Access point processing method and device of power grid high-capacity user access request

Abstract

The invention provides an access point processing method and device for a high-capacity user access request of a power grid. According to the method, a digital power grid frame model is constructed based on comprehensive parameters of a power grid, an optimal access point is automatically calculated by combining power utilization application data of a user and applying a UVV task allocation method based on a multi-population genetic algorithm, and a construction work order is automatically generated and pushed to the user for construction. According to the intelligent power grid access point processing method, automatic calculation based on the digital power grid model and the multi-population genetic algorithm is realized, the processing efficiency and accuracy of the power grid access request are greatly improved, the risks of manual intervention and human errors are reduced, and meanwhile, the configuration of power grid resources is optimized. The problems of low efficiency, poor accuracy, unreasonable resource allocation, insufficient dynamic adjustment and the like in manual processing of large-capacity user access requests in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation, and in particular to an access point processing method for access requests of large-capacity users of a power grid, an access point processing device for access requests of large-capacity users of a power grid, a computer-readable storage medium and a power grid operation system. Background Art

[0002] With the continuous development of intelligent and digital technologies in power grids, the management and operation modes of power grids are gradually transforming towards automation and intelligence. In particular, the demand for large-capacity users to access the power grid is increasing. How to efficiently and scientifically handle user access requests has become an important issue facing the power industry. In the existing technology, the processing of power grid access requests usually relies on manual judgment and empirical analysis. This process is not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in inaccurate access point selection, which in turn affects the reasonable allocation of power grid loads and the optimal use of resources.

[0003] At present, when processing requests for large-capacity users to access the power grid, it is often necessary to manually process and analyze information such as each user's access location, load capacity, and load properties. This process is very cumbersome when the number of users is large, and it is easy to make calculation errors or analysis delays. In addition, most systems in the existing technology lack efficient automated processing capabilities, making it difficult to quickly calculate the best access point based on the access needs of different users, and lack dynamic optimization evaluation of access points, and often can only rely on fixed rules or manual decisions. Summary of the invention

[0004] The main purpose of the present application is to provide an access point processing method for access requests of large-capacity users in a power grid, an access point processing device for access requests of large-capacity users in a power grid, a computer-readable storage medium, a processor and an electronic device, so as to at least solve the problems of low efficiency, poor accuracy, unreasonable resource allocation, insufficient dynamic adjustment, etc. in the manual processing of access requests of large-capacity users in the prior art.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, there is provided an access point processing method for access requests of large-capacity users of a power grid, comprising: constructing a digital power grid framework model based on comprehensive parameters of the power grid to obtain a first target model, wherein the comprehensive parameters at least include physical equipment, network topology, operating parameters and environmental factors of the power grid; obtaining electricity application data, wherein the electricity application data at least includes user coordinates, power demand properties and power demand capacity, and the power demand properties are used to characterize the purpose of electricity; according to the digital power grid framework model and the power application data, performing calculations through a UVV task allocation method based on a multi-population genetic algorithm to determine the target access point of the user's power supply line in the power grid; creating a construction work order based on the target access point and pushing it to the user.

[0006] Optionally, based on the comprehensive parameters of the power grid, a digital power grid model is constructed to obtain a first target model, including: analyzing based on the comprehensive parameters, extracting the power grid equipment parameters from the comprehensive parameters, and constructing a simulation model of the power equipment based on the power grid equipment parameters to obtain a power grid equipment model, wherein the power grid equipment parameters include equipment parameters, operating parameters and fault responses of each power equipment in the power grid; analyzing based on the comprehensive parameters, extracting the network layout of the power grid from the comprehensive parameters, and constructing a topology model of the power grid based on the network layout to obtain a power grid topology model; analyzing based on the comprehensive parameters, extracting real-time operating data from the comprehensive parameters to obtain the operating parameters, wherein the operating parameters include voltage, current, power and frequency; analyzing based on the comprehensive parameters, extracting external environmental data from the comprehensive parameters to obtain the environmental factors, wherein the environmental factors include weather changes, natural disasters and fluctuations in application demand; simulating the power grid through a mathematical model based on the power grid equipment model, the power grid topology model, the operating parameters and the environmental factors to achieve real-time analysis of the power grid and obtain the first target model.

[0007] Optionally, after acquiring the electricity application data, the method further includes: performing an integrity check based on the electricity application data to determine whether there are missing values ​​in the electricity application data, and if there are missing values, sending first target information to the user, the first target information being used to prompt the user to re-upload the electricity application data; performing analysis based on the electricity application data to extract coordinate values ​​in the electricity application data, and converting the coordinate values ​​into a preset format; and performing position matching with a power grid topology model in the first target model based on the converted coordinate values ​​to obtain a target position association.

[0008] Optionally, according to the digital power grid model and the electricity application data, a UVV task allocation method based on a multi-population genetic algorithm is used to calculate and determine the target access point of the user power supply line in the power grid, including: initializing the population based on the access points included in the digital power grid model to obtain multiple first target populations; a first calculation step, calculating the fitness corresponding to the access point in each of the first target populations through a fitness function based on the electricity application data and the target position association; a second calculation step, performing local genetic operations on the inside of each of the first target populations to obtain a second target population, and performing global genetic operations on all of the second target populations to obtain a third target population; repeating the first calculation step and the second calculation step in sequence at least once until the number of stagnation times reaches a preset value or the number of iterations reaches a maximum number of iterations, and determining the access point corresponding to the maximum fitness value in all the third target populations as the target access point.

[0009] Optionally, a population is initialized based on the access points included in the digital power grid network model to obtain multiple first target populations, including: based on the power grid area division of the power grid topology model in the digital power grid network model, the counting points in each power grid area are divided into the same population to obtain multiple fourth target populations; and one access point is randomly selected in each of the fourth target populations as the initial solution of the fourth target population to obtain the first target population.

[0010] Optionally, calculating the fitness corresponding to each access point in the first target population through a fitness function based on the electricity application data includes: constructing the fitness function based on a deviation between the load capacity of the access point and the power demand capacity, an interference deviation of the access point to the existing load of the power grid after the access point accesses the user, a construction cost of the access point accessing the user, and the total transmission efficiency of the power grid.

