Photovoltaic power generation efficiency evaluation method and system based on unmanned aerial vehicle detection data

Abstract

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power generation efficiency evaluation method and system based on unmanned aerial vehicle detection data, and the method comprises the steps: obtaining a reference coordinate set through a reference photovoltaic power generation unit group, and obtaining a fitting detection path sequence based on the reference coordinate set, and sequentially driving the detection unmanned aerial vehicles by using the fitting detection paths in the fitting detection path sequence, monitoring the unmanned aerial vehicle coordinates of the detection unmanned aerial vehicles in driving in real time, and when the unmanned aerial vehicle coordinates are the detection coordinates, obtaining a target photovoltaic image based on the detection angles corresponding to the detection nodes. And obtaining an evaluation loss coefficient by using the target photovoltaic image, the data collection unit, the data analysis unit and the data evaluation unit, obtaining the reference power generation efficiency based on the reference photovoltaic power generation unit corresponding to the detection node, and calculating the product of the evaluation loss coefficient and the reference power generation efficiency to obtain the evaluation power generation efficiency. According to the invention, the evaluation of the photovoltaic power generation efficiency can be intelligently and accurately realized.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a photovoltaic power generation efficiency evaluation method and system based on unmanned aerial vehicle detection data. Background Art

[0002] With the continuous growth of global demand for clean energy, photovoltaic power generation has been widely used as an important renewable energy technology. Correspondingly, how to accurately and intelligently evaluate the efficiency of photovoltaic power generation has become an urgent problem to be solved.

[0003] At present, traditional methods for evaluating photovoltaic power generation efficiency mostly use monitoring equipment installed on the ground in advance to evaluate the power generation efficiency of photovoltaic power generation units, or use manual inspections to test each photovoltaic power generation unit to evaluate the power generation efficiency of the photovoltaic power generation unit.

[0004] Although the above method can realize the evaluation of photovoltaic power generation efficiency, it consumes a lot of manpower and material resources and has great limitations when evaluating the power generation efficiency of photovoltaic power generation units. Therefore, intelligently and accurately realizing the evaluation of photovoltaic power generation efficiency has become an urgent problem to be solved. Summary of the invention

[0005] The present invention provides a photovoltaic power generation efficiency evaluation method based on unmanned aerial vehicle detection data and a computer-readable storage medium, the main purpose of which is to intelligently and accurately realize the evaluation of photovoltaic power generation efficiency.

[0006] To achieve the above purpose, the present invention provides a photovoltaic power generation efficiency evaluation method based on drone detection data, comprising: receiving an efficiency evaluation instruction, and confirming an efficiency evaluation environment based on the efficiency evaluation instruction, wherein the efficiency evaluation environment includes a detection drone, a reference photovoltaic power generation unit group, and an efficiency evaluation system, wherein the efficiency evaluation system includes: a data collection unit, a data analysis unit, a data evaluation unit, and a result feedback unit, wherein the reference photovoltaic power generation unit group includes a plurality of reference photovoltaic power generation units; A reference coordinate set is obtained by using a reference photovoltaic power generation unit group, wherein the reference coordinate set includes a plurality of reference coordinates, and the reference coordinates correspond to the reference photovoltaic power generation units one by one, and a fitting detection path sequence is obtained based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, the fitting detection path includes a plurality of detection nodes, and the detection nodes include detection angles and detection coordinates; The detection drones are sequentially driven using the fitting detection paths in the fitting detection path sequence, and the drone coordinates of the driving detection drones are monitored in real time, and when the drone coordinates are the detection coordinates, a target photovoltaic image is acquired based on the detection angle corresponding to the detection node; Obtaining an evaluation loss factor using a target photovoltaic image, a data collection unit, a data analysis unit, and a data evaluation unit; Acquiring a reference power generation efficiency based on a reference photovoltaic power generation unit corresponding to the detection node; The product of the evaluation loss coefficient and the reference power generation efficiency is calculated to obtain the evaluation power generation efficiency, and the result feedback unit is used to send the evaluation power generation efficiency to the initiator of the efficiency evaluation instruction to realize the power generation efficiency evaluation of the reference photovoltaic power generation unit group.

[0007] Optionally, acquiring a fitting detection path sequence based on the reference coordinate set includes: Identify reference regression coordinates as a starting point and an end point, and cluster the reference coordinates in the reference coordinate set using a pre-constructed clustering method to obtain one or more clustered coordinate sets; One or more cluster center coordinates are identified based on one or more cluster coordinate sets, wherein the cluster center coordinates correspond one-to-one to the cluster coordinate sets, the Euclidean distance between each of the one or more cluster center coordinates and the reference regression coordinates is calculated to obtain a retrieval order distance, the retrieval order distances are summarized to obtain a retrieval order distance set, the retrieval order distances in the retrieval order distance set are sorted in ascending order to obtain a retrieval order sequence, and a retrieval coordinate group sequence is obtained based on the retrieval order sequence, wherein the retrieval coordinate group sequence includes one or more retrieval coordinate groups, and the retrieval coordinate groups correspond one-to-one to the retrieval order distances in the retrieval order sequence, and the retrieval coordinate groups correspond one-to-one to the cluster coordinate sets; Extracting retrieval coordinate groups from the retrieval coordinate group sequence in sequence, and performing the following operations on the extracted retrieval coordinate groups: Acquire an identification coordinate group based on the search coordinate group, wherein the identification coordinate group includes a plurality of identification coordinates, and the identification coordinates correspond to the reference coordinates one by one; Acquire a path fitting model for fitting the path, obtain the fitting path using the path fitting model and the identification coordinate group, identify multiple detection nodes in the fitting path using the identification coordinates in the identification coordinate group to obtain an initial partition path, obtain a fitting detection path based on the initial partition path and the reference regression coordinates, identify the explored coordinate set of the fitting detection path, eliminate the explored coordinate set in the reference coordinate set, and use the reference coordinate set from which the explored coordinate set is eliminated as the reference coordinate set, return to the step of clustering the reference coordinates in the reference coordinate set using the pre-constructed clustering method to obtain one or more clustered coordinate sets, summarize the fitting detection paths to obtain a fitting detection path set, sort the fitting detection paths in the fitting detection path set in the order of the time corresponding to the acquisition of the fitting detection paths from the earliest to the latest, and obtain a fitting detection path sequence.

[0008] Optionally, acquiring a fitting detection path based on the initial segmentation path and the reference regression coordinates includes: Identify the first detection node in the initial partition path to obtain the initial detection node, obtain the initial flight distance using the identification coordinates and reference regression coordinates corresponding to the initial detection node, and identify the fitting partition path in the initial partition path using the initial detection node and the preset node progressive length; The last detection node is extracted from the fitting partition path to obtain a regression detection node, and the regression flight distance is obtained using the identification coordinates corresponding to the regression detection node and the reference regression coordinates; Identify the number of detection nodes in the fitting partition path, obtain the number of detection nodes, obtain the initial energy consumption of the detection UAV, and calculate the detection energy consumption based on the initial flight distance, the regression flight distance, the fitting partition path and the number of detection nodes; The product of the initial energy consumption and the preset energy consumption ratio is calculated to obtain a safe energy consumption threshold, and a fitting detection path is obtained based on the detection energy consumption and the safe energy consumption threshold.

[0009] Optionally, the calculating the detection energy consumption based on the initial flight distance, the regression flight distance, the fitting division path and the number of detection nodes includes: The fitting detection distance is obtained based on the fitting partition path, and the detection energy consumption is calculated using the fitting detection distance and the pre-constructed energy consumption evaluation relationship, wherein the energy consumption evaluation relationship is as follows: ; in, represents the detection energy consumption, , They are the preset flight coefficient and hover coefficient respectively. Indicates the number of detection nodes, represents the quality of the detection drone, It indicates the distance that the detection drone needs to fly when conducting detection. , , They represent the fitting detection distance, the initial flight distance and the regression flight distance respectively, represents the propulsion efficiency of the detection drone, represents the optimal cruising speed, Indicates the flight speed of the drone. Indicates the preset single hover time.

