An intelligent substation site selection and line selection planning method, system, device and medium

Abstract

The present invention relates to a method, system, device and medium for intelligent site selection and route selection planning of a substation, belonging to the field of substation site selection, and includes: using unmanned aerial vehicle (UAV) oblique photography to collect information of a target site selection area and generate a three-dimensional model, then determining multiple environmentally sensitive areas and their sensitivity weights based on the three-dimensional model, and simultaneously obtaining load demand data and construction cost data of the area. Then, a multi-objective evaluation model is established to optimize multiple candidate site selection schemes obtained to obtain the optimal site selection scheme. Finally, route selection planning is carried out on the optimal site selection scheme based on an adaptive algorithm, and the optimal route selection scheme is obtained by adjusting the route selection strategy in real time. This application solves the technical problem that the existing substation site selection and route selection planning lack the support of intelligent algorithms, resulting in low planning accuracy and efficiency, and achieves the technical effect of improving the accuracy and efficiency of substation project site selection and route selection planning through collaborative optimization of intelligent algorithms.

Description

Technical Field

[0001] The present invention relates to the field of substation site selection, and particularly to a method, system, device and medium for intelligent substation site selection and line selection planning. Background Art

[0002] With the acceleration of the urbanization process and the continuous growth of power demand, the site selection and line selection planning of substation projects have become increasingly important. The site selection and line selection planning of substations are key links in the construction of power systems, and their rationality directly affects the reliability, economy and environmental impact of the entire power system.

[0003] Currently, the site selection and line selection planning of substations mainly rely on manual experience for judgment and decision-making. This traditional manual planning method has the following deficiencies: First, planners need to comprehensively consider multiple factors such as geographical environment, load demand, construction cost, etc. It is easy to generate deviations when manually processing such complex multi-dimensional information, resulting in low planning accuracy; Second, the manual planning method requires a large amount of time for scheme comparison and adjustment, and the planning efficiency is low; Third, in actual projects, environmental conditions and load demands often change dynamically, and it is difficult for traditional manual planning methods to respond to these changes in a timely manner and make flexible adjustments. Therefore, the lack of intelligent algorithm support in the existing technology for substation site selection and line selection planning leads to low planning accuracy and efficiency. Summary of the Invention

[0004] In view of the technical problem that the lack of intelligent algorithm support in the existing technology for substation site selection and line selection planning leads to low planning accuracy and efficiency, the present invention provides a method, system, device and medium for intelligent substation site selection and line selection planning to solve this problem.

[0005] The technical solutions of the present invention to solve the above technical problems are as follows:

[0006] In the first aspect, the present invention provides a method for intelligent substation site selection and line selection planning, including: using an unmanned aerial vehicle (UAV) oblique photography to collect information on the target site selection area, obtaining the geographical information data of the target site selection area, and generating a three-dimensional model of the target area based on the geographical information data; determining multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtaining the regional sensitivity weights of the multiple environmentally sensitive areas; obtaining the load demand data and construction cost data of the target site selection area; establishing a multi-objective evaluation model, and based on the geographical information data, the regional sensitivity weights of the multiple environmentally sensitive areas, the load demand data and the construction cost data, combining the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area, and optimizing the multiple candidate site selection schemes to obtain the optimal site selection scheme; performing line selection planning on the optimal site selection scheme based on an adaptive algorithm, and obtaining the optimal line selection scheme by adjusting the line selection strategy in real time.

[0007] In a second aspect, the present invention provides a substation intelligent site selection and line selection planning system, comprising: a three-dimensional model establishment module, configured to collect information of a target site selection area by using drone oblique photography, obtain geographical information data of the target site selection area, and generate a three-dimensional model of the target area according to the geographical information data; an environmental impact determination module, configured to determine a plurality of environmentally sensitive areas based on the three-dimensional model of the target area, and obtain the regional sensitivity weights of the plurality of environmentally sensitive areas; a regional data acquisition module, configured to acquire load demand data and construction cost data of the target site selection area; an optimal site selection determination module, configured to establish a multi-objective evaluation model, and based on the geographical information data, the regional sensitivity weights of the plurality of environmentally sensitive areas, the load demand data, and the construction cost data, combine the multi-objective evaluation model and an improved multi-objective genetic algorithm to obtain a plurality of candidate site selection schemes in the target site selection area, and optimize the plurality of candidate site selection schemes to obtain an optimal site selection scheme; an optimal line selection determination module, configured to perform line selection planning on the optimal site selection scheme based on an adaptive algorithm, and obtain an optimal line selection scheme by adjusting the line selection strategy in real time.

