Power distribution network resource allocation method and device, computer equipment, computer readable storage medium and computer program product

Abstract

The invention relates to a power distribution network resource allocation method and device, computer equipment, a storage medium and a computer program product. Relates to the power technology field. The method comprises the following steps: acquiring power grid benefit data and power grid performance data of a plurality of regions; according to the power grid benefit data of each region, determining an effect characterization value of each region; the effect characterization value represents the improvement effect of the power distribution network resource distribution benefit; and adjusting a resource allocation scheme for each region according to the power grid performance data and the effect representation value of each region. By adopting the method, the resource allocation efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a distribution network resource allocation method, device, computer equipment, computer-readable storage medium and computer program product. Background Art

[0002] As the scale and complexity of power grids increase, the market competition faced by power grid companies is becoming more intense. However, traditional resource allocation decisions rely on manual experience and lack scientific quantitative evaluation methods, resulting in resource waste and inefficient resource allocation. Therefore, benefit-cost ratio analysis and intelligent evaluation methods have emerged.

[0003] At present, benefit-cost ratio analysis and intelligent evaluation methods have been applied to power resource allocation management, but the evaluation is relatively rough in the process of quantitative evaluation, and there is a lack of a comprehensive benefit-cost ratio analysis system. Therefore, the current distribution network resource allocation management method cannot systematically or quantitatively evaluate the resource allocation benefits during the resource allocation process, and there is a problem of low resource allocation efficiency. Summary of the invention

[0004] Based on this, it is necessary to provide a distribution network resource allocation method, apparatus, computer equipment, computer-readable storage medium and computer program product to address the above-mentioned technical problem of low resource allocation efficiency.

[0005] In a first aspect, the present application provides a method for allocating resources in a power distribution network, comprising:

[0006] Obtain grid efficiency data and grid performance data for multiple regions;

[0007] Determine the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation benefit;

[0008] According to the power grid performance data and effect characterization values ​​of each region, the resource allocation plan for each region is adjusted.

[0009] In one of the embodiments, the resource allocation plan for each region is adjusted according to the power grid performance data and the effect characterization value of each region, including: determining at least two regions whose power grid performance data have a difference less than a first threshold; if the difference between the effect characterization value of any one region of the at least two regions and the effect characterization value of the other region is greater than a second threshold, adjusting the resource allocation plan for any one region.

[0010] In one of the embodiments, adjusting the resource allocation plan for each region based on the power grid performance data and effect characterization values ​​of each region also includes: determining two regions where the difference in power grid performance data is greater than a third threshold; if the difference between the effect characterization value of the region with lower power grid performance data and the effect characterization value of the region with higher power grid performance data among the two regions is less than a fourth threshold, adjusting the resource allocation plan for the region with lower power grid performance data.

[0011] In one embodiment, the grid benefit data includes the total amount of resource allocation and the improvement range of multiple predetermined indicators; the effect characterization value of each region is determined based on the grid benefit data of each region, including: for each region, processing the improvement range of each indicator of the region to obtain the total improvement range; obtaining the ratio of the total improvement range to the total amount of resource allocation as the effect characterization value of the region.

[0012] In one of the embodiments, after adjusting the resource allocation plan for each region according to the power grid performance data and effect characterization value of each region, it includes: establishing a dynamic adjustment mechanism and monitoring and updating the effect characterization value; when the effect characterization value is lower than a preset effect characterization threshold, adjusting the resource allocation plan for each region based on the dynamic adjustment mechanism to ensure that the effect characterization value is not lower than the preset effect characterization threshold.

[0013] In a second aspect, the present application also provides a distribution network resource allocation device, comprising:

[0014] A data acquisition module, used to acquire power grid benefit data and power grid performance data of multiple regions;

[0015] An effect characterization value determination module is used to determine the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation benefit;

[0016] The resource allocation scheme adjustment module is used to adjust the resource allocation scheme for each region according to the power grid performance data and effect characterization value of each region.

