A method and system for screening and optimizing effective data of photovoltaic power generation

Abstract

The present invention relates to the technical field of power operation and maintenance data processing. The present invention provides a method and system for optimizing the screening of effective data for photovoltaic power generation. Aiming at the problem of reduced accuracy in screening effective data for photovoltaic power generation in new energy power plants under extreme natural environments, the present invention constructs geographic image data of photovoltaic power stations through digital twin technology, and combines the comparison of real-time plant natural environment data with theoretical natural environment information and natural environment simulation models to achieve accurate screening of data. At the same time, a genetic algorithm is used to optimize the operating data of the photovoltaic power station after screening, so that the photovoltaic power station of the new energy power plant can operate efficiently and stably under changes in the natural environment. The present invention improves the accuracy of screening effective data for photovoltaic power generation, and provides strong support for the intelligent operation and maintenance of photovoltaic power stations.

Description

Technical Field

[0001] The present invention relates to the technical field of power operation and maintenance data processing, and in particular to a method and system for screening and optimizing effective photovoltaic power generation data. Background Art

[0002] Photovoltaic power generation effective data screening technology helps to improve the efficient and stable operation of photovoltaic power stations. Photovoltaic power generation effective data screening technology mainly collects, cleans, screens and analyzes the massive data generated by photovoltaic power stations to eliminate abnormal data and retain effective data of photovoltaic power stations, so as to accurately evaluate the operating status and power generation efficiency of photovoltaic power stations. Photovoltaic power generation effective data screening technology uses the data acquisition module to collect power generation data and environmental parameters of the power station in real time through photovoltaic array sensors, meteorological sensors and other equipment. Environmental parameters include light intensity and temperature. Subsequently, the data processing module verifies, parses and converts the received data frames and stores them in the database. In the data screening process, a variety of algorithms and models, such as Gaussian processes, are used to perform uncertainty analysis on the IV curve to obtain confidence intervals, thereby improving the accuracy of the data.

[0003] The use of effective data screening technology for photovoltaic power generation involves multiple links. First, through the data observation link, the character strings in the time series are converted into numbers and plotted into curves to intuitively understand the changing trends of parameters such as current and voltage. Then, in the data cleaning link, tools such as operating condition classifiers are used to eliminate fault states and outliers to ensure the accuracy and consistency of data from new energy power plants. On this basis, the voltage, current and other data obtained by screening are combined with the engineering model of the photovoltaic power station to solve the IV curve, and the confidence interval is obtained through uncertainty analysis methods to evaluate the performance of photovoltaic modules and the power generation efficiency of the power station.

[0004] The photovoltaic power generation effective data screening technology has the following technical pain points in the actual use process. The actual application scenarios in the field of new energy power plants are complex, including different types of photovoltaic power stations, different geographical environments and climatic conditions, different operation and maintenance management levels, etc. In new energy power plants under extreme natural environments, the accuracy of photovoltaic power generation effective data screening is reduced due to the complex natural environmental factors in the actual application scenarios, resulting in the power generation efficiency and operation and maintenance management of the photovoltaic power station being affected. In order to solve this technical pain point, the present invention provides a photovoltaic power generation effective data screening optimization method and system. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method and system for optimizing the screening of effective data for photovoltaic power generation, so as to solve the problem that the accuracy of effective data screening for photovoltaic power generation is reduced due to the complex natural environmental factors in actual application scenarios in new energy power plants under extreme natural environments.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] In a first aspect, the present invention provides a method for screening and optimizing effective data of photovoltaic power generation, comprising:

[0008] Step S101, obtaining regional range information of a photovoltaic power station and equipment information of a photovoltaic power station, wherein the equipment information of the photovoltaic power station includes equipment attributes of the photovoltaic power station, equipment coordinate information of the photovoltaic power station, and equipment operation information of the photovoltaic power station, wherein the regional range information of the photovoltaic power station includes coordinate information within the regional range of the photovoltaic power station and the regional geographical environment of the photovoltaic power station, and substituting the regional range information of the photovoltaic power station and the equipment information of the photovoltaic power station into the digital twin model to generate geographic image data of the photovoltaic power station;

[0009] Step S102, obtaining the coordinates of the control point of the photovoltaic power station, determining the mapping position of the coordinate position of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station according to the coordinate position of the control point of the photovoltaic power station, obtaining the coordinate information of the coordinate position of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, correcting the coordinate information of the coordinate position of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station based on the coordinates of the control point of the photovoltaic power station, obtaining the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, and correcting the coordinate data in the geographic image data of the photovoltaic power station according to the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station to obtain the corrected coordinate information of the photovoltaic power station;

[0010] Step S103, obtain the inspection equipment information of the photovoltaic power station, substitute the inspection equipment information of the photovoltaic power station and the corrected coordinate information of the photovoltaic power station into the preset inspection path model of the inspection equipment to obtain the inspection path of the inspection equipment, receive the real-time natural environment data of the factory area collected by the inspection equipment on the inspection path, obtain the real-time natural environment data of the factory area, and obtain the natural environment information corresponding to the corrected coordinate information of the photovoltaic power station from the data interface according to the corrected coordinate information of the photovoltaic power station to obtain the theoretical natural environment information, group the theoretical natural environment information, the basis for grouping the theoretical natural environment information is grouping per hour to obtain theoretical natural environment grouping data, and compare the real-time natural environment data of the factory area with the theoretical natural environment grouping data based on time. The natural environment data is matched according to the matching benchmark to obtain a natural environment data period matching result, wherein the natural environment data matching result includes natural environment period matching success data and natural environment period matching failure data, wherein the natural environment period matching success data includes theoretical natural environment grouping data and real-time plant area natural environment data corresponding to the theoretical natural environment grouping data, and the theoretical natural environment grouping data is compared with the real-time plant area natural environment data corresponding to the theoretical natural environment grouping data. If the natural environment data comparison error is greater than a preset error value, the theoretical natural environment information is stopped from being used, and the real-time plant area natural environment data and the corrected photovoltaic power station coordinate information are substituted into a preset natural environment simulation model to obtain the natural environment simulation data corresponding to the photovoltaic power station coordinates;

[0011] Step S104, obtaining the operation data of the photovoltaic power station, marking the data source of the operation data of the photovoltaic power station to obtain the operation data of the photovoltaic power station with the data source mark, matching the data source mark with the corrected coordinate information of the photovoltaic power station to obtain the position corresponding to the operation data of the photovoltaic power station in the geographic image of the photovoltaic power station, substituting the natural environment simulation data corresponding to the coordinates of the photovoltaic power station into the geographic image of the photovoltaic power station to obtain the natural environment change information in the geographic image of the photovoltaic power station, monitoring the natural environment change information in the geographic image of the photovoltaic power station, if the natural environment change in the geographic image of the photovoltaic power station exceeds a preset range value, then retrieving the position coordinate information corresponding to the natural environment change in the geographic image of the photovoltaic power station when it exceeds the preset range value to obtain the natural environment change area, and determining the data source mark corresponding to the natural environment change area according to the position coordinate information, performing data retrieval in a preset database according to the data source mark corresponding to the natural environment, and obtaining the operation data of the photovoltaic power station with the data source mark;

[0012] Step 105, performing validity detection on the photovoltaic power station operation data with the data source mark to obtain the validity detection result of the photovoltaic power station operation data, the validity detection result of the photovoltaic power station operation data includes valid data of the photovoltaic power station operation data and invalid data of the photovoltaic power station operation data, deleting the invalid data of the photovoltaic power station operation data in the photovoltaic power station operation data to obtain the screened photovoltaic power station operation data, substituting the screened photovoltaic power station operation data into a preset photovoltaic power station operation and maintenance model to obtain the photovoltaic power station operation and maintenance result after the natural environment changes, if the photovoltaic power station operation and maintenance result is that it cannot operate normally, then the screened photovoltaic power station operation data is replaced. Genetic algorithms are used for optimization to obtain optimized photovoltaic power station operation data, and the optimized photovoltaic power station operation data are substituted into a preset photovoltaic power station operation and maintenance model to obtain optimized photovoltaic power station operation and maintenance results of natural environment changes. If the corrected photovoltaic power station operation and maintenance results after the natural environment changes cannot operate normally, the genetic algorithm is used to continue to optimize the screened photovoltaic power station operation data until the optimized photovoltaic power station operation data is substituted into the preset photovoltaic power station operation and maintenance model, and the output photovoltaic power station operation and maintenance results after the natural environment changes are normal operation or the maximum number of optimization iterations is reached, then the genetic algorithm is stopped to continue to optimize the screened photovoltaic power station operation data.

[0013] Furthermore, the photovoltaic power generation effective data screening and optimization method of the present invention, step S103 includes:

[0014] If the natural environment data comparison error is less than the preset error value, then the theoretical natural environment information corresponding to the natural environment data comparison error is collected;

[0015] Obtain historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment, and use the environmental data comparison error, historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment to build an environmental simulation model;

[0016] The real-time plant area natural environment data is processed using a natural environment simulation model to obtain plant area natural environment simulation data, and the plant area natural environment simulation data is compared with theoretical natural environment information to obtain a plant area natural environment data comparison result;

[0017] The plant area natural environment data comparison results are grouped according to time to obtain the plant area natural environment data comparison result grouping data, and the natural environment data error threshold is compared for each group of data in the plant area natural environment data comparison result grouping data to filter out data greater than the natural environment data error threshold, and the natural environment prediction time information of the data greater than the natural environment data error threshold is retrieved to obtain the estimated natural environment change time;

[0018] Collect the inspection route of the real-time inspection equipment, generate the opposite inspection path of the inspection equipment based on the inspection route of the real-time inspection equipment, obtain the re-inspection inspection route, configure the expected natural environment change time and the re-inspection inspection route into the re-inspection inspection equipment, and when the expected natural environment change time is reached, receive the data collected by the re-inspection inspection equipment on the re-inspection inspection route as the re-inspection natural environment data;

[0019] The re-inspected natural environment data will be compared with the factory area natural environment simulation data. If the re-inspected natural environment data are inconsistent with the factory area natural environment simulation data, the natural environment simulation model will be optimized.

[0020] Furthermore, the photovoltaic power generation effective data screening and optimization method of the present invention, step S103 includes:

[0021] Aligning the re-inspected natural environment data and the plant area natural environment simulation data according to the same timestamp and spatial coordinates, and calculating the difference between the re-inspected natural environment data and the plant area natural environment simulation data, wherein the difference between the re-inspected natural environment data and the plant area natural environment simulation data includes a numerical difference and a trend difference;

[0022] Identify the difference between the re-inspected natural environment data and the plant area natural environment simulation data to obtain a difference identification result, wherein the difference identification result includes an area and a time period where the difference between the re-inspected natural environment data and the plant area natural environment simulation data exceeds a preset threshold;

[0023] The difference recognition results are matched in the preset knowledge base to obtain the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station, and the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station is matched in the preset model library to obtain the model to be trained, and the re-inspected natural environment data, the plant natural environment simulation data and the difference recognition results are used to train the model to be trained to obtain the optimized natural environment simulation model.