[0008] In a third aspect, the present application provides an electronic device, which includes: a processor; a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the substation intelligent site selection and line selection planning method provided by the present application.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium, storing a computer program, and the computer program is configured to execute the substation intelligent site selection and line selection planning method provided by the present application.

[0010] The beneficial effects of the present invention are as follows:

[0011] Use unmanned aerial vehicle (UAV) oblique photography to collect information on the target site selection area, obtain the geographical information data of the target site selection area, generate a three-dimensional model of the target area based on the geographical information data, and accurately grasp the geographical environment characteristics of the site selection area; determine multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtain the regional sensitivity weights of the multiple environmentally sensitive areas to quantitatively evaluate the environmental impact of the site selection; obtain the load demand data and construction cost data of the target site selection area to provide important parameters for subsequent optimization calculations; establish a multi-objective evaluation model, combine the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area, and optimize the multiple candidate site selection schemes to obtain the optimal site selection scheme, realizing the automatic optimization of the site selection scheme; perform route planning on the optimal site selection scheme based on the adaptive algorithm, and obtain the optimal route plan by adjusting the route strategy in real time to ensure the optimal route planning result in a complex dynamic environment. Through the above technical solutions, multiple intelligent algorithms are coordinated to achieve the technical effect of improving the accuracy and efficiency of substation engineering site selection and route planning through collaborative optimization of intelligent algorithms. Brief Description of the Drawings

[0012] Figure 1 It is a schematic flowchart of a substation intelligent site selection and route planning method provided by the present invention;

[0013] Figure 2 It is a schematic structural diagram of a substation intelligent site selection and route planning system provided by the present invention;

[0014] Figure 3 It is a schematic structural diagram of an electronic device provided by the present invention;

[0015] Figure 4 It is a schematic structural diagram of a computer-readable storage medium provided by the present invention.

[0016] In the drawings, the components represented by each reference numeral are as follows:

[0017] 11. Three-dimensional model establishment module; 12. Environmental impact determination module; 13. Regional data acquisition module; 14. Optimal site selection determination module; 15. Optimal route determination module; 200. Electronic device; 210. Memory; 220. Processor; 211. First computer program; 300. Computer-readable storage medium; 311. Second computer program. Detailed Embodiments

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.

[0019] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0020] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present invention.

[0021] Embodiment 1:

[0022] As Figure 1 shown, the embodiment of the present invention provides a method for intelligent site selection and route selection planning of a substation, including:

[0023] S100: Use an unmanned aerial vehicle (UAV) oblique photography to collect information on the target site selection area, obtain the geographic information data of the target site selection area, and generate a three-dimensional model of the target area according to the geographic information data.

[0024] Specifically, first, geographic information of the target site selection area is collected through the drone oblique photography technology. Geographic information system software (such as ArcGIS, QGIS) is used to process the collected oblique photography data to obtain geographic information data including terrain, land use type, road and building distribution, etc., and its accuracy can reach within 0.1 meter. Then, a three-dimensional model of the target site selection area is generated based on the obtained geographic information data. This three-dimensional model can be used to identify the locations of key environmentally sensitive areas such as ecological protection areas, residential areas, rivers, etc., providing data support for subsequent environmental impact assessment. At the same time, the three-dimensional model can also be used to analyze the characteristics of the site such as slope, flatness and obstacles, providing a basis for the estimation of construction costs.

[0025] By obtaining the three-dimensional model of the target area, geographic constraint conditions are provided for subsequent site selection and route selection planning to ensure the applicability and scientific nature of the site selection process.

[0026] S200: Determine multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtain the regional sensitivity weights of the multiple environmentally sensitive areas.

[0027] Specifically, first, by analyzing the already generated three-dimensional model of the target area, the locations of key environmentally sensitive areas such as ecological protection areas, residential areas, rivers, etc. are identified to determine multiple environmentally sensitive areas. Then, different protection weights are set for different types of environmentally sensitive areas. Specifically, the weights are assigned based on the regional nature. For example, the weight of the ecological protection area can be set to 1.0, the residential area to 0.6, and the industrial area to 0.3.

[0028] By establishing the regional sensitivity weights of multiple environmentally sensitive areas, environmental constraint conditions are provided for subsequent site selection assessment to ensure that different environmentally sensitive areas can be reasonably protected during the site selection process.

[0029] S300: Obtain the load demand data and construction cost data of the target site selection area.

[0030] Specifically, for the load demand data, first, the historical power grid data and urban planning data are collected as basic data. Then, the time series analysis method is used to conduct a stationarity test on the historical load data, and the ARIMA model is used to capture the trend and seasonal characteristics of the load change. At the same time, machine learning methods such as random forest are used to incorporate multi-dimensional influencing factors such as climate conditions and urban development planning into the prediction model. Finally, prediction models with three time scales of 5 years, 10 years, and 15 years are established respectively to determine the load demand data.