[0017] In one of the embodiments, the resource allocation scheme adjustment module is also used to determine at least two regions where the difference between power grid performance data is less than a first threshold; if the difference between the effect characterization value of any one of the at least two regions and the effect characterization value of the other regions is greater than a second threshold, the resource allocation scheme for any one region is adjusted.

[0018] In a third aspect, the present application further provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0019] Obtain grid efficiency data and grid performance data for multiple regions;

[0020] Determine the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation benefit;

[0021] According to the power grid performance data and effect characterization values ​​of each region, the resource allocation plan for each region is adjusted.

[0022] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0023] Obtain grid efficiency data and grid performance data for multiple regions;

[0024] Determine the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation benefit;

[0025] According to the power grid performance data and effect characterization values ​​of each region, the resource allocation plan for each region is adjusted.

[0026] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, which implements the following steps when executed by a processor:

[0027] Obtain grid efficiency data and grid performance data for multiple regions;

[0028] Determine the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation benefit;

[0029] According to the power grid performance data and effect characterization values ​​of each region, the resource allocation plan for each region is adjusted.

[0030] The above-mentioned distribution network resource allocation method, device, computer equipment, storage medium and computer program product have the following beneficial effects in the process of distribution network resource allocation: by obtaining the power grid benefit data and power grid performance data of multiple regions; then determining the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation benefit; finally, adjusting the resource allocation plan for each region according to the power grid performance data and effect characterization value of each region. The above process can ensure that resources are applied to the areas that need improvement the most by analyzing the power grid benefit data and power grid performance data of each region; by determining the effect characterization value, the resource allocation benefit of the distribution network in each region can be quantified to promote the resource utilization rate of the power grid; and adjusting the resource allocation according to the power grid performance data and the effect characterization value can enhance the power supply reliability of the power grid and further improve the resource allocation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A schematic diagram of a flow chart of a method for allocating resources in a power distribution network in one embodiment;

[0033] Figure 2 A schematic diagram of a flow chart of a resource allocation scheme adjustment step in an embodiment;

[0034] Figure 3 A schematic diagram of a flow chart of a resource allocation scheme adjustment step in another embodiment;

[0035] Figure 4 is a detailed flow chart of a method for allocating resources in a power distribution network in an embodiment;

[0036] Figure 5 It is a structural block diagram of a distribution network resource allocation device in one embodiment;

[0037] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0038] This application introduces benefit-cost ratio analysis and horizontal comparison mechanism to systematically and quantitatively evaluate the benefits of resource allocation, optimize the decision-making process, improve resource allocation efficiency, and ensure reasonable allocation of resources.

[0039] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0040] In one embodiment, Figure 1 As shown, a method for allocating resources in a distribution network is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps S102 to S106. Among them:

[0041] Step S102, obtaining power grid benefit data and power grid performance data of multiple regions.

[0042] Among them, the grid benefit data refers to the benefit indicators related to grid operation collected from multiple regions, which may include multiple indicators for evaluating the overall performance of the grid; the grid performance data refers to the technical indicators of the grid operation status.

[0043] Optionally, grid efficiency data can be obtained from the operating data of the power company, such as power supply capacity data, failure rate data and user satisfaction data provided by the power company; grid performance data can be measured by installing sensors at grid nodes, such as measuring voltage, frequency and load.

[0044] Step S104, determining the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the benefit of distribution network resource allocation.

[0045] Among them, the effect characterization value is a quantitative indicator calculated based on the power grid efficiency data of each region, which represents the effect of improving the efficiency of distribution network resource allocation.

[0046] Specifically, the effect of the distribution network on improving the resource allocation efficiency of each region can be determined by calculating the ratio between the data included in the power grid efficiency data.

[0047] Step S106: adjusting the resource allocation plan for each region according to the power grid performance data and effect characterization value of each region.

[0048] Among them, the resource allocation plan can represent the allocation plan of human, material, financial and other resources during the transformation and operation of the power grid.

[0049] Specifically, various regions can be ranked according to the effect characterization value, and resources can be allocated preferentially to regions with greater benefit improvements, thereby rapidly improving the performance of the power grid. Different resource allocation plans can also be set for different regions based on specific power grid performance numbers. For example, regions with high failure rates can be set as requiring enhanced equipment maintenance and upgrades, while regions with overloads can be set as requiring increased power supply capacity.

