Power grid planning method, system and device and computer readable storage medium

Abstract

The invention discloses a power grid planning method, system and equipment and a computer readable storage medium, and aims to greatly reduce manual operation and improve data processing accuracy and efficiency by automatically collecting and processing GIS map data, a power grid operation state and load prediction data. And path planning is performed by combining an A algorithm with a GIS technology, so that the path is optimal and feasible, and the planning period is remarkably shortened. The system comprises a data acquisition module, a problem definition module, a path planning module, a cost calculation module, a path optimization module, a scheme generation module, a visual display module and the like, all the modules work cooperatively, and systematization and standardization of the planning process are achieved. Through an intelligent optimization algorithm, the planning scheme quality is improved, multiple factors are comprehensively considered, and it is ensured that the scheme is scientific, reasonable and economical. The modular design enables the system to be easy to expand and optimize, adapts to power grid planning problems of different scales and complexities, improves the adaptability and flexibility of planning, and provides a comprehensive and efficient solution for power grid planning.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a power grid planning method, system, device and computer-readable storage medium. Background Art

[0002] Grid planning is an important part of the power system, involving the layout design of the transmission and distribution networks, equipment selection, line path determination, etc. Traditional grid planning methods usually rely on the experience and expertise of planners to manually design line paths and estimate engineering costs.

[0003] Traditional power grid planning methods have the following significant problems:

[0004] 1) Large workload and low efficiency: Power grid planning involves a lot of data analysis and calculation work, such as the collection and processing of geographic information, the selection and evaluation of line paths, etc. Manual operation is not only time-consuming and labor-intensive, but also prone to errors, affecting work efficiency.

[0005] 2) Reliance on experience and lack of systematicity: Traditional methods rely heavily on the personal experience of planners, and there may be large differences between different planners, resulting in inconsistent quality and reliability of planning schemes. In addition, manual operations make it difficult to systematically consider all influencing factors, and the rationality and scientificity of planning results are often difficult to guarantee.

[0006] 3) Inaccurate cost estimation: The cost of power grid projects involves equipment, materials, construction and other aspects. Traditional cost estimation is usually based on experience and historical data, which is difficult to accurately reflect the current market situation, resulting in large deviations in cost estimation and affecting the scientific nature of investment decisions.

[0007] 4) Poor adaptability: With the continuous growth of electricity demand and the expansion of power grid scale, power grid planning faces a more complex environment and higher requirements. Traditional methods are difficult to adapt to such changes and cannot respond to various power grid problems in a timely and effective manner.

[0008] Therefore, there is an urgent need for a power grid planning method, system, device and readable storage medium to solve the problems of low efficiency, lack of scientificity, inaccurate cost estimation, poor adaptability and human errors in the current power grid planning method. Summary of the invention

[0009] In order to solve the above problems, the present invention provides a power grid planning method, system, device and computer-readable storage medium. The method combines a path planning algorithm, a geographic information system (GIS) and an engineering cost database to provide an efficient and intelligent power grid planning solution suitable for the construction and transformation of transmission and distribution networks.

[0010] A first aspect of an embodiment of the present invention discloses a power grid planning method, comprising the following steps:

[0011] Step S1, obtaining GIS map data, power grid operation status data, and load forecast data;

[0012] Step S2: define the power grid problem to be solved based on the collected data; formulate planning objectives and constraints for solving the problem based on the power grid problem to be solved;

[0013] Step S3: Use the A algorithm to perform path planning and generate a planned path in combination with GIS map data;

[0014] Step S4, combining the planned path and the grid cost database data, and using the cost calculation formula B to calculate the construction cost;

[0015] Step S5, integrating the path planning results and the engineering cost estimation results to generate a power grid planning scheme; wherein the generated power grid planning scheme at least includes: a detailed path planning diagram and an engineering cost budget table;

[0016] Step S6: Optimize the planned path using genetic algorithm and particle swarm algorithm to further reduce the path length and cost.

[0017] Preferably, the method further comprises the following steps:

[0018] Step S7: Provide a GIS-based visualization tool to display the planned path and project cost distribution.