[0011] Optionally, performing a local genetic operation within each of the first target populations to obtain a second target population, and performing a global genetic operation based on all of the second target populations to obtain a third target population, including: based on the fitness of each of the access points, selecting a preset number of access points from each of the first target populations to obtain a fifth target population, wherein the selection method includes one or more of tournament selection, roulette selection, and elite selection; performing a crossover operation and a mutation operation on the fifth target population and the first target population in sequence to obtain the second target population; performing an immigration operation on each of the second target populations to obtain multiple a sixth target population; performing operations based on each of the sixth target populations through a preset constraint matrix to verify whether an access point in the sixth target population violates a preset constraint condition, and deleting the access point that violates the preset constraint condition to obtain a seventh target population; performing forgetting detection and stagnation detection in sequence based on the seventh target population to obtain the third target population, wherein, when the forgetting detection passes, the number of iterations increases, and when the detection fails, the number of iterations decreases, and when the stagnation detection passes, the number of stagnations is reset to zero, and when the detection fails, the number of stagnations increases.

[0012] According to another aspect of the present application, an access point processing device for access requests of large-capacity users of a power grid is provided, comprising: a first construction unit, used to construct a digital power grid framework model based on comprehensive parameters of the power grid to obtain a first target model, wherein the comprehensive parameters at least include physical equipment, network topology, operating parameters and environmental factors of the power grid; an acquisition unit, used to acquire electricity application data, wherein the electricity application data at least includes user coordinates, power demand properties and power demand capacity, and the power demand properties are used to characterize the purpose of electricity; a calculation unit, used to determine the target access point of the user's power supply line in the power grid by performing calculations based on the digital power grid framework model and the power application data through a UVV task allocation method based on a multi-population genetic algorithm; and a sending unit, used to create a construction work order based on the target access point and push it to the user.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0014] According to another aspect of the present application, a power grid operation system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the methods described.

[0015] Applying the technical solution of this application, a digital power grid model is constructed based on the comprehensive parameters of the power grid, and combined with the user's electricity application data, the UVV task allocation method based on a multi-population genetic algorithm is used to automatically calculate the optimal access point, and automatically generate a construction work order, which is pushed to the user for construction use. This application uses an intelligent power grid access point processing method to achieve automated calculation based on a digital power grid model and a multi-population genetic algorithm, greatly improving the processing efficiency and accuracy of power grid access requests, reducing the risk of manual intervention and human error, and optimizing the configuration of power grid resources. It solves the problems of low efficiency, poor accuracy, unreasonable resource allocation, and insufficient dynamic adjustment in the manual processing of large-capacity user access requests in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for processing an access point of a large-capacity user access request for a power grid provided in an embodiment of the present application is shown;

[0017] Figure 2 A schematic flow chart of a method for processing an access point of a large-capacity user access request for a power grid provided in accordance with an embodiment of the present application is shown;

[0018] Figure 3 A schematic diagram of a UVV task allocation method based on a multi-population genetic algorithm provided according to an embodiment of the present application is shown;

[0019] Figure 4 An algorithm flow chart of an access point processing method for a large-capacity user access request of a power grid based on blockchain provided in accordance with an embodiment of the present application is shown;

[0020] Figure 5 A structural block diagram of an access point processing device for a large-capacity user access request of a power grid provided in accordance with an embodiment of the present application is shown.

[0021] The above drawings include the following reference numerals:

[0022] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] As introduced in the background technology, most systems in the prior art lack efficient automated processing capabilities, and it is difficult to quickly calculate the best access point according to the access requirements of different users. In addition, there is a lack of dynamic optimization evaluation of the access point, and they can often only rely on fixed rules or manual decisions. In order to solve the problems of low efficiency, poor accuracy, unreasonable resource allocation, insufficient dynamic adjustment, etc. in the manual processing of large-capacity user access requests in the prior art, the embodiments of the present application provide an access point processing method for access requests of large-capacity users of a power grid, an access point processing device for access requests of large-capacity users of a power grid, a computer-readable storage medium, and a power grid operation system.

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0028] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of an access point processing method for a large-capacity user access request of a power grid according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0029] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] In this embodiment, an access point processing method for a large-capacity user access request of a power grid running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] Figure 2 FIG. 1 is a flow chart of a method for processing an access point of a large-capacity user access request of a power grid according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0032] Step S201, constructing a digital power grid model based on comprehensive parameters of the power grid to obtain a first target model, wherein the comprehensive parameters at least include physical equipment, network topology, operating parameters and environmental factors of the power grid;

[0033] Specifically, by building a digital grid model based on the comprehensive parameters of the grid (including physical equipment, network topology, operating parameters and environmental factors), it can fully and accurately reflect the actual operation status and power distribution of the grid. This process enables the subsequent selection of user access points to take into account the distribution and capacity of grid resources, avoiding the problem of ignoring the overall load balance and potential bottlenecks of the grid in traditional methods.

[0034] Step S202, obtaining electricity application data, where the electricity application data at least includes user coordinates, electricity demand properties and electricity demand capacity, where the electricity demand properties are used to characterize the use of electricity;

[0035] Specifically, by obtaining the user's specific electricity application data, especially the user's coordinates, the nature of the electricity demand, and the electricity demand capacity, an access plan can be tailored for each user. The collection and analysis of this data ensures that the access point selection not only meets the load requirements of the power grid, but also accurately matches the best access point according to the user's actual electricity demand and purpose, avoiding the one-size-fits-all situation in traditional processing methods and improving the flexibility and accuracy of user access.

[0036] Step S203, according to the digital power grid model and the power application data, a UVV task allocation method based on a multi-population genetic algorithm is used to calculate and determine the target access point of the user's power supply line in the power grid;

[0037] Specifically, the UVV task allocation method based on multi-population genetic algorithm is used for calculation, which can efficiently search for the optimal access point in a complex power grid environment. This intelligent algorithm can handle multiple task allocations at the same time, optimize the selection process of power grid access points, and dynamically adjust according to power grid load, user needs and other factors to ensure that each user's access point selection achieves load balance and resource optimization while meeting the stability of the power grid.

[0038] Step S204: Create a construction work order based on the target access point and push it to the user.

[0039] Specifically, the UVV task allocation method based on multi-population genetic algorithm is used for calculation, which can efficiently search for the optimal access point in a complex power grid environment. This intelligent algorithm can handle multiple task allocations at the same time, optimize the selection process of power grid access points, and dynamically adjust according to power grid load, user needs and other factors to ensure that each user's access point selection achieves load balance and resource optimization while meeting the stability of the power grid.

