Comprehensive management method and system for grazing-light complementary wildfire disaster resistance of mountain photovoltaic power station

Abstract

The invention belongs to the technical field of mountain photovoltaic power station operation management, and particularly relates to a grazing-light complementary wildfire disaster resistance comprehensive management method and system for a mountain photovoltaic power station. The method comprises the following steps: identifying image data of each photovoltaic power station by using an image identification network model to obtain the growth condition of current weeds and shrubs under each photovoltaic array; taking multiple optimization targets as a target function, and taking internal facilities, matrix positions, road conditions and uncertain factors of the photovoltaic power station as constraint conditions; wherein the weather condition, the growth condition of weeds and shrubs and the fault position and time of the photovoltaic module serve as uncertain factors of the photovoltaic power station; and solving the objective function by adopting a multi-objective optimization algorithm to obtain an optimal comprehensive management strategy. The multiple optimization targets comprise the operation and maintenance mode of the mountain photovoltaic power station, the cleaning frequency and route of the photovoltaic module, the goat grazing route and the optimal weeding frequency. The intelligent comprehensive management of the mountain photovoltaic power station is realized, the operation and maintenance efficiency is improved, and the wildfire risk is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation and management of mountain photovoltaic power stations, and specifically relates to a comprehensive management method and system for pastoral-photovoltaic complementary protection against wildfire disasters in mountain photovoltaic power stations. Background Art

[0002] With the growing global demand for renewable energy, photovoltaic power stations, as an important component of clean energy, are increasingly being built, especially in underutilized land such as mountains. However, the construction and operation of mountain photovoltaic power stations face many challenges, such as complex terrain, sensitive ecological environment, and difficult operation and maintenance. Especially in some arid and semi-arid areas, weeds and bushes around mountain photovoltaic power stations are prone to become a source of wildfire risk, posing a serious threat to the safe operation of power stations. In order to meet these challenges, traditional mountain photovoltaic power station management methods often focus on a single operation and maintenance or fire prevention measure, lacking comprehensive considerations. For example, some power stations use regular manual weeding to reduce the risk of wildfires, but this method is not only inefficient and costly, but can also damage the ecological environment around the power station. At the same time, due to the complex mountain terrain, it is difficult for operation and maintenance personnel to quickly reach every corner of the power station, resulting in low operation and maintenance efficiency and safety hazards. In recent years, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, new possibilities have been provided for the intelligent management of mountain photovoltaic power stations. By constructing a mathematical model of a photovoltaic power station, it is possible to accurately describe the internal facilities, array positions and road conditions of the power station, providing basic data support for subsequent operation and maintenance and fire prevention management. At the same time, image recognition technology can be used to monitor the growth of weeds and shrubs in the power station in real time, providing a scientific basis for determining the frequency of weeding. In addition, combined with eco-friendly management methods such as goat grazing, it can not only reduce weeding costs, but also promote the ecological balance around the power station.

[0003] In the field of photovoltaic power generation, the operation and maintenance management strategies of onshore photovoltaic power stations have become increasingly mature, but the comprehensive operation and maintenance management strategies of mountain photovoltaic power stations involving fire prevention and goat breeding are still in urgent need of exploration. Take Nanpiao District, Huludao City, Liaoning Province as an example. The area is located in a hilly area, and the mountains are roughly distributed in a grid shape. Before the large-scale installation of photovoltaic power stations in the mountains, several large wildfires had occurred in the area, and goat breeding had not been carried out on a large scale. With the installation of photovoltaic power stations, the weeds and shrubs under and between photovoltaic panels are denser than before the installation of photovoltaic panels, and the fire hazards are becoming increasingly prominent. How to organically combine photovoltaic power station fire prevention with goat breeding to form pastoral-photovoltaic complementarity has put forward higher requirements for the comprehensive operation and maintenance management strategies of mountain photovoltaic power stations. The existing operation and maintenance strategies of onshore photovoltaic power stations mainly focus on the daily operation and maintenance, fault handling and energy efficiency improvement of power stations, but rarely involve complex factors such as fire prevention and goat breeding. Therefore, these strategies cannot be directly applied to pastoral-photovoltaic complementary photovoltaic power stations with wildfire protection needs. Summary of the invention