[0024] Furthermore, the photovoltaic power generation effective data screening and optimization method of the present invention, step S104 comprises:

[0025] If the natural environment changes in the geographic image of the photovoltaic power station do not exceed the preset range value, the natural environment changes in the geographic image of the photovoltaic power station will continue to be monitored. In the process of monitoring the natural environment changes in the geographic image of the photovoltaic power station, the natural environment change information in the geographic image of the photovoltaic power station is substituted into the preset natural environment change monitoring model to output the real-time natural environment change monitoring results;

[0026] The construction process of the natural environment change monitoring model includes: collecting natural environment data from various sensors and equipment of photovoltaic power stations, the natural environment data includes light intensity, temperature, humidity and wind speed, and also collecting operation data of photovoltaic power stations, the operation data of photovoltaic power stations includes current, voltage and power generation, pre-processing the collected data, and screening out the natural environment change characteristics from the pre-processed data, and the natural environment change characteristics are used to reflect the change trend of the natural environment;

[0027] The deep learning model is trained using the characteristics of natural environment changes to obtain a natural environment change detection model.

[0028] Furthermore, the photovoltaic power generation effective data screening and optimization method of the present invention, step S105 comprises:

[0029] The screened photovoltaic power station operation data is encoded to form an initial population, where each individual represents a solution, i.e., a combination of photovoltaic power station operation data;

[0030] Receive the fitness function, which is used to evaluate the quality of each individual, calculate the fitness value of each individual in the initial population, and sort the population according to the fitness value;

[0031] According to the fitness value, a part of individuals are selected as parents to generate the next generation, and the selected parent individuals are crossovered to generate new individuals. The crossover operation includes single-point crossover and multi-point crossover.

[0032] Perform mutation operations on newly generated individuals to increase the diversity of the population, and merge the new individuals generated by crossover and mutation operations with the parent individuals to form a new population;

[0033] Calculate the fitness value of each individual in the new population, sort the population according to the fitness value, check whether the maximum number of optimization iterations is reached or an individual that meets the requirements is found, if the termination condition is not met, continue to perform genetic operations, if the termination condition is met, stop the optimization process, and output the optimized photovoltaic power station operation data.

[0034] In a second aspect, the present invention provides a photovoltaic power generation effective data screening and optimization system, which uses the photovoltaic power generation effective data screening and optimization method as described above, including:

[0035] A data acquisition unit is used to acquire regional range information of a photovoltaic power station and equipment information of a photovoltaic power station, wherein the equipment information of the photovoltaic power station includes equipment attributes of the photovoltaic power station, equipment coordinate information of the photovoltaic power station and equipment operation information of the photovoltaic power station, wherein the regional range information of the photovoltaic power station includes coordinate information within the regional range of the photovoltaic power station and the regional geographical environment of the photovoltaic power station, and the regional range information of the photovoltaic power station and the equipment information of the photovoltaic power station are substituted into the digital twin model to generate geographic image data of the photovoltaic power station;

[0036] a position generating unit, for acquiring the coordinates of the control point of the photovoltaic power station, determining the mapping position of the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station according to the coordinates of the control point of the photovoltaic power station, obtaining the coordinate information of the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, correcting the coordinate information of the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station based on the coordinates of the control point of the photovoltaic power station, obtaining the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, and correcting the coordinate data in the geographic image data of the photovoltaic power station according to the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, to obtain the corrected coordinate information of the photovoltaic power station;

[0037] The natural environment monitoring unit is used to obtain the inspection equipment information of the photovoltaic power station, substitute the inspection equipment information of the photovoltaic power station and the corrected coordinate information of the photovoltaic power station into the preset inspection path model of the inspection equipment to obtain the inspection path of the inspection equipment, receive the real-time natural environment data of the plant area collected by the inspection equipment on the inspection path, obtain the real-time natural environment data of the plant area, and obtain the natural environment information corresponding to the corrected coordinate information of the photovoltaic power station from the data interface according to the corrected coordinate information of the photovoltaic power station to obtain the theoretical natural environment information, group the theoretical natural environment information, the basis for grouping the theoretical natural environment information is grouping per hour to obtain the theoretical natural environment grouping data, and group the real-time natural environment data of the plant area with the theoretical natural environment grouping data by hour The natural environment data period matching result is obtained by matching the time interval as the matching benchmark, and the natural environment data matching result includes the natural environment period matching success data and the natural environment period matching failure data, and the natural environment period matching success data includes the theoretical natural environment grouping data and the real-time plant area natural environment data corresponding to the theoretical natural environment grouping data. The theoretical natural environment grouping data and the real-time plant area natural environment data corresponding to the theoretical natural environment grouping data are compared with each other. If the natural environment data comparison error is greater than the preset error value, the theoretical natural environment information is stopped from being used, and the real-time plant area natural environment data and the corrected photovoltaic power station coordinate information are substituted into the preset natural environment simulation model to obtain the natural environment simulation data corresponding to the photovoltaic power station coordinates;

[0038] a data screening unit, for acquiring operation data of a photovoltaic power station, marking the data source of the operation data of the photovoltaic power station to obtain operation data of the photovoltaic power station with the data source mark, matching the data source mark with the corrected coordinate information of the photovoltaic power station to obtain the position corresponding to the operation data of the photovoltaic power station in the geographic image of the photovoltaic power station, substituting the natural environment simulation data corresponding to the coordinates of the photovoltaic power station into the geographic image of the photovoltaic power station to obtain the natural environment change information in the geographic image of the photovoltaic power station, monitoring the natural environment change information in the geographic image of the photovoltaic power station, if the natural environment change in the geographic image of the photovoltaic power station exceeds a preset range value, retrieving the position coordinate information corresponding to the natural environment change in the geographic image of the photovoltaic power station when the natural environment change exceeds the preset range value to obtain the natural environment change area, and determining the data source mark corresponding to the natural environment change area according to the position coordinate information, performing data retrieval in a preset database according to the data source mark corresponding to the natural environment, and obtaining the operation data of the photovoltaic power station with the data source mark;

[0039] The optimization unit is used to perform validity detection on the photovoltaic power station operation data with the data source mark to obtain the validity detection result of the photovoltaic power station operation data, wherein the validity detection result of the photovoltaic power station operation data includes valid data of the photovoltaic power station operation data and invalid data of the photovoltaic power station operation data, delete the invalid data of the photovoltaic power station operation data in the photovoltaic power station operation data, obtain the screened photovoltaic power station operation data, substitute the screened photovoltaic power station operation data into the preset photovoltaic power station operation and maintenance model, obtain the photovoltaic power station operation and maintenance result after the natural environment changes, and if the photovoltaic power station operation and maintenance result is that it cannot operate normally, the screened photovoltaic power station operation data is replaced. The optimized photovoltaic power station operation data is obtained by using a genetic algorithm for optimization, and the optimized photovoltaic power station operation data is substituted into a preset photovoltaic power station operation and maintenance model to obtain an optimized photovoltaic power station operation and maintenance result of the natural environment change. If the corrected photovoltaic power station operation and maintenance result after the natural environment change is unable to operate normally, the genetic algorithm is used to continue to optimize the screened photovoltaic power station operation data until the optimized photovoltaic power station operation data is substituted into the preset photovoltaic power station operation and maintenance model and the output photovoltaic power station operation and maintenance result after the natural environment change is normal operation or the maximum number of optimization iterations is reached, then the genetic algorithm is stopped to continue to optimize the screened photovoltaic power station operation data.

[0040] Furthermore, in the photovoltaic power generation effective data screening and optimization system of the present invention, the natural environment monitoring unit is also used for:

[0041] If the natural environment data comparison error is less than the preset error value, then the theoretical natural environment information corresponding to the natural environment data comparison error is collected;

[0042] Obtain historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment, and use the environmental data comparison error, historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment to build an environmental simulation model;

[0043] The real-time plant area natural environment data is processed using a natural environment simulation model to obtain plant area natural environment simulation data, and the plant area natural environment simulation data is compared with theoretical natural environment information to obtain a plant area natural environment data comparison result;

[0044] The plant area natural environment data comparison results are grouped according to time to obtain the plant area natural environment data comparison result grouping data, and the natural environment data error threshold is compared for each group of data in the plant area natural environment data comparison result grouping data to filter out data greater than the natural environment data error threshold, and the natural environment prediction time information of the data greater than the natural environment data error threshold is retrieved to obtain the estimated natural environment change time;

[0045] Collect the inspection route of the real-time inspection equipment, generate the opposite inspection path of the inspection equipment based on the inspection route of the real-time inspection equipment, obtain the re-inspection inspection route, configure the expected natural environment change time and the re-inspection inspection route into the re-inspection inspection equipment, and when the expected natural environment change time is reached, receive the data collected by the re-inspection inspection equipment on the re-inspection inspection route as the re-inspection natural environment data;

[0046] The re-inspected natural environment data will be compared with the factory area natural environment simulation data. If the re-inspected natural environment data are inconsistent with the factory area natural environment simulation data, the natural environment simulation model will be optimized.

[0047] Furthermore, in the photovoltaic power generation effective data screening and optimization system of the present invention, the natural environment monitoring unit is also used for:

[0048] Aligning the re-inspected natural environment data and the plant area natural environment simulation data according to the same timestamp and spatial coordinates, and calculating the difference between the re-inspected natural environment data and the plant area natural environment simulation data, wherein the difference between the re-inspected natural environment data and the plant area natural environment simulation data includes a numerical difference and a trend difference;

[0049] Identify the difference between the re-inspected natural environment data and the plant area natural environment simulation data to obtain a difference identification result, wherein the difference identification result includes an area and a time period where the difference between the re-inspected natural environment data and the plant area natural environment simulation data exceeds a preset threshold;

[0050] The difference recognition results are matched in the preset knowledge base to obtain the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station, and the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station is matched in the preset model library to obtain the model to be trained, and the re-inspected natural environment data, the plant natural environment simulation data and the difference recognition results are used to train the model to be trained to obtain the optimized natural environment simulation model.

[0051] Furthermore, in the photovoltaic power generation effective data screening and optimization system of the present invention, the data screening unit is also used for:

[0052] If the natural environment changes in the geographic image of the photovoltaic power station do not exceed the preset range value, the natural environment changes in the geographic image of the photovoltaic power station will continue to be monitored. In the process of monitoring the natural environment changes in the geographic image of the photovoltaic power station, the natural environment change information in the geographic image of the photovoltaic power station is substituted into the preset natural environment change monitoring model to output the real-time natural environment change monitoring results;

[0053] The construction process of the natural environment change monitoring model includes: collecting natural environment data from various sensors and equipment of photovoltaic power stations, the natural environment data includes light intensity, temperature, humidity and wind speed, and also collecting operation data of photovoltaic power stations, the operation data of photovoltaic power stations includes current, voltage and power generation, pre-processing the collected data, and screening out the natural environment change characteristics from the pre-processed data, and the natural environment change characteristics are used to reflect the change trend of the natural environment;

[0054] The deep learning model is trained using the characteristics of natural environment changes to obtain a natural environment change detection model.