[0031] For the construction cost data, first analyze the slope, flatness and obstacle distribution of the site based on the acquired 3D model, evaluate the construction conditions and difficulty coefficient, then estimate the land acquisition cost in combination with the local land use plan and market price. At the same time, obtain the latest price data of building materials, equipment, etc. through market research, and obtain relevant planning requirements from the urban planning department, so as to determine the construction cost data.

[0032] By obtaining the load demand data and construction cost data, it provides an important basis for the subsequent establishment of a multi-objective evaluation model and scheme optimization.

[0033] S400: Establish a multi-objective evaluation model. Based on the geographic information data, the regional sensitivity weights of multiple environmentally sensitive areas, the load demand data and the construction cost data, combine the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area, and optimize the multiple candidate site selection schemes to obtain the optimal site selection scheme.

[0034] Specifically, first, establish a multi-objective evaluation model for substation site selection, including a construction cost evaluation model, an environmental impact evaluation model and a power supply reliability evaluation model. The construction cost evaluation model mainly considers land acquisition costs, construction costs and material costs; the environmental impact evaluation model is evaluated based on the weights and distances of environmentally sensitive areas; the power supply reliability evaluation model is evaluated through load coverage and power supply losses.

[0035] Then, use an improved multi-objective genetic algorithm to optimize the site selection scheme. During the optimization process, first generate an initial scheme population, perform non-dominated sorting and crowding degree calculation on the schemes in the population through the multi-objective evaluation model, and generate new schemes through dynamic mutation and generalization mutation strategies. Through multiple iterations of optimization, the optimal site selection scheme is finally obtained, realizing the automatic optimization of the site selection scheme.

[0036] S500: Based on the adaptive algorithm, conduct route planning for the optimal site selection scheme, and obtain the optimal route selection scheme by adjusting the route selection strategy in real time.

[0037] Specifically, after obtaining the optimal site selection scheme, use the adaptive algorithm to plan and design the substation line.

[0038] First, perform initialization and parameter settings, and set the initial weight for all candidate paths i→j , where i and j represent the starting point and ending point of the path respectively; select the number m of candidate path schemes; set the weight iteration rate and the upper and lower limits of the weight .

[0039] At the same time, determine the objective function for route selection positioning, and use the following formula:

[0040]

[0041] Among them, C is the total line cost, E is the environmental impact of the line, and R is the power supply reliability. 、 、 are the target weights, which are used to balance the priorities of different targets.

[0042] Then, path construction and selection are carried out. The next node j is selected based on the following formula with probability:

[0043]

[0044] Among them, is the weight on the current path at time t, is the heuristic information (such as the reciprocal of the path length), are the weight factors, which respectively control the relative importance of the weight and the heuristic information, represents the set of optional next nodes of node i.

[0045] When the selection of the path i→j is completed, the weight on this path needs to be updated:

[0046]

[0047] Among them, is the control factor for weight update, is the newly added weight value on the path. To ensure the stability of the algorithm, it is necessary to ensure that the updated weight remains within the set interval, that is, .

[0048] In the actual execution process, the algorithm perceives external changes by obtaining dynamic data such as new load demands and geographical obstacle changes in real time. To adapt to environmental changes, the weight iteration rate is dynamically adjusted:

[0049]

[0050] Among them, t is the current iteration number, is the preset maximum iteration number. Such a design ensures that the iteration rate gradually decreases during the later search process, which is beneficial to the convergence of the algorithm.

[0051] To improve the reliability of line planning, a redundancy optimization model is introduced:

[0052]

[0053] Among them, represents the optimization cost of line redundancy, is the actual length of the i-th line, is the number of alternative paths in the i-th line, is the minimum number of alternative paths required by the system, represents the redundancy cost coefficient, which is used to balance the relationship between the line length and the number of alternative paths.

[0054] During the iterative optimization process, when it is detected that the current path plan is significantly affected by the environment (such as the emergence of new geographical obstacles or load changes), the algorithm will perform local perturbations to search for a better solution again. The entire optimization process stops when the maximum number of iterations is reached or the optimal path remains unchanged for several consecutive times, and the final optimal line plan is output. If the environmental conditions continue to change, the system will re-execute the path planning process based on the latest acquired environmental data.

[0055] Through the application of the adaptive algorithm, the dynamic optimization of the line planning is realized, and the adaptability of the planning scheme to complex environments is improved.