[0004] The purpose of the present invention is to provide a comprehensive management method and system for mountain photovoltaic power stations to resist wildfire disasters by complementing pastoral and photovoltaic power stations, so as to solve the problem of inapplicability of on-road operation and maintenance strategies in mountain photovoltaic power stations in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a comprehensive management method for preventing wildfire disasters by using a photovoltaic power station in a mountainous area and a photovoltaic complementarity system, comprising: Obtaining a pre-built mathematical model of a photovoltaic power station; wherein the mathematical model of the photovoltaic power station includes internal facilities, array locations, and road conditions of the photovoltaic power station; Acquire current image data of each photovoltaic array in a mountain photovoltaic power station; wherein the current image data includes images of weeds and shrubs; input each current image data into a pre-trained image recognition network model to obtain the current growth conditions of weeds and shrubs under each photovoltaic array; Determine the comprehensive management optimization target; wherein the comprehensive management optimization target is a multi-optimization target, including the operation and maintenance mode of mountain photovoltaic power station, the frequency and route of photovoltaic module cleaning, the goat grazing route and the optimal weeding frequency; The comprehensive management optimization goal is taken as the objective function, and the internal facilities, array location, road conditions and uncertain factors of the photovoltaic power station are taken as constraints; among them, weather conditions, weed and shrub growth, and the location and time of photovoltaic module failure are taken as uncertain factors of the photovoltaic power station; a multi-objective optimization algorithm is used to solve the objective function to obtain the optimal comprehensive management strategy.

[0006] Furthermore, the image recognition network model is trained in the following manner: Determine the model architecture and initialize model parameters of the image recognition network model; Obtain pre-processed images of weeds, shrubs and goats in the photovoltaic array and use them as training sets for the model; The image recognition network model is trained based on the training set; after the training is completed, the model is tested with the test set, and the training is completed when the preset conditions are met.

[0007] Furthermore, in the mathematical model of the photovoltaic power station, the road condition is represented in the form of a road graph, the road graph G=(V,E), where V is a node set, E is an edge set, and each edge e∈E has a weight w(e) representing the attribute of the road.

[0008] Furthermore, the current image data of each photovoltaic array in the mountain photovoltaic power station is obtained, including: Use a drone to take photos of each photovoltaic array in a photovoltaic power station; each photovoltaic array corresponds to a photo taken at a different angle.

[0009] Furthermore, the uncertainty factors of the photovoltaic power station are represented in the form of an uncertainty distribution curve.

[0010] Furthermore, a multi-objective optimization algorithm is used to solve the objective function and obtain the optimal comprehensive management strategy, including: Select genetic algorithm GA as the solution method, set the initial parameters of the algorithm, and initialize the population; determine the objective function and constraint conditions of the comprehensive management optimization goal; Evaluate the quality of each solution in the population according to the fitness function, and select the solution as the parent based on the fitness for subsequent crossover and mutation operations; Perform crossover on the selected parent generation to generate new offspring solutions; perform random mutation on the offspring solutions; and form a new population with the newly generated offspring solutions and the parent generation solutions; The solution in the new population is selected according to the fitness function, and the process of crossover, mutation and population update is repeated until the preset number of iterations is reached or the termination condition is met, and the Pareto optimal solution set is obtained.

[0011] Furthermore, the photovoltaic power station mathematical model is a mixed integer linear programming model.