[0010] Optionally, the acquiring a fitting detection path based on the detection energy consumption and the safety energy consumption threshold includes: Compare the detected energy consumption with the safety energy consumption threshold. If the detected energy consumption is less than or equal to the safety energy consumption threshold, update the node incremental length using the preset iterative step value, and use the updated node incremental length as the node incremental length. Return to the step of using the initial detected node and the preset node incremental length to confirm the fitting partition path in the initial partition path, until it is confirmed that the detected energy consumption is greater than the safety energy consumption threshold, summarize the detected energy consumption, and obtain a detected energy consumption set. The following operations are performed for each detection energy consumption in the detection energy consumption set: Calculate the difference between the safety energy consumption threshold and the detection energy consumption to obtain the evaluation energy consumption, summarize the evaluation energy consumption, obtain the evaluation energy consumption set, and confirm the target evaluation energy consumption based on the evaluation energy consumption set, wherein the target evaluation energy consumption is greater than or equal to 0, and the target evaluation energy consumption is the minimum evaluation energy consumption in the evaluation energy consumption set, and the fitted detection path is the fitted partitioning path corresponding to the target evaluation energy consumption.

[0011] Optionally, acquiring a target photovoltaic image based on a detection angle corresponding to a detection node includes: An initial monitoring image is acquired based on a preset detection angle, and an initial photovoltaic image is extracted from the initial monitoring image using a pre-built photovoltaic recognition model; The initial photovoltaic image is corrected using the detection angle and detection angle corresponding to the detection node to obtain the target photovoltaic image.

[0012] Optionally, the obtaining of the evaluation loss coefficient by using the target photovoltaic image, the data collection unit, the data analysis unit and the data evaluation unit includes: Confirming receipt of a data collection instruction from a data collection unit, parsing the data collection instruction, and obtaining a grayscale gradient set, wherein the grayscale gradient set includes a plurality of gradient grayscale values; Confirming receipt of a data evaluation instruction from a data evaluation unit, parsing the data evaluation instruction, and obtaining a reference grayscale range; A plurality of first reference grayscale values ​​are obtained based on a preset extraction value and a reference grayscale range, and the following operation is performed on each of the plurality of first reference grayscale values: Identify a fitted photovoltaic power generation unit based on a first reference grayscale value, obtain a fitted loss efficiency using the fitted photovoltaic power generation unit, associate the first reference grayscale value and the fitted loss efficiency to obtain a fitted coordinate, and summarize the fitted coordinates to obtain a fitted coordinate set; Confirming receipt of a data analysis instruction from a data analysis unit, parsing the data analysis instruction, obtaining a curve fitting model, mapping all fitting coordinates in the fitting coordinate set to a pre-constructed reference coordinate system to obtain a mapping coordinate set, and obtaining a fitting loss curve using the curve fitting model and the mapping coordinate set; Performing grayscale transformation on the target photovoltaic image to obtain a target grayscale image; Based on the target grayscale image, a pre-constructed region growing algorithm and a grayscale gradient set, one or more fitting region image groups are obtained, wherein the fitting region image groups include one or more initial fitting region images, and the fitting region image groups correspond to the gradient grayscale values ​​one by one; The following operations are performed on each of the one or more fitted region image groups: Acquire a fitting image area based on one or more initial fitting region images corresponding to the fitting region image group, acquire a target image area of ​​a target photovoltaic image, calculate a ratio of the fitting image area to the target image area, and obtain a fitting ratio; Obtaining a fitting grayscale mean value based on the fitting region image group, and using the fitting grayscale mean value to retrieve a target loss efficiency from the fitting loss curve; The target loss efficiency and the fitting ratio are associated to obtain a target loss node, the target loss nodes are summarized to obtain a target loss node set, and the target loss node set is used to calculate the evaluation loss coefficient.

[0013] Optionally, the target impairment node set is used to calculate the assessed impairment coefficient, and the calculation formula is as follows: ; in, represents the assessment impairment coefficient, Indicates that the target damaged nodes have target damage nodes, , They represent the target damaged node set. The fitting ratio and target loss efficiency corresponding to each target loss node.

[0014] Optionally, obtaining a reference power generation efficiency based on a reference photovoltaic power generation unit corresponding to the detection node includes: Acquire reference environmental data of a reference photovoltaic power generation unit, wherein the reference environmental data includes: irradiation intensity, ambient temperature and ambient humidity; Obtaining predicted power generation efficiency based on the reference environmental data and a pre-trained efficiency prediction model; The service life of the reference photovoltaic power generation unit is obtained, and the age depreciation coefficient is obtained based on the service life. The product of the age depreciation coefficient and the predicted power generation efficiency is calculated to obtain the reference power generation efficiency.

[0015] To achieve the above purpose, the present invention also provides a photovoltaic power generation efficiency evaluation system based on drone detection data, comprising: An evaluation environment confirmation module is used to receive an efficiency evaluation instruction and confirm an efficiency evaluation environment based on the efficiency evaluation instruction, wherein the efficiency evaluation environment includes a detection drone, a reference photovoltaic power generation unit group and an efficiency evaluation system, wherein the efficiency evaluation system includes: a data collection unit, a data analysis unit, a data evaluation unit and a result feedback unit, wherein the reference photovoltaic power generation unit group includes a plurality of reference photovoltaic power generation units; A detection path fitting module is used to obtain a reference coordinate set using a reference photovoltaic power generation unit group, wherein the reference coordinate set includes a plurality of reference coordinates, and the reference coordinates correspond to the reference photovoltaic power generation units one by one, and obtain a fitting detection path sequence based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, and the fitting detection path includes a plurality of detection nodes, and the detection nodes include detection angles and detection coordinates; A photovoltaic image detection module is used to sequentially drive the detection drone using the fitting detection paths in the fitting detection path sequence, and to monitor the drone coordinates of the driving detection drone in real time, and to obtain a target photovoltaic image based on the detection angle corresponding to the detection node when the drone coordinates are the detection coordinates; A power generation efficiency evaluation module, used to obtain an evaluation loss factor using a target photovoltaic image, a data collection unit, a data analysis unit, and a data evaluation unit; Acquiring a reference power generation efficiency based on a reference photovoltaic power generation unit corresponding to the detection node; The product of the evaluation loss coefficient and the reference power generation efficiency is calculated to obtain the evaluation power generation efficiency, and the result feedback unit is used to send the evaluation power generation efficiency to the initiator of the efficiency evaluation instruction to realize the power generation efficiency evaluation of the reference photovoltaic power generation unit group.

[0016] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising: A memory storing at least one instruction; and a processor executing the instruction stored in the memory to implement the above-mentioned photovoltaic power generation efficiency evaluation method based on drone detection data.

[0017] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned photovoltaic power generation efficiency evaluation method based on drone detection data.