[0056] Furthermore, the embodiments of the present application further include:

[0057] S410: Establish a construction cost evaluation model, an environmental impact evaluation model, and a power supply reliability evaluation model;

[0058] S420: Summarize the construction cost evaluation model, the environmental impact evaluation model, and the power supply reliability evaluation model to generate the multi-objective evaluation model.

[0059] In a preferred embodiment, first, three evaluation models are established respectively, namely a construction cost evaluation model, an environmental impact evaluation model, and a power supply reliability evaluation model.

[0060] The construction cost evaluation model adopts the following formula:

[0061]

[0062] Wherein, is the total cost, is the land acquisition cost (depending on the location and area), is the construction cost (determined by the terrain complexity and construction conditions), is the material cost.

[0063] The environmental impact evaluation model adopts the following formula:

[0064]

[0065] Wherein, is the environmental sensitivity weight of region i, and di is the distance from the substation to region i.

[0066] The power supply reliability evaluation model uses the following formula:

[0067]

[0068] Among them, R is the power supply reliability, f is the covered load demand, F is the total load demand, and L is the power supply loss.

[0069] Preferably, in order to achieve a balance between multiple goals, a risk assessment model is introduced:

[0070]

[0071] Among them, is the total risk assessment value of the site selection plan, is the occurrence probability of the i-th type of risk event, is the impact degree of the i-th type of risk event.

[0072] At the same time, a dynamic weight adjustment model is adopted:

[0073]

[0074] Among them, is the dynamic weight of the i-th goal at time t, is the initial weight, is the sensitivity coefficient, is the current constraint variable, is the maximum value of the constraint variable.

[0075] Through the comprehensive application of the above models, a complete multi-objective evaluation model is formed to provide support for the subsequent evaluation of the site selection plan.

[0076] Furthermore, the embodiments of the present application further include:

[0077] S430: Generate multiple first site selection plans based on a preset population size to form a first population;

[0078] S440: Evaluate the multiple first site selection plans according to the multi-objective evaluation model to obtain multiple first plan scores;

[0079] S450: Perform non-dominated sorting and crowding degree calculation on the multiple first site selection plans according to the multiple first plan scores, and determine multiple first mutation plans based on the non-dominated sorting result and the crowding degree calculation result;

[0080] S460: Perform mutation operations on the multiple first mutation plans based on the mutation probability to obtain multiple first mutated plans, and evaluate the multiple first mutated plans according to the multi-objective evaluation model to obtain multiple first mutated plan scores;

[0081] S470: Select multiple second site selection plans from the multiple first site selection plans and the multiple first mutated site selection plans according to the multiple first plan scores and the multiple first mutated plan scores to form a second population;

[0082] S480: Iteratively execute. When the preset number of iterations is reached, obtain the Nth population, where the Nth population has multiple Nth site selection plans;

[0083] S490: Use the multiple Nth site selection plans as multiple candidate site selection plans.

[0084] In a preferred embodiment, first, multiple first site selection plans are generated within the target site selection area according to the preset population size. Each site selection plan includes information such as the site selection location, floor area, distance relationship with the environmentally sensitive area, and load coverage. These first site selection plans constitute the first population. Then, the established multi-objective evaluation model is used to evaluate each site selection plan in the first population. The specific evaluation process includes calculating the construction cost score, environmental impact score, and power supply reliability score, and combining the dynamic weights to obtain the comprehensive score of each plan as the first plan score. Next, non-dominated sorting is performed based on the obtained first plan scores, and the site selection plans in the first population are stratified according to the quality. At the same time, the crowding degree of each site selection plan is calculated to ensure the diversity of the population. According to the non-dominated sorting result and the crowding degree calculation result, multiple first candidate mutated plans are selected.

[0085] After that, mutation operations are performed on the selected first candidate mutated plans to generate multiple first mutated plans. Then, these mutated plans are evaluated using the multi-objective evaluation model to obtain multiple first mutated plan scores. Next, according to the first plan scores and the first mutated plan scores, high-quality plans are selected from the first site selection plans and the first mutated site selection plans to form a second population. The selection process follows the elitist strategy to ensure that excellent individuals are retained. Repeat the above steps for population iteration until the preset number of iterations is reached to obtain the Nth population containing multiple Nth site selection plans. Finally, the site selection plans in the Nth population are used as candidate site selection plans to lay a foundation for subsequent plan optimization.

[0086] Through the coordinated cooperation of the above steps, the optimization process of the site selection plan based on the improved multi-objective genetic algorithm is realized, and multiple candidate site selection plans are determined to provide support for obtaining the optimal site selection plan.

[0087] Further, the mutation probability gradually increases as the number of iterations increases.