[0019] Preferably, when the path planning is performed using the A algorithm in step S3, the following steps are included:

[0020] SA, initialization step, in which the start and end nodes are defined and the cost estimates of the nodes are initialized;

[0021] SB, search step, in which the node with the minimum cost is selected from the open list for expansion in combination with GIS map data, adjacent nodes are generated, and the node cost and parent node information are updated;

[0022] SC, a path generation step, in which the search step is repeated until the end node is found or the open list is empty, and then the planned path is obtained by backtracking from the end node; wherein the planned path takes into account the terrain, roads and power grid equipment locations based on the GIS map data.

[0023] Preferably, step A comprises the following steps:

[0024] SA1, define the starting node SSS and the ending node GGG;

[0025] SA2, add the starting node SSS to the open list and initialize the cost estimate of each node;

[0026] Among them, for the starting node SSS, its actual cost g(S) is initialized to 0, that is, g(S) = 0; for the terminal node GGG, h(S) is defined as the heuristic estimated cost from the starting node to the terminal node, and the heuristic estimated cost is calculated based on GIS map data using Euclidean distance or Manhattan distance;

[0027] Preferably, step B comprises the following steps:

[0028] SB1. Select the node nnn with the minimum cost from the open list, and define the cost calculation formula of node nnn as f(n)=g(n)+h(n), where g(n) is the actual cost from the starting node to node nnn, and h(n) is the estimated cost from node nnn to the end node;

[0029] SB2, move node nnn to the closed list;

[0030] SB3, generate the adjacent nodes of node nnn, and calculate the cost of each adjacent node by considering the terrain, roads and the location of existing power grid equipment in combination with GIS map data;

[0031] SB4. Define each adjacent node as mmm and its cost as g(m); for each adjacent node mmm, if it is in the open list or closed list, update its cost g(m) and parent node information; otherwise, add it to the open list and set its parent node to nnn.

[0032] Preferably, the construction costs in step S4 at least include: line erection costs, substation construction costs, material costs, and labor costs; the power grid cost database data at least includes: information on cost standards for various types of equipment, materials, and construction;

[0033] The calculation formula B in step S4 is:

[0034] Total cost

[0035] Among them, Cline is the line construction cost, Cstation is the substation construction cost, Cmaterial is the material cost, and Clabor is the labor cost.

[0036] Preferably, step S6 of "optimizing the planned path using a genetic algorithm or a particle swarm algorithm to further reduce the path length and cost" includes:

[0037] S61, initializing the population: generating a number of initial paths as population individuals;

[0038] S62, fitness function: define a fitness function fff, whose value is a comprehensive evaluation value of path cost and construction cost, and the formula is: f(path)=w1×path_cost+w2×construction_cost; where w1 and w2 are weight coefficients, path_cost is the path cost, and construction_cost is the construction cost;

[0039] S63, crossover and mutation: Perform crossover and mutation operations on selected individuals to generate a new population;

[0040] S64, Iteration: Repeat the selection, crossover and mutation operations until convergence to the optimal path.

[0041] A second aspect of an embodiment of the present invention discloses a power grid planning system, which adopts the power grid planning method described in the first aspect, and includes:

[0042] Data acquisition module 1: used to obtain GIS map data, power grid operation status data and load forecast data to provide basic information support for subsequent planning;

[0043] Problem definition module 2: It is used to define the specific problems that need to be solved in the power grid planning process based on the collected data, and to formulate clear planning goals and corresponding constraints based on these problems to ensure the rationality and feasibility of the planning scheme;

[0044] Path planning module 3: used to use A algorithm for path planning and generate a preliminary planning path in combination with GIS map data;

[0045] Cost calculation module 4: Combine the planned path and the grid cost database data to calculate the construction cost using the cost calculation formula;

[0046] Solution generation module 5: integrates the path planning results and engineering cost estimation results to generate a power grid planning solution;

[0047] Path optimization module 6: Use genetic algorithm, particle swarm algorithm and other intelligent optimization algorithms to further optimize the planned path to reduce the path length and reduce the cost;

[0048] Visualization display module 7: Provides GIS-based visualization tools to display the planned path and project cost distribution;

[0049] Among them, the path planning module 3 includes: an initialization submodule 31 for defining the starting point, end point nodes and initializing the node cost estimation value, a search submodule 32 for selecting the node with the smallest cost from the open list for expansion and updating the node information, and a path generation submodule 33 for finding the end point node by backtracking and generating the final planned path.