[0050] In the above distribution network resource allocation method, the grid benefit data and grid performance data of multiple regions are obtained; then, the effect characterization value of each region is determined according to the grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation efficiency; finally, the resource allocation plan for each region is adjusted according to the grid performance data and effect characterization value of each region. The above process can ensure that resources are applied to the areas that need improvement the most by analyzing the grid benefit data and grid performance data of each region; by determining the effect characterization value, the resource allocation efficiency of the distribution network in each region can be quantified to promote the resource utilization of the grid; and adjusting the resource allocation according to the grid performance data and the effect characterization value can enhance the power supply reliability of the grid and further improve the resource allocation efficiency.

[0051] In an exemplary embodiment, Figure 2 As shown, step S106 adjusts the resource allocation plan for each region according to the power grid performance data and effect characterization values ​​of each region, including: step S202, determining at least two regions whose power grid performance data have a difference less than a first threshold; step S204, if the difference between the effect characterization value of any one region of the at least two regions and the effect characterization value of other regions is greater than a second threshold, then adjusting the resource allocation plan for any one region.

[0052] Among them, the first threshold represents the maximum value of the load density similarity in the power grid performance data between regions; the second threshold represents the maximum value of the difference between the effect characterization value of any region in the two regions and the effect characterization value of other regions.

[0053] Specifically, at least two regions are determined whose load density difference of the power grid is less than the first threshold, that is, at least two regions are determined, and the load density of the power grids of at least two regions is very similar, and the difference is less than the first threshold; the difference between the effect characterization value of any one of the at least two regions and the effect characterization values ​​of other regions is greater than the second threshold, that is, the effect characterization value of any one of the at least two regions is very similar to the effect characterization values ​​of other regions, and the difference between the effect characterization value of any one region and the effect characterization values ​​of other regions is less than the second threshold.

[0054] More specifically, the load density of a power grid may refer to the electricity demand per unit area. If the difference between the effect characterization value of any one of at least two regions and the effect characterization values ​​of other regions is greater than a second threshold, that is, there is a large gap between the effect characterization value of one region and that of other regions, then it is necessary to adjust the resource allocation plan of the region so that the effect characterization values ​​of all regions are very close, that is, the difference between the effect characterization values ​​of all regions is less than the second threshold.

[0055] In this embodiment, by comparing the grid performance data and effect characterization values ​​between different regions, the rational allocation of resources is ensured. For areas with poor grid operation effects, they can be improved in a timely manner through additional support and resources, ultimately improving the overall efficiency of the power system and ensuring the effective use of resources.

[0056] In one embodiment, Figure 3 As shown, step S106 adjusts the resource allocation plan for each region according to the power grid performance data and effect characterization value of each region, and also includes: step S302, determining two regions whose power grid performance data have a difference greater than a third threshold; step S304, if the difference between the effect characterization value of the region with lower power grid performance data and the effect characterization value of the region with higher power grid performance data in the two regions is less than a fourth threshold, then adjusting the resource allocation plan for the region with lower power grid performance data.

[0057] Among them, the third threshold represents the maximum value of the degree of grid level proximity in the grid performance data between the two regions; the fourth threshold represents the maximum value of the difference between the effect characterization value of the region with lower grid performance data and the effect characterization value of the region with higher grid performance data in the two regions.

[0058] Specifically, two regions are determined whose difference in grid performance data is less than the third threshold, that is, the grid levels of the two regions are very similar and the difference is less than the third threshold; the difference between the effect characterization value of the region with a lower grid level and the effect characterization value of the region with a higher grid level in the two regions is greater than the third threshold, that is, the effect characterization values ​​of the two regions are very similar, and the difference between the effect characterization value of one region and the effect characterization value of the other region is less than the fourth threshold.