[0088] Specifically, during the execution of the improved multi-objective genetic algorithm, in order to improve the global search ability of the algorithm, the initial value of the mutation probability is set to 0.1. As the number of iterations increases, the mutation probability will gradually increase. By gradually increasing the mutation probability with the increase of the number of iterations, in the initial stage of the algorithm, a smaller mutation probability is adopted to maintain the basic characteristics of the population and avoid the loss of excellent genes caused by excessive mutation; in the middle stage of the algorithm, the mutation probability gradually increases, enhancing the diversity of the population, expanding the search space, and improving the ability of the algorithm to jump out of the local optimum; in the later stage of the algorithm, a larger mutation probability can inject new vitality into the population, prevent the algorithm from converging prematurely, and continuously maintain the ability to search for new solutions.

[0089] Through the dynamic mutation probability, while maintaining the stability of the population, the global search ability of the algorithm is enhanced, and the optimization effect of the site selection scheme is improved.

[0090] Furthermore, in the mutation operation process, a generalization mutation strategy is adopted, and each mutation candidate solution is perturbed and mutated multiple times according to the preset number of perturbation times.

[0091] In a preferred embodiment, when performing the mutation operation on the mutation candidate solution, a generalization mutation strategy is adopted. For example, the preset number of perturbation times is set to 10, and each mutation candidate solution is perturbed and mutated multiple times. Through generalization mutation, in each mutation operation, the same mutation candidate solution is perturbed to different degrees multiple times, thereby generating multiple mutated solutions. Through multiple perturbations, exploration can be carried out in different directions of the mutation candidate solution, increasing the diversity of the mutation results. Each perturbation makes a slight change on the basis of the original solution, including fine-tuning of the site selection location, changes in the floor area, etc. This way of multiple small-scale perturbations can explore the solution space more comprehensively and increase the possibility of finding a better solution.

[0092] Through the application of the generalization mutation strategy, the effectiveness of the mutation operation is improved, and the optimization performance of the algorithm is enhanced.

[0093] Furthermore, the embodiments of the present application further include:

[0094] S4100: Select the optimal candidate site selection solution according to multiple candidate site selection solutions;

[0095] S4110: Randomly select two candidate site selection solutions from the multiple candidate site selection solutions as the first random candidate site selection solution and the second random candidate site selection solution;

[0096] S4120: Based on the preset mutation formula, combine the optimal candidate site selection solution, the first random candidate site selection solution, and the second random candidate site selection solution to generate a mutated candidate site selection solution;

[0097] S4130: Evaluate the optimal candidate site selection plan and the mutated candidate site selection plan according to the multi-objective evaluation model, and determine whether the mutated candidate site selection plan is superior to the optimal candidate site selection plan;

[0098] S4140: Cross the mutated candidate site selection plan and the optimal candidate site selection plan to obtain an updated candidate site selection plan;

[0099] S4150: When the updated candidate site selection plan is superior to the optimal candidate site selection plan, use the updated candidate site selection plan as the optimal candidate site selection plan;

[0100] S4160: When the updated candidate site selection plan is not superior to the optimal candidate site selection plan, keep the optimal candidate site selection plan;

[0101] S4170: Iteratively execute. When the preset number of evolution times is reached, use the optimal candidate site selection plan as the most optimal site selection plan.

[0102] In a preferred embodiment, first, from multiple candidate site selection plans, according to the scoring results of the multi-objective evaluation model, select the plan with the highest score as the optimal candidate site selection plan. Then, randomly select two plans from the remaining candidate site selection plans as the first random candidate site selection plan and the second random candidate site selection plan respectively. The purpose of random selection is to increase the diversity of plan search. Subsequently, use a preset mutation formula, combined with the optimal candidate site selection plan, the first random candidate site selection plan, and the second random candidate site selection plan, to generate a mutated candidate site selection plan. Mutation operations can generate new candidate plans and expand the search space. Next, use the multi-objective evaluation model to evaluate the optimal candidate site selection plan and the mutated candidate site selection plan. By comparing the evaluation results, determine whether the mutated candidate site selection plan is superior to the optimal candidate site selection plan.

[0103] Next, perform a crossover operation on the mutated candidate site selection plan and the optimal candidate site selection plan to generate an updated candidate site selection plan. Crossover operations can inherit the excellent features of the two parent plans. By comparing the evaluation results, if the updated candidate site selection plan is superior to the optimal candidate site selection plan, then use the updated candidate site selection plan as the new optimal candidate site selection plan; otherwise, keep the original optimal candidate site selection plan unchanged. Repeat the above steps for iterative optimization. When the preset number of evolution times is reached, determine the final optimal candidate site selection plan as the most optimal site selection plan.

[0104] Through the above iterative execution, continuously optimize the site selection plan, and finally obtain the most optimal site selection plan that meets the multi-objective requirements.