[0012] In a second aspect, the present invention provides a comprehensive management device for preventing wildfire disasters by complementing pastoral and photovoltaic power stations in mountainous areas, comprising: A model building module, used to obtain a pre-built mathematical model of a photovoltaic power station; wherein the mathematical model of the photovoltaic power station includes internal facilities, array locations and road conditions of the photovoltaic power station; An image recognition module is used to obtain current image data of each photovoltaic array in a mountain photovoltaic power station; wherein the current image data includes images of weeds and shrubs; each current image data is input into a pre-trained image recognition network model to obtain the current growth conditions of weeds and shrubs under each photovoltaic array; A target determination module is used to determine the comprehensive management optimization target; wherein the comprehensive management optimization target is a multi-optimization target, including the operation and maintenance mode of mountain photovoltaic power stations, the frequency and route of photovoltaic module cleaning, the goat grazing route and the optimal weeding frequency; The target solving module is used to take the comprehensive management optimization target as the objective function, and the internal facilities, array location, road conditions and uncertain factors of the photovoltaic power station as constraints; among them, weather conditions and the growth of weeds and shrubs, as well as the location and time of photovoltaic module failures are used as uncertain factors of the photovoltaic power station; a multi-objective optimization algorithm is used to solve the objective function to obtain the optimal comprehensive management strategy.

[0013] According to a third aspect of the present invention, an electronic device is provided, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the above-mentioned integrated management method for preventing wildfire disasters by complementary pastoral and photovoltaic power stations in mountainous areas.

[0014] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the integrated management method for resisting wildfire disasters by complementary pastoral and photovoltaic power stations in mountainous areas is implemented as described above.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The method of the present invention constructs a mathematical model of a photovoltaic power station including key information such as the internal facilities, array position and road conditions of the photovoltaic power station; through the photovoltaic power station image data, the image recognition network model that has been pre-trained is input to obtain the current growth of weeds and shrubs under each photovoltaic array, which provides a scientific basis for determining the weeding frequency and planning the goat grazing route. Combined with the actual situation of the mountain photovoltaic power station, multiple optimization targets are determined, including the operation and maintenance inspection method of the mountain photovoltaic power station, the cleaning frequency and route of the photovoltaic components, the optimal goat grazing route and weeding frequency, etc. The comprehensive management optimization target is used as the objective function, and the internal facilities, array position, road conditions and uncertain factors (such as weather conditions, weed shrub growth, photovoltaic component failure location and time, etc.) of the photovoltaic power station are used as constraints. A multi-objective optimization algorithm is selected to solve the objective function to obtain the optimal comprehensive management strategy. Intelligent and integrated management of mountain photovoltaic power stations is realized. The operation and maintenance efficiency is improved, the fire risk is reduced, the harmonious development of goat breeding and photovoltaic power stations is promoted, and strong support is provided for the sustainable development of mountain photovoltaic power stations. The present invention provides a comprehensive management device for preventing wildfire disasters by combining pastoral and photovoltaic complementarity in a mountain photovoltaic power station, an electronic device and a computer-readable storage medium, which also solve the problems raised in the background technology section. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a flow chart of a comprehensive management method for preventing wildfire disasters by using photovoltaic power stations and pastoral-photovoltaic complementarity in a mountain area according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a comprehensive management device for preventing wildfire disasters by combining pastoral and photovoltaic complementation in a mountain photovoltaic power station according to an embodiment of the present invention; Figure 3 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0018] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0019] Example 1 To achieve intelligent integrated management of mountain photovoltaic power stations, a series of technical problems need to be solved. For example, how to build an accurate mathematical model of photovoltaic power stations, how to train an efficient image recognition network model, how to determine the integrated management optimization goals and build the corresponding objective function, and how to select a suitable multi-objective optimization algorithm for solving them. These problems require in-depth research and exploration to form a complete, efficient, and eco-friendly integrated management method for mountain photovoltaic power stations and pastoral-photovoltaic complementarity to resist wildfire disasters.