[0040] Through this embodiment, a method for processing access points for access requests from large-capacity users of the power grid is proposed, which can significantly improve the processing efficiency and accuracy of access requests from the power grid. Through the construction of a digital power grid model and the application of intelligent algorithms, the system can automatically and accurately calculate the optimal access point, automatically generate construction work orders and push them to users, simplifying the subsequent construction process, reducing waiting time, and improving user experience. Overall, this method not only improves the efficiency of power grid resource allocation, but also optimizes the overall experience of the user access process, and solves the problems of low efficiency, poor accuracy, unreasonable resource allocation, and insufficient dynamic adjustment in the manual processing of large-capacity user access requests in the prior art.

[0041] As a possible implementation method, a digital power grid model is constructed based on the comprehensive parameters of the power grid to obtain the first target model, including:

[0042] Step S301, performing analysis based on comprehensive parameters, extracting power grid equipment parameters from the comprehensive parameters, and constructing a simulation model of the power equipment based on the power grid equipment parameters to obtain a power grid equipment model, wherein the power grid equipment parameters include equipment parameters, operating parameters, and fault responses of each power equipment in the power grid;

[0043] Specifically, by analyzing the comprehensive parameters of the power grid, the key parameters of each power device in the power grid are extracted, such as device type, rated power, device operating status, fault response characteristics, etc. Based on these parameters, a simulation model of the power equipment is constructed to simulate the performance and response of the power equipment under different working conditions. Through this simulation model, the operating status of the equipment can be monitored in real time, and equipment failures can be predicted and responded to.

[0044] Step S302, performing analysis based on the comprehensive parameters, extracting the network layout of the power grid in the comprehensive parameters, and constructing a topological model of the power grid based on the network layout to obtain a power grid topological model;

[0045] Specifically, the network layout of the power grid is analyzed, including the spatial configuration and connection mode of each substation, transmission line, distribution equipment, etc. in the power grid. By extracting the network layout information, a topological model of the power grid is constructed, and then the connection relationship between the various components of the power grid is obtained. This model can help identify key nodes and possible load bottlenecks in the power grid, and provide a basis for the selection of access points and load scheduling.

[0046] Step S303, performing analysis based on the comprehensive parameters, extracting real-time operation data from the comprehensive parameters, and obtaining operation parameters, where the operation parameters include voltage, current, power, and frequency;

[0047] Specifically, the real-time operation data of the power grid is analyzed, including the voltage, current, power, frequency and other operation parameters in the power grid. By extracting and analyzing these real-time data, dynamic monitoring of the power grid operation status can be achieved, and potential problems in the power grid operation, such as voltage fluctuations, load overload and other abnormal conditions, can be discovered in time.

[0048] Step S304, performing analysis based on the comprehensive parameters, extracting external environment data in the comprehensive parameters, and obtaining environmental factors, which include weather changes, natural disasters, and fluctuations in application demand;

[0049] Specifically, the impact of the external environment on the operation of the power grid is analyzed, and environmental factors including weather changes, natural disasters (such as typhoons and earthquakes), and fluctuations in application demand (such as industrial demand peaks) are extracted. By considering these external factors, the operation performance of the power grid under different environmental conditions can be more accurately evaluated, and corresponding adjustment measures can be made.

[0050] Step S305 , simulating the power grid through a mathematical model based on the power grid equipment model, the power grid topology model, the operating parameters and the environmental factors, so as to realize real-time analysis of the power grid and obtain a first target model.

[0051] Specifically, the entire power grid is simulated through a mathematical model, combining the power grid equipment model, power grid topology model, real-time operating parameters and environmental factors. This simulation can not only reflect the operating status of the power grid in real time, but also predict the future operating trend of the power grid according to different load demands and environmental changes, providing data support for power grid dispatching, maintenance and expansion.

[0052] As a possible implementation manner, after obtaining the electricity application data, the method further includes:

[0053] Step S401, performing integrity check based on the electricity application data to determine whether there are missing values ​​in the electricity application data, and sending first target information to the user if there are missing values, the first target information is used to prompt the user to re-upload the electricity application data;

[0054] Specifically, the power application data submitted by the user is checked for completeness to see if there are any missing values ​​(such as whether the user coordinates, power demand capacity and other information are missing). If missing values ​​are found, the system will automatically send a prompt message (first target information) to the user, requiring the user to re-upload the complete power application data. This can avoid inaccurate or failed subsequent calculations and access point selection due to missing data.

[0055] Step S402, analyzing the electricity application data to extract coordinate values ​​in the electricity application data, and converting the coordinate values ​​into a preset format;

[0056] Specifically, the power application data is analyzed, the user's coordinate values ​​(such as longitude and latitude information) are extracted, and converted into a system preset format. This process ensures that the user's coordinate information can be compatible with the power grid topology model, thereby achieving accurate access point calculation.

[0057] Step S403: Based on the converted coordinate values, position matching is performed with the power grid topology model in the first target model to obtain a target position association.

[0058] Specifically, the user's coordinates are matched with the grid topology model to ensure that the user's access point corresponds to the actual location in the grid. Through this matching, the system can accurately determine the best location for the user to access the grid, avoiding errors or matching errors in traditional methods.

[0059] As a possible implementation, Figure 3 is a schematic diagram of a UVV task allocation method based on a multi-population genetic algorithm provided by the present application, such as Figure 3 As shown, the algorithm first initializes the population and sets the generation counter gen1 for local genetic operations and the generation counter gen2 for global genetic operations. The algorithm divides the population into multiple subpopulations, each of which performs local genetic operations independently, including selection, crossover, and mutation. Next, global genetic operations are performed, involving immigration, evaluating whether individuals violate constraints, and elite retention. The algorithm checks whether the gen1 counter needs to be reset (forgetting) or whether it is stagnant. If so, the gen2 counter is reset. Subsequently, the values ​​of gen1 and gen2 are updated. If there is no forgetting, gen1 is reduced by 1 and then increased by 1, indicating that the generation of local genetic operations has increased. If there is no stagnation, gen2 is increased by 1, indicating that the generation of global genetic operations has increased. If gen1 does not exceed the maximum number of generations MaxGen, or gen2 does not exceed the maximum stagnation count MaxTrappedCount, the algorithm will continue to iterate. Finally, when the termination condition is met, the algorithm ends and returns the optimal solution found.