[0105] Furthermore, the preset mutation formula is:

[0106]

[0107] Among them, is the mutation candidate site selection scheme, is the optimal candidate site selection scheme, is the mutation factor, is the first random candidate site selection scheme, is the second random candidate site selection scheme.

[0108] In a preferred embodiment, the preset mutation formula is:

[0109]

[0110] Among them, represents the mutation candidate site selection scheme generated through the mutation operation, including specific information such as the site location and floor area of the substation; represents the current optimal candidate site selection scheme, which is the scheme with the highest score in the previous optimization process; represents the mutation factor, which is used to control the intensity of the mutation, and its value range is usually between 0 and 1; represents the first random candidate site selection scheme randomly selected from the candidate site selection schemes; represents the second random candidate site selection scheme randomly selected from the candidate site selection schemes.

[0111] Taking the current optimal candidate site selection scheme as the benchmark, the perturbation direction and magnitude are determined through the difference information of the two randomly selected schemes and . The term represents a difference vector in the solution space, and multiplying it by the mutation factor can control the amplitude of the perturbation. Adding this perturbation term to the optimal candidate site selection scheme can generate a new candidate scheme near the optimal candidate site selection scheme.

[0112] Mutating the optimal candidate site selection scheme through the above preset mutation formula ensures that the newly generated scheme will not deviate too far from the current optimal candidate site selection scheme; taking the difference between the two randomly selected schemes as the perturbation term increases the randomness and diversity of the search; through the adjustment of the mutation factor, the intensity of the perturbation can be flexibly controlled, achieving a balance between local search and global search, and effectively improving the search ability and optimization effect of the algorithm.

[0113] The substation intelligent site selection and line selection planning method provided by the embodiments of the present invention has at least the following technical effects:

[0114] Use unmanned aerial vehicle (UAV) oblique photography to collect information on the target site selection area, obtain the geographical information data of the target site selection area, generate a three-dimensional model of the target area based on the geographical information data, visually display the area characteristics, and facilitate analysis and evaluation. Determine multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtain the regional sensitivity weights of the multiple environmentally sensitive areas to ensure that the site selection plan meets the environmental protection requirements. Obtain the load demand data and construction cost data of the target site selection area to provide important information for subsequent site selection evaluation; establish a multi-objective evaluation model, combine the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection plans in the target site selection area, and optimize the multiple candidate site selection plans to obtain the optimal site selection plan and find the best balance point among multiple objectives. Based on the adaptive algorithm, conduct route planning for the optimal site selection plan, and obtain the optimal route plan by adjusting the route selection strategy in real time to ensure finding the optimal route plan in a complex and changeable environment, thereby improving the accuracy and efficiency of substation project site selection and route planning.

[0115] Embodiment 2:

[0116] As Figure 2 shown, based on the same inventive concept as the substation intelligent site selection and route planning method provided in Embodiment 1, the embodiment of the present invention further provides a substation intelligent site selection and route planning system, including:

[0117] A three-dimensional model establishment module 11, configured to use UAV oblique photography to collect information on the target site selection area, obtain the geographical information data of the target site selection area, and generate a three-dimensional model of the target area based on the geographical information data;

[0118] An environmental impact determination module 12, configured to determine multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtain the regional sensitivity weights of the multiple environmentally sensitive areas;

[0119] A regional data acquisition module 13, configured to obtain the load demand data and construction cost data of the target site selection area;

[0120] An optimal site selection determination module 14, configured to establish a multi-objective evaluation model, based on the geographical information data, the regional sensitivity weights of the multiple environmentally sensitive areas, the load demand data and the construction cost data, combine the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection plans in the target site selection area, and optimize the multiple candidate site selection plans to obtain the optimal site selection plan;

[0121] An optimal route determination module 15, configured to conduct route planning for the optimal site selection plan based on the adaptive algorithm, and obtain the optimal route plan by adjusting the route selection strategy in real time.

[0122] Further, the optimal site selection determination module 14 includes the following execution steps:

[0123] Establish a construction cost evaluation model, an environmental impact evaluation model, and a power supply reliability evaluation model;

[0124] Summarize the construction cost evaluation model, the environmental impact evaluation model, and the power supply reliability evaluation model to generate the multi-objective evaluation model.