[0059] More specifically, the grid level may refer to the degree of perfection of the grid infrastructure. If the difference between the effect characterization value of the region with a lower grid level and the effect characterization value of the region with a higher grid level is greater than the third threshold, that is, the effect characterization value of the region with a higher grid level is significantly lower than the effect characterization value of the region with a lower grid level, then it is necessary to adjust the resource allocation plan of the region with a lower grid level so that the effect characterization values ​​of all regions are very close, that is, the difference between the effect characterization values ​​of all regions is less than the fourth threshold.

[0060] In this embodiment, by obtaining the difference between the effect characterization values ​​of regions with low power grid levels and those of regions with high power grid levels, the rationality of resource allocation can be judged in a timely manner, and then the resource allocation plan can be adjusted in a timely manner, effectively narrowing the power grid performance gap between different regions, promoting balanced development among regions, and at the same time, improving the overall performance and reliability of the power grid.

[0061] Furthermore, in one embodiment, the grid benefit data includes the total amount of resource allocation and the improvement range of a plurality of predetermined indicators; step S104 determines the effect characterization value of each region based on the grid benefit data of each region, including: for each region, processing the improvement range of each indicator of the region to obtain the total improvement range; obtaining the ratio of the total improvement range to the total amount of resource allocation as the effect characterization value of the region.

[0062] Among them, the various indicators of the region can include load growth after power grid transformation, grid perfection, safe power supply capacity, voltage quality, etc. The calculation method of the effect representation value of each region is as follows:

[0063] , where BCR (Benefit Cost Ratio) represents the effect representation value.

[0064] Specifically, the improvement rates of various indicators of the region are processed to obtain the total improvement rate, and the processing methods may include the weighted average method, that is, setting corresponding weights according to the importance of each indicator, and further calculating the total improvement rate by weighted calculation; it may also include the simple average method, that is, summing up the improvement rates of various indicators, and then dividing it by the total number of indicators, and the resulting value is used as the total improvement rate; it may also be calculated by other calculation methods, which are not limited here.

[0065] In this embodiment, by obtaining the total improvement of each region based on the improvement of various indicators in each region, the degree of improvement in the power grid performance and other aspects of the region can be more accurately obtained; by calculating the ratio of the total improvement to the total amount of resource allocation, regions with high resource utilization efficiency can be effectively identified, which helps to optimize resource allocation, more reasonably allocate limited resources, and improve the overall efficiency of the power grid.

[0066] In an exemplary embodiment, the multiple indicators may be determined in the following manner: obtaining historical effect representation values ​​of each region; and identifying multiple indicators that affect the historical effect representation values ​​as the multiple indicators.

[0067] Among them, the historical effect representation value represents a quantitative assessment of the power grid performance in a certain region within a specific period of time, reflecting the comprehensive performance of the region in terms of resource utilization, service quality, etc.

[0068] In this embodiment, by analyzing historical data, the trend of data changes can be identified, and then multiple indicators can be determined to provide a basis for resource allocation and subsequent operation of the power grid.

[0069] Furthermore, in one embodiment, after adjusting the resource allocation plan for each region according to the power grid performance data and effect characterization value of each region in step S106, it includes: establishing a dynamic adjustment mechanism and monitoring and updating the effect characterization value; when the effect characterization value is lower than a preset effect characterization threshold, adjusting the resource allocation plan for each region based on the dynamic adjustment mechanism to ensure that the effect characterization value is not lower than the preset effect characterization threshold.

[0070] Among them, the dynamic adjustment mechanism allows timely adjustment of resource allocation, strategies and action plans based on changes in real-time data and historical effect representation values ​​to adapt to changes in the external environment and internal needs; the preset effect representation threshold is the minimum standard of effect representation values ​​determined in advance. If it is lower than this standard, it means that measures need to be taken to make improvements.

[0071] In this embodiment, by establishing a dynamic adjustment mechanism and monitoring the effect characterization value, problems in power grid management can be dealt with in a timely manner to ensure that the effect characterization value of each region is not lower than a preset effect characterization threshold.

[0072] The present application provides a method for allocating resources in a power distribution network. In order to better understand the process of the above-mentioned method for allocating resources in a power distribution network, Figure 4 As shown, the specific process of the distribution network resource allocation method of the present application is described in detail below, including the following steps:

[0073] Step S402, collecting power grid benefit data and power grid performance data of multiple regions.