[0018] The present invention is to solve the problem described in the background technology. An embodiment of the present invention uses a reference photovoltaic power generation unit group to obtain a reference coordinate set, wherein the reference coordinate set includes multiple reference coordinates, and the reference coordinates correspond to the reference photovoltaic power generation unit one by one, and a fitting detection path sequence is obtained based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, and the fitting detection path includes multiple detection nodes, and the detection nodes include detection angles and detection coordinates. It can be seen that the embodiment of the present invention, before realizing the power generation efficiency evaluation of the reference photovoltaic power generation unit, takes into account that the location of the reference photovoltaic power generation unit may be different. Therefore, the embodiment of the present invention combines the energy consumption that can be stored by the detection drone, and plans a path for realizing the reference photovoltaic power generation unit in the reference photovoltaic power generation unit group. Furthermore, by fitting the detection path, it is possible to realize the detection of the reference photovoltaic power generation unit, and can ensure the safety of the detection drone and reduce the energy consumption required for detecting the reference photovoltaic power generation unit, thereby improving the intelligence of the embodiment of the present invention. The embodiment of the present invention utilizes the fitting detection path in the fitting detection path sequence to sequentially drive the detection drone, and monitors the drone coordinates of the detection drone in real time during driving. When the drone coordinates are the detection coordinates, the target photovoltaic image is obtained based on the detection angle corresponding to the detection node, and the evaluation loss coefficient is obtained using the target photovoltaic image, the data collection unit, the data analysis unit and the data evaluation unit. The reference power generation efficiency is obtained based on the reference photovoltaic power generation unit corresponding to the detection node, and the product of the evaluation loss coefficient and the reference power generation efficiency is calculated to obtain the evaluation power generation efficiency. It can be seen that when evaluating the power generation efficiency of the reference photovoltaic power generation unit, the embodiment of the present invention takes into account the dust coating condition, service life, ambient temperature, ambient humidity and irradiation intensity of the reference photovoltaic power generation unit, and combines the factors that may affect the reference photovoltaic power generation unit to fit different loss coefficients, thereby improving the accuracy of the power generation efficiency evaluation of the reference photovoltaic power generation unit. Therefore, the present invention can intelligently and accurately realize the evaluation of photovoltaic power generation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a process for evaluating photovoltaic power generation efficiency based on drone detection data provided by an embodiment of the present invention; Figure 2 A functional module diagram of a photovoltaic power generation efficiency evaluation system based on drone detection data provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device for implementing the photovoltaic power generation efficiency evaluation method based on drone detection data provided by an embodiment of the present invention.

[0020] Description of reference numerals: 1. Electronic device; 10. Processor; 11. Memory; 12. Bus; 100. Photovoltaic power generation efficiency evaluation system based on UAV detection data; 101. Evaluation environment confirmation module; 102. Detection path fitting module; 103. Photovoltaic image detection module; 104. Power generation efficiency evaluation module.

[0021] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0023] The embodiment of the present application provides a photovoltaic power generation efficiency evaluation method based on drone detection data. The execution subject of the photovoltaic power generation efficiency evaluation method based on drone detection data includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the photovoltaic power generation efficiency evaluation method based on drone detection data can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0024] Reference Figure 1 FIG. 1 is a flow chart of a method for evaluating photovoltaic power generation efficiency based on drone detection data provided by an embodiment of the present invention. In this embodiment, the method for evaluating photovoltaic power generation efficiency based on drone detection data includes: S1. Receive an efficiency evaluation instruction, and confirm an efficiency evaluation environment based on the efficiency evaluation instruction, wherein the efficiency evaluation environment includes a detection drone, a reference photovoltaic power generation unit group and an efficiency evaluation system, wherein the efficiency evaluation system includes: a data collection unit, a data analysis unit, a data evaluation unit and a result feedback unit, wherein the reference photovoltaic power generation unit group includes multiple reference photovoltaic power generation units.

[0025] It should be explained that the efficiency evaluation instruction refers to an instruction for realizing the evaluation of photovoltaic power generation efficiency, the efficiency evaluation environment refers to the necessary environment for realizing the evaluation of photovoltaic power generation efficiency, and the efficiency evaluation environment includes a detection drone, a reference photovoltaic power generation unit group and an efficiency evaluation system, and the detection drone refers to a drone used to realize data collection for the reference photovoltaic power generation unit group in the efficiency evaluation environment. The reference photovoltaic power generation unit group refers to a collection of reference photovoltaic power generation units to be evaluated for efficiency, and the reference photovoltaic power generation unit refers to a photovoltaic power generation unit. The photovoltaic power generation unit is the core part of the photovoltaic power generation system and is responsible for converting the photon energy in sunlight into electrical energy. The efficiency evaluation system refers to an APP or applet used to realize the efficiency evaluation of the reference photovoltaic power generation unit group, and the efficiency evaluation system includes: a data collection unit, a data analysis unit, a data evaluation unit and a result feedback unit. For the specific application of the unit, please refer to the subsequent embodiments.

[0026] For example, Xiao Zhang, as the person in charge of the photovoltaic power generation system, in order to improve the power generation efficiency of the photovoltaic power generation system, therefore, after installing the photovoltaic power generation unit, Xiao Zhang issues the efficiency evaluation instruction, and Xiao Zhang confirms the efficiency evaluation environment. By adjusting the angle of the reference photovoltaic power generation unit in the reference photovoltaic power generation unit group in the efficiency evaluation environment, the angle with the highest power generation efficiency of each reference photovoltaic power generation unit in the reference photovoltaic power generation unit group can be clearly identified, and the reference photovoltaic power generation unit can be adjusted at this angle to improve the power generation efficiency of the photovoltaic power generation system.

[0027] S2. Obtain a reference coordinate set using a reference photovoltaic power generation unit group, wherein the reference coordinate set includes multiple reference coordinates, and the reference coordinates correspond one-to-one to the reference photovoltaic power generation units, and obtain a fitting detection path sequence based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, the fitting detection path includes multiple detection nodes, and the detection nodes include detection angles and detection coordinates.

[0028] It should be explained that the reference coordinates refer to the coordinates used to characterize the reference photovoltaic power generation unit. Optionally, three-dimensional coordinates are used as the reference coordinates.

[0029] Further, acquiring a fitting detection path sequence based on the reference coordinate set includes: Identify reference regression coordinates as a starting point and an end point, and cluster the reference coordinates in the reference coordinate set using a pre-constructed clustering method to obtain one or more clustered coordinate sets; One or more cluster center coordinates are identified based on one or more cluster coordinate sets, wherein the cluster center coordinates correspond one-to-one to the cluster coordinate sets, the Euclidean distance between each of the one or more cluster center coordinates and the reference regression coordinates is calculated to obtain a retrieval order distance, the retrieval order distances are summarized to obtain a retrieval order distance set, the retrieval order distances in the retrieval order distance set are sorted in ascending order to obtain a retrieval order sequence, and a retrieval coordinate group sequence is obtained based on the retrieval order sequence, wherein the retrieval coordinate group sequence includes one or more retrieval coordinate groups, and the retrieval coordinate groups correspond one-to-one to the retrieval order distances in the retrieval order sequence, and the retrieval coordinate groups correspond one-to-one to the cluster coordinate sets; Extracting retrieval coordinate groups from the retrieval coordinate group sequence in sequence, and performing the following operations on the extracted retrieval coordinate groups: Acquire an identification coordinate group based on the search coordinate group, wherein the identification coordinate group includes a plurality of identification coordinates, and the identification coordinates correspond to the reference coordinates one by one; Acquire a path fitting model for fitting the path, obtain the fitting path using the path fitting model and the identification coordinate group, identify multiple detection nodes in the fitting path using the identification coordinates in the identification coordinate group to obtain an initial partition path, obtain a fitting detection path based on the initial partition path and the reference regression coordinates, identify the explored coordinate set of the fitting detection path, eliminate the explored coordinate set in the reference coordinate set, and use the reference coordinate set from which the explored coordinate set is eliminated as the reference coordinate set, return to the step of clustering the reference coordinates in the reference coordinate set using the pre-constructed clustering method to obtain one or more clustered coordinate sets, summarize the fitting detection paths to obtain a fitting detection path set, sort the fitting detection paths in the fitting detection path set in the order of the time corresponding to the acquisition of the fitting detection paths from the earliest to the latest, and obtain a fitting detection path sequence.