[0125] Further, the optimal site selection determination module 14 further includes the following execution steps:

[0126] Generate a plurality of first site selection plans based on a preset population size to form a first population;

[0127] Evaluate the plurality of first site selection plans according to the multi-objective evaluation model to obtain a plurality of first plan scores;

[0128] Perform non-dominated sorting and crowding degree calculation on the plurality of first site selection plans according to the plurality of first plan scores, and determine a plurality of first candidate mutation plans based on the non-dominated sorting result and the crowding degree calculation result;

[0129] Perform a mutation operation on the plurality of first candidate mutation plans based on a mutation probability to obtain a plurality of first mutation plans, and evaluate the plurality of first mutation plans according to the multi-objective evaluation model to obtain a plurality of first mutation plan scores;

[0130] Select a plurality of second site selection plans from the plurality of first site selection plans and the plurality of first mutation plans according to the plurality of first plan scores and the plurality of first mutation plan scores to form a second population;

[0131] Iteratively execute. When a preset number of iterations is reached, obtain the Nth population, where the Nth population has a plurality of Nth site selection plans;

[0132] Use the plurality of Nth site selection plans as a plurality of candidate site selection plans.

[0133] Further, the mutation probability gradually increases as the number of iterations increases.

[0134] Further, the mutation operation process adopts a generalization mutation strategy, and performs multiple perturbation mutations on each candidate mutation plan according to a preset number of perturbation times.

[0135] Further, the optimal site selection determination module 14 further includes the following execution steps:

[0136] Select an optimal candidate site selection plan according to the plurality of candidate site selection plans;

[0137] Randomly select two candidate site selection schemes from the multiple candidate site selection schemes as the first random candidate site selection scheme and the second random candidate site selection scheme;

[0138] Based on a preset mutation formula, combine the optimal candidate site selection scheme, the first random candidate site selection scheme, and the second random candidate site selection scheme to generate a mutated candidate site selection scheme;

[0139] According to the multi-objective evaluation model, evaluate the optimal candidate site selection scheme and the mutated candidate site selection scheme to determine whether the mutated candidate site selection scheme is superior to the optimal candidate site selection scheme;

[0140] Cross the mutated candidate site selection scheme and the optimal candidate site selection scheme to obtain an updated candidate site selection scheme;

[0141] When the updated candidate site selection scheme is superior to the optimal candidate site selection scheme, use the updated candidate site selection scheme as the optimal candidate site selection scheme;

[0142] When the updated candidate site selection scheme is not superior to the optimal candidate site selection scheme, keep the optimal candidate site selection scheme;

[0143] Iteratively execute. When the preset number of evolution times is reached, use the optimal candidate site selection scheme as the optimal site selection scheme.

[0144] Further, the preset mutation formula is:

[0145]

[0146] Wherein, is the mutated candidate site selection scheme, is the optimal candidate site selection scheme, is the mutation factor, is the first random candidate site selection scheme, is the second random candidate site selection scheme.

[0147] Embodiment III:

[0148] Please refer to Figure 3 , Figure 3 which is the schematic diagram of the embodiment of the electronic device provided by the embodiment of the present invention. As Figure 3 shown, an electronic device 200 provided by the embodiment of the present invention includes a memory 210, a processor 220, and a first computer program 211 stored on the memory 210 and executable on the processor 220. When the processor 220 executes the first computer program 211, it implements the intelligent substation site selection and line selection planning method.

[0149] Embodiment IV:

[0150] Please refer toFigure 4 , Figure 4 This is a schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. As Figure 4 shown, this embodiment provides a computer-readable storage medium 300, on which a second computer program 311 is stored. When the second computer program 311 is executed by a processor, it implements a substation intelligent site selection and line selection planning method.

[0151] It should be noted that in the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0152] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0154] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide means for implementing the functions in the processFigure 1 One or more processes and / or boxes Figure 1 Steps of the functions specified in one or more boxes

[0156] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic inventive concept.

[0157] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent substation site selection and line selection planning method, characterized in that, Including: Using an unmanned aerial vehicle (UAV) oblique photography to collect information on the target site selection area, obtaining the geographical information data of the target site selection area, and generating a three-dimensional model of the target area based on the geographical information data; Determining multiple environmentally sensitive areas based on the three-dimensional model of the target area, and obtaining the regional sensitivity weights of the multiple environmentally sensitive areas; Obtaining the load demand data and construction cost data of the target site selection area; Establishing a multi-objective evaluation model, based on the geographical information data, the regional sensitivity weights of the multiple environmentally sensitive areas, the load demand data and the construction cost data, combining the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area, and optimizing the multiple candidate site selection schemes to obtain the optimal site selection scheme; Performing a route planning on the optimal site selection scheme based on an adaptive algorithm, and obtaining the optimal route scheme by adjusting the route strategy in real time; Among them, combining the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection schemes in the target site selection area includes: Generating multiple first site selection schemes based on a preset population size to form a first population; Evaluating the multiple first site selection schemes respectively according to the multi-objective evaluation model to obtain multiple first scheme scores; Performing non-dominated sorting and crowding degree calculation on the multiple first site selection schemes according to the multiple first scheme scores, and determining multiple first schemes to be mutated based on the non-dominated sorting result and the crowding degree calculation result; Performing a mutation operation on the multiple first schemes to be mutated based on a mutation probability to obtain multiple first mutated schemes, and evaluating the multiple first mutated schemes respectively according to the multi-objective evaluation model to obtain multiple first mutated scheme scores; wherein, the mutation probability gradually increases with the increase of the iteration times; Selecting multiple second site selection schemes from the multiple first site selection schemes and the multiple first mutated schemes according to the multiple first scheme scores and the multiple first mutated scheme scores to form a second population; Performing iterative execution, and when the preset iteration times are reached, obtaining the Nth population, wherein the Nth population has multiple Nth site selection schemes; Taking the multiple Nth site selection schemes as multiple candidate site selection schemes.