[0020] The present invention proposes a comprehensive management method for mountain photovoltaic power stations to resist wildfire disasters by complementing pastoral and photovoltaic power stations. The method aims to achieve intelligent comprehensive management of mountain photovoltaic power stations, improve operation and maintenance efficiency, reduce wildfire risks, and promote ecological balance around power stations by constructing a mathematical model of the photovoltaic power station, using image recognition technology to monitor the growth of weeds and shrubs in real time, determining comprehensive management optimization goals and constructing objective functions, and using multi-objective optimization algorithms to solve the optimal comprehensive management strategy.

[0021] like Figure 1 As shown, a comprehensive management method for preventing wildfire disasters by complementing mountain photovoltaic power stations with grazing and photovoltaic power plants includes: S1. Obtain a pre-built mathematical model of a photovoltaic power station; wherein the mathematical model of the photovoltaic power station includes internal facilities, array locations and road conditions of the photovoltaic power station.

[0022] Optionally, this solution mathematically models the internal facilities, array positions, road conditions, etc. of the photovoltaic power station, and linearizes them in the final form of a mixed integer linear programming model. The photovoltaic power station mathematical model is a mixed-integer linear programming model (MILP).

[0023] In some more specific embodiments: the internal facilities include inverters, transformers, control rooms, etc., for example, the facilities are integrated F ={ f 1, f 2,…, fn},in fi Indicates i The location and capacity of each facility. The array position includes the layout and orientation of the photovoltaic panels, as well as the coordinate position. Each photovoltaic array can have one or more coordinate points, for example, the array set A ={ a 1, a 2,…, 2.},in aj Indicates jThe road conditions include the width, length, slope, etc. of the road in the photovoltaic power station, which are represented by edges and nodes in graph theory. For example, the road graph G=(V,E), where V is the node set, E is the edge set, and each edge e∈E has a weight w(e) representing the attributes of the road.

[0024] S2. Obtain current image data of each photovoltaic array in a mountain photovoltaic power station; wherein the current image data includes images of weeds and shrubs; input each of the current image data into a pre-trained image recognition network model to obtain the current growth conditions of weeds and shrubs under each photovoltaic array.

[0025] In step S2, the current image data of each photovoltaic array in the mountain photovoltaic power station is obtained, including: using a drone to take photos of each photovoltaic array in the photovoltaic power station; wherein each photovoltaic array corresponds to photos taken at different angles. The drone is combined with a camera to shoot the scene, and the photovoltaic array with more weeds and shrubs is accurately identified through the image recognition algorithm. According to the boundary of 10 cm from the bottom edge of the photovoltaic module, the photovoltaic array above this range is defined as having more weeds and shrubs, and goats or manual weeding are required.

[0026] In an optional embodiment, an electronic fence is set up in each photovoltaic array to prevent goats from leaving the photovoltaic field, and weeds and shrubs that goats cannot eat are recorded and weeding work is carried out on them at a fixed point. The photovoltaic array can be photographed by a camera, and the neural network can identify the corresponding weeds and shrubs in the picture, which are pushed to the operation and maintenance personnel every month, and then the weeds and shrubs that goats do not eat are removed at a fixed point.

[0027] In step S2, the image recognition network model is trained in the following manner: determining the model architecture of the image recognition network model and initializing the model parameters; obtaining the preprocessed images of weeds, shrubs and goats in the photovoltaic array and using them as the training set of the model; training the image recognition network model based on the training set; after the training is completed, the model is tested with the test set, and the training is completed after the preset conditions are met.

[0028] In an optional embodiment, when constructing an image recognition network model (CNN, Convolutional Neural Network), first build an image recognition training environment, then determine the number of neurons and the number of neural network layers in the image recognition algorithm, and establish a neural network, and train the neural network through images of weeds and shrubs in the photovoltaic array. Adjust the training parameters to achieve the optimal training effect on the neural network. Then use new images to test the trained neural network. The trained neural network can be tested using a set of weed and shrub images of different types, heights, years, seasons, and goat gnawing conditions. In this way, the ability of identifying weeds and shrubs in the photovoltaic array and the gnawing conditions of goats proposed in this patent can be tested.