[0030] It should be explained that the reference regression coordinates refer to the coordinates of the corresponding positions when the detection drone takes off and flies back. For example, the detection drone takes off from the base station, and after completing the detection of the reference photovoltaic power generation unit, it flies back to the base station. The three-dimensional coordinates corresponding to the base station are the reference regression coordinates. The clustered coordinate set refers to the clustered reference coordinate set obtained after clustering the reference coordinates in the reference coordinate set using a clustering algorithm. Generally speaking, the number of clustered coordinate sets is related to the clustering algorithm used and the reference coordinates in the reference coordinate set. Optionally, the k-means clustering algorithm is used as the clustering method, and other technologies can achieve the same effect, which will not be repeated here. For example, the k-means clustering algorithm is used to cluster the reference coordinates in the reference coordinate set into 3 clusters, and the reference coordinates included in each cluster constitute the clustered coordinate set.

[0031] Furthermore, the cluster center coordinates refer to the coordinates of the cluster center corresponding to the cluster coordinate set. The technology for identifying the cluster center coordinates in the cluster coordinate set is an existing technology and will not be repeated here. The path fitting model refers to a model or method that can fit the path. Optionally, the ant colony algorithm is used as the path fitting model. Other technologies can achieve the same effect and will not be repeated here. The identification coordinates refer to the reference coordinates after identification. The purpose of identifying the reference coordinates here is to achieve the distinction between the reference coordinates, thereby improving the accuracy of path planning for the detection drone. For example, the retrieval coordinate group includes three reference coordinates, and the three reference coordinates are (1, 2, 1), (3, 2, 2) and (3, 5, 6). Then, each of the three reference coordinates is identified to obtain three identification coordinates, and the three identification coordinates are: 1-(1, 2, 1), 2-(3, 2, 2) and 3-(3, 5, 6).

[0032] It should be explained that the fitting path refers to the path obtained by using the path fitting model to identify each identified coordinate in the coordinate group as a necessary point. The detection node refers to the node corresponding to each reference photovoltaic power generation unit, and the node includes a detection angle for characterizing the inclination corresponding to the reference photovoltaic power generation unit and a detection coordinate for characterizing the coordinate corresponding to the reference photovoltaic power generation unit. The explored coordinate set refers to the set of detection coordinates included in the fitting detection path. Here, the definition of the detection coordinates is the same as the definition of the reference coordinates, and will not be repeated here. Generally speaking, when installing a photovoltaic power generation unit, the coordinates and inclination of the installed photovoltaic power generation unit will be recorded.

[0033] Furthermore, the acquiring of the fitting detection path based on the initial segmentation path and the reference regression coordinates includes: Identify the first detection node in the initial partition path to obtain the initial detection node, obtain the initial flight distance using the identification coordinates and reference regression coordinates corresponding to the initial detection node, and identify the fitting partition path in the initial partition path using the initial detection node and the preset node progressive length; The last detection node is extracted from the fitting partition path to obtain a regression detection node, and the regression flight distance is obtained using the identification coordinates corresponding to the regression detection node and the reference regression coordinates; Identify the number of detection nodes in the fitting partition path, obtain the number of detection nodes, obtain the initial energy consumption of the detection UAV, and calculate the detection energy consumption based on the initial flight distance, the regression flight distance, the fitting partition path and the number of detection nodes; The product of the initial energy consumption and the preset energy consumption ratio is calculated to obtain a safe energy consumption threshold, and a fitting detection path is obtained based on the detection energy consumption and the safe energy consumption threshold.

[0034] It is understandable that the initial flight distance refers to the distance required for the detection drone to fly from the starting point to the first detection node that needs to be detected. Generally, before using the detection drone for detection, the relevant parameters of the detection drone will be set. The relevant parameters include but are not limited to: the flight altitude of the detection drone, the flight speed of the detection drone, and the flight path of the detection drone. Therefore, the initial flight distance can be calculated by the set relevant parameters, identification coordinates and reference regression coordinates. For ease of understanding, the identification coordinates and reference regression coordinates are both processed as two-dimensional coordinates, and the flight path of the detection drone is a straight line. Therefore, the initial flight path can be obtained by dividing the path corresponding to the detection drone flying from the starting point to the initial detection node into one or more line segments, and accumulating the distance of each line segment.

[0035] It should be explained that the node progressive length refers to the number of detection nodes that can be selected in the initial partition path. For example, the value corresponding to the initial detection node in the initial partition path is 1, indicating that the initial detection node is the first node that needs to be detected in the initial partition path. The node progressive length is 1, and the fitting partition path includes the first detection node in the initial partition path and the second detection node in the initial partition path. Here, the second detection node is the regression detection node, that is, the node progressive length can be used to confirm the fitting partition path including two detection nodes in the initial partition path. Generally speaking, the method of obtaining the regression flight distance is the same as the method of obtaining the initial flight distance, which will not be repeated here.

[0036] Furthermore, the calculation of detection energy consumption based on the initial flight distance, the regression flight distance, the fitting division path and the number of detection nodes includes: The fitting detection distance is obtained based on the fitting partition path, and the detection energy consumption is calculated using the fitting detection distance and the pre-constructed energy consumption evaluation relationship, wherein the energy consumption evaluation relationship is as follows: ; in, represents the detection energy consumption, , They are the preset flight coefficient and hover coefficient respectively. Indicates the number of detection nodes, represents the quality of the detection drone, It indicates the distance that the detection drone needs to fly when conducting detection. , , They represent the fitting detection distance, the initial flight distance and the regression flight distance respectively, represents the propulsion efficiency of the detection drone, represents the optimal cruising speed, Indicates the flight speed of the drone. Indicates the preset single hover time.

[0037] It should be explained that the method for obtaining the fitting detection distance is the same as the method for obtaining the initial flight distance, and no further description is given here. Propulsion efficiency refers to the efficiency of the detection drone when performing detection, wherein the propulsion efficiency includes but is not limited to: the propeller efficiency of the detection drone, the motor efficiency of the detection drone, and optionally, the propulsion efficiency is obtained by experiment. The same effect can be achieved by other technologies, and no further description is given here. The flight coefficient refers to the parameter that affects the flight of the detection drone, wherein the factors that affect the flight of the detection drone include but are not limited to: the starting resistance coefficient of the detection drone, and the windward area of ​​the detection drone. The hovering coefficient refers to the parameter that affects the hovering of the detection drone, wherein the factors that affect the hovering of the detection drone include but are not limited to: gravity acceleration, air density. Optionally, the flight coefficient and the hovering coefficient are obtained respectively by experimental calibration method, and the same effect can be achieved by other technologies, and no further description is given here. The optimal cruising speed is the speed at which the detection drone has the lowest fuel consumption rate, the longest endurance time or the best comprehensive performance under specific conditions. The flight speed refers to the actual speed at which the detection drone is set when flying. The purpose of setting a single hovering time is to collect an image of the environment where the reference photovoltaic power generation unit is located and to determine a correlation coefficient of the environment where the reference photovoltaic power generation unit is located, wherein the correlation coefficient includes: irradiation intensity, ambient temperature, and ambient humidity.

[0038] Furthermore, the acquiring of a fitting detection path based on the detection energy consumption and the safety energy consumption threshold comprises: Compare the detected energy consumption with the safety energy consumption threshold. If the detected energy consumption is less than or equal to the safety energy consumption threshold, update the node incremental length using the preset iterative step value, and use the updated node incremental length as the node incremental length. Return to the step of using the initial detected node and the preset node incremental length to confirm the fitting partition path in the initial partition path, until it is confirmed that the detected energy consumption is greater than the safety energy consumption threshold, summarize the detected energy consumption, and obtain a detected energy consumption set. The following operations are performed for each detection energy consumption in the detection energy consumption set: Calculate the difference between the safety energy consumption threshold and the detection energy consumption to obtain the evaluation energy consumption, summarize the evaluation energy consumption, obtain the evaluation energy consumption set, and confirm the target evaluation energy consumption based on the evaluation energy consumption set, wherein the target evaluation energy consumption is greater than or equal to 0, and the target evaluation energy consumption is the minimum evaluation energy consumption in the evaluation energy consumption set, and the fitted detection path is the fitted partitioning path corresponding to the target evaluation energy consumption.