[0060] According to the digital grid model and electricity application data, the UVV task allocation method based on multi-population genetic algorithm is used to determine the target access point of the user's power supply line in the grid, including:

[0061] Step S501, initializing a population based on access points included in a digital power grid model to obtain a plurality of first target populations;

[0062] Specifically, the population is initialized first according to the access point information contained in the digital power grid model. The access points in the power grid model represent potential access points at different locations in the power grid, which will serve as the initial solutions for the genetic algorithm population. By initializing multiple first target populations, the algorithm provides a diverse initial solution space for the optimization calculation of access points, providing a basis for the subsequent evolution of the genetic algorithm.

[0063] Step S502, a first calculation step, calculating the fitness corresponding to each access point in the first target population through a fitness function based on the association between the electricity application data and the target location;

[0064] Specifically, based on the correlation between the electricity application data and the target location, the fitness function is used to calculate the fitness value of each access point in the first target population. The fitness function will evaluate each access point based on the power demand, the grid conditions of the access point, and other relevant factors to determine its pros and cons. Access points with high fitness values ​​represent the best access points that better meet user needs.

[0065] Step S503, a second calculation step, performing local genetic operations within each first target population to obtain a second target population, and performing global genetic operations based on all second target populations to obtain a third target population;

[0066] Specifically, the algorithm first performs local genetic operations on each first target population, including selection, crossover, and mutation operations, to obtain the second target population. Local genetic operations simulate the natural selection mechanism to screen out individuals with higher fitness and generate new offspring. Then, all second target populations will undergo global genetic operations to further integrate the excellent solutions in each local population to obtain the third target population. Global genetic operations ensure the diversity of the population and increase the efficiency of global search.

[0067] Step S504, repeating the first calculation step and the second calculation step at least once in sequence until the number of stagnation times reaches a preset value or the number of iterations reaches a maximum number of iterations, and determining the access points corresponding to the maximum fitness values ​​in all the third target populations as target access points.

[0068] Specifically, by repeating the aforementioned calculation steps, fitness calculation, local genetic operations and global genetic operations are continuously performed until the preset number of stagnation or the maximum number of iterations is reached. Each iteration will gradually evolve the population, and the access point selection will become more and more accurate. When the stop condition is met, the system will select the access point corresponding to the maximum fitness value from the third target population and determine it as the final target access point.

[0069] It can be seen that through multiple rounds of iteration, the genetic algorithm can gradually optimize the selection of access points and maximize the fitness and matching degree of access points. The setting of the iteration stop condition avoids excessive calculation and improves the efficiency of the algorithm. When the target access point is finally determined, the system can ensure that the access point that best meets user needs and meets the grid conditions is selected, optimizing the configuration and use of grid resources.

[0070] As a possible implementation method, a population is initialized based on access points included in the digital power grid model to obtain multiple first target populations, including:

[0071] Step S601, based on the grid area division of the grid topology model in the digital grid model, the counting points in each grid area are divided into the same population to obtain a plurality of fourth target populations;

[0072] Specifically, the grid area is divided according to the grid topology information in the digital grid model. Each grid area includes a number of access points, all of which will be classified as the same population to form multiple fourth target populations. The division of the grid area usually takes into account the geographical location, grid load conditions and other grid operation factors to ensure that the access points in each area are similar to some extent or have the same demand characteristics.

[0073] Step S602: randomly select an access point in each fourth target population as an initial solution of the fourth target population to obtain the first target population.

[0074] Specifically, an access point is randomly selected from each fourth target population as the initial solution of the population, and these initial solutions will form the first target population. Randomly selecting initial solutions is an important part of the genetic algorithm, which can ensure the diversity of the population and provide a rich solution space for the subsequent optimization process. These initial solutions will serve as the basic individuals in the genetic algorithm optimization process and participate in the subsequent fitness evaluation and genetic operations.

[0075] As a possible implementation method, the fitness corresponding to each access point in the first target population is calculated by a fitness function based on the electricity application data, including:

[0076] The fitness function is constructed based on the deviation between the load capacity of the access point and the power demand capacity, the interference deviation of the access point to the existing load of the power grid after the access point is connected to the user, the construction cost of the access point to the user and the total transmission efficiency of the power grid.

[0077] Specifically, by incorporating multiple key factors into the fitness function, the pros and cons of each access point can be comprehensively evaluated. Taking into account factors such as load capacity, grid stability, construction cost, and transmission efficiency, the fitness function can not only optimize the location and capacity matching of access points, but also effectively avoid unnecessary burdens on the grid or excessive construction costs. This multi-dimensional evaluation ensures that the selected access point maximizes the optimization of grid resources while meeting user needs, improving the overall efficiency and stability of the grid.

[0078] As a possible implementation method, a local genetic operation is performed within each first target population to obtain a second target population, and a global genetic operation is performed based on all second target populations to obtain a third target population, including:

[0079] Step S701, based on the fitness of each access point, select a preset number of access points from each first target population to obtain a fifth target population, wherein the selection method includes one or more of tournament selection, roulette selection and elite selection;

[0080] Specifically, a new population is formed by selecting suitable individuals from the first target population according to the fitness of the access point. Access points with higher fitness represent better adaptability to the power grid, and are therefore selected preferentially. The selection method may include: tournament selection is to randomly select multiple individuals to form a competition group, and select the individuals with the strongest fitness to join the new population. Roulette selection is to select individuals based on the ratio of the individual's fitness value to the total fitness, and individuals with higher fitness have a greater probability of being selected. Elite selection is to directly retain the individuals with the best fitness to the next generation to ensure that the algorithm does not lose excellent solutions.

[0081] Step S702, performing a crossover operation and a mutation operation in sequence based on the fifth target population and the first target population to obtain a second target population;

[0082] Specifically, crossover and mutation are two core genetic operations in genetic algorithms, which are used to explore new solution spaces. The crossover operation is to form new individuals by exchanging individual genes in the fifth target population, thereby generating new possible solutions. The mutation operation is to randomly modify individual genes to generate different gene combinations to avoid local concentration in the solution space.

[0083] Step S703, performing an immigration operation based on each second target population to obtain multiple sixth target populations;

[0084] Specifically, the immigration operation introduces external individuals or exchanges individuals with other populations to break the excessive concentration within the population and enhance the diversity of the population. It avoids excessive convergence between local populations and enhances the global search ability of the genetic algorithm.