[0029] S3. Determine the comprehensive management optimization goal; wherein the comprehensive management optimization goal is a multi-optimization goal, including the operation and maintenance method of the mountain photovoltaic power station, the frequency and route of photovoltaic module cleaning, the goat grazing route and the optimal weeding frequency.

[0030] Specifically, the power generation of mountain photovoltaic power stations, goat breeding results and wildfire prevention effects are taken as multi-objective optimization goals, and combined with uncertain factors such as sudden failures, the optimal operation and maintenance method of mountain photovoltaic power stations, the optimal frequency and route of photovoltaic module cleaning, the optimal goat grazing route and the optimal weeding frequency are obtained under different inclination angles of photovoltaic modules, different types of weeds and shrubs, and different weather conditions, so as to maximize the power generation of mountain photovoltaic power stations, goat breeding and wildfire prevention benefits.

[0031] S4. The comprehensive management optimization goal is taken as the objective function, and the internal facilities, array location, road conditions and uncertain factors of the photovoltaic power station are taken as constraints; among them, weather conditions and the growth of weeds and shrubs, as well as the location and time of photovoltaic module failure are taken as uncertain factors of the photovoltaic power station; a multi-objective optimization algorithm is used to solve the objective function to obtain the optimal comprehensive management strategy.

[0032] Alternatively, in this solution, the objective function can be expressed as f(x)=( f 1(x), f 2(x), f 3(x), f4 (x)), where x is the vector of decision variables. For example, f 1(x) can represent the cost or time of operation and maintenance. f 2(x) can represent the cleaning efficiency of the photovoltaic module, f 3(x) can represent the income from goat grazing, f4(x) can represent the cost or effect of weeding. Constraints are used to limit the range of decision variables. For example, the operation and maintenance route must meet road conditions; the cleaning frequency and route must consider the location and orientation of the photovoltaic array; the grazing route must avoid the photovoltaic array and sensitive areas; the weeding frequency must consider the growth of weeds and shrubs and fire prevention requirements.

[0033] Optionally, in this solution, the decision variable x may include: the path and schedule of operation and maintenance; the frequency and route of cleaning of photovoltaic modules; the route and time of goat grazing; the frequency and area of ​​weeding.

[0034] In step S4, the uncertainty factors of the photovoltaic power station are represented in the form of an uncertainty distribution curve. Specifically, the uncertainty factors such as weather conditions, weed and shrub growth, photovoltaic module fault location, fault time, etc. are analyzed and modeled, and the uncertainty distribution curve is established based on historical data to clarify the correlation between various uncertainties.

[0035] In an optional embodiment, historical weather data, vegetation growth data, photovoltaic module failure records, etc. are collected. The distribution characteristics of each uncertain factor are analyzed, such as normal distribution, Poisson distribution, etc. A distribution curve is established for each uncertain factor based on historical data. The correlation between different uncertain factors is analyzed, such as the relationship between weather changes and vegetation growth.

[0036] Optionally, the weather conditions can be represented by random variables W The value range can be a combination of meteorological parameters such as temperature, humidity, wind speed, and wind direction. The growth of weeds and shrubs can be represented by random variables G The value can be a combination of parameters such as the height and density of weeds and shrubs. The failure of photovoltaic modules can be represented by random variables F It is represented by, and its value can be a combination of the location, time, type and other information of the faulty component.

[0037] In step S4, a multi-objective optimization algorithm is used to solve the objective function to obtain the optimal comprehensive management strategy, including: selecting a genetic algorithm GA as a solution method, setting the initial parameters of the algorithm, and initializing the population; determining the objective function and constraints of the comprehensive management optimization goal; evaluating the pros and cons of each solution in the population according to the fitness function, and selecting a solution as the parent generation according to the fitness for subsequent crossover and mutation operations; crossovering the selected parent generation to generate a new child solution; randomly mutating the child solution; forming a new population with the newly generated child solution and the parent solution; selecting a solution in the new population according to the fitness function, and repeating the process of crossover, mutation and updating the population until a preset number of iterations is reached or the termination condition is met, and a Pareto optimal solution set is obtained.