[0055] Furthermore, in the photovoltaic power generation effective data screening and optimization system of the present invention, the optimization unit is also used for:

[0056] The screened photovoltaic power station operation data is encoded to form an initial population, where each individual represents a solution, i.e., a combination of photovoltaic power station operation data;

[0057] Receive the fitness function, which is used to evaluate the quality of each individual, calculate the fitness value of each individual in the initial population, and sort the population according to the fitness value;

[0058] According to the fitness value, a part of individuals are selected as parents to generate the next generation, and the selected parent individuals are crossovered to generate new individuals. The crossover operation includes single-point crossover and multi-point crossover.

[0059] Perform mutation operations on newly generated individuals to increase the diversity of the population, and merge the new individuals generated by crossover and mutation operations with the parent individuals to form a new population;

[0060] Calculate the fitness value of each individual in the new population, sort the population according to the fitness value, check whether the maximum number of optimization iterations is reached or an individual that meets the requirements is found, if the termination condition is not met, continue to perform genetic operations, if the termination condition is met, stop the optimization process, and output the optimized photovoltaic power station operation data.

[0061] Beneficial effects of the present invention:

[0062] The present invention generates accurate geographic image data of photovoltaic power stations by substituting the regional scope information and equipment information of photovoltaic power stations into the digital twin model, providing a reliable basis for subsequent data screening. The real-time natural environment data of the plant area collected by the inspection equipment is compared with the theoretical natural environment information, and combined with the natural environment simulation model, the accuracy of the natural environment data is effectively improved, thereby improving the screening accuracy of effective photovoltaic power generation data.

[0063] Real-time collection and analysis of photovoltaic power station operation data and natural environment data can promptly detect and respond to anomalies and changes in the data, improving the real-time nature of data processing. When the natural environment data comparison error is small, the flexibility and accuracy of data processing are further enhanced by building and optimizing the natural environment simulation model, and rechecking the collection and comparison of inspection data.

[0064] By testing the validity of photovoltaic power station operation data and optimizing genetic algorithms, the accuracy of power station operation and maintenance data is improved, providing strong support for operation and maintenance decisions. When the natural environment changes beyond the preset range, it can timely retrieve and filter out the operation data closely related to the natural environment changes, providing data guarantee for responding to extreme weather and natural disasters. Through the precise screening and optimization of effective photovoltaic power generation data, the interference of invalid data and abnormal data on power station operation evaluation is reduced, and the accuracy of power generation efficiency evaluation is improved. The optimized data screening and optimization methods improve the operation and maintenance efficiency and help extend the service life of photovoltaic power stations.

[0065] This invention combines digital twin technology, natural environment simulation, deep learning and other advanced technologies to promote the intelligent development of photovoltaic power generation technology. By constructing a natural environment change detection model and a genetic algorithm optimization model, it realizes the intelligent monitoring and optimization of the operating status of photovoltaic power stations, providing new ideas and methods for the further development of photovoltaic power generation technology.

[0066] In summary, the present invention has made positive contributions to the technological progress and industrial development in the field of photovoltaic power generation by improving the accuracy of data screening, enhancing the real-time and flexibility of data processing, optimizing the operation and maintenance decision-making of photovoltaic power stations, improving the power generation efficiency and operation and maintenance efficiency of photovoltaic power stations, and promoting the intelligent development of photovoltaic power generation technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0068] Figure 1 A schematic diagram of the flow chart of the photovoltaic power generation effective data screening and optimization method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0070] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0071] In a first aspect, the present invention provides a method for screening and optimizing effective data of photovoltaic power generation, comprising:

[0072] Step S101, obtaining regional range information of a photovoltaic power station and equipment information of a photovoltaic power station, wherein the equipment information of the photovoltaic power station includes equipment attributes of the photovoltaic power station, equipment coordinate information of the photovoltaic power station, and equipment operation information of the photovoltaic power station, wherein the regional range information of the photovoltaic power station includes coordinate information within the regional range of the photovoltaic power station and the regional geographical environment of the photovoltaic power station, and substituting the regional range information of the photovoltaic power station and the equipment information of the photovoltaic power station into the digital twin model to generate geographic image data of the photovoltaic power station;

[0073] In step S101, the present invention proposes a key data preprocessing and scenario modeling method, aiming to provide accurate basic data for subsequent data screening and optimization. The specific steps are as follows:

[0074] Obtaining the regional scope information of the photovoltaic power station includes the coordinate information within the regional scope of the photovoltaic power station, that is, the longitude and latitude of the geographical location of the power station, and the geographical environment information of the photovoltaic power station area, such as topography, landform, vegetation coverage, etc. This information helps to construct the geographical environment background of the photovoltaic power station.

[0075] The equipment information of photovoltaic power stations includes the properties of photovoltaic power station equipment (such as equipment type, model, manufacturer, etc.), equipment coordinate information (i.e. the specific location of the equipment in the photovoltaic power station) and equipment operation information (such as current operating status, historical operating data, etc.). This information is crucial for understanding the layout and operating status of the equipment in the power station.

[0076] Substitute the above-obtained regional range information and equipment information into the digital twin model. A digital twin is a virtual model that integrates physical entities, sensors, data, and analysis, and can reflect the state and changes of physical entities in real time. In the present invention, the digital twin model is used to construct a virtual image of a photovoltaic power station, that is, the geographic image data of a photovoltaic power station.

[0077] Through the processing of the digital twin model, geographic image data containing information such as the geographical environment, equipment layout and operating status of the photovoltaic power station area is generated. The data not only includes the geographical location and equipment coordinates of the power station, but also reflects the interaction between the power station and the surrounding environment, providing an intuitive and accurate basis for subsequent data screening and optimization.

[0078] Step S101 provides the necessary basic data for the subsequent steps. By constructing the geographic image data of the photovoltaic power station, the present invention can more accurately understand the actual operation of the power station, thereby more effectively screening and optimizing the effective data of photovoltaic power generation. At the same time, this also reflects the innovation and practicality of the present invention in data preprocessing and scene modeling.

[0079] Step S102, obtaining the coordinates of the control point of the photovoltaic power station, determining the mapping position of the coordinate position of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station according to the coordinate position of the control point of the photovoltaic power station, obtaining the coordinate information of the coordinate position of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, correcting the coordinate information of the coordinate position of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station based on the coordinates of the control point of the photovoltaic power station, obtaining the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, and correcting the coordinate data in the geographic image data of the photovoltaic power station according to the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station to obtain the corrected coordinate information of the photovoltaic power station;

[0080] In step S102, the present invention proposes a method for accurately correcting the coordinate information of the geographic image data of a photovoltaic power station, aiming to improve the accuracy of data processing. The specific steps are as follows:

[0081] Control points are points with clear coordinate information and identifiable features selected from the geographic image data of a photovoltaic power station, such as the entrance of a power station and the location of important equipment. The coordinate information of the control points is usually obtained through GPS measurement or other positioning technologies.

[0082] In the geographic image data of the photovoltaic power station, the corresponding position in the image data, that is, the mapping position, is determined according to the actual coordinate information of the control point. It is necessary to improve the consistency between the geographic coordinate system of the image data and the actual coordinate system for accurate matching.

[0083] Based on the coordinates of the control points, the coordinate information of the corresponding position in the geographic image data is corrected. Since the image data has certain deformation or errors, these errors can be eliminated by correcting the coordinates of the control points to improve the accuracy of the image data.

[0084] After correction, the coordinates of the control points in the geographic image data of the photovoltaic power station are obtained, which more accurately reflect the actual location. Using the coordinates of the control points in the geographic image of the photovoltaic power station as a benchmark, other coordinate data in the geographic image data are corrected to improve the consistency and accuracy of the coordinate information in the entire geographic image data set, providing a reliable basis for subsequent data analysis and processing.

[0085] Step S102 improves the accuracy of the geographic image data of the photovoltaic power station. By accurately correcting the coordinate information, the present invention provides more accurate basic data for subsequent data screening, analysis and optimization, which helps to improve the operation and maintenance efficiency and power generation efficiency of the photovoltaic power station. At the same time, this also reflects the refinement and precision of the present invention in data processing and scene modeling.

[0086] Step S103, obtain the inspection equipment information of the photovoltaic power station, substitute the inspection equipment information of the photovoltaic power station and the corrected coordinate information of the photovoltaic power station into the preset inspection path model of the inspection equipment to obtain the inspection path of the inspection equipment, receive the real-time natural environment data of the factory area collected by the inspection equipment on the inspection path, obtain the real-time natural environment data of the factory area, and obtain the natural environment information corresponding to the corrected coordinate information of the photovoltaic power station from the data interface according to the corrected coordinate information of the photovoltaic power station to obtain the theoretical natural environment information, group the theoretical natural environment information, the basis for grouping the theoretical natural environment information is grouping per hour to obtain theoretical natural environment grouping data, and compare the real-time natural environment data of the factory area with the theoretical natural environment grouping data based on time. The natural environment data is matched according to the matching benchmark to obtain a natural environment data period matching result, wherein the natural environment data matching result includes natural environment period matching success data and natural environment period matching failure data, wherein the natural environment period matching success data includes theoretical natural environment grouping data and real-time plant area natural environment data corresponding to the theoretical natural environment grouping data, and the theoretical natural environment grouping data is compared with the real-time plant area natural environment data corresponding to the theoretical natural environment grouping data. If the natural environment data comparison error is greater than a preset error value, the theoretical natural environment information is stopped from being used, and the real-time plant area natural environment data and the corrected photovoltaic power station coordinate information are substituted into a preset natural environment simulation model to obtain the natural environment simulation data corresponding to the photovoltaic power station coordinates;

[0087] In step S103, the present invention proposes a natural environment information verification and optimization method combining real-time data of inspection equipment with theoretical data, aiming to improve the accuracy of natural environment data of photovoltaic power stations. The specific steps are as follows:

[0088] The information of the inspection equipment of the photovoltaic power station is obtained, including the type of inspection equipment, performance parameters, current location, inspection plan, etc. The inspection equipment is usually used to monitor the equipment operation status and natural environment of the photovoltaic power station.

[0089] Substitute the inspection equipment information and the corrected PV power station coordinate information into the preset inspection equipment inspection path model. The model automatically generates the optimal inspection path based on the layout of the power station, equipment distribution and inspection requirements.

[0090] When the inspection equipment moves on the preset inspection route, it will collect real-time natural environment data of the plant, such as light intensity, temperature, humidity, wind speed, etc. The data reflects the current natural environment conditions of the power station.

[0091] According to the corrected PV power station coordinate information, the theoretical natural environment information of the corresponding location is obtained from the data interface. The information usually comes from meteorological stations, historical data records or model predictions, and represents the natural environment change trend of the location within a certain period of time.