[0085] Step S704, performing calculations based on each sixth target population through a preset constraint matrix to verify whether the access points in the sixth target population violate the preset constraint conditions, and deleting the access points that violate the preset constraint conditions to obtain the seventh target population;

[0086] Specifically, to ensure the rationality of the access point, the algorithm checks whether each individual meets the operating constraints of the power grid (such as load capacity, regional load balancing, etc.). Individuals that violate the constraints will be deleted or replaced to ensure that the final solution meets the actual power grid needs.

[0087] Step S705, based on the seventh target population, forgetting detection and stagnation detection are performed in sequence to obtain a third target population, wherein, for the forgetting detection, if the detection is passed, the number of iterations increases, and if the detection is not passed, the number of iterations decreases, and for the stagnation detection, if the detection is passed, the number of stagnations is reset to zero, and if the detection is not passed, the number of stagnations increases.

[0088] Specifically, forgetting detection is to monitor the change of the solution and determine whether the solution is "forgotten". If the current solution does not bring obvious improvement, the number of iterations is reduced. Stagnation detection is to mark the population as stagnant and increase the number of stagnation if no new optimal solution appears after a certain number of iterations to prevent the waste of computing resources.

[0089] Furthermore, forgetting detection and stagnation detection can effectively prevent the genetic algorithm from falling into local search and continuing invalid iterations. By reducing the number of invalid iterations, the utilization efficiency of computing resources is optimized, ensuring that the algorithm stops in time after reaching a satisfactory result, and improving the efficiency of the overall optimization process.

[0090] In the above embodiment, when calculating the fitness, the key factors used for reference include but are not limited to the current value, voltage value, branch switch, nearby transformer, nearby residents, line loss value and substation line loss value of the access point, as shown in Table 1.

[0091] Table 1

[0092]

[0093]

[0094] In another embodiment of the present application, Figure 4As shown, the key factors of each access point are extracted to generate samples, and the samples are summed through the summation layer to obtain the first feature (including the optimal individual of the previous iteration). In addition, the population segmentation and fitting calculation are performed based on the user work order to obtain the second feature. In the blockchain part, the parent individual is determined based on the second feature and the user work order, and the access key factor is determined based on the first feature and the second feature. Then, based on the access key factor and whether the user accepts and the effect of the access key factor on the parent individual, the child individual is obtained, and then the child individual is selected, crossover and mutation operations are performed based on the fitness, and finally the optimal individual is determined. Repeat the above process for iteration until the convergence condition is met or the maximum number of iterations is reached, and the optimal individual is used until the user reports the installation.

[0095] The embodiment of the present application also provides an access point processing device for access requests of large-capacity users of a power grid. It should be noted that the access point processing device for access requests of large-capacity users of a power grid in the embodiment of the present application can be used to execute the access point processing method for access requests of large-capacity users of a power grid provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0096] The following introduces an access point processing device for large-capacity user access requests of a power grid provided in an embodiment of the present application.

[0097] Figure 5 1 is a structural block diagram of an access point processing device for a large-capacity user access request of a power grid according to an embodiment of the present application. Figure 5 As shown, the device includes: a first constructing unit 10 , an acquiring unit 20 , a calculating unit 30 and a sending unit 40 .

[0098] A first construction unit 10 is used to construct a digital power grid model based on comprehensive parameters of the power grid to obtain a first target model, wherein the comprehensive parameters at least include physical devices, network topology, operating parameters and environmental factors of the power grid;

[0099] Specifically, by building a digital grid model based on the comprehensive parameters of the grid (including physical equipment, network topology, operating parameters and environmental factors), it can fully and accurately reflect the actual operation status and power distribution of the grid. This process enables the subsequent selection of user access points to take into account the distribution and capacity of grid resources, avoiding the problem of ignoring the overall load balance and potential bottlenecks of the grid in traditional methods.

[0100] The acquisition unit 20 is used to acquire electricity application data, where the electricity application data at least includes user coordinates, electricity demand properties and electricity demand capacity, where the electricity demand properties are used to characterize the use of electricity;

[0101] Specifically, by obtaining the user's specific electricity application data, especially the user's coordinates, the nature of the electricity demand, and the electricity demand capacity, an access plan can be tailored for each user. The collection and analysis of this data ensures that the access point selection not only meets the load requirements of the power grid, but also accurately matches the best access point according to the user's actual electricity demand and purpose, avoiding the one-size-fits-all situation in traditional processing methods and improving the flexibility and accuracy of user access.

[0102] The calculation unit 30 is used to determine the target access point of the user's power supply line in the power grid by performing calculations based on the UVV task allocation method based on a multi-population genetic algorithm according to the digital power grid network model and the power application data;

[0103] Specifically, the UVV task allocation method based on multi-population genetic algorithm is used for calculation, which can efficiently search for the optimal access point in a complex power grid environment. This intelligent algorithm can handle multiple task allocations at the same time, optimize the selection process of power grid access points, and dynamically adjust according to power grid load, user needs and other factors to ensure that each user's access point selection achieves load balance and resource optimization while meeting the stability of the power grid.

[0104] The sending unit 40 is used to create a construction work order based on the target access point and push it to the user.

[0105] Specifically, the UVV task allocation method based on multi-population genetic algorithm is used for calculation, which can efficiently search for the optimal access point in a complex power grid environment. This intelligent algorithm can handle multiple task allocations at the same time, optimize the selection process of power grid access points, and dynamically adjust according to power grid load, user needs and other factors to ensure that each user's access point selection achieves load balance and resource optimization while meeting the stability of the power grid.

[0106] Through this embodiment, an access point processing device for access requests of large-capacity users in a power grid is proposed. The device includes: a first construction unit, an acquisition unit, a calculation unit and a sending unit. The first construction unit is used to construct a digital power grid model based on the comprehensive parameters of the power grid to obtain a first target model, and the comprehensive parameters at least include the physical equipment, network topology, operation parameters and environmental factors of the power grid; the acquisition unit is used to obtain power application data, and the power application data at least includes user coordinates, power demand properties and power demand capacity, and the power demand properties are used to characterize the purpose of electricity; the calculation unit is used to calculate according to the digital power grid model and the power application data through the UVV task allocation method based on the multi-population genetic algorithm to determine the target access point of the user's power supply line in the power grid; the sending unit is used to create a construction work order based on the target access point and push it to the user. The device realizes automatic calculation based on the digital power grid model and the multi-population genetic algorithm through the intelligent power grid access point processing method, greatly improves the processing efficiency and accuracy of the power grid access request, reduces the risk of manual intervention and human error, and optimizes the configuration of power grid resources. The invention solves the problems of low efficiency, poor accuracy, unreasonable resource allocation, insufficient dynamic adjustment, etc. in the manual processing of large-capacity user access requests in the prior art.