[0039] It is understandable that when the detection energy consumption is less than the safety energy consumption threshold, it indicates that the detection drone can complete the preset task and return safely. The iterative step value refers to the value used to increase the node incremental length. For example, the node incremental length is 1, the iterative step value is 2, and the updated node incremental length is 3. Generally speaking, the detection drone needs to fly the initial flight distance and the return flight distance every time it conducts detection. Therefore, combining the initial energy consumption of the detection drone and maximizing the detection of the detection node can improve the efficiency of the detection drone at work. Here, the form of confirming the fitting division path is adopted to improve the efficiency of the detection drone at work. The purpose of setting the energy consumption ratio is to improve the safety of the detection drone during detection, that is, to ensure that the detection drone can return safely. Optionally, the energy consumption ratio is a manually set value, and other technologies can achieve the same effect, which will not be repeated here. The initial energy consumption refers to the energy stored by the detection drone.

[0040] S3. Use the fitting detection paths in the fitting detection path sequence to sequentially drive the detection drones, and monitor the drone coordinates of the driving detection drones in real time. When the drone coordinates are the detection coordinates, obtain the target photovoltaic image based on the detection angle corresponding to the detection node.

[0041] It should be understood that each fitting detection path in the fitting detection path sequence includes one or more detection nodes. Therefore, the fitting detection paths in the fitting detection path sequence can be used to drive the detection drone in sequence, thereby realizing the detection of the reference photovoltaic power generation unit group. Generally speaking, the embodiments of the present invention can also use multiple detection drones to realize the detection of the reference photovoltaic power generation unit included in the fitting detection path in the fitting detection path sequence, which will not be repeated here. Optionally, the detection of the position of the detection drone is realized by pre-installing a GPS unit in the detection drone, that is, the drone coordinates of the detection drone are obtained in real time through the GPS unit. Generally speaking, the drone coordinates are the detection coordinates here, which means that the detection coordinates are the same as the drone coordinates. For example, the drone coordinates and the detection coordinates are both represented in the form of longitude and latitude. When the longitude and latitude corresponding to the drone coordinates are the same as the longitude and latitude corresponding to the detection coordinates, the drone coordinates are confirmed to be the detection coordinates.

[0042] It should be explained that the step of acquiring the target photovoltaic image based on the detection angle corresponding to the detection node includes: An initial monitoring image is acquired based on a preset detection angle, and an initial photovoltaic image is extracted from the initial monitoring image using a pre-built photovoltaic recognition model; The initial photovoltaic image is corrected using the detection angle and detection angle corresponding to the detection node to obtain the target photovoltaic image.

[0043] Further, the detection angle refers to the angle used to realize the shooting of the environment. For example, the direction perpendicular to the ground is used to realize the collection of the image in the environment. Therefore, the angle corresponding to the direction perpendicular to the ground is the detection angle, and the image in the environment collected is the initial monitoring image. The photovoltaic recognition model refers to a model that can recognize and extract the image corresponding to the reference photovoltaic power generation unit in the initial monitoring image. Optionally, a pre-trained neural network model is used as the photovoltaic recognition model, and other technologies can achieve the same effect, which will not be repeated here. The initial photovoltaic image refers to the image used to characterize the reference photovoltaic power generation unit identified in the initial detection image using the photovoltaic recognition model. Correcting the initial photovoltaic image using the detection angle and detection angle corresponding to the detection node means: correcting the initial photovoltaic image to an image used to characterize the front of the reference photovoltaic power generation unit, that is, the corrected initial photovoltaic image is an image taken when it is perpendicular to the reference photovoltaic power generation unit. Here, the corrected initial photovoltaic image is the target photovoltaic image. Optionally, the correction of the initial photovoltaic image is realized by combining the detection angle and the detection angle through a geometric correction method, and other technologies can achieve the same effect, which will not be repeated here.

[0044] It is understandable that when capturing images of the reference photovoltaic power generation unit, directly photographing the reference photovoltaic power generation unit at an angle perpendicular to the reference photovoltaic power generation unit may also cause noise in the initial photovoltaic image due to the emission of sunlight. Therefore, the acquisition of the initial photovoltaic image by setting the detection angle can improve the accuracy of the initial photovoltaic image, thereby laying the foundation for the subsequent evaluation of the power generation efficiency of the reference photovoltaic power generation unit.

[0045] S4. Obtain the evaluation loss factor using the target photovoltaic image, the data collection unit, the data analysis unit and the data evaluation unit.

[0046] It should be explained that the method of obtaining the evaluation loss coefficient by using the target photovoltaic image, the data collection unit, the data analysis unit and the data evaluation unit includes: Confirming receipt of a data collection instruction from a data collection unit, parsing the data collection instruction, and obtaining a grayscale gradient set, wherein the grayscale gradient set includes a plurality of gradient grayscale values; Confirming receipt of a data evaluation instruction from a data evaluation unit, parsing the data evaluation instruction, and obtaining a reference grayscale range; A plurality of first reference grayscale values ​​are obtained based on a preset extraction value and a reference grayscale range, and the following operation is performed on each of the plurality of first reference grayscale values: Identify a fitted photovoltaic power generation unit based on a first reference grayscale value, obtain a fitted loss efficiency using the fitted photovoltaic power generation unit, associate the first reference grayscale value and the fitted loss efficiency to obtain a fitted coordinate, and summarize the fitted coordinates to obtain a fitted coordinate set; Confirming receipt of a data analysis instruction from a data analysis unit, parsing the data analysis instruction, obtaining a curve fitting model, mapping all fitting coordinates in the fitting coordinate set to a pre-constructed reference coordinate system to obtain a mapping coordinate set, and obtaining a fitting loss curve using the curve fitting model and the mapping coordinate set; Performing grayscale transformation on the target photovoltaic image to obtain a target grayscale image; Based on the target grayscale image, a pre-constructed region growing algorithm and a grayscale gradient set, one or more fitting region image groups are obtained, wherein the fitting region image groups include one or more initial fitting region images, and the fitting region image groups correspond to the gradient grayscale values ​​one by one; The following operations are performed on each of the one or more fitted region image groups: Acquire a fitting image area based on one or more initial fitting region images corresponding to the fitting region image group, acquire a target image area of ​​a target photovoltaic image, calculate a ratio of the fitting image area to the target image area, and obtain a fitting ratio; Obtaining a fitting grayscale mean value based on the fitting region image group, and using the fitting grayscale mean value to retrieve a target loss efficiency from the fitting loss curve; The target loss efficiency and the fitting ratio are associated to obtain a target loss node, the target loss nodes are summarized to obtain a target loss node set, and the target loss node set is used to calculate the evaluation loss coefficient.

[0047] It is understandable that the gradient grayscale value is a grayscale value used to evaluate the dust coverage of the reference photovoltaic power generation unit surface. The reference grayscale range is used to express the range of grayscale values ​​of the reference photovoltaic power generation unit surface after the reference photovoltaic power generation unit surface is covered with dust. Optionally, the reference grayscale range is obtained by artificial setting. Optionally, a linear interval method is used to extract multiple first reference grayscale values ​​in the reference grayscale range using the extracted value. For example, the reference grayscale range is (0, 10) and the extracted value is 5. Then, using the linear interval method, 5 first reference grayscale values ​​can be extracted in the reference grayscale range, and the 5 first reference grayscale values ​​are: 0, 2.5, 5, 7.5 and 10 respectively. The fitted photovoltaic power generation unit refers to a photovoltaic power generation unit with dust coverage on the surface, and the grayscale value of the image corresponding to the dust coverage of the fitted photovoltaic power generation unit is the first reference grayscale value. Optionally, a dust simulation material, a photovoltaic power generation unit and a first reference grayscale value are used to construct a fitted photovoltaic power generation unit.