[0074] Specifically, the grid benefit data may include the total amount of resource allocation and the extent of improvement of various indicators, among which the total amount of resource allocation may include the total amount of resources planned for resource allocation, etc., and the extent of improvement of various indicators may include the load growth after grid transformation, grid perfection, safe power supply capacity, voltage quality, etc.; the grid performance data may include grid level and load density, among which the grid level may include the degree of perfection of grid infrastructure, etc., and the load density may include data such as electricity demand per unit area.

[0075] Step S404, calculating effect representation values ​​of multiple regions.

[0076] Specifically, we first quantify the improvement of each indicator. We can use specific quantification methods and calculation formulas to convert the improvement of each indicator into quantifiable data, and then use the effect representation value formula to calculate the effect representation value of each region.

[0077] Step S406: performing a horizontal comparison of multiple regions based on the power grid performance data.

[0078] Optionally, a horizontal comparison can be made among counties with similar grid levels and load densities, because their BCRs should be similar. If the BCR of a county is significantly lower than that of other similar counties, it means that the resource allocation efficiency of the county is not high and there may be excessive resource allocation. A horizontal comparison can also be made among regions based on the grid level. If the BCR of a county is significantly lower than that of a county with a higher grid level, it means that the resource allocation direction of the county may be unreasonable and the resource allocation strategy needs to be adjusted.

[0079] Step S408, integrate the above data and establish a dynamic adjustment mechanism.

[0080] Specifically, resource allocation decisions can be verified and optimized based on the BCR analysis results. The specific steps are: first, verify the effectiveness of the comprehensive scoring mechanism to ensure the scientific nature of the scoring rules and weight distribution; then, for counties and districts with low resource allocation efficiency, analyze the reasons and formulate optimization adjustment plans, including adjusting the direction of resource allocation and optimizing project configuration; then establish a dynamic adjustment mechanism, regularly monitor and update BCR, adjust project rankings and resource allocation plans according to actual conditions and resource changes, and ensure flexibility and adaptability of decision-making.

[0081] Furthermore, the efficiency of the method proposed in the present application can be verified, for example, by comparing the first project preference set obtained based on the comprehensive scoring mechanism with the second project preference set selected by manual decision-making. The specific steps are: adopt a systematic comprehensive scoring mechanism to score the projects in multiple dimensions to form a first project preference set; then select the second project preference set based on expert experience and decision-making; then perform a BCR comparison, that is, calculate the BCR of the two preference sets respectively, and make an overall comparison. The plan with a higher BCR is the better plan. Through this comparative verification, it can be ensured that the first project preference set selected by the comprehensive scoring mechanism is not lower than the second project preference set selected by manual decision-making in terms of resource allocation efficiency, that is, the method proposed in the present application is more efficient.

[0082] Through the above embodiments, the scientificity and rationality of distribution network resource allocation management can be effectively improved, the resource allocation efficiency can be optimized, the economic and social benefits of the power grid system can be significantly improved, and the sustainable development of the power grid system can be promoted.

[0083] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0084] Based on the same inventive concept, the embodiment of the present application also provides a distribution network resource allocation device for implementing the distribution network resource allocation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more distribution network resource allocation device embodiments provided below can refer to the limitations of the distribution network resource allocation method above, and will not be repeated here.

[0085] In an exemplary embodiment, Figure 5 As shown, a distribution network resource allocation device is provided, comprising: a data acquisition module 501, an effect characterization value determination module 502 and a resource allocation scheme adjustment module 503, wherein:

[0086] The data acquisition module 501 is used to acquire the power grid benefit data and power grid performance data of multiple regions;

[0087] The effect characterization value determination module 502 is used to determine the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation benefit;

[0088] The resource allocation scheme adjustment module 503 is used to adjust the resource allocation scheme for each region according to the power grid performance data and effect characterization value of each region.