[0050] A third aspect of an embodiment of the present invention discloses a computer device, comprising: a memory 100, a processor 200, and executable instructions stored in the memory and executable in the processor, wherein the processor implements the method described in the first aspect when executing the executable instructions.

[0051] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium having computer-executable instructions stored thereon, wherein the executable instructions, when executed by a processor, implement the method described in the first aspect.

[0052] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0053] 1. Embodiment 1 of this case greatly reduces the workload of manual operation and improves the accuracy and efficiency of data processing by automatically collecting and processing GIS map data, power grid operation status data and load forecast data. By using algorithm A for path planning, multiple feasible planning paths can be quickly generated, which significantly shortens the planning cycle compared to traditional manual design. Moreover, the method of this case defines the power grid problem based on the collected data, and formulates clear planning goals and constraints, making the planning process more systematic and standardized. Combining algorithm A and GIS technology, multiple factors such as geographic information and load demand are systematically considered to ensure the scientificity and rationality of the planning scheme and reduce dependence on manual experience. By optimizing the path and cost through the algorithm, multiple influencing factors can be comprehensively considered, the rationality and scientificity of the planning scheme can be improved, and the uncertainty caused by human experience can be reduced. Combining the power grid cost library data and cost calculation formula B, the construction cost can be estimated more accurately, reflecting the current market situation, and providing a scientific basis for investment decisions. And through the optimization algorithm in step S6, the path and engineering costs are further optimized, the total cost is reduced, and the economy and feasibility are improved. Through the joint setting of steps S2, S3 and S6, the method of this case can define the problem, formulate planning goals and constraints, use algorithm A for preliminary path planning, and use the optimization algorithm to dynamically adjust and optimize the planned path, so as to flexibly respond to the complex environment and changing demands brought about by the expansion of the power grid scale and changes in demand, and improve the adaptability of the plan.

[0054] 2. This case uses the A algorithm combined with GIS map data for path planning to ensure the optimality and feasibility of path planning; the A algorithm can efficiently find the optimal path from the starting point to the end point, especially when considering complex terrain, roads and obstacles. By using the heuristic evaluation function, the A algorithm can reduce unnecessary searches as much as possible, thereby improving search efficiency. In addition, combined with GIS map data, the A algorithm can generate a more accurate and reasonable path planning solution.

[0055] 3. The second embodiment of this case quickly collects the required data through the data acquisition module, providing a data basis for subsequent planning. The problem definition module clarifies the planning objectives and constraints, and improves the pertinence and efficiency of the planning process. The path planning module uses the efficient A algorithm for path planning, which can quickly generate a preliminary planning path. The cost calculation module and the path optimization module comprehensively consider the path length and construction costs, and optimize the planning scheme in terms of economy and feasibility. The quality of the planning scheme is further improved by the application of intelligent optimization algorithms such as genetic algorithms and particle swarm algorithms. The detailed planning scheme and budget table generated by the scheme generation module provide decision makers with comprehensive planning results. The visualization display module helps decision makers better understand the planning scheme through intuitive graphical displays, improving the accuracy and efficiency of decision-making. The entire system adopts a modular design, and each module is relatively independent and cooperative, which is easy to expand and optimize according to actual needs, so that the system can adapt to power grid planning problems of different scales and complexities, and improve the adaptability and flexibility of planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 It is a schematic diagram of the process of the first embodiment of the present invention;

[0058] Figure 2 It is a structural block diagram of the second embodiment of the present invention.

[0059] Figure 3 It is a structural diagram of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including 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.

[0062] The embodiments of the present invention disclose a power grid planning method, system, device and computer-readable storage medium, which can simplify the identification construction process and improve the identification efficiency, and are also conducive to the precise tuning of the identification method in the later stage.

[0063] Embodiment 1

[0064] like Figure 1 As shown, a power grid planning method includes the following steps:

[0065] Step S1, obtaining GIS map data, power grid operation status data, and load forecast data;

[0066] Step S2: Based on the collected data, define the grid problems that need to be solved; formulate planning objectives and constraints for solving the problems based on the grid problems that need to be solved; the grid problems that need to be solved include: new transmission lines, site selection for distribution stations, etc. Clarifying planning objectives and constraints includes: minimizing engineering costs, meeting load requirements, etc.