[0038] In an optional embodiment, based on the growth of weeds and shrubs in different months and at different photovoltaic module inclination angles (fixed and not adjustable), a multi-objective optimization algorithm (GA, genetic algorithm) is used to rationally plan the photovoltaic module cleaning route and frequency, and adjust the photovoltaic field inspection route and frequency to ensure that goat weeding is beneficial to the photovoltaic power station inspection route and fire protection needs, thereby maximizing the interests of the three parties: power generation of mountain photovoltaic power stations, goat breeding, and forest fire prevention.

[0039] In an optional embodiment, the control variables, state variables, objective equations and constraints of the multi-objective optimization algorithm are determined, and a mathematical environment that can represent the photovoltaic power station is built. The multi-objective optimization algorithm GA is compiled through Python, and the mathematical environment is connected through the Gurobi planning optimization operator, the initial state is established through historical data, and the optimal solution is achieved by adjusting the parameters. The strategy is actually applied in the photovoltaic power station to maximize the multi-objective benefits.

[0040] In a more specific embodiment, a process of solving a multi-objective optimization algorithm is provided as follows: (1) Algorithm initialization Set the initial parameters of the genetic algorithm (GA), such as population size, number of iterations, crossover probability, mutation probability, etc.

[0041] Initialize the population, that is, generate a set of random solutions, which will serve as the starting point for the algorithm search.

[0042] (2) Objective function and constraint processing Objective function construction: Convert the comprehensive management optimization goal into a specific mathematical expression to form an objective function.

[0043] Constraint processing: The internal facilities of the photovoltaic power station, the location of the array, the road conditions and uncertain factors (such as weather conditions, the growth of weeds and shrubs, the location and time of photovoltaic module failure) are taken as constraints.

[0044] (3) Algorithm iteration and optimization Selection operation: Evaluate the quality of each solution in the population according to the fitness function (usually related to the objective function). Select the solution with higher fitness as the parent for subsequent crossover and mutation operations.

[0045] Crossover operation: Crossover the selected parent generation to generate a new child solution.

[0046] Mutation operation: Randomly mutate the offspring solution to introduce new genetic information.

[0047] Update the population: The newly generated child solutions and the parent solutions are combined into a new population. The fitness function is used to evaluate the quality of each solution in the new population, and the solutions with higher fitness are retained.

[0048] Iteration and termination: Repeat the above process of selection, crossover, mutation and population update until the preset number of iterations is reached or other termination conditions are met. During the iteration process, the algorithm will gradually approach the optimal solution set (i.e., the Pareto optimal solution set).

[0049] (4) Result analysis and decision-making Result analysis: The optimal solution set obtained by the algorithm is analyzed to evaluate the performance of each solution in terms of operation and maintenance cost, power generation efficiency, goat grazing benefits, etc. According to actual needs, the most appropriate solution is selected as the final comprehensive management strategy.

[0050] Example 2 like Figure 2 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a mountain photovoltaic power station and pastoral photovoltaic complementary comprehensive management device for resisting wildfire disasters, which is characterized by comprising: A model building module, used to obtain a pre-built mathematical model of a photovoltaic power station; wherein the mathematical model of the photovoltaic power station includes internal facilities, array locations and road conditions of the photovoltaic power station; An image recognition module is used to obtain current image data of each photovoltaic array in a mountain photovoltaic power station; wherein the current image data includes images of weeds and shrubs; each current image data is input into a pre-trained image recognition network model to obtain the current growth conditions of weeds and shrubs under each photovoltaic array; A target determination module is used to determine the comprehensive management optimization target; wherein the comprehensive management optimization target is a multi-optimization target, including the operation and maintenance mode of mountain photovoltaic power stations, the frequency and route of photovoltaic module cleaning, the goat grazing route and the optimal weeding frequency; The target solving module is used to take the comprehensive management optimization target as the objective function, and the internal facilities, array location, road conditions and uncertain factors of the photovoltaic power station as constraints; among them, weather conditions and the growth of weeds and shrubs, as well as the location and time of photovoltaic module failures are used as uncertain factors of the photovoltaic power station; a multi-objective optimization algorithm is used to solve the objective function to obtain the optimal comprehensive management strategy.