[0107] As a possible implementation manner, the first construction unit includes: a first construction module, a second construction module, a first analysis module, a second analysis module and a third analysis module.

[0108] The first construction module is used to analyze based on the comprehensive parameters, extract the power grid equipment parameters in the comprehensive parameters, and construct a simulation model of the power equipment based on the power grid equipment parameters to obtain a power grid equipment model, where the power grid equipment parameters include equipment parameters, operating parameters and fault responses of each power equipment in the power grid;

[0109] Specifically, by analyzing the comprehensive parameters of the power grid, the key parameters of each power device in the power grid are extracted, such as device type, rated power, device operating status, fault response characteristics, etc. Based on these parameters, a simulation model of the power equipment is constructed to simulate the performance and response of the power equipment under different working conditions. Through this simulation model, the operating status of the equipment can be monitored in real time, and equipment failures can be predicted and responded to.

[0110] The second construction module is used to analyze based on the comprehensive parameters, extract the network layout of the power grid in the comprehensive parameters, and construct a topological model of the power grid based on the network layout to obtain a power grid topological model;

[0111] Specifically, the network layout of the power grid is analyzed, including the spatial configuration and connection mode of each substation, transmission line, distribution equipment, etc. in the power grid. By extracting the network layout information, a topological model of the power grid is constructed, and then the connection relationship between the various components of the power grid is obtained. This model can help identify key nodes and possible load bottlenecks in the power grid, and provide a basis for the selection of access points and load scheduling.

[0112] The first analysis module is used to perform analysis based on the comprehensive parameters, extract the real-time operation data in the comprehensive parameters, and obtain the operation parameters, which include voltage, current, power and frequency;

[0113] Specifically, the real-time operation data of the power grid is analyzed, including the voltage, current, power, frequency and other operation parameters in the power grid. By extracting and analyzing these real-time data, dynamic monitoring of the power grid operation status can be achieved, and potential problems in the power grid operation, such as voltage fluctuations, load overload and other abnormal conditions, can be discovered in time.

[0114] The second analysis module is used to perform analysis based on the comprehensive parameters, extract external environment data from the comprehensive parameters, and obtain environmental factors, which include weather changes, natural disasters, and fluctuations in application demand;

[0115] Specifically, the impact of the external environment on the operation of the power grid is analyzed, and environmental factors including weather changes, natural disasters (such as typhoons and earthquakes), and fluctuations in application demand (such as industrial demand peaks) are extracted. By considering these external factors, the operation performance of the power grid under different environmental conditions can be more accurately evaluated, and corresponding adjustment measures can be made.

[0116] The third analysis module is used to simulate the power grid through a mathematical model based on the power grid equipment model, the power grid topology model, the operating parameters and the environmental factors, so as to realize the real-time analysis of the power grid and obtain the first target model.

[0117] Specifically, the entire power grid is simulated through a mathematical model, combining the power grid equipment model, power grid topology model, real-time operating parameters and environmental factors. This simulation can not only reflect the operating status of the power grid in real time, but also predict the future operating trend of the power grid according to different load demands and environmental changes, providing data support for power grid dispatching, maintenance and expansion.

[0118] As a possible implementation manner, the device further includes: a verification unit, an analysis unit and a matching unit.

[0119] A verification unit, configured to perform integrity verification based on the electricity application data to determine whether there are missing values ​​in the electricity application data, and if there are missing values, send first target information to the user, the first target information being used to prompt the user to re-upload the electricity application data;

[0120] Specifically, the power application data submitted by the user is checked for completeness to see if there are any missing values ​​(such as whether the user coordinates, power demand capacity and other information are missing). If missing values ​​are found, the system will automatically send a prompt message (first target information) to the user, requiring the user to re-upload the complete power application data. This can avoid inaccurate or failed subsequent calculations and access point selection due to missing data.

[0121] An analysis unit, used to perform analysis based on the electricity application data to extract coordinate values ​​in the electricity application data and convert the coordinate values ​​into a preset format;

[0122] Specifically, the power application data is analyzed, the user's coordinate values ​​(such as longitude and latitude information) are extracted, and converted into a system preset format. This process ensures that the user's coordinate information can be compatible with the power grid topology model, thereby achieving accurate access point calculation.

[0123] The matching unit is used to perform position matching with the power grid topology model in the first target model based on the converted coordinate value to obtain a target position association.

[0124] Specifically, the user's coordinates are matched with the grid topology model to ensure that the user's access point corresponds to the actual location in the grid. Through this matching, the system can accurately determine the best location for the user to access the grid, avoiding errors or matching errors in traditional methods.

[0125] As a possible implementation, the computing unit includes: an initialization module, a fitness module, a genetic module and an iteration module.

[0126] An initialization module, used to initialize a population based on access points included in the digital power grid model to obtain a plurality of first target populations;

[0127] A fitness module, used in the first calculation step, calculates the fitness corresponding to each access point in the first target population through a fitness function based on the association between the electricity application data and the target location;

[0128] A genetic module is used in the second calculation step to perform a local genetic operation on each first target population to obtain a second target population, and to perform a global genetic operation on all second target populations to obtain a third target population;

[0129] The iteration module is used to repeat the first calculation step and the second calculation step in sequence at least once until the number of stagnation times reaches a preset value or the number of iterations reaches a maximum number of iterations, and to determine the access points corresponding to the maximum fitness values ​​in all the third target populations as target access points.

[0130] It can be seen that through multiple rounds of iteration, the genetic algorithm can gradually optimize the selection of access points and maximize the fitness and matching degree of access points. The setting of the iteration stop condition avoids excessive calculation and improves the efficiency of the algorithm. When the target access point is finally determined, the system can ensure that the access point that best meets user needs and meets the grid conditions is selected, optimizing the configuration and use of grid resources.

[0131] As a possible implementation manner, the initialization module includes: a division submodule and a selection submodule.

[0132] A division submodule, for dividing the grid areas based on the grid topology model in the digital grid model, dividing the input points in each grid area into the same population, and obtaining a plurality of fourth target populations;

[0133] Specifically, the grid area is divided according to the grid topology information in the digital grid model. Each grid area includes a number of access points, all of which will be classified as the same population to form multiple fourth target populations. The division of the grid area usually takes into account the geographical location, grid load conditions and other grid operation factors to ensure that the access points in each area are similar to some extent or have the same demand characteristics.