[0067] Step S3: Use the A algorithm to perform path planning and generate a planned path in combination with GIS map data; the A algorithm is a shortest path algorithm based on heuristic search.

[0068] Step S4, combining the planned path and the grid cost database data, and using the cost calculation formula B to calculate the construction cost;

[0069] Step S5, integrating the path planning results and the engineering cost estimation results to generate a power grid planning scheme; wherein the generated power grid planning scheme at least includes: a detailed path planning diagram and an engineering cost budget table;

[0070] Step S6: Optimize the planned path using genetic algorithm and particle swarm algorithm to further reduce the path length and cost. In the specific implementation, the GIS map data obtained includes: power grid equipment location, terrain, roads and other information. Accurate geographic data can be obtained through the Geographic Information System (GIS) platform. The collection of power grid operation status and load forecast data is obtained by analyzing the power monitoring system and historical data.

[0071] As mentioned above, this case greatly reduces the workload of manual operations and improves the accuracy and efficiency of data processing by automatically collecting and processing GIS map data, power grid operation status data and load forecast data. By using the A algorithm for path planning, multiple feasible planning paths can be quickly generated, which significantly shortens the planning cycle compared to traditional manual design. Moreover, the method of this case defines the power grid problem based on the collected data, and formulates clear planning goals and constraints, making the planning process more systematic and standardized. Combining the A algorithm and GIS technology, multiple factors such as geographic information and load demand are systematically considered to ensure the scientificity and rationality of the planning scheme and reduce dependence on manual experience. By optimizing the path and cost through the algorithm, multiple influencing factors can be comprehensively considered, the rationality and scientificity of the planning scheme can be improved, and the uncertainty caused by human experience can be reduced. Combining the power grid cost library data and the cost calculation formula B, the construction cost can be estimated more accurately, reflecting the current market situation, and providing a scientific basis for investment decisions. And through the optimization algorithm in step S6, the path and engineering costs are further optimized, the total cost is reduced, and the economy and feasibility are improved. Through the joint setting of steps S2, S3 and S6, the method of this case can define the problem, formulate planning goals and constraints, use algorithm A for preliminary path planning, and use the optimization algorithm to dynamically adjust and optimize the planned path, so as to flexibly respond to the complex environment and changing demands brought about by the expansion of the power grid scale and changes in demand, and improve the adaptability of the plan.

[0072] As a preferred implementation, the automatic power grid planning method of this case also includes:

[0073] Step S7: Provide a GIS-based visualization tool to display the planned path and project cost distribution. In this way, providing a GIS-based visualization tool can intuitively display the planned path and project cost distribution, which is convenient for planners to adjust and optimize according to actual conditions.

[0074] As a preferred implementation method, the specific implementation steps of the A algorithm path planning are as follows:

[0075] SA, initialization step, in which the starting and ending nodes are defined and the cost estimates of the nodes are initialized; the details are as follows:

[0076] SA1, define the starting node SSS and the ending node GGG;

[0077] SA2, add the starting node SSS to the open list and initialize the cost estimate of each node;

[0078] Among them, for the starting node SSS, its actual cost g(S) is initialized to 0, that is, g(S)=0g(S)=0g(S)=0; for the terminal node GGG, h(S) is defined as the heuristic estimated cost from the starting node to the terminal node, and the heuristic estimated cost is calculated based on GIS map data using Euclidean distance or Manhattan distance;

[0079] SB, search step, in which the node with the lowest cost is selected from the open list for expansion in combination with GIS map data, adjacent nodes are generated, and the node cost and parent node information are updated; the details are as follows:

[0080] SB1. Select the node nnn with the minimum cost from the open list, and define the cost calculation formula of node nnn as f(n)=g(n)+h(n), where g(n) is the actual cost from the starting node to node nnn, and h(n) is the estimated cost from node nnn to the end node;

[0081] SB2, move node nnn to the closed list;

[0082] SB3, generate the adjacent nodes of node nnn, and calculate the cost of each adjacent node by considering the terrain, roads and the location of existing power grid equipment in combination with GIS map data;

[0083] SB4. Define each adjacent node as mmm and its cost as g(m); for each adjacent node mmm, if it is in the open list or closed list, update its cost g(m) and parent node information; otherwise, add it to the open list and set its parent node to nnn.