[0089] Furthermore, in one embodiment, the resource allocation scheme adjustment module 503 is also used to determine at least two regions where the difference between the power grid performance data is less than a first threshold; if the difference between the effect characterization value of any one of the at least two regions and the effect characterization value of other regions is greater than a second threshold, the resource allocation scheme for any one region is adjusted.

[0090] Furthermore, in one embodiment, the resource allocation scheme adjustment module 503 is also used to determine two regions where the difference in grid performance data is greater than a third threshold; if the difference between the effect characterization value of the region with lower grid performance data and the effect characterization value of the region with higher grid performance data in the two regions is less than a fourth threshold, the resource allocation scheme for the region with lower grid performance data is adjusted.

[0091] Furthermore, in one embodiment, the effect characterization value determination module 502 is also used to process the improvement of various indicators in each region to obtain a total improvement; and obtain the ratio of the total improvement to the total amount of resource allocation as the effect characterization value of the region.

[0092] Furthermore, in one embodiment, the resource allocation scheme adjustment module 503 is also used to establish a dynamic adjustment mechanism and monitor and update the effect characterization value; when the effect characterization value is lower than a preset effect characterization threshold, the resource allocation scheme of each region is adjusted based on the dynamic adjustment mechanism to ensure that the effect characterization value is not lower than the preset effect characterization threshold.

[0093] Each module in the above-mentioned distribution network resource allocation device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0094] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store distribution network resource allocation data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a distribution network resource allocation method is implemented.

[0095] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0096] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0097] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0098] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0099] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0100] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0101] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A distribution network resource allocation method, characterized in that: The method comprises: Obtain grid efficiency data and grid performance data for multiple regions; Determine the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation benefit; According to the power grid performance data and effect characterization values ​​of each region, the resource allocation plan for each region is adjusted.

2. The method according to claim 1, characterized in that The adjusting the resource allocation scheme for each region according to the power grid performance data and effect characterization value of each region includes: determining at least two regions where a difference between grid performance data is less than a first threshold; If the difference between the effect characterization value of any one of the at least two regions and the effect characterization values ​​of the other regions is greater than a second threshold, the resource allocation scheme for the any one region is adjusted.

3. The method according to claim 1, characterized in that According to the power grid performance data and effect characterization values ​​of the respective regions, adjusting the resource allocation scheme for the respective regions further includes: identifying two regions where the difference between the grid performance data is greater than a third threshold; If the difference between the effect characterization value of the region with lower grid performance data and the effect characterization value of the region with higher grid performance data in the two regions is less than a fourth threshold, the resource allocation plan for the region with lower grid performance data is adjusted.

4. The method according to claim 1, characterized in that: Grid efficiency data includes the total amount of resource allocation and the extent of improvement of multiple predetermined indicators; Determining the effect characterization value of each region according to the power grid benefit data of each region includes: For each region, the improvement of each indicator in the region is processed to obtain the total improvement; The ratio of the total improvement to the total amount of resource allocation is obtained as the effect representation value of the region.

5. The method according to claim 1, characterized in that After adjusting the resource allocation scheme for each region according to the power grid performance data and effect characterization value of each region, the method further comprises: Establishing a dynamic adjustment mechanism and monitoring and updating the effect representation value; When the effect characterization value is lower than a preset effect characterization threshold, the resource allocation schemes of the various regions are adjusted based on the dynamic adjustment mechanism to ensure that the effect characterization value is not lower than the preset effect characterization threshold.

6. A distribution network resource allocation device, characterized in that: The device comprises: A data acquisition module, used to acquire power grid benefit data and power grid performance data of multiple regions; An effect characterization value determination module is used to determine the effect characterization value of each region according to the power grid benefit data of each region; the effect characterization value represents the effect of improving the distribution network resource allocation benefit; The resource allocation scheme adjustment module is used to adjust the resource allocation scheme for each region according to the power grid performance data and effect characterization value of each region.

7. The device according to claim 6, characterized in that The resource allocation scheme adjustment module is also used to determine at least two regions where the difference between power grid performance data is less than a first threshold; if the difference between the effect characterization value of any region among the at least two regions and the effect characterization value of other regions is greater than a second threshold, the resource allocation scheme for any region is adjusted.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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