[0048] It should be explained that the fitting loss efficiency refers to the ratio of the actual power of the photovoltaic power generation unit to the rated power. The curve fitting model is a model used to fit multiple discrete points into a curve. Optionally, a polynomial fitting model is used as the curve fitting model. Other technologies can achieve the same effect, which will not be repeated here. Optionally, a two-dimensional coordinate system is used as the reference coordinate system. Other technologies can achieve the same effect, which will not be repeated here. The regional growing algorithm is a prior art and will not be repeated here.

[0049] It should be understood that the fitting image area is used to characterize the total area of ​​the initial fitting region image in the fitting region image group. Optionally, the number of pixels is used as the fitting image area. The same effect can be achieved by using other technologies, which will not be repeated here. Generally, the fitting image area corresponding to one or more fitting region image groups is the target image area. The target image area is the area used to characterize the target photovoltaic image, and the method for obtaining the target image area is the same as the method for obtaining the fitting image area, which will not be repeated here. Grayscale transformation refers to the technology of converting a color image into a gray image, and grayscale transformation is a prior art and will not be repeated here.

[0050] Furthermore, the fitting grayscale mean is the mean of the grayscale values ​​of one or more corresponding initial fitting region images in the fitting region image group. For example, if the fitting region image group includes three initial fitting region images, the fitting grayscale mean is the mean of the grayscale values ​​corresponding to the three initial fitting region images.

[0051] It can be understood that retrieving the target loss efficiency in the fitting loss curve using the fitting grayscale mean value means: retrieving the fitting loss efficiency corresponding to the grayscale value having the same fitting grayscale mean value in the fitting loss curve.

[0052] Furthermore, the target loss node set is used to calculate the evaluation loss coefficient, and the calculation formula is as follows: ; in, represents the assessment impairment coefficient, Indicates that the target damaged nodes have target damage nodes, , They represent the target damaged node set. The fitting ratio and target loss efficiency corresponding to each target loss node.

[0053] S5. Obtain a reference power generation efficiency based on the reference photovoltaic power generation unit corresponding to the detection node.

[0054] It should be explained that the step of obtaining the reference power generation efficiency based on the reference photovoltaic power generation unit corresponding to the detection node includes: Acquire reference environmental data of a reference photovoltaic power generation unit, wherein the reference environmental data includes: irradiation intensity, ambient temperature and ambient humidity; Obtaining predicted power generation efficiency based on the reference environmental data and a pre-trained efficiency prediction model; The service life of the reference photovoltaic power generation unit is obtained, and the age depreciation coefficient is obtained based on the service life. The product of the age depreciation coefficient and the predicted power generation efficiency is calculated to obtain the reference power generation efficiency.

[0055] It is understandable that the service life refers to the time when the reference photovoltaic power generation unit is put into use. Optionally, the age depreciation coefficient is obtained through the empirical model and the service life. Other technologies can achieve the same effect, which will not be repeated here. The age depreciation coefficient refers to a parameter related to the service life, and the parameter is used to express the ratio of the actual power generation power of the reference photovoltaic power generation unit to the rated power after a period of time.

[0056] Furthermore, the efficiency prediction model refers to a model for predicting the ratio between the actual power generation and the rated power generation of a reference photovoltaic power generation unit under reference environmental data. Optionally, a short-term prediction digital twin model is used as the efficiency prediction model. Other technologies can achieve the same effect, which will not be repeated here. The predicted power generation efficiency refers to the ratio between the actual power generation and the rated power generation of a reference photovoltaic power generation unit under reference environmental data.

[0057] It should be explained that the irradiation intensity refers to the solar irradiation energy received per unit area per unit time by the reference photovoltaic power generation unit. The ambient temperature and ambient humidity refer to the temperature and humidity of the environment corresponding to the reference photovoltaic power generation unit, respectively.

[0058] S6. Calculate the product of the evaluation loss coefficient and the reference power generation efficiency to obtain the evaluation power generation efficiency, and use the result feedback unit to send the evaluation power generation efficiency to the initiator of the efficiency evaluation instruction to achieve the power generation efficiency evaluation of the reference photovoltaic power generation unit group.

[0059] It should be explained that the evaluation of power generation efficiency is used to characterize the ratio of the power generation power of the reference photovoltaic power generation unit to the rated power.

[0060] The present invention is to solve the problem described in the background technology. An embodiment of the present invention uses a reference photovoltaic power generation unit group to obtain a reference coordinate set, wherein the reference coordinate set includes multiple reference coordinates, and the reference coordinates correspond to the reference photovoltaic power generation unit one by one, and a fitting detection path sequence is obtained based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, and the fitting detection path includes multiple detection nodes, and the detection nodes include detection angles and detection coordinates. It can be seen that the embodiment of the present invention, before realizing the power generation efficiency evaluation of the reference photovoltaic power generation unit, takes into account that the location of the reference photovoltaic power generation unit may be different. Therefore, the embodiment of the present invention combines the energy consumption that can be stored by the detection drone, and plans a path for realizing the reference photovoltaic power generation unit in the reference photovoltaic power generation unit group. Furthermore, by fitting the detection path, it is possible to realize the detection of the reference photovoltaic power generation unit, and can ensure the safety of the detection drone and reduce the energy consumption required for detecting the reference photovoltaic power generation unit, thereby improving the intelligence of the embodiment of the present invention. The embodiment of the present invention utilizes the fitting detection path in the fitting detection path sequence to sequentially drive the detection drone, and monitors the drone coordinates of the detection drone in real time during driving. When the drone coordinates are the detection coordinates, the target photovoltaic image is obtained based on the detection angle corresponding to the detection node, and the evaluation loss coefficient is obtained using the target photovoltaic image, the data collection unit, the data analysis unit and the data evaluation unit. The reference power generation efficiency is obtained based on the reference photovoltaic power generation unit corresponding to the detection node, and the product of the evaluation loss coefficient and the reference power generation efficiency is calculated to obtain the evaluation power generation efficiency. It can be seen that when evaluating the power generation efficiency of the reference photovoltaic power generation unit, the embodiment of the present invention takes into account the dust coating condition, service life, ambient temperature, ambient humidity and irradiation intensity of the reference photovoltaic power generation unit, and combines the factors that may affect the reference photovoltaic power generation unit to fit different loss coefficients, thereby improving the accuracy of the power generation efficiency evaluation of the reference photovoltaic power generation unit. Therefore, the present invention can intelligently and accurately realize the evaluation of photovoltaic power generation efficiency.

[0061] like Figure 2 , is a functional module diagram of a photovoltaic power generation efficiency evaluation system based on UAV detection data provided by an embodiment of the present invention.