[0084] SC, a path generation step, in which the search step is repeated until the end node is found or the open list is empty, and then the planned path is obtained by backtracking from the end node; wherein the planned path takes into account the terrain, roads and power grid equipment locations based on the GIS map data to improve the feasibility and rationality of the path.

[0085] As mentioned above, the A algorithm is used in combination with GIS map data for path planning to ensure the optimality and feasibility of path planning; the A algorithm can efficiently find the optimal path from the starting point to the end point, especially when considering complex terrain, roads and obstacles. By using the heuristic evaluation function, the A algorithm can reduce unnecessary searches as much as possible, thereby improving search efficiency. In addition, combined with GIS map data, the A algorithm can generate more accurate and reasonable path planning solutions.

[0086] As a preferred implementation, the construction costs in step S4 at least include: line erection costs, substation construction costs, material costs, and labor costs; the power grid cost database data at least includes: information on cost standards for various types of equipment, materials, and construction;

[0087] The calculation formula B for calculating the cost in step S4 is:

[0088] Total cost

[0089] Among them, Cline is the line construction cost, Cstation is the substation construction cost, Cmaterial is the material cost, and Clabor is the labor cost.

[0090] As mentioned above, using the cost database data to automatically calculate the costs of various projects ensures the accuracy and reliability of cost estimates, optimizes investment decisions, and reduces budget deviations. Using the cost calculation formula B and clearly listing all cost components, as well as using standard data in the power grid cost database to calculate the cost of each part, can greatly improve the accuracy of cost calculation.

[0091] As a preferred implementation, step S6 of "optimizing the planned path using a genetic algorithm and a particle swarm algorithm to further reduce the path length and cost" includes:

[0092] S61. Initialize the population:

[0093] Generate several initial paths as population individuals.

[0094] This step is the starting point of the genetic algorithm, which generates a set of initial paths randomly or based on a certain strategy as the starting point of the algorithm search.

[0095] S62, fitness function:

[0096] Define the fitness function f(path), whose value is the comprehensive evaluation value of path cost and construction cost.

[0097] The formula is: f(path) = w1×path_cost+w2×construction_cost, where w1 and w2 are weight coefficients, path_cost is the path cost, and construction_cost is the construction cost. The fitness function is used to evaluate the quality of each path individual and is the basis for selection in the genetic algorithm.

[0098] S63, Crossover and Mutation:

[0099] Perform crossover and mutation operations on the selected individuals to generate a new population.

[0100] Crossover operation is one of the main ways to generate new individuals in genetic algorithms, which generates offspring individuals by exchanging some genes of two parent individuals.

[0101] The mutation operation randomly changes part of an individual's genes to increase the diversity of the population.

[0102] S64, Iteration:

[0103] The selection, crossover and mutation operations are repeated until the optimal path is converged.

[0104] The iterative process continuously evolves the population, gradually approaching the optimal solution through operations such as selection, crossover and mutation.

[0105] Among them, genetic algorithm and particle swarm algorithm are both optimization algorithms based on swarm intelligence, which have the characteristics of parallel search and global optimization.

[0106] As mentioned above, by combining genetic algorithm and particle swarm algorithm, we can comprehensively consider the path length and construction cost, find a more economical and reasonable path planning solution, which not only considers the short distance of the path, but also the construction cost, thus achieving the dual optimization of path planning and engineering economy. Through the iterative process, the algorithm can continuously evolve the population and gradually approach the optimal solution, thereby improving the search efficiency and accuracy. By introducing crossover and mutation operations, the algorithm can jump out of the local optimal solution and avoid falling into the problem of premature convergence. In addition, genetic algorithm and particle swarm algorithm have good scalability and can be improved and optimized according to actual needs to adapt to more complex path planning problems.