[0134] The selection submodule is used to randomly select an access point in each fourth target population as an initial solution of the fourth target population to obtain the first target population.

[0135] Specifically, an access point is randomly selected from each fourth target population as the initial solution of the population, and these initial solutions will form the first target population. Randomly selecting initial solutions is an important part of the genetic algorithm, which can ensure the diversity of the population and provide a rich solution space for the subsequent optimization process. These initial solutions will serve as the basic individuals in the genetic algorithm optimization process and participate in the subsequent fitness evaluation and genetic operations.

[0136] As a possible implementation, the fitness module includes: a fitness construction submodule.

[0137] The fitness construction submodule is used to construct a fitness function based on the deviation between the load capacity of the access point and the power demand capacity, the interference deviation of the access point to the existing load of the power grid after the access point is connected to the user, the construction cost of the access point to the user and the total transmission efficiency of the power grid.

[0138] Specifically, by incorporating multiple key factors into the fitness function, the pros and cons of each access point can be comprehensively evaluated. Taking into account factors such as load capacity, grid stability, construction cost, and transmission efficiency, the fitness function can not only optimize the location and capacity matching of access points, but also effectively avoid unnecessary burdens on the grid or excessive construction costs. This multi-dimensional evaluation ensures that the selected access point maximizes the optimization of grid resources while meeting user needs, improving the overall efficiency and stability of the grid.

[0139] As a possible implementation, the genetic module includes: a selection submodule, a first operation submodule, a second operation submodule, a calculation submodule and a detection submodule.

[0140] A selection submodule, configured to select a preset number of access points from each first target population based on the fitness of each access point, to obtain a fifth target population, wherein the selection method includes one or more of a tournament selection, a roulette selection, and an elite selection;

[0141] Specifically, a new population is formed by selecting suitable individuals from the first target population according to the fitness of the access point. Access points with higher fitness represent better adaptability to the power grid, and are therefore selected preferentially. The selection method may include: tournament selection is to randomly select multiple individuals to form a competition group, and select the individuals with the strongest fitness to join the new population. Roulette selection is to select individuals based on the ratio of the individual's fitness value to the total fitness, and individuals with higher fitness have a greater probability of being selected. Elite selection is to directly retain the individuals with the best fitness to the next generation to ensure that the algorithm does not lose excellent solutions.

[0142] A first operation submodule is used to perform a crossover operation and a mutation operation in sequence based on the fifth target population and the first target population to obtain a second target population;

[0143] Specifically, crossover and mutation are two core genetic operations in genetic algorithms, which are used to explore new solution spaces. The crossover operation is to form new individuals by exchanging individual genes in the fifth target population, thereby generating new possible solutions. The mutation operation is to randomly modify individual genes to generate different gene combinations to avoid local concentration in the solution space.

[0144] A second operation submodule is used to perform an immigration operation based on each second target population to obtain multiple sixth target populations;

[0145] Specifically, the immigration operation introduces external individuals or exchanges individuals with other populations to break the excessive concentration within the population and enhance the diversity of the population. It avoids excessive convergence between local populations and enhances the global search ability of the genetic algorithm.

[0146] An operation submodule, configured to perform operations based on each sixth target population through a preset constraint matrix to verify whether an access point in the sixth target population violates a preset constraint condition, and delete the access point that violates the preset constraint condition to obtain a seventh target population;

[0147] Specifically, to ensure the rationality of the access point, the algorithm checks whether each individual meets the operating constraints of the power grid (such as load capacity, regional load balancing, etc.). Individuals that violate the constraints will be deleted or replaced to ensure that the final solution meets the actual power grid needs.

[0148] The detection submodule is used to perform forgetting detection and stagnation detection in sequence based on the seventh target population to obtain a third target population, wherein, for the forgetting detection, if the detection is passed, the number of iterations increases, and if the detection is not passed, the number of iterations decreases, and for the stagnation detection, if the detection is passed, the number of stagnations is reset to zero, and if the detection is not passed, the number of stagnations increases.

[0149] Specifically, forgetting detection is to monitor the change of the solution and determine whether the solution is "forgotten". If the current solution does not bring obvious improvement, the number of iterations is reduced. Stagnation detection is to mark the population as stagnant and increase the number of stagnation if no new optimal solution appears after a certain number of iterations to prevent the waste of computing resources.

[0150] The access point processing device for the access request of a large-capacity user of the power grid includes a processor and a memory. The first construction unit, the acquisition unit, the calculation unit and the sending unit are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement the corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0151] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to solve the problems of low efficiency, poor accuracy, unreasonable resource allocation, insufficient dynamic adjustment, etc. in the prior art of manually processing large-capacity user access requests.

[0152] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0153] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the access point processing method for the access request of a large-capacity user of the power grid.

[0154] An embodiment of the present invention provides a power grid operation system, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include an access point processing method for executing the above-mentioned power grid large-capacity user access request.

[0155] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0156] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0157] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0160] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0161] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0162] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0163] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0164] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0165] 1) The access point processing method for large-capacity user access requests of the power grid of this application can significantly improve the processing efficiency and accuracy of power grid access requests. Through the construction of a digital power grid model and the application of intelligent algorithms, the system can automatically and accurately calculate the optimal access point, automatically generate construction work orders and push them to users, simplifying the subsequent construction process, reducing waiting time, and improving user experience. Overall, this method not only improves the efficiency of power grid resource allocation, but also optimizes the overall experience of the user access process, and solves the problems of low efficiency, poor accuracy, unreasonable resource allocation, and insufficient dynamic adjustment in the manual processing of large-capacity user access requests in the prior art.