[0051] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for implementing the above-mentioned embodiment of a comprehensive management method for preventing wildfire disasters by complementing pastoral and photovoltaic power stations in mountainous areas; The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0052] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the integrated management method of mountain photovoltaic power station pastoral-photovoltaic complementarity to resist wildfire disasters in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0053] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0054] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0055] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a comprehensive management method for preventing wildfire disasters by complementing pastoral photovoltaic power stations in mountainous areas. The processor 102 can execute the plurality of instructions to implement: A pre-built mathematical model of a photovoltaic power station is obtained; wherein the mathematical model of the photovoltaic power station includes internal facilities, array locations and road conditions of the photovoltaic power station.

[0056] The current image data of each photovoltaic array in the mountain photovoltaic power station is obtained; wherein the current image data includes images of weeds and shrubs; each of the current image data is input into a pre-trained image recognition network model to obtain the current growth conditions of weeds and shrubs under each photovoltaic array.

[0057] Determine the comprehensive management optimization goal; wherein, the comprehensive management optimization goal is a multi-optimization goal, including the operation and maintenance method of mountain photovoltaic power stations, the frequency and route of photovoltaic module cleaning, the goat grazing route and the optimal weeding frequency.

[0058] The comprehensive management optimization goal is taken as the objective function, and the internal facilities, array location, road conditions and uncertain factors of the photovoltaic power station are taken as constraints; among them, weather conditions, weed and shrub growth, and the location and time of photovoltaic module failure are taken as uncertain factors of the photovoltaic power station; a multi-objective optimization algorithm is used to solve the objective function to obtain the optimal comprehensive management strategy.

[0059] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0060] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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.

[0061] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0064] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A comprehensive management method for preventing wildfire disasters by combining photovoltaic power stations with pastoral and photovoltaic complementarity in mountainous areas, characterized in that: include: Acquire a pre-built mathematical model of a photovoltaic power station; wherein the mathematical model of the photovoltaic power station includes internal facilities, array locations and road conditions of the photovoltaic power station; Acquire current image data of each photovoltaic array in a mountain photovoltaic power station; wherein the current image data includes images of weeds and shrubs; input each current image data into a pre-trained image recognition network model to obtain the current growth conditions of weeds and shrubs under each photovoltaic array; Determine the comprehensive management optimization target; wherein the comprehensive management optimization target is a multi-optimization target, including the operation and maintenance mode of mountain photovoltaic power station, the frequency and route of photovoltaic module cleaning, the goat grazing route and the optimal weeding frequency; The comprehensive management optimization goal is taken as the objective function, and the internal facilities, array location, road conditions and uncertain factors of the photovoltaic power station are taken as constraints; among them, weather conditions, weed and shrub growth, and the location and time of photovoltaic module failure are taken as uncertain factors of the photovoltaic power station; a multi-objective optimization algorithm is used to solve the objective function to obtain the optimal comprehensive management strategy.

2. The integrated management method for resisting wildfire disasters by combining pastoral and photovoltaic complementary power stations in mountainous areas according to claim 1 is characterized in that: The image recognition network model is trained as follows: Determine the model architecture and initialize model parameters of the image recognition network model; Obtain pre-processed images of weeds, shrubs and goats in the photovoltaic array and use them as training sets for the model; Train the image recognition network model based on the training set; After the training is completed, the model is tested with the test set. When the preset conditions are met, the training is completed.