[0092] The theoretical natural environment information is grouped every hour to obtain theoretical natural environment grouping data, which helps to align and match the theoretical data with the real-time data in time.

[0093] The real-time plant area natural environment data and the theoretical natural environment grouping data are matched based on time to obtain the natural environment data period matching results. The matching results include successfully matched data and unmatched data.

[0094] For the successfully matched natural environment data, the theoretical natural environment group data is compared with the corresponding real-time plant area natural environment data. If the error between the two is greater than the preset error value, it is considered that the theoretical natural environment information is inaccurate or invalid in the current time period.

[0095] When the theoretical natural environment information is judged to be inaccurate, the information is stopped from being used, and the real-time plant natural environment data and the corrected PV power station coordinate information are substituted into the preset natural environment simulation model. The model can simulate the natural environment change trend of the location of the PV power station based on real-time data and historical data.

[0096] Through the processing of the natural environment simulation model, the natural environment simulation data of the corresponding position of the photovoltaic power station coordinates are obtained. The data more accurately reflects the current and future natural environment conditions of the power station, providing strong support for subsequent data analysis and decision-making.

[0097] Step S103 realizes dual verification and optimization of the natural environment information of the photovoltaic power station by combining real-time inspection data with theoretical data. When the theoretical data is inaccurate, it can be switched to a simulation model based on real-time data in a timely manner, thereby improving the accuracy of subsequent data analysis and decision-making, reflecting the flexibility and practicality of the present invention in data processing and analysis.

[0098] Step S104, obtaining the operation data of the photovoltaic power station, marking the data source of the operation data of the photovoltaic power station to obtain the operation data of the photovoltaic power station with the data source mark, matching the data source mark with the corrected coordinate information of the photovoltaic power station to obtain the position corresponding to the operation data of the photovoltaic power station in the geographic image of the photovoltaic power station, substituting the natural environment simulation data corresponding to the coordinates of the photovoltaic power station into the geographic image of the photovoltaic power station to obtain the natural environment change information in the geographic image of the photovoltaic power station, monitoring the natural environment change information in the geographic image of the photovoltaic power station, if the natural environment change in the geographic image of the photovoltaic power station exceeds a preset range value, then retrieving the position coordinate information corresponding to the natural environment change in the geographic image of the photovoltaic power station when it exceeds the preset range value to obtain the natural environment change area, and determining the data source mark corresponding to the natural environment change area according to the position coordinate information, performing data retrieval in a preset database according to the data source mark corresponding to the natural environment, and obtaining the operation data of the photovoltaic power station with the data source mark;

[0099] In step S104, the present invention proposes an effective screening method for photovoltaic power station operation data, which combines geographic imaging technology, natural environment simulation and data source labeling technology to more accurately identify and screen photovoltaic power station operation data that is closely related to natural environment changes. The specific steps are as follows:

[0100] First, collect the operating data of the photovoltaic power station including key indicators such as current, voltage, and power generation.

[0101] Then, the data source of these operating data is tagged. Data source tagging means assigning a unique identifier or code to each piece of operating data, which can indicate the source or location of the data, such as a specific PV module, inverter or sensor.

[0102] The photovoltaic power station operation data with data source tags is matched with the corrected photovoltaic power station coordinate information. The corrected coordinate information provides a more accurate geographical location of each device or sensor in the power station. Through the matching process, the specific location corresponding to each operation data in the photovoltaic power station geographic image can be determined.

[0103] Substitute the natural environment simulation data corresponding to the coordinates of the photovoltaic power station into the geographic image of the photovoltaic power station. The natural environment simulation data is generated based on a variety of information such as meteorological models, historical data, and real-time monitoring data, and can reflect the changing trend of the natural environment in the power station area. Monitor the natural environment change information in the geographic image, including changes in parameters such as light intensity, temperature, humidity, and wind speed.

[0104] Set the preset range values ​​of natural environment changes, which are determined based on the operating experience of the power station, equipment performance, safety standards, etc. If the natural environment changes in the geographical image of the photovoltaic power station are monitored to exceed the preset range values, further processing is performed.

[0105] When the natural environment changes beyond the preset range, the location coordinate information corresponding to the natural environment change area in the geographic image is retrieved, and the data source tag corresponding to the natural environment change area is determined based on the location coordinate information.

[0106] Finally, these data source tags are used to perform data retrieval in a preset database to obtain photovoltaic power station operation data with data source tags that are closely related to natural environmental changes. The present invention can accurately screen out photovoltaic power station operation data that is closely related to natural environmental changes, and provide strong support for subsequent data analysis and power station operation and maintenance decisions.

[0107] Step 105, performing validity detection on the photovoltaic power station operation data with the data source mark to obtain the validity detection result of the photovoltaic power station operation data, the validity detection result of the photovoltaic power station operation data includes valid data of the photovoltaic power station operation data and invalid data of the photovoltaic power station operation data, deleting the invalid data of the photovoltaic power station operation data in the photovoltaic power station operation data to obtain the screened photovoltaic power station operation data, substituting the screened photovoltaic power station operation data into a preset photovoltaic power station operation and maintenance model to obtain the photovoltaic power station operation and maintenance result after the natural environment changes, if the photovoltaic power station operation and maintenance result is that it cannot operate normally, then the screened photovoltaic power station operation data is replaced. Genetic algorithms are used for optimization to obtain optimized photovoltaic power station operation data, and the optimized photovoltaic power station operation data are substituted into a preset photovoltaic power station operation and maintenance model to obtain optimized photovoltaic power station operation and maintenance results of natural environment changes. If the corrected photovoltaic power station operation and maintenance results after the natural environment changes cannot operate normally, the genetic algorithm is used to continue to optimize the screened photovoltaic power station operation data until the optimized photovoltaic power station operation data is substituted into the preset photovoltaic power station operation and maintenance model, and the output photovoltaic power station operation and maintenance results after the natural environment changes are normal operation or the maximum number of optimization iterations is reached, then the genetic algorithm is stopped to continue to optimize the screened photovoltaic power station operation data.

[0108] In step S105, the present invention proposes a method for comprehensive optimization processing of photovoltaic power station operation data, aiming to improve the normal operation of the power station under natural environmental changes through effectiveness detection and genetic algorithm optimization. The specific steps are as follows:

[0109] The validity of photovoltaic power station operation data with data source tags is checked, and the process involves evaluation of multiple aspects such as data integrity, rationality, and consistency.

[0110] The test results divide the operating data into two categories: valid data and invalid data. Valid data refers to data that is within the expected range, logically reasonable and valuable to power plant operation and maintenance; while invalid data contains errors, abnormalities or redundant information.

[0111] The detected invalid data is deleted from the original running data, and only valid data is retained. This process helps to reduce data noise and improve the accuracy and efficiency of subsequent analysis.

[0112] Substitute the screened photovoltaic power station operation data into the preset photovoltaic power station operation and maintenance model. This model is a comprehensive analysis tool based on power station operation data and environmental parameters, which can evaluate the operation and maintenance status of the power station, predict potential failures and make optimization suggestions.

[0113] Through model calculations, the operation and maintenance results of the photovoltaic power station after the natural environment changes are obtained. The results can reflect the operating status and performance of the power station under the current natural environment.

[0114] Analyze the results of the operation and maintenance model output to determine whether the photovoltaic power station can operate normally. If the operation and maintenance results show that the power station cannot operate normally, further optimization processing is required.

[0115] The genetic algorithm is applied to optimize the selected photovoltaic power station operation data. Genetic algorithm is a search algorithm that simulates natural selection and genetic mechanism and can gradually approach the optimal solution through an iterative process.

[0116] During the optimization process, the algorithm will encode, select, crossover and mutate the operating data to generate a more optimized data combination. The optimized data will be substituted into the photovoltaic power station operation and maintenance model for evaluation again.

[0117] If the optimized photovoltaic power station operation data is substituted into the operation and maintenance model, and the output photovoltaic power station operation and maintenance results after the natural environment changes are still unable to operate normally, the genetic algorithm will continue to be used to optimize the screened operation data.

[0118] This process will be repeated until the operation and maintenance model outputs the result that the power plant can operate normally, or the preset maximum number of optimization iterations is reached.

[0119] When the result output by the photovoltaic power station operation and maintenance model is that the power station can operate normally, or the genetic algorithm optimization reaches the maximum number of iterations, the optimization process is stopped. The final optimized photovoltaic power station operation data and the corresponding operation and maintenance results are output, and the data will provide valuable reference and guidance for the actual operation and maintenance of the power station. The present invention can effectively improve the operation stability and efficiency of photovoltaic power stations under changes in the natural environment, reduce operation and maintenance costs, and extend the service life of the power station.

[0120] Specifically, the photovoltaic power generation effective data screening and optimization method of the present invention, step S103 includes:

[0121] If the natural environment data comparison error is less than the preset error value, then the theoretical natural environment information corresponding to the natural environment data comparison error is collected;

[0122] Obtain historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment, and use the environmental data comparison error, historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment to build an environmental simulation model;

[0123] The real-time plant area natural environment data is processed using a natural environment simulation model to obtain plant area natural environment simulation data, and the plant area natural environment simulation data is compared with theoretical natural environment information to obtain a plant area natural environment data comparison result;

[0124] The plant area natural environment data comparison results are grouped according to time to obtain the plant area natural environment data comparison result grouping data, and the natural environment data error threshold is compared for each group of data in the plant area natural environment data comparison result grouping data to filter out data greater than the natural environment data error threshold, and the natural environment prediction time information of the data greater than the natural environment data error threshold is retrieved to obtain the estimated natural environment change time;

[0125] Collect the inspection route of the real-time inspection equipment, generate the opposite inspection path of the inspection equipment based on the inspection route of the real-time inspection equipment, obtain the re-inspection inspection route, configure the expected natural environment change time and the re-inspection inspection route into the re-inspection inspection equipment, and when the expected natural environment change time is reached, receive the data collected by the re-inspection inspection equipment on the re-inspection inspection route as the re-inspection natural environment data;

[0126] The re-inspected natural environment data will be compared with the factory area natural environment simulation data. If the re-inspected natural environment data are inconsistent with the factory area natural environment simulation data, the natural environment simulation model will be optimized.

[0127] In step S103, the specific steps include:

[0128] Firstly, the real-time plant natural environment data is compared with the theoretical natural environment information, and the data comparison error is calculated.

[0129] If the data comparison error is less than the preset error value, the current natural environment data is considered to be relatively accurate and no further optimization is required. However, in this scenario, in order to further improve the accuracy of the data, the theoretical natural environment information corresponding to the natural environment data comparison error will still be collected for subsequent analysis.

[0130] Obtain the historical factory environment data collected by the inspection equipment on the inspection route, as well as the corresponding historical factory theoretical environment information.