[0107] Embodiment 2

[0108] like Figure 2 As shown, a power grid planning system adopts the power grid planning method described in Example 1, including:

[0109] Data acquisition module 1: used to obtain GIS map data, power grid operation status data and load forecast data to provide basic information support for subsequent planning;

[0110] Problem definition module 2: It is used to define the specific problems that need to be solved in the power grid planning process based on the collected data, and to formulate clear planning goals and corresponding constraints based on these problems to ensure the rationality and feasibility of the planning scheme;

[0111] Path planning module 3: used to use A algorithm for path planning and generate a preliminary planning path in combination with GIS map data;

[0112] Cost calculation module 4: Combine the planned path and the grid cost database data to calculate the construction cost using the cost calculation formula;

[0113] Solution generation module 5: integrates the path planning results and engineering cost estimation results to generate a power grid planning solution;

[0114] Path optimization module 6: Use genetic algorithm, particle swarm algorithm and other intelligent optimization algorithms to further optimize the planned path to reduce the path length and reduce the cost;

[0115] Visualization display module 7: Provides GIS-based visualization tools to display the planned path and project cost distribution;

[0116] Among them, the path planning module 3 includes: an initialization submodule 31 for defining the starting point, end point nodes and initializing the node cost estimation value, a search submodule 32 for selecting the node with the smallest cost from the open list for expansion and updating the node information, and a path generation submodule 33 for finding the end point node by backtracking and generating the final planned path.

[0117] As mentioned above, the required data is quickly collected through the data acquisition module, providing a data basis for subsequent planning. The problem definition module clarifies the planning objectives and constraints, improving the pertinence and efficiency of the planning process. The path planning module uses the efficient A algorithm for path planning, which can quickly generate a preliminary planning path. The cost calculation module and the path optimization module comprehensively consider the path length and construction costs, and optimize the planning scheme in terms of economy and feasibility. The quality of the planning scheme is further improved by the application of intelligent optimization algorithms such as genetic algorithms and particle swarm algorithms. The detailed planning scheme and budget table generated by the scheme generation module provide decision makers with comprehensive planning results. The visualization display module helps decision makers better understand the planning scheme through intuitive graphical displays, improving the accuracy and efficiency of decision-making. The entire system adopts a modular design, and each module is relatively independent and cooperative, which is easy to expand and optimize according to actual needs, so that the system can adapt to power grid planning problems of different scales and complexities, and improve the adaptability and flexibility of planning.

[0118] Embodiment 3

[0119] like Figure 3 As shown, a computer device includes: a memory 100, a processor 200, and executable instructions stored in the memory and executable in the processor, and the processor implements the method described in Example 1 when executing the executable instructions.

[0120] Embodiment 4

[0121] A computer-readable storage medium stores computer-executable instructions, wherein the executable instructions, when executed by a processor, implement the method described in Embodiment 1.

[0122] In summary, the present invention significantly improves the efficiency, accuracy and adaptability of power grid planning through intelligent and automated means, optimizes the overall layout and operation effect of the power grid, and provides strong technical support for the construction and transformation of the power grid.

[0123] The above is a detailed introduction to a power grid planning method, system, device and computer-readable storage medium disclosed in an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in the field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A power grid planning method, characterized in that: The following steps are involved: Step S1, obtaining GIS map data, power grid operation status data, and load forecast data; Step S2: define the power grid problem to be solved based on the collected data; formulate planning objectives and constraints for solving the problem based on the power grid problem to be solved; Step S3: Use the A algorithm to perform path planning and generate a planned path in combination with GIS map data; Step S4, combining the planned path and the grid cost database data, and using the cost calculation formula B to calculate the construction cost; Step S5, integrating the path planning results and the engineering cost estimation results to generate a power grid planning scheme; wherein the generated power grid planning scheme at least includes: a detailed path planning diagram and an engineering cost budget table; Step S6: Optimize the planned path using genetic algorithm and particle swarm algorithm to further reduce the path length and cost.

2. The power grid planning method according to claim 1, characterized in that: The following steps are also included: Step S7: Provide a GIS-based visualization tool to display the planned path and project cost distribution.