[0062] The photovoltaic power generation efficiency evaluation system 100 based on drone detection data of the present invention can be installed in an electronic device. According to the functions implemented, the photovoltaic power generation efficiency evaluation system 100 based on drone detection data can include an evaluation environment confirmation module 101, a detection path fitting module 102, a photovoltaic image detection module 103 and a power generation efficiency evaluation module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0063] The evaluation environment confirmation module 101 is used to receive the efficiency evaluation instruction and confirm the efficiency evaluation environment based on the efficiency evaluation instruction, wherein the efficiency evaluation environment includes a detection drone, a reference photovoltaic power generation unit group and an efficiency evaluation system, wherein the efficiency evaluation system includes: a data collection unit, a data analysis unit, a data evaluation unit and a result feedback unit, wherein the reference photovoltaic power generation unit group includes a plurality of reference photovoltaic power generation units; The detection path fitting module 102 is used to obtain a reference coordinate set using a reference photovoltaic power generation unit group, wherein the reference coordinate set includes a plurality of reference coordinates, and the reference coordinates correspond to the reference photovoltaic power generation units one by one, and obtain a fitting detection path sequence based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, and the fitting detection path includes a plurality of detection nodes, and the detection nodes include detection angles and detection coordinates; The photovoltaic image detection module 103 is used to sequentially drive the detection drone using the fitting detection paths in the fitting detection path sequence, and monitor the drone coordinates of the driving detection drone in real time, and when the drone coordinates are the detection coordinates, obtain the target photovoltaic image based on the detection angle corresponding to the detection node; The power generation efficiency evaluation module 104 is used to obtain the evaluation loss factor by using the target photovoltaic image, the data collection unit, the data analysis unit and the data evaluation unit; Acquiring a reference power generation efficiency based on a reference photovoltaic power generation unit corresponding to the detection node; The product of the evaluation loss coefficient and the reference power generation efficiency is calculated to obtain the evaluation power generation efficiency, and the result feedback unit is used to send the evaluation power generation efficiency to the initiator of the efficiency evaluation instruction to realize the power generation efficiency evaluation of the reference photovoltaic power generation unit group.

[0064] In detail, each module in the photovoltaic power generation efficiency evaluation system 100 based on drone detection data in the embodiment of the present invention is used in the same manner as above. Figure 1 The photovoltaic power generation efficiency evaluation method based on UAV detection data described in the present invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0065] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a photovoltaic power generation efficiency evaluation method based on drone detection data provided by an embodiment of the present invention.

[0066] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a photovoltaic power generation efficiency evaluation method program based on drone detection data.

[0067] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the photovoltaic power generation efficiency evaluation method program based on drone detection data, but also can be used to temporarily store data that has been output or is to be output.

[0068] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a photovoltaic power generation efficiency evaluation method program based on drone detection data, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0069] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0070] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0071] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0072] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0073] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0074] The photovoltaic power generation efficiency evaluation method program based on drone detection data stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: receiving an efficiency evaluation instruction, and confirming an efficiency evaluation environment based on the efficiency evaluation instruction, wherein the efficiency evaluation environment includes a detection drone, a reference photovoltaic power generation unit group, and an efficiency evaluation system, wherein the efficiency evaluation system includes: a data collection unit, a data analysis unit, a data evaluation unit, and a result feedback unit, wherein the reference photovoltaic power generation unit group includes a plurality of reference photovoltaic power generation units; A reference coordinate set is obtained by using a reference photovoltaic power generation unit group, wherein the reference coordinate set includes a plurality of reference coordinates, and the reference coordinates correspond to the reference photovoltaic power generation units one by one, and a fitting detection path sequence is obtained based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, the fitting detection path includes a plurality of detection nodes, and the detection nodes include detection angles and detection coordinates; The detection drones are sequentially driven using the fitting detection paths in the fitting detection path sequence, and the drone coordinates of the driving detection drones are monitored in real time, and when the drone coordinates are the detection coordinates, a target photovoltaic image is acquired based on the detection angle corresponding to the detection node; Obtaining an evaluation loss factor using a target photovoltaic image, a data collection unit, a data analysis unit, and a data evaluation unit; Acquiring a reference power generation efficiency based on a reference photovoltaic power generation unit corresponding to the detection node; The product of the evaluation loss coefficient and the reference power generation efficiency is calculated to obtain the evaluation power generation efficiency, and the result feedback unit is used to send the evaluation power generation efficiency to the initiator of the efficiency evaluation instruction to realize the power generation efficiency evaluation of the reference photovoltaic power generation unit group.

[0075] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0076] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0077] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement: receiving an efficiency evaluation instruction, and confirming an efficiency evaluation environment based on the efficiency evaluation instruction, wherein the efficiency evaluation environment includes a detection drone, a reference photovoltaic power generation unit group, and an efficiency evaluation system, wherein the efficiency evaluation system includes: a data collection unit, a data analysis unit, a data evaluation unit, and a result feedback unit, wherein the reference photovoltaic power generation unit group includes a plurality of reference photovoltaic power generation units; A reference coordinate set is obtained by using a reference photovoltaic power generation unit group, wherein the reference coordinate set includes a plurality of reference coordinates, and the reference coordinates correspond to the reference photovoltaic power generation units one by one, and a fitting detection path sequence is obtained based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, the fitting detection path includes a plurality of detection nodes, and the detection nodes include detection angles and detection coordinates; The detection drones are sequentially driven using the fitting detection paths in the fitting detection path sequence, and the drone coordinates of the driving detection drones are monitored in real time, and when the drone coordinates are the detection coordinates, a target photovoltaic image is acquired based on the detection angle corresponding to the detection node; Obtaining an evaluation loss factor using a target photovoltaic image, a data collection unit, a data analysis unit, and a data evaluation unit; Acquiring a reference power generation efficiency based on a reference photovoltaic power generation unit corresponding to the detection node; The product of the evaluation loss coefficient and the reference power generation efficiency is calculated to obtain the evaluation power generation efficiency, and the result feedback unit is used to send the evaluation power generation efficiency to the initiator of the efficiency evaluation instruction to realize the power generation efficiency evaluation of the reference photovoltaic power generation unit group.

[0078] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0079] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0081] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A photovoltaic power generation efficiency evaluation method based on drone detection data, characterized in that: The method comprises: receiving an efficiency evaluation instruction, and confirming an efficiency evaluation environment based on the efficiency evaluation instruction, wherein the efficiency evaluation environment includes a detection drone, a reference photovoltaic power generation unit group, and an efficiency evaluation system, wherein the efficiency evaluation system includes: a data collection unit, a data analysis unit, a data evaluation unit, and a result feedback unit, wherein the reference photovoltaic power generation unit group includes a plurality of reference photovoltaic power generation units; A reference coordinate set is obtained by using a reference photovoltaic power generation unit group, wherein the reference coordinate set includes a plurality of reference coordinates, and the reference coordinates correspond to the reference photovoltaic power generation units one by one, and a fitting detection path sequence is obtained based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, the fitting detection path includes a plurality of detection nodes, and the detection nodes include detection angles and detection coordinates; The detection drones are sequentially driven using the fitting detection paths in the fitting detection path sequence, and the drone coordinates of the detection drones being driven are monitored in real time, and when the drone coordinates are the detection coordinates, a target photovoltaic image is acquired based on the detection angle corresponding to the detection node; Obtaining an evaluation loss factor using a target photovoltaic image, a data collection unit, a data analysis unit, and a data evaluation unit; Acquiring a reference power generation efficiency based on a reference photovoltaic power generation unit corresponding to the detection node; The product of the evaluation loss coefficient and the reference power generation efficiency is calculated to obtain the evaluation power generation efficiency, and the result feedback unit is used to send the evaluation power generation efficiency to the initiator of the efficiency evaluation instruction to realize the power generation efficiency evaluation of the reference photovoltaic power generation unit group.