[0166] 2) The access point processing device for large-capacity user access requests of the power grid of the present application comprises: a first construction unit, an acquisition unit, a calculation unit and a sending unit. The first construction unit is used to construct a digital power grid model based on the comprehensive parameters of the power grid to obtain a first target model, and the comprehensive parameters at least include the physical equipment, network topology, operation parameters and environmental factors of the power grid; the acquisition unit is used to obtain power application data, and the power application data at least includes user coordinates, power demand properties and power demand capacity, and the power demand properties are used to characterize the purpose of electricity; the calculation unit is used to determine the target access point of the user's power supply line in the power grid according to the digital power grid model and the power application data through the UVV task allocation method based on the multi-population genetic algorithm; the sending unit is used to create a construction work order based on the target access point and push it to the user. The device realizes the automatic calculation based on the digital power grid model and the multi-population genetic algorithm through the intelligent power grid access point processing method, greatly improves the processing efficiency and accuracy of the power grid access request, reduces the risk of manual intervention and human error, and optimizes the configuration of power grid resources. The invention solves the problems of low efficiency, poor accuracy, unreasonable resource allocation, insufficient dynamic adjustment, etc. in the manual processing of large-capacity user access requests in the prior art.

[0167] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for processing access point requests of large-capacity users in a power grid, characterized in that: include: Based on comprehensive parameters of the power grid, a digital power grid model is constructed to obtain a first target model, wherein the comprehensive parameters at least include physical equipment, network topology, operating parameters and environmental factors of the power grid; Acquiring electricity application data, wherein the electricity application data at least includes user coordinates, electricity demand properties and electricity demand capacity, wherein the electricity demand properties are used to characterize the use of electricity; According to the digital power grid model and the power application data, a UVV task allocation method based on a multi-population genetic algorithm is used to calculate and determine a target access point of the user's power supply line in the power grid; A construction work order is created based on the target access point and pushed to the user.

2. The method according to claim 1, characterized in that: Based on the comprehensive parameters of the power grid, a digital power grid model is constructed to obtain the first target model, including: Analyze based on the comprehensive parameters, extract the power grid equipment parameters in the comprehensive parameters, and construct a simulation model of the power equipment based on the power grid equipment parameters to obtain a power grid equipment model, wherein the power grid equipment parameters include equipment parameters, operating parameters and fault responses of each power equipment in the power grid; Analyzing based on the comprehensive parameters, extracting the network layout of the power grid in the comprehensive parameters, and constructing a topological model of the power grid based on the network layout to obtain a power grid topological model; Analyze based on the comprehensive parameters, extract the real-time operation data in the comprehensive parameters, and obtain the operation parameters, wherein the operation parameters include voltage, current, power and frequency; Performing analysis based on the comprehensive parameters, extracting external environment data in the comprehensive parameters, and obtaining the environmental factors, wherein the environmental factors include weather changes, natural disasters, and fluctuations in application demand; Based on the power grid equipment model, the power grid topology model, the operating parameters and the environmental factors, the power grid is simulated through a mathematical model to achieve real-time analysis of the power grid and obtain the first target model.

3. The method according to claim 2, characterized in that After obtaining the electricity application data, the method further includes: Performing an integrity check based on the electricity application data to determine whether there are missing values ​​in the electricity application data, and sending first target information to the user if there are missing values, the first target information being used to prompt the user to re-upload the electricity application data; Performing analysis based on the electricity application data to extract coordinate values ​​in the electricity application data, and converting the coordinate values ​​into a preset format; Based on the converted coordinate values, position matching is performed with the power grid topology model in the first target model to obtain a target position association.

4. The method according to claim 3, characterized in that: According to the digital power grid model and the power application data, a UVV task allocation method based on a multi-population genetic algorithm is used to calculate and determine a target access point of the user power supply line in the power grid, including: Initializing a population based on access points included in the digital power grid model to obtain a plurality of first target populations; A first calculation step, calculating the fitness corresponding to each access point in the first target population through a fitness function based on the association between the electricity application data and the target location; A second calculation step is to perform a local genetic operation within each of the first target populations to obtain a second target population, and to perform a global genetic operation on all of the second target populations to obtain a third target population; The first calculation step and the second calculation step are sequentially repeated at least once until the number of stagnation times reaches a preset value or the number of iterations reaches a maximum number of iterations, and the access points corresponding to the maximum fitness values ​​in all the third target populations are determined as the target access points.

5. The method according to claim 4, characterized in that Initializing a population based on the access points included in the digital power grid model to obtain a plurality of first target populations, including: Based on the grid area division of the grid topology model in the digital grid grid model, the counting points in each grid area are divided into the same population to obtain a plurality of fourth target populations; An access point is randomly selected from each of the fourth target populations as an initial solution of the fourth target population to obtain a first target population.

6. The method according to claim 4, characterized in that Calculating the fitness corresponding to each access point in the first target population by using a fitness function based on the electricity application data includes: The fitness function is constructed based on the deviation of the load capacity of the access point and the power demand capacity, the interference deviation of the existing load of the power grid after the access point accesses the user, the construction cost of the access point accessing the user and the total transmission efficiency of the power grid.

7. The method according to claim 4, characterized in that Performing a local genetic operation within each of the first target populations to obtain a second target population, and performing a global genetic operation on all of the second target populations to obtain a third target population, including: Based on the fitness of each of the access points, select a preset number of access points from each of the first target populations to obtain a fifth target population, wherein the selection method includes one or more of tournament selection, roulette selection, and elite selection; Performing a crossover operation and a mutation operation in sequence based on the fifth target population and the first target population to obtain the second target population; Performing an immigration operation based on each of the second target populations to obtain a plurality of sixth target populations; Performing operations based on each of the sixth target populations through a preset constraint matrix to verify whether an access point in the sixth target population violates a preset constraint condition, and deleting the access point that violates the preset constraint condition to obtain a seventh target population; Based on the seventh target population, forgetting detection and stagnation detection are performed in sequence to obtain the third target population, wherein, if the forgetting detection passes, the number of iterations increases, and if the detection fails, the number of iterations decreases, and if the stagnation detection passes, the number of stagnations is reset to zero, and if the detection fails, the number of stagnations increases.

8. An access point processing device for access requests of large-capacity users in a power grid, characterized in that: include: A first construction unit is used to construct a digital power grid model based on comprehensive parameters of the power grid to obtain a first target model, wherein the comprehensive parameters at least include physical equipment, network topology, operating parameters and environmental factors of the power grid; An acquisition unit, used to acquire electricity application data, wherein the electricity application data at least includes user coordinates, electricity demand properties and electricity demand capacity, wherein the electricity demand properties are used to characterize the use of electricity; A calculation unit, configured to determine a target access point of a user's power supply line in the power grid by performing calculations based on the digital power grid model and the power application data through a UVV task allocation method based on a multi-population genetic algorithm; The sending unit is used to create a construction work order based on the target access point and push it to the user.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A power grid operation system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

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