to the second reliability index, a third weight corresponding to the third reliability index, a fourth weight corresponding to the fourth reliability index, and a fifth weight corresponding to the fifth reliability index; the sum of the first weight, the second weight, the third weight, the fourth weight, and the fifth weight is 1; Perform calculation based on the first weight, the second weight, the third weight, the fourth weight, the fifth weight, the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index to obtain a reference reliability index; Determining power supply stability parameters of the target power supply enterprise; determining a target adjustment factor corresponding to the power supply stability parameter; The reference reliability index is adjusted based on the target adjustment factor to obtain the target reliability index.

4. The method according to claim 3, wherein Determining the power supply stability parameter of the target power supply enterprise includes: Obtain n power supply devices corresponding to the target power supply enterprise; n is an integer greater than 1; Determine a device failure rate corresponding to each of the n power supply devices to obtain n device failure rates; The power supply stability parameter of the target power supply enterprise is determined based on the n device failure rates.

5. The method according to claim 4, wherein The determining of the device failure rate corresponding to each of the n power supply devices to obtain n device failure rates includes: Obtaining a fault downtime and an operating time of a first power supply device within the preset time period; the first power supply device is any one of the n power supply devices; Determine a reference failure rate of the first power supply device based on the fault downtime and the operating time; Obtaining the number of failures of the first power supply device within the preset time period; determining a target adjustment parameter corresponding to the number of failures; The reference failure rate is optimized based on the target adjustment parameter to obtain a device failure rate corresponding to the first power supply device.

6. The method according to claim 5, wherein The determining the power supply stability parameter of the target power supply enterprise based on the n device failure rates includes: Determine an average of the n device failure rates to obtain an average device failure rate; Obtaining the mapping relationship between equipment failure rate and power supply stability parameters; Determine a power supply stability parameter corresponding to the average device failure rate based on the mapping relationship to obtain a reference power supply stability parameter; Determining the standard deviation of the failure rates of the n devices; determining a target fine-tuning parameter corresponding to the standard deviation; The reference power supply stability parameter is adjusted based on the target fine-tuning parameter to obtain the power supply stability parameter of the target power supply enterprise.

7. The method according to claim 1, wherein Determining the reliability benchmarking level of the target power supply enterprise based on the target reliability index to obtain the target reliability benchmarking level includes: When the target reliability index is greater than or equal to a first threshold, determining that the target reliability benchmarking level is level one; When the target reliability index is less than the first threshold and greater than or equal to a second threshold, determining that the target reliability benchmarking level is level two; When the target reliability index is less than the second threshold and greater than or equal to a third threshold, determining that the target reliability benchmarking level is level three; When the target reliability index is less than the third threshold and greater than or equal to a fourth threshold, determining that the target reliability benchmarking level is level four; When the target reliability index is less than the fourth threshold, the target reliability benchmarking level is determined to be level five.

8. A classification and evaluation device for power supply enterprises based on power supply reliability benchmarking, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to obtain the power sales volume, power supply area, and distribution network ring rate of the target power supply enterprise within a preset time period, as well as the population density and gross domestic product of the target area corresponding to the target power supply enterprise within the preset time period; The processing unit is configured to determine a first reliability index of the target power supply enterprise based on the power sales volume and the preset power sales volume, determine a second reliability index of the target power supply enterprise based on the power supply area and the preset power supply area, determine a third reliability index of the target power supply enterprise based on the distribution network ring network rate and the preset distribution network ring network rate, determine a fourth reliability index of the target power supply enterprise based on the population density and the preset population density, and determine a fifth reliability index of the target power supply enterprise based on the gross domestic product and the preset gross domestic product; Determine a target reliability index of the target power supply enterprise based on the first reliability index, the second reliability index, the third reliability index, the fourth reliability index, and the fifth reliability index; The reliability benchmarking level of the target power supply enterprise is determined based on the target reliability index to obtain a target reliability benchmarking level.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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