3. The power grid planning method according to claim 1, characterized in that: When the path planning is performed using the A algorithm in step S3, the following steps are included: SA, initialization step, in which the start and end nodes are defined and the cost estimates of the nodes are initialized; SB, search step, in which the node with the lowest cost is selected from the open list for expansion in combination with GIS map data, adjacent nodes are generated, and the node cost and parent node information are updated; SC, a path generation step, in which the search step is repeated until the end node is found or the open list is empty, and then the planned path is obtained by backtracking from the end node; wherein the planned path takes into account the terrain, roads and power grid equipment locations based on the GIS map data.

4. The power grid planning method according to claim 3, characterized in that: Step A includes the following steps: SA1, define the starting node SSS and the ending node GGG; SA2, add the starting node SSS to the open list and initialize the cost estimate of each node; Among them, for the starting node SSS, its actual cost g(S) is initialized to 0, that is, g(S) = 0; for the end node GGG, it is defined as h(S) as the heuristic estimated cost from the starting node to the end node, and the heuristic estimated cost is calculated based on GIS map data using Euclidean distance or Manhattan distance.

5. The power grid planning method according to claim 3 or 4, characterized in that: Step B includes the following steps: SB1. Select the node nnn with the minimum cost from the open list, and define the cost calculation formula of node nnn as f(n)=g(n)+h(n), where g(n) is the actual cost from the starting node to node nnn, and h(n) is the estimated cost from node nnn to the end node; SB2, move node nnn to the closed list; SB3, generate the adjacent nodes of node nnn, and calculate the cost of each adjacent node by considering the terrain, roads and the location of existing power grid equipment in combination with GIS map data; SB4. Define each adjacent node as mmm and its cost as g(m); for each adjacent node mmm, if it is in the open list or closed list, update its cost g(m) and parent node information; otherwise, add it to the open list and set its parent node to nnn.

6. The power grid planning method according to claim 1, characterized in that: The construction costs in step S4 at least include: line erection costs, substation construction costs, material costs, and labor costs; the power grid cost database data at least includes: information on cost standards for various types of equipment, materials, and construction; The calculation formula B for calculating the cost in step S4 is: Total cost Among them, Cline is the line construction cost, Cstation is the substation construction cost, Cmaterial is the material cost, and Clabor is the labor cost.

7. The power grid planning method according to claim 1 or 6, characterized in that: Step S6 of "optimizing the planned path using genetic algorithm and particle swarm algorithm to further reduce the path length and cost" includes: S61, initializing the population: generating a number of initial paths as population individuals; S62, fitness function: define a fitness function fff, whose value is a comprehensive evaluation value of path cost and construction cost, and the formula is: f(path)=w1×path_cost+w2×construction_cost; where w1 and w2 are weight coefficients, path_cost is the path cost, and construction_cost is the construction cost; S63, crossover and mutation: Perform crossover and mutation operations on selected individuals to generate a new population; S64, Iteration: Repeat the selection, crossover and mutation operations until convergence to the optimal path.

8. A power grid planning system, characterized in that: The power grid planning method according to any one of claims 1 to 7 comprises: Data acquisition module (1): used to obtain GIS map data, power grid operation status data and load forecast data to provide basic information support for subsequent planning; Problem definition module (2): used to define specific problems that need to be solved in the power grid planning process based on the collected data, and to formulate clear planning goals and corresponding constraints based on these problems to ensure the rationality and feasibility of the planning scheme; Path planning module (3): used to perform path planning using the A algorithm and generate a preliminary planned path in combination with GIS map data; Cost calculation module (4): combining the planned path and the grid cost database data, and using the cost calculation formula to calculate the construction cost; Solution generation module (5): integrates the path planning results and the engineering cost estimation results to generate a power grid planning solution; Path optimization module (6): using genetic algorithm, particle swarm algorithm and other intelligent optimization algorithms to further optimize the planned path to reduce the path length and reduce the cost; Visualization display module (7): provides GIS-based visualization tools to display the planned path and project cost distribution; The path planning module (3) includes: an initialization submodule (31) for defining a starting point, an end point node and initializing an estimated value of a node cost; a search submodule (32) for selecting a node with the smallest cost from an open list for expansion and updating node information; and a path generation submodule (33) for finding an end point node by backtracking and generating a final planned path.

9. A computer device comprising: A memory (100), a processor (200), and executable instructions stored in the memory and executable in the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the executable instructions.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the executable instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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