[0131] A natural environment simulation model is constructed using natural environment data comparison errors, historical factory natural environment data of inspection equipment inspection paths, and historical factory theoretical natural environment information. The model can simulate and predict current natural environment data based on historical data and error information.

[0132] The real-time natural environment data of the factory area is input into the constructed natural environment simulation model, and the data is processed to obtain the natural environment simulation data of the factory area.

[0133] The obtained plant area natural environment simulation data was compared with the theoretical natural environment information again to evaluate the accuracy of the simulation model.

[0134] The comparison results of the natural environment data of the factory area are grouped according to time to obtain grouped data of data comparison results in multiple time periods.

[0135] The data in each time period are compared with the natural environment data error threshold, and the data with errors greater than the preset threshold are screened out. The data indicate abnormal or uncertain natural environment changes.

[0136] The natural environment prediction time information corresponding to the data greater than the error threshold is retrieved to obtain the estimated natural environment change time.

[0137] According to the inspection route of the real-time inspection equipment, a reverse inspection path is generated as the re-inspection inspection route.

[0138] The expected time of natural environment change and re-inspection route are configured into the re-inspection equipment.

[0139] When the expected natural environment change time arrives, the re-inspection inspection equipment will inspect along the re-inspection inspection route and collect corresponding natural environment data as re-inspection natural environment data.

[0140] Compare the re-inspected natural environment data with the previous plant area natural environment simulation data.

[0141] If there is an inconsistency between the two, it means that there are errors or deficiencies in the natural environment simulation model and it needs to be optimized.

[0142] Based on the comparison results and error analysis, the natural environment simulation model is adjusted and optimized to improve its prediction accuracy.

[0143] Through the above steps, the present invention can further improve the accuracy of natural environment data by constructing a natural environment simulation model, re-inspecting patrol data collection and comparison, etc., when the natural environment data comparison error is small, thereby providing more reliable data support for the operation and maintenance management of photovoltaic power stations.

[0144] Specifically, the photovoltaic power generation effective data screening and optimization method of the present invention, step S103 includes:

[0145] Aligning the re-inspected natural environment data and the plant area natural environment simulation data according to the same timestamp and spatial coordinates, and calculating the difference between the re-inspected natural environment data and the plant area natural environment simulation data, wherein the difference between the re-inspected natural environment data and the plant area natural environment simulation data includes a numerical difference and a trend difference;

[0146] Identify the difference between the re-inspected natural environment data and the plant area natural environment simulation data to obtain a difference identification result, wherein the difference identification result includes an area and a time period where the difference between the re-inspected natural environment data and the plant area natural environment simulation data exceeds a preset threshold;

[0147] The difference recognition results are matched in the preset knowledge base to obtain the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station, and the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station is matched in the preset model library to obtain the model to be trained, and the re-inspected natural environment data, the plant natural environment simulation data and the difference recognition results are used to train the model to be trained to obtain the optimized natural environment simulation model.

[0148] In the further refinement of step S103, the present invention proposes a method for finely analyzing, identifying and optimizing the model for the difference between the re-inspected natural environment data and the plant area natural environment simulation data, the specific contents of which are as follows:

[0149] First, the re-inspected natural environment data and the factory natural environment simulation data are accurately aligned according to the same timestamp and spatial coordinates to improve the consistency between the two in time and space dimensions.

[0150] Subsequently, the difference between the aligned re-inspected natural environment data and the plant area natural environment simulation data is calculated. The difference includes not only the specific numerical difference, but also the difference in the data change trend, so as to comprehensively evaluate the inconsistency between the two.

[0151] The calculated differences are carefully identified to determine which regions and time periods have differences that exceed the preset thresholds. Differences that exceed the thresholds are considered significant inconsistencies and require special attention.

[0152] The difference identification results will clearly indicate which areas (such as specific geographic locations or equipment locations) and which periods (such as specific time periods or seasons) have significant inconsistencies in natural environmental data.

[0153] The difference identification results are input into the preset knowledge base for matching to obtain information on the impact of these differences on the operation and maintenance decisions of the photovoltaic power station. The knowledge base contains rules and knowledge based on historical data and expert experience, which can help quickly understand the practical significance of data differences.

[0154] Next, the impact of the differences on the operation and maintenance decisions of the photovoltaic power station is matched in the preset model library to find a suitable model to be trained. The model library contains a variety of models for processing the relationship between natural environment data and photovoltaic power station operation data, which can provide suitable solutions according to specific needs.

[0155] The training model is trained using the re-inspected natural environment data, the plant natural environment simulation data, and the difference recognition results. Through the training process, the model can learn the complex relationship between data differences and the operating status of the photovoltaic power station.

[0156] After training, an optimized natural environment simulation model is obtained. The model can more accurately simulate and predict the impact of natural environment changes on the operation of photovoltaic power stations, providing more reliable data support for subsequent operation and maintenance decisions.

[0157] Through the above steps, the present invention not only realizes the detailed analysis of the differences between the re-inspected natural environment data and the plant natural environment simulation data, but also continuously optimizes the natural environment simulation model through methods such as knowledge base matching, model library query and model training, thereby improving its prediction accuracy and adaptability to natural environment changes, which helps photovoltaic power stations maintain efficient and stable operation in complex and changeable natural environments and improves the efficiency and effectiveness of operation and maintenance management.

[0158] Specifically, the photovoltaic power generation effective data screening and optimization method of the present invention, step S104 includes:

[0159] If the natural environment changes in the geographic image of the photovoltaic power station do not exceed the preset range value, the natural environment changes in the geographic image of the photovoltaic power station will continue to be monitored. In the process of monitoring the natural environment changes in the geographic image of the photovoltaic power station, the natural environment change information in the geographic image of the photovoltaic power station is substituted into the preset natural environment change monitoring model to output the real-time natural environment change monitoring results;

[0160] The construction process of the natural environment change monitoring model includes: collecting natural environment data from various sensors and equipment of photovoltaic power stations, the natural environment data includes light intensity, temperature, humidity and wind speed, and also collecting operation data of photovoltaic power stations, the operation data of photovoltaic power stations includes current, voltage and power generation, pre-processing the collected data, and screening out the natural environment change characteristics from the pre-processed data, and the natural environment change characteristics are used to reflect the change trend of the natural environment;

[0161] The deep learning model is trained using the characteristics of natural environment changes to obtain a natural environment change detection model.

[0162] In step S104, the present invention proposes a method for continuously monitoring the natural environment changes in the geographic image of the photovoltaic power station and outputting real-time monitoring results using the natural environment change monitoring model, which not only helps to timely discover and respond to the impact of natural environment changes on the operation of the photovoltaic power station, but also improves the accuracy and efficiency of natural environment change detection through the training of the deep learning model. The specific contents are as follows:

[0163] When the natural environment changes in the geographic image of the photovoltaic power station do not exceed the preset range value, the system will continue to monitor the natural environment changes. The continuous monitoring process improves the photovoltaic power station's ability to operate stably under various environmental conditions and respond to potential environmental changes in a timely manner.

[0164] During the monitoring process, the natural environment change information in the geographic image of the photovoltaic power station is input into the preset natural environment change monitoring model in real time. The model can output corresponding monitoring results based on the input natural environment change information and provide decision support for operation and maintenance personnel.

[0165] The natural environment data is collected from various sensors and equipment in the photovoltaic power station, including key indicators such as light intensity, temperature, humidity and wind speed. At the same time, the operation data of the photovoltaic power station, such as current, voltage and power generation, are also collected to fully understand the operation status of the power station.

[0166] The collected data are preprocessed, including data cleaning, denoising, normalization and other steps to improve the quality and usability of the data.

[0167] Screening out features that can reflect the changing trends of the natural environment from the preprocessed data is crucial for building an accurate and effective natural environment change monitoring model.

[0168] The deep learning model is trained using the selected characteristics of natural environment changes. Through a large amount of data input and iterative model optimization, the model can accurately identify and predict the changing trends of the natural environment.

[0169] The trained natural environment change monitoring model can output real-time monitoring results based on the input natural environment change information. The results include specific indicators of natural environment changes, change trends, and the impact on the operation of photovoltaic power stations, providing comprehensive and accurate information support for operation and maintenance personnel.

[0170] Through the implementation of step S104, the present invention realizes continuous and accurate monitoring of natural environment changes in the geographic image of the photovoltaic power station, and improves the intelligence level of monitoring through the training of the deep learning model, which helps operation and maintenance personnel to promptly discover and respond to the impact of natural environment changes on the operation of the photovoltaic power station, and ensure the efficient and stable operation of the power station.

[0171] Specifically, the photovoltaic power generation effective data screening and optimization method of the present invention, step S105 includes:

[0172] The screened photovoltaic power station operation data is encoded to form an initial population, where each individual represents a solution, i.e., a combination of photovoltaic power station operation data;

[0173] Receive the fitness function, which is used to evaluate the quality of each individual, calculate the fitness value of each individual in the initial population, and sort the population according to the fitness value;

[0174] According to the fitness value, a part of individuals are selected as parents to generate the next generation, and the selected parent individuals are crossovered to generate new individuals. The crossover operation includes single-point crossover and multi-point crossover.

[0175] Perform mutation operations on newly generated individuals to increase the diversity of the population, and merge the new individuals generated by crossover and mutation operations with the parent individuals to form a new population;

[0176] Calculate the fitness value of each individual in the new population, sort the population according to the fitness value, check whether the maximum number of optimization iterations is reached or an individual that meets the requirements is found, if the termination condition is not met, continue to perform genetic operations, if the termination condition is met, stop the optimization process, and output the optimized photovoltaic power station operation data.

[0177] In step S105, the specific steps of using the genetic algorithm to continue optimizing the screened photovoltaic power station operation data can be divided into the following four stages:

[0178] Phase 1: Initialize the population;

[0179] Data encoding: First, the screened photovoltaic power plant operation data is encoded to form an initial population. Each individual represents a solution, that is, a combination or configuration of photovoltaic power plant operation data.

[0180] Phase 2: Fitness assessment;

[0181] Fitness function: Define a fitness function to evaluate the quality of each individual. This function can be determined based on the operation and maintenance results of the photovoltaic power station after the natural environment changes output by the photovoltaic power station operation and maintenance model. For example, individuals with normal operation results will obtain higher fitness values.

[0182] Evaluate the population: Calculate the fitness value of each individual in the initial population and sort the population according to the fitness value.

[0183] Stage 3: Genetic manipulation;

[0184] Selection: According to the fitness value, a part of individuals are selected as parents to generate the next generation. Common selection methods include roulette selection, tournament selection, etc.

[0185] Crossover: Perform a crossover operation on the selected parent individuals to generate new individuals. The crossover operation can include single-point crossover, multi-point crossover, etc.

[0186] Mutation: Perform mutation operations on newly generated individuals to increase the diversity of the population. Mutation operations can include changing a gene value or gene fragment of an individual.

[0187] Phase 4: Iteration and termination;

[0188] Generate a new population: merge the new individuals generated by crossover and mutation operations with the parent individuals to form a new population.