3. The integrated management method for resisting wildfire disasters by combining pastoral and photovoltaic complementary power stations in mountainous areas according to claim 1 is characterized in that: In the mathematical model of the photovoltaic power station, the road condition is represented in the form of a road graph, the road graph G=(V,E), where V is a node set, E is an edge set, and each edge e∈E has a weight w(e) representing the attribute of the road.

4. The integrated management method for preventing wildfire disasters by combining pastoral and photovoltaic complementation in mountain photovoltaic power stations according to claim 1 is characterized in that: Obtain the current image data of each photovoltaic array in the mountain photovoltaic power station, including: Use a drone to take photos of each photovoltaic array in a photovoltaic power station; each photovoltaic array corresponds to a photo taken at a different angle.

5. The integrated management method for preventing wildfire disasters by combining pastoral and photovoltaic complementation in mountain photovoltaic power stations according to claim 1 is characterized in that: The uncertainty factors of photovoltaic power plants are expressed in the form of uncertainty distribution curves.

6. The integrated management method for preventing wildfire disasters by combining pastoral and photovoltaic complementation in mountain photovoltaic power stations according to claim 1 is characterized in that: The multi-objective optimization algorithm is used to solve the objective function and obtain the optimal comprehensive management strategy, including: Select genetic algorithm GA as the solution method, set the initial parameters of the algorithm, and initialize the population; determine the objective function and constraint conditions of the comprehensive management optimization goal; Evaluate the quality of each solution in the population according to the fitness function, and select the solution as the parent based on the fitness for subsequent crossover and mutation operations; Perform crossover on the selected parent generation to generate new offspring solutions; perform random mutation on the offspring solutions; and form a new population with the newly generated offspring solutions and the parent generation solutions; The solution in the new population is selected according to the fitness function, and the process of crossover, mutation and population update is repeated until the preset number of iterations is reached or the termination condition is met, and the Pareto optimal solution set is obtained.

7. The integrated management method for preventing wildfire disasters by combining photovoltaic power station with pastoral-photovoltaic complementation in mountainous areas according to claim 1 is characterized in that: The photovoltaic power station mathematical model is a mixed integer linear programming model.

8. A comprehensive management device for preventing wildfire disasters by combining photovoltaic power station with pastoral-photovoltaic complementation in mountainous areas, characterized in that: include: A model building module, used to obtain a pre-built mathematical model of a photovoltaic power station; wherein the mathematical model of the photovoltaic power station includes internal facilities, array locations and road conditions of the photovoltaic power station; An image recognition module is used to obtain current image data of each photovoltaic array in a mountain photovoltaic power station; wherein the current image data includes images of weeds and shrubs; each current image data is input into a pre-trained image recognition network model to obtain the current growth conditions of weeds and shrubs under each photovoltaic array; A target determination module is used to determine the comprehensive management optimization target; wherein the comprehensive management optimization target is a multi-optimization target, including the operation and maintenance mode of mountain photovoltaic power stations, the frequency and route of photovoltaic module cleaning, the goat grazing route and the optimal weeding frequency; The target solving module is used to take the comprehensive management optimization target as the objective function, and the internal facilities, array location, road conditions and uncertain factors of the photovoltaic power station as constraints; among them, weather conditions and the growth of weeds and shrubs, as well as the location and time of photovoltaic module failures are used as uncertain factors of the photovoltaic power station; a multi-objective optimization algorithm is used to solve the objective function to obtain the optimal comprehensive management strategy.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the integrated management method for resisting wildfire disasters by complementary pastoral and photovoltaic power stations in mountainous areas as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, it implements the comprehensive management method for mountain photovoltaic power station pastoral-photovoltaic complementarity to resist wildfire disasters as described in any one of claims 1 to 7.

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