2. The intelligent substation site selection and line selection planning method according to claim 1, wherein Establishing a multi-objective evaluation model includes: Establishing a construction cost evaluation model, an environmental impact evaluation model, and a power supply reliability evaluation model; Summarizing the construction cost evaluation model, the environmental impact evaluation model and the power supply reliability evaluation model to generate the multi-objective evaluation model.

3. The intelligent substation site selection and line selection planning method according to claim 1, characterized in that The mutation operation process adopts a generalization mutation strategy, and performs multiple perturbation mutations on each scheme to be mutated according to a preset number of perturbation times.

4. The intelligent substation site selection and line selection planning method according to claim 1, characterized in that Optimizing the multiple candidate site selection schemes to obtain the optimal site selection scheme includes: Selecting the optimal candidate site selection scheme according to the multiple candidate site selection schemes; Randomly selecting two candidate site selection schemes from the multiple candidate site selection schemes as the first random candidate site selection scheme and the second random candidate site selection scheme; Generating a mutated candidate site selection scheme based on a preset mutation formula, combining the optimal candidate site selection scheme, the first random candidate site selection scheme and the second random candidate site selection scheme; Evaluate the optimal candidate site selection plan and the mutated candidate site selection plan according to the multi-objective evaluation model, and determine whether the mutated candidate site selection plan is superior to the optimal candidate site selection plan; Cross the mutated candidate site selection plan and the optimal candidate site selection plan to obtain an updated candidate site selection plan; When the updated candidate site selection plan is superior to the optimal candidate site selection plan, use the updated candidate site selection plan as the optimal candidate site selection plan; When the updated candidate site selection plan is not superior to the optimal candidate site selection plan, keep the optimal candidate site selection plan; Execute iteratively. When the preset number of evolution times is reached, use the optimal candidate site selection plan as the optimal site selection plan.

5. The intelligent substation site selection and line selection planning method according to claim 4, characterized in that The preset mutation formula is: Among them, is a candidate site selection scheme for mutation, is the optimal candidate site selection scheme, is a mutation factor and is the first randomly selected candidate site selection scheme, is the second randomly selected candidate site selection scheme.

6. An intelligent substation site selection and line selection planning system, characterized in that, For implementing the substation intelligent site selection and line selection planning method according to any one of claims 1-5, including: A three-dimensional model establishment module, which is used to collect information on the target site selection area by using drone oblique photography, obtain the geographical information data of the target site selection area, and generate a three-dimensional model of the target area according to the geographical information data; An environmental impact determination module, which is used to determine multiple environmentally sensitive areas based on the three-dimensional model of the target area and obtain the regional sensitivity weights of the multiple environmentally sensitive areas; A regional data acquisition module, which is used to acquire the load demand data and construction cost data of the target site selection area; An optimal site selection determination module, which is used to establish a multi-objective evaluation model, based on the geographical information data, the regional sensitivity weights of multiple environmentally sensitive areas, the load demand data and the construction cost data, combine the multi-objective evaluation model and the improved multi-objective genetic algorithm to obtain multiple candidate site selection plans in the target site selection area, and optimize the multiple candidate site selection plans to obtain the optimal site selection plan; An optimal line selection determination module, which is used to perform line selection planning on the optimal site selection plan based on the adaptive algorithm, and obtain the optimal line selection plan by adjusting the line selection strategy in real time.

7. An electronic device, characterized in that, Including: A memory for storing computer software programs; A processor for reading and executing the computer software program, thereby implementing the substation intelligent site selection and line selection planning method according to any one of claims 1-5.

8. A non-transitory computer-readable storage medium, characterized in that, The computer software program is stored in the storage medium, and when the computer software program is executed by the processor, the substation intelligent site selection and line selection planning method according to any one of claims 1-5 is implemented.

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