[0189] Evaluate the new population: Calculate the fitness value of each individual in the new population and sort the population according to the fitness value.

[0190] Termination condition: Check whether the termination condition is met, such as reaching the maximum number of optimization iterations or finding an individual that meets the requirements.

[0191] If the termination condition is not met, the process returns to stage three and continues to perform genetic operations; if the termination condition is met, the optimization process is stopped and the optimized photovoltaic power station operation data is output.

[0192] Through the genetic algorithm operation in the above four stages, the operating data of the selected photovoltaic power station can be continuously optimized to make it more adaptable to changes in the natural environment, thereby improving the operation and maintenance efficiency and power generation efficiency of the photovoltaic power station.

[0193] In a second aspect, the present invention provides a photovoltaic power generation effective data screening and optimization system, which uses the photovoltaic power generation effective data screening and optimization method as described above, including:

[0194] A data acquisition unit is used to acquire regional range information of a photovoltaic power station and equipment information of a photovoltaic power station, wherein the equipment information of the photovoltaic power station includes equipment attributes of the photovoltaic power station, equipment coordinate information of the photovoltaic power station and equipment operation information of the photovoltaic power station, wherein the regional range information of the photovoltaic power station includes coordinate information within the regional range of the photovoltaic power station and the regional geographical environment of the photovoltaic power station, and the regional range information of the photovoltaic power station and the equipment information of the photovoltaic power station are substituted into the digital twin model to generate geographic image data of the photovoltaic power station;

[0195] a position generating unit, for acquiring the coordinates of the control point of the photovoltaic power station, determining the mapping position of the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station according to the coordinates of the control point of the photovoltaic power station, obtaining the coordinate information of the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, correcting the coordinate information of the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station based on the coordinates of the control point of the photovoltaic power station, obtaining the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, and correcting the coordinate data in the geographic image data of the photovoltaic power station according to the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, to obtain the corrected coordinate information of the photovoltaic power station;

[0196] The natural environment monitoring unit is used to obtain the inspection equipment information of the photovoltaic power station, substitute the inspection equipment information of the photovoltaic power station and the corrected coordinate information of the photovoltaic power station into the preset inspection path model of the inspection equipment to obtain the inspection path of the inspection equipment, receive the real-time natural environment data of the plant area collected by the inspection equipment on the inspection path, obtain the real-time natural environment data of the plant area, and obtain the natural environment information corresponding to the corrected coordinate information of the photovoltaic power station from the data interface according to the corrected coordinate information of the photovoltaic power station to obtain the theoretical natural environment information, group the theoretical natural environment information, the basis for grouping the theoretical natural environment information is grouping per hour to obtain the theoretical natural environment grouping data, and group the real-time natural environment data of the plant area with the theoretical natural environment grouping data by hour The natural environment data period matching result is obtained by matching the time interval as the matching benchmark, and the natural environment data matching result includes the natural environment period matching success data and the natural environment period matching failure data, and the natural environment period matching success data includes the theoretical natural environment grouping data and the real-time plant area natural environment data corresponding to the theoretical natural environment grouping data. The theoretical natural environment grouping data and the real-time plant area natural environment data corresponding to the theoretical natural environment grouping data are compared with each other. If the natural environment data comparison error is greater than the preset error value, the theoretical natural environment information is stopped from being used, and the real-time plant area natural environment data and the corrected photovoltaic power station coordinate information are substituted into the preset natural environment simulation model to obtain the natural environment simulation data corresponding to the photovoltaic power station coordinates;

[0197] a data screening unit, for acquiring operation data of a photovoltaic power station, marking the data source of the operation data of the photovoltaic power station to obtain operation data of the photovoltaic power station with the data source mark, matching the data source mark with the corrected coordinate information of the photovoltaic power station to obtain the position corresponding to the operation data of the photovoltaic power station in the geographic image of the photovoltaic power station, substituting the natural environment simulation data corresponding to the coordinates of the photovoltaic power station into the geographic image of the photovoltaic power station to obtain the natural environment change information in the geographic image of the photovoltaic power station, monitoring the natural environment change information in the geographic image of the photovoltaic power station, if the natural environment change in the geographic image of the photovoltaic power station exceeds a preset range value, retrieving the position coordinate information corresponding to the natural environment change in the geographic image of the photovoltaic power station when the natural environment change exceeds the preset range value to obtain the natural environment change area, and determining the data source mark corresponding to the natural environment change area according to the position coordinate information, performing data retrieval in a preset database according to the data source mark corresponding to the natural environment, and obtaining the operation data of the photovoltaic power station with the data source mark;

[0198] The optimization unit is used to perform validity detection on the photovoltaic power station operation data with the data source mark to obtain the validity detection result of the photovoltaic power station operation data, wherein the validity detection result of the photovoltaic power station operation data includes valid data of the photovoltaic power station operation data and invalid data of the photovoltaic power station operation data, delete the invalid data of the photovoltaic power station operation data in the photovoltaic power station operation data, obtain the screened photovoltaic power station operation data, substitute the screened photovoltaic power station operation data into the preset photovoltaic power station operation and maintenance model, obtain the photovoltaic power station operation and maintenance result after the natural environment changes, and if the photovoltaic power station operation and maintenance result is that it cannot operate normally, the screened photovoltaic power station operation data is replaced. The optimized photovoltaic power station operation data is obtained by using a genetic algorithm for optimization, and the optimized photovoltaic power station operation data is substituted into a preset photovoltaic power station operation and maintenance model to obtain an optimized photovoltaic power station operation and maintenance result of the natural environment change. If the corrected photovoltaic power station operation and maintenance result after the natural environment change is unable to operate normally, the genetic algorithm is used to continue to optimize the screened photovoltaic power station operation data until the optimized photovoltaic power station operation data is substituted into the preset photovoltaic power station operation and maintenance model and the output photovoltaic power station operation and maintenance result after the natural environment change is normal operation or the maximum number of optimization iterations is reached, then the genetic algorithm is stopped to continue to optimize the screened photovoltaic power station operation data.

[0199] Specifically, the photovoltaic power generation effective data screening and optimization system of the present invention, the natural environment monitoring unit is also used for:

[0200] If the natural environment data comparison error is less than the preset error value, then the theoretical natural environment information corresponding to the natural environment data comparison error is collected;

[0201] Obtain historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment, and use the environmental data comparison error, historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment to build an environmental simulation model;

[0202] The real-time plant area natural environment data is processed using a natural environment simulation model to obtain plant area natural environment simulation data, and the plant area natural environment simulation data is compared with theoretical natural environment information to obtain a plant area natural environment data comparison result;

[0203] The plant area natural environment data comparison results are grouped according to time to obtain the plant area natural environment data comparison result grouping data, and the natural environment data error threshold is compared for each group of data in the plant area natural environment data comparison result grouping data to filter out data greater than the natural environment data error threshold, and the natural environment prediction time information of the data greater than the natural environment data error threshold is retrieved to obtain the estimated natural environment change time;

[0204] Collect the inspection route of the real-time inspection equipment, generate the opposite inspection path of the inspection equipment based on the inspection route of the real-time inspection equipment, obtain the re-inspection inspection route, configure the expected natural environment change time and the re-inspection inspection route into the re-inspection inspection equipment, and when the expected natural environment change time is reached, receive the data collected by the re-inspection inspection equipment on the re-inspection inspection route as the re-inspection natural environment data;

[0205] The re-inspected natural environment data will be compared with the factory area natural environment simulation data. If the re-inspected natural environment data are inconsistent with the factory area natural environment simulation data, the natural environment simulation model will be optimized.

[0206] Specifically, in the photovoltaic power generation effective data screening and optimization system of the present invention, the natural environment monitoring unit is further used to: align the re-inspected natural environment data and the plant area natural environment simulation data according to the same timestamp and spatial coordinates, and calculate the difference between the re-inspected natural environment data and the plant area natural environment simulation data, wherein the difference between the re-inspected natural environment data and the plant area natural environment simulation data includes a numerical difference and a trend difference;

[0207] Identify the difference between the re-inspected natural environment data and the plant area natural environment simulation data to obtain a difference identification result, wherein the difference identification result includes an area and a time period where the difference between the re-inspected natural environment data and the plant area natural environment simulation data exceeds a preset threshold;

[0208] The difference recognition results are matched in the preset knowledge base to obtain the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station, and the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station is matched in the preset model library to obtain the model to be trained, and the re-inspected natural environment data, the plant natural environment simulation data and the difference recognition results are used to train the model to be trained to obtain the optimized natural environment simulation model.

[0209] Specifically, the photovoltaic power generation effective data screening and optimization system of the present invention, the data screening unit includes:

[0210] If the natural environment changes in the geographic image of the photovoltaic power station do not exceed the preset range value, the natural environment changes in the geographic image of the photovoltaic power station will continue to be monitored. In the process of monitoring the natural environment changes in the geographic image of the photovoltaic power station, the natural environment change information in the geographic image of the photovoltaic power station is substituted into the preset natural environment change monitoring model to output the real-time natural environment change monitoring results;

[0211] The construction process of the natural environment change monitoring model includes: collecting natural environment data from various sensors and equipment of photovoltaic power stations, the natural environment data includes light intensity, temperature, humidity and wind speed, and also collecting operation data of photovoltaic power stations, the operation data of photovoltaic power stations includes current, voltage and power generation, pre-processing the collected data, and screening out the natural environment change characteristics from the pre-processed data, and the natural environment change characteristics are used to reflect the change trend of the natural environment;

[0212] The deep learning model is trained using the characteristics of natural environment changes to obtain a natural environment change detection model.

[0213] Specifically, the photovoltaic power generation effective data screening and optimization system of the present invention, the optimization unit is also used for

[0214] The screened photovoltaic power station operation data is encoded to form an initial population, where each individual represents a solution, i.e., a combination of photovoltaic power station operation data;

[0215] Receive the fitness function, which is used to evaluate the quality of each individual, calculate the fitness value of each individual in the initial population, and sort the population according to the fitness value;

[0216] According to the fitness value, a part of individuals are selected as parents to generate the next generation, and the selected parent individuals are crossovered to generate new individuals. The crossover operation includes single-point crossover and multi-point crossover.

[0217] Perform mutation operations on newly generated individuals to increase the diversity of the population, and merge the new individuals generated by crossover and mutation operations with the parent individuals to form a new population;

[0218] Calculate the fitness value of each individual in the new population, sort the population according to the fitness value, check whether the maximum number of optimization iterations is reached or an individual that meets the requirements is found, if the termination condition is not met, continue to perform genetic operations, if the termination condition is met, stop the optimization process, and output the optimized photovoltaic power station operation data.