2. The photovoltaic power generation efficiency evaluation method based on drone detection data according to claim 1, characterized in that: The acquiring a fitting detection path sequence based on the reference coordinate set comprises: Identify reference regression coordinates as a starting point and an end point, and cluster the reference coordinates in the reference coordinate set using a pre-constructed clustering method to obtain one or more clustered coordinate sets; One or more cluster center coordinates are identified based on one or more cluster coordinate sets, wherein the cluster center coordinates correspond one-to-one to the cluster coordinate sets, the Euclidean distance between each of the one or more cluster center coordinates and the reference regression coordinates is calculated to obtain a retrieval order distance, the retrieval order distances are summarized to obtain a retrieval order distance set, the retrieval order distances in the retrieval order distance set are sorted in ascending order to obtain a retrieval order sequence, and a retrieval coordinate group sequence is obtained based on the retrieval order sequence, wherein the retrieval coordinate group sequence includes one or more retrieval coordinate groups, and the retrieval coordinate groups correspond one-to-one to the retrieval order distances in the retrieval order sequence, and the retrieval coordinate groups correspond one-to-one to the cluster coordinate sets; Extracting retrieval coordinate groups from the retrieval coordinate group sequence in sequence, and performing the following operations on the extracted retrieval coordinate groups: Acquire an identification coordinate group based on the search coordinate group, wherein the identification coordinate group includes a plurality of identification coordinates, and the identification coordinates correspond to the reference coordinates one by one; Acquire a path fitting model for fitting the path, obtain the fitting path using the path fitting model and the identification coordinate group, identify multiple detection nodes in the fitting path using the identification coordinates in the identification coordinate group to obtain an initial partition path, obtain a fitting detection path based on the initial partition path and the reference regression coordinates, identify the explored coordinate set of the fitting detection path, eliminate the explored coordinate set in the reference coordinate set, and use the reference coordinate set from which the explored coordinate set is eliminated as the reference coordinate set, return to the step of clustering the reference coordinates in the reference coordinate set using the pre-constructed clustering method to obtain one or more clustered coordinate sets, summarize the fitting detection paths to obtain a fitting detection path set, sort the fitting detection paths in the fitting detection path set in the order of the time corresponding to the acquisition of the fitting detection paths from the earliest to the latest, and obtain a fitting detection path sequence.

3. The photovoltaic power generation efficiency evaluation method based on drone detection data as claimed in claim 2, characterized in that: The acquiring of the fitting detection path based on the initial segmentation path and the reference regression coordinates includes: Identify the first detection node in the initial partition path to obtain the initial detection node, obtain the initial flight distance using the identification coordinates and reference regression coordinates corresponding to the initial detection node, and identify the fitting partition path in the initial partition path using the initial detection node and the preset node progressive length; The last detection node is extracted from the fitting partition path to obtain a regression detection node, and the regression flight distance is obtained using the identification coordinates corresponding to the regression detection node and the reference regression coordinates; Identify the number of detection nodes in the fitting partition path, obtain the number of detection nodes, obtain the initial energy consumption of the detection UAV, and calculate the detection energy consumption based on the initial flight distance, the regression flight distance, the fitting partition path and the number of detection nodes; The product of the initial energy consumption and the preset energy consumption ratio is calculated to obtain a safe energy consumption threshold, and a fitting detection path is obtained based on the detection energy consumption and the safe energy consumption threshold.

4. The photovoltaic power generation efficiency evaluation method based on drone detection data as claimed in claim 3 is characterized in that: The calculation of detection energy consumption based on the initial flight distance, the regression flight distance, the fitting division path and the number of detection nodes includes: The fitting detection distance is obtained based on the fitting partition path, and the detection energy consumption is calculated using the fitting detection distance and the pre-constructed energy consumption evaluation relationship, wherein the energy consumption evaluation relationship is as follows: ; in, represents the detection energy consumption, , They are the preset flight coefficient and hover coefficient respectively. Indicates the number of detection nodes, represents the quality of the detection drone, It indicates the distance that the detection drone needs to fly when conducting detection. , , They represent the fitting detection distance, the initial flight distance and the regression flight distance respectively, represents the propulsion efficiency of the detection drone, represents the optimal cruising speed, Indicates the flight speed of the drone. Indicates the preset single hover time.

5. The photovoltaic power generation efficiency evaluation method based on drone detection data according to claim 4 is characterized in that: The acquiring a fitting detection path based on the detection energy consumption and the safety energy consumption threshold comprises: Compare the detected energy consumption with the safety energy consumption threshold. If the detected energy consumption is less than or equal to the safety energy consumption threshold, update the node incremental length using the preset iterative step value, and use the updated node incremental length as the node incremental length. Return to the step of using the initial detected node and the preset node incremental length to confirm the fitting partition path in the initial partition path, until it is confirmed that the detected energy consumption is greater than the safety energy consumption threshold, summarize the detected energy consumption, and obtain a detected energy consumption set. The following operations are performed for each detection energy consumption in the detection energy consumption set: Calculate the difference between the safety energy consumption threshold and the detection energy consumption to obtain the evaluation energy consumption, summarize the evaluation energy consumption, obtain the evaluation energy consumption set, and confirm the target evaluation energy consumption based on the evaluation energy consumption set, wherein the target evaluation energy consumption is greater than or equal to 0, and the target evaluation energy consumption is the minimum evaluation energy consumption in the evaluation energy consumption set, and the fitted detection path is the fitted partitioning path corresponding to the target evaluation energy consumption.

6. The photovoltaic power generation efficiency evaluation method based on drone detection data according to claim 5, characterized in that: The step of acquiring a target photovoltaic image based on a detection angle corresponding to a detection node includes: An initial monitoring image is acquired based on a preset detection angle, and an initial photovoltaic image is extracted from the initial monitoring image using a pre-built photovoltaic recognition model; The initial photovoltaic image is corrected using the detection angle and detection angle corresponding to the detection node to obtain the target photovoltaic image.

7. The photovoltaic power generation efficiency evaluation method based on drone detection data according to claim 6, characterized in that: The method of obtaining the evaluation loss coefficient by using the target photovoltaic image, the data collection unit, the data analysis unit and the data evaluation unit includes: Confirming receipt of a data collection instruction from a data collection unit, parsing the data collection instruction, and obtaining a grayscale gradient set, wherein the grayscale gradient set includes a plurality of gradient grayscale values; Confirming receipt of a data evaluation instruction from a data evaluation unit, parsing the data evaluation instruction, and obtaining a reference grayscale range; A plurality of first reference grayscale values ​​are obtained based on a preset extraction value and a reference grayscale range, and the following operation is performed on each of the plurality of first reference grayscale values: Identify a fitted photovoltaic power generation unit based on a first reference grayscale value, obtain a fitted loss efficiency using the fitted photovoltaic power generation unit, associate the first reference grayscale value and the fitted loss efficiency to obtain a fitted coordinate, and summarize the fitted coordinates to obtain a fitted coordinate set; Confirming receipt of a data analysis instruction from a data analysis unit, parsing the data analysis instruction, obtaining a curve fitting model, mapping all fitting coordinates in the fitting coordinate set to a pre-constructed reference coordinate system to obtain a mapping coordinate set, and obtaining a fitting loss curve using the curve fitting model and the mapping coordinate set; Performing grayscale transformation on the target photovoltaic image to obtain a target grayscale image; Acquire one or more fitting region image groups based on the target grayscale image, a pre-constructed region growing algorithm and a grayscale gradient set, wherein the fitting region image groups include one or more initial fitting region images, and the fitting region image groups correspond to the gradient grayscale values ​​one by one; The following operations are performed on each of the one or more fitted region image groups: Acquire a fitting image area based on one or more initial fitting region images corresponding to the fitting region image group, acquire a target image area of ​​a target photovoltaic image, calculate a ratio of the fitting image area to the target image area, and obtain a fitting ratio; Obtaining a fitting grayscale mean value based on the fitting region image group, and using the fitting grayscale mean value to retrieve a target loss efficiency from the fitting loss curve; The target loss efficiency and the fitting ratio are associated to obtain a target loss node, the target loss nodes are summarized to obtain a target loss node set, and the target loss node set is used to calculate the evaluation loss coefficient.

8. The photovoltaic power generation efficiency evaluation method based on drone detection data according to claim 7, characterized in that: The target loss node set is used to calculate the evaluation loss coefficient, and the calculation formula is as follows: ; in, represents the assessment impairment coefficient, Indicates that the target damaged nodes have target damage nodes, , They represent the target damaged node set. The fitting ratio and target loss efficiency corresponding to each target loss node.

9. The photovoltaic power generation efficiency evaluation method based on drone detection data according to claim 8, characterized in that: The obtaining of a reference power generation efficiency based on a reference photovoltaic power generation