[0219] The present invention solves the problem that the accuracy of photovoltaic power generation effective data screening is reduced due to complex natural environmental factors in actual application scenarios in new energy power plants under extreme natural environments through a photovoltaic power generation effective data screening optimization method and system. The specific solution includes the following key steps:

[0220] The regional scope information and equipment information of the photovoltaic power station are obtained, and this information is substituted into the digital twin model to generate the geographic image data of the photovoltaic power station. Through the digital twin technology, a virtual mirror image of the photovoltaic power station is constructed, which provides accurate geographic coordinates and environmental information basis for subsequent data processing and screening.

[0221] The control point coordinates of the photovoltaic power station are obtained, their mapping positions are determined in the geographic image data, and the coordinate information in the geographic image data is corrected based on the control point coordinates to obtain more accurate photovoltaic power station coordinate information, thereby improving the accuracy of subsequent data processing.

[0222] The real-time plant area natural environment data collected by the inspection equipment is obtained and compared with the theoretical natural environment information obtained through the corrected photovoltaic power station coordinate information. If the comparison error is greater than the preset error value, the real-time plant area natural environment data and the corrected photovoltaic power station coordinate information are substituted into the natural environment simulation model to obtain natural environment simulation data corresponding to the photovoltaic power station coordinates that are closer to the actual situation. If the comparison error is less than the preset error value, the natural environment simulation model is further constructed and optimized to improve the prediction accuracy of the natural environment data.

[0223] Substitute the natural environment simulation data corresponding to the photovoltaic power station coordinates into the geographic image to monitor the natural environment change information. If the natural environment changes beyond the preset range, the corresponding photovoltaic power station operation data is retrieved from the database according to the location coordinate information of the changed area. Perform validity check on the retrieved photovoltaic power station operation data, delete invalid data, and obtain the filtered valid operation data.

[0224] Substitute the filtered effective operation data into the photovoltaic power station operation and maintenance model to evaluate the operation and maintenance results of the photovoltaic power station after the natural environment changes. If the operation and maintenance results cannot operate normally, the genetic algorithm is used to optimize the operation data until the optimized data is substituted into the operation and maintenance model to output the normal operation and maintenance results or the maximum number of optimization iterations is reached.

[0225] Through the above steps, the present invention effectively solves the problem of reduced accuracy in screening effective data for photovoltaic power generation in new energy power plants under extreme natural environments. Through key technical means such as digital twin technology, natural environment data comparison and simulation, natural environment change monitoring and data screening, and data optimization and operation and maintenance decision-making, the accuracy of screening effective data for photovoltaic power generation is improved, providing support for the efficient and stable operation of photovoltaic power stations.

Claims

1. A method for screening and optimizing effective data of photovoltaic power generation, characterized in that: include: Step S101, obtaining regional range information of a photovoltaic power station and equipment information of a photovoltaic power station, wherein the equipment information of the photovoltaic power station includes equipment attributes of the photovoltaic power station, equipment coordinate information of the photovoltaic power station, and equipment operation information of the photovoltaic power station, wherein the regional range information of the photovoltaic power station includes coordinate information within the regional range of the photovoltaic power station and the regional geographical environment of the photovoltaic power station, and substituting the regional range information of the photovoltaic power station and the equipment information of the photovoltaic power station into the digital twin model to generate geographic image data of the photovoltaic power station; Step S102, obtaining the coordinates of the control point of the photovoltaic power station, determining the mapping position of the coordinate position of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station according to the coordinate position of the control point of the photovoltaic power station, obtaining the coordinate information of the coordinate position of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, correcting the coordinate information of the coordinate position of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station based on the coordinates of the control point of the photovoltaic power station, obtaining the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, and correcting the coordinate data in the geographic image data of the photovoltaic power station according to the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station to obtain the corrected coordinate information of the photovoltaic power station; Step S103, obtain the inspection equipment information of the photovoltaic power station, substitute the inspection equipment information of the photovoltaic power station and the corrected coordinate information of the photovoltaic power station into the preset inspection path model of the inspection equipment to obtain the inspection path of the inspection equipment, receive the real-time natural environment data of the factory area collected by the inspection equipment on the inspection path, obtain the real-time natural environment data of the factory area, and obtain the natural environment information corresponding to the corrected coordinate information of the photovoltaic power station from the data interface according to the corrected coordinate information of the photovoltaic power station to obtain the theoretical natural environment information, group the theoretical natural environment information, the basis for grouping the theoretical natural environment information is grouping per hour to obtain theoretical natural environment grouping data, and compare the real-time natural environment data of the factory area with the theoretical natural environment grouping data based on time. The natural environment data is matched according to the matching benchmark to obtain a natural environment data period matching result, wherein the natural environment data matching result includes natural environment period matching success data and natural environment period matching failure data, wherein the natural environment period matching success data includes theoretical natural environment grouping data and real-time plant area natural environment data corresponding to the theoretical natural environment grouping data, and the theoretical natural environment grouping data is compared with the real-time plant area natural environment data corresponding to the theoretical natural environment grouping data. If the natural environment data comparison error is greater than a preset error value, the theoretical natural environment information is stopped from being used, and the real-time plant area natural environment data and the corrected photovoltaic power station coordinate information are substituted into a preset natural environment simulation model to obtain the natural environment simulation data corresponding to the photovoltaic power station coordinates; Step S104, obtaining the operation data of the photovoltaic power station, marking the data source of the operation data of the photovoltaic power station to obtain the operation data of the photovoltaic power station with the data source mark, matching the data source mark with the corrected coordinate information of the photovoltaic power station to obtain the position corresponding to the operation data of the photovoltaic power station in the geographic image of the photovoltaic power station, substituting the natural environment simulation data corresponding to the coordinates of the photovoltaic power station into the geographic image of the photovoltaic power station to obtain the natural environment change information in the geographic image of the photovoltaic power station, monitoring the natural environment change information in the geographic image of the photovoltaic power station, if the natural environment change in the geographic image of the photovoltaic power station exceeds a preset range value, then retrieving the position coordinate information corresponding to the natural environment change in the geographic image of the photovoltaic power station when it exceeds the preset range value to obtain the natural environment change area, and determining the data source mark corresponding to the natural environment change area according to the position coordinate information, performing data retrieval in a preset database according to the data source mark corresponding to the natural environment, and obtaining the operation data of the photovoltaic power station with the data source mark; Step 105, performing validity detection on the photovoltaic power station operation data with the data source mark to obtain the validity detection result of the photovoltaic power station operation data, the validity detection result of the photovoltaic power station operation data includes valid data of the photovoltaic power station operation data and invalid data of the photovoltaic power station operation data, deleting the invalid data of the photovoltaic power station operation data in the photovoltaic power station operation data to obtain the screened photovoltaic power station operation data, substituting the screened photovoltaic power station operation data into a preset photovoltaic power station operation and maintenance model to obtain the photovoltaic power station operation and maintenance result after the natural environment changes, if the photovoltaic power station operation and maintenance result is that it cannot operate normally, then the screened photovoltaic power station operation data is replaced. Genetic algorithms are used for optimization to obtain optimized photovoltaic power station operation data, and the optimized photovoltaic power station operation data are substituted into a preset photovoltaic power station operation and maintenance model to obtain optimized photovoltaic power station operation and maintenance results for natural environment changes. If the corrected photovoltaic power station operation and maintenance results after the natural environment changes cannot operate normally, the genetic algorithm is used to continue to optimize the screened photovoltaic power station operation data until the optimized photovoltaic power station operation data is substituted into the preset photovoltaic power station operation and maintenance model for operation, and the output photovoltaic power station operation and maintenance results after the natural environment changes are normal operation or the maximum number of optimization iterations is reached, then the genetic algorithm is stopped to continue to optimize the screened photovoltaic power station operation data.

2. The photovoltaic power generation effective data screening and optimization method according to claim 1, characterized in that: The step S103 includes: If the natural environment data comparison error is less than the preset error value, then the theoretical natural environment information corresponding to the natural environment data comparison error is collected; Obtain historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment, and use the environmental data comparison error, historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment to build an environmental simulation model; The real-time plant area natural environment data is processed using a natural environment simulation model to obtain plant area natural environment simulation data, and the plant area natural environment simulation data is compared with theoretical natural environment information to obtain a plant area natural environment data comparison result; The plant area natural environment data comparison results are grouped according to time to obtain the plant area natural environment data comparison result grouping data, and the natural environment data error threshold is compared for each group of data in the plant area natural environment data comparison result grouping data to filter out data greater than the natural environment data error threshold, and the natural environment prediction time information of the data greater than the natural environment data error threshold is retrieved to obtain the estimated natural environment change time; Collect the inspection route of the real-time inspection equipment, generate the opposite inspection path of the inspection equipment based on the inspection route of the real-time inspection equipment, obtain the re-inspection inspection route, configure the expected natural environment change time and the re-inspection inspection route into the re-inspection inspection equipment, and when the expected natural environment change time is reached, receive the data collected by the re-inspection inspection equipment on the re-inspection inspection route as the re-inspection natural environment data; The re-inspected natural environment data will be compared with the factory area natural environment simulation data. If the re-inspected natural environment data are inconsistent with the factory area natural environment simulation data, the natural environment simulation model will be optimized.

3. The photovoltaic power generation effective data screening and optimization method according to claim 2, characterized in that: The step S103 includes: Aligning the re-inspected natural environment data and the plant area natural environment simulation data according to the same timestamp and spatial coordinates, and calculating the difference between the re-inspected natural environment data and the plant area natural environment simulation data, wherein the difference between the re-inspected natural environment data and the plant area natural environment simulation data includes a numerical difference and a trend difference; Identify the difference between the re-inspected natural environment data and the plant area natural environment simulation data to obtain a difference identification result, wherein the difference identification result includes an area and a time period where the difference between the re-inspected natural environment data and the plant area natural environment simulation data exceeds a preset threshold; The difference recognition results are matched in the preset knowledge base to obtain the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station, and the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station is matched in the preset model library to obtain the model to be trained, and the re-inspected natural environment data, the plant natural environment simulation data and the difference recognition results are used to train the model to be trained to obtain the optimized natural environment simulation model.

4. The photovoltaic power generation effective data screening and optimization method according to claim 1, characterized in that: The step S104 includes: If the natural environment changes in the geographic image of the photovoltaic power station do not exceed the preset range value, the natural environment changes in the geographic image of the photovoltaic power station will continue to be monitored. In the process of monitoring the natural environment changes in the geographic image of the photovoltaic power station, the natural environment change information in the geographic image of the photovoltaic power station is substituted into the preset natural environment change monitoring model to output the real-time natural environment change monitoring results; The construction process of the natural environment change monitoring model includes: collecting natural environment data from various sensors and equipment of photovoltaic power stations, the natural environment data includes light intensity, temperature, humidity and wind speed, and also collecting operation data of photovoltaic power stations, the operation data of photovoltaic power stations includes current, voltage and power generation, pre-processing the collected data, and screening out the natural environment change characteristics from the pre-processed data, and the natural environment change characteristics are used to reflect the change trend of the natural environment; The deep learning model is trained using the characteristics of natural environment changes to obtain a natural environment change detection model.

5. The photovoltaic power generation effective data screening and optimization method according to claim 1, characterized in that: The step S105 includes: The screened photovoltaic power station operation data is encoded to form an initial population, where each individual represents a solution, i.e., a combination of photovoltaic power station operation data; Receive the fitness function, which is used to evaluate the quality of each individual, calculate the fitness value of each individual in the initial population, and sort the population according to the fitness value; According to the fitness value, a part of individuals are selected as parents to generate the next generation, and the selected parent individuals are crossovered to generate new individuals. The crossover operation includes single-point crossover and multi-point crossover. Perform mutation operations on newly generated individuals to increase the diversity of the population, and merge the new individuals generated by crossover and mutation operations with the parent individuals to form a new population; Calculate the fitness value of each individual in the new population, sort the population according to the fitness value, check whether the maximum number of optimization iterations is reached or an individual that meets the requirements is found, if the termination condition is not met, continue to perform genetic operations, if the termination condition is met, stop the optimization process, and output the optimized photovoltaic power station operation data.

6. A photovoltaic power generation effective data screening and optimization system, using the photovoltaic power generation effective data screening and optimization method according to any one of claims 1 to 5, characterized in that: include: A data acquisition unit is used to acquire regional range information of a photovoltaic power station and equipment information of a photovoltaic power station, wherein the equipment information of the photovoltaic power station includes equipment attributes of the photovoltaic power station, equipment coordinate information of the photovoltaic power station and equipment operation information of the photovoltaic power station, wherein the regional range information of the photovoltaic power station includes coordinate information within the regional range of the photovoltaic power station and the regional geographical environment of the photovoltaic power station, and the regional range information of the photovoltaic power station and the equipment information of the photovoltaic power station are substituted into the digital twin model to generate geographic image data of the photovoltaic power station; a position generating unit, for acquiring the coordinates of the control point of the photovoltaic power station, determining the mapping position of the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station according to the coordinates of the control point of the photovoltaic power station, obtaining the coordinate information of the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, correcting the coordinate information of the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station based on the coordinates of the control point of the photovoltaic power station, obtaining the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, and correcting the coordinate data in the geographic image data of the photovoltaic power station according to the coordinates of the control point of the photovoltaic power station in the geographic image data of the photovoltaic power station, to obtain the corrected coordinate information of the photovoltaic power station; The natural environment monitoring unit is used to obtain the inspection equipment information of the photovoltaic power station, substitute the inspection equipment information of the photovoltaic power station and the corrected coordinate information of the photovoltaic power station into the preset inspection path model of the inspection equipment to obtain the inspection path of the inspection equipment, receive the real-time natural environment data of the plant area collected by the inspection equipment on the inspection path, obtain the real-time natural environment data of the plant area, and obtain the natural environment information corresponding to the corrected coordinate information of the photovoltaic power station from the data interface according to the corrected coordinate information of the photovoltaic power station to obtain the theoretical natural environment information, group the theoretical natural environment information, the basis for grouping the theoretical natural environment information is grouping per hour to obtain the theoretical natural environment grouping data, and group the real-time natural environment data of the plant area with the theoretical natural environment grouping data by hour The natural environment data period matching result is obtained by matching the time interval as the matching benchmark, and the natural environment data matching result includes the natural environment period matching success data and the natural environment period matching failure data, and the natural environment period matching success data includes the theoretical natural environment grouping data and the real-time plant area natural environment data corresponding to the theoretical natural environment grouping data. The theoretical natural environment grouping data and the real-time plant area natural environment data corresponding to the theoretical natural environment grouping data are compared with each other. If the natural environment data comparison error is greater than the preset error value, the theoretical natural environment information is stopped from being used, and the real-time plant area natural environment data and the corrected photovoltaic power station coordinate information are substituted into the preset natural environment simulation model to obtain the natural environment simulation data corresponding to the photovoltaic power station coordinates; a data screening unit, for acquiring operation data of a photovoltaic power station, marking the data source of the operation data of the photovoltaic power station to obtain operation data of the photovoltaic power station with the data source mark, matching the data source mark with the corrected coordinate information of the photovoltaic power station to obtain the position corresponding to the operation data of the photovoltaic power station in the geographic image of the photovoltaic power station, substituting the natural environment simulation data corresponding to the coordinates of the photovoltaic power station into the geographic image of the photovoltaic power station to obtain the natural environment change information in the geographic image of the photovoltaic power station, monitoring the natural environment change information in the geographic image of the photovoltaic power station, if the natural environment change in the geographic image of the photovoltaic power station exceeds a preset range value, retrieving the position coordinate information corresponding to the natural environment change in the geographic image of the photovoltaic power station when the natural environment change exceeds the preset range value to obtain the natural environment change area, and determining the data source mark corresponding to the natural environment change area according to the position coordinate information, performing data retrieval in a preset database according to the data source mark corresponding to the natural environment, and obtaining the operation data of the photovoltaic power station with the data source mark; The optimization unit is used to perform validity detection on the photovoltaic power station operation data with the data source mark to obtain the validity detection result of the photovoltaic power station operation data, wherein the validity detection result of the photovoltaic power station operation data includes valid data of the photovoltaic power station operation data and invalid data of the photovoltaic power station operation data, delete the invalid data of the photovoltaic power station operation data in the photovoltaic power station operation data, obtain the screened photovoltaic power station operation data, substitute the screened photovoltaic power station operation data into the preset photovoltaic power station operation and maintenance model, obtain the photovoltaic power station operation and maintenance result after the natural environment changes, and if the photovoltaic power station operation and maintenance result is that it cannot operate normally, the screened photovoltaic power station operation data is replaced. The optimized photovoltaic power station operation data is obtained by using a genetic algorithm for optimization, and the optimized photovoltaic power station operation data is substituted into a preset photovoltaic power station operation and maintenance model to obtain an optimized photovoltaic power station operation and maintenance result of the natural environment change. If the corrected photovoltaic power station operation and maintenance result after the natural environment change is unable to operate normally, the genetic algorithm is used to continue to optimize the screened photovoltaic power station operation data until the optimized photovoltaic power station operation data is substituted into the preset photovoltaic power station operation and maintenance model and the output photovoltaic power station operation and maintenance result after the natural environment change is normal operation or the maximum number of optimization iterations is reached, then the genetic algorithm is stopped to continue to optimize the screened photovoltaic power station operation data.

7. The photovoltaic power generation effective data screening and optimization system according to claim 6, characterized in that: The natural environment monitoring unit is also used for: If the natural environment data comparison error is less than the preset error value, then the theoretical natural environment information corresponding to the natural environment data comparison error is collected; Obtain historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment, and use the environmental data comparison error, historical plant environment data and historical plant theoretical environment information of the inspection path of the inspection equipment to build an environmental simulation model; The real-time plant area natural environment data is processed using a natural environment simulation model to obtain plant area natural environment simulation data, and the plant area natural environment simulation data is compared with theoretical natural environment information to obtain a plant area natural environment data comparison result; The plant area natural environment data comparison results are grouped according to time to obtain the plant area natural environment data comparison result grouping data, and the natural environment data error threshold is compared for each group of data in the plant area natural environment data comparison result grouping data to filter out data greater than the natural environment data error threshold, and the natural environment prediction time information of the data greater than the natural environment data error threshold is retrieved to obtain the estimated natural environment change time; Collect the inspection route of the real-time inspection equipment, generate the opposite inspection path of the inspection equipment based on the inspection route of the real-time inspection equipment, obtain the re-inspection inspection route, configure the expected natural environment change time and the re-inspection inspection route into the re-inspection inspection equipment, and when the expected natural environment change time is reached, receive the data collected by the re-inspection inspection equipment on the re-inspection inspection route as the re-inspection natural environment data; The re-inspected natural environment data will be compared with the factory area natural environment simulation data. If the re-inspected natural environment data are inconsistent with the factory area natural environment simulation data, the natural environment simulation model will be optimized.

8. The photovoltaic power generation effective data screening and optimization system according to claim 7, characterized in that: The natural environment monitoring unit is also used for: Aligning the re-inspected natural environment data and the plant area natural environment simulation data according to the same timestamp and spatial coordinates, and calculating the difference between the re-inspected natural environment data and the plant area natural environment simulation data, wherein the difference between the re-inspected natural environment data and the plant area natural environment simulation data includes a numerical difference and a trend difference; Identify the difference between the re-inspected natural environment data and the plant area natural environment simulation data to obtain a difference identification result, wherein the difference identification result includes an area and a time period where the difference between the re-inspected natural environment data and the plant area natural environment simulation data exceeds a preset threshold; The difference recognition results are matched in the preset knowledge base to obtain the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station, and the impact information of the differences on the operation and maintenance decision-making of the photovoltaic power station is matched in the preset model library to obtain the model to be trained, and the re-inspected natural environment data, the plant natural environment simulation data and the difference recognition results are used to train the model to be trained to obtain the optimized natural environment simulation model.

9. The photovoltaic power generation effective data screening and optimization system according to claim 6, characterized in that: The data screening unit is further used for: If the natural environment changes in the geographic image of the photovoltaic power station do not exceed the preset range value, the natural environment changes in the geographic image of the photovoltaic power station will continue to be monitored. In the process of monitoring the natural environment changes in the geographic image of the photovoltaic power station, the natural environment change information in the geographic image of the photovoltaic power station is substituted into the preset natural environment change monitoring model to output the real-time natural environment change monitoring results; The construction process of the natural environment change monitoring model includes: collecting natural environment data from various sensors and equipment of photovoltaic power stations, the natural environment data includes light intensity, temperature, humidity and wind speed, and also collecting operation data of photovoltaic power stations, the operation data of photovoltaic power stations includes current, voltage and power generation, pre-processing the collected data, and screening out the natural environment change characteristics from the pre-processed data, and the natural environment change characteristics are used to reflect the change trend of the natural environment; The deep learning model is trained using the characteristics of natural environment changes to obtain a natural environment change detection model.

10. The photovoltaic power generation effective data screening and optimization system according to claim 6, characterized in that: The optimization unit is further used for: The screened photovoltaic power station operation data is encoded to form an initial population, where each individual represents a solution, i.e., a combination of photovoltaic power station operation data; Receive the fitness function, which is used to evaluate the quality of each individual, calculate the fitness value of each individual in the initial population, and sort the population according to the fitness value; According to the fitness value, a part of individuals are selected as parents to generate the next generation, and the selected parent individuals are crossovered to generate new individuals. The crossover operation includes single-point crossover and multi-point crossover. Perform mutation operations on newly generated individuals to increase the diversity of the population, and merge the new individuals generated by crossover and mutation operations with the parent individuals to form a new population; Calculate the fitness value of each individual in the new population, sort the population according to the fitness value, check whether the maximum number of optimization iterations is reached or an individual that meets the requirements is found, if the termination condition is not met, continue to perform genetic operations, if the termination condition is met, stop the optimization process, and output the optimized photovoltaic power station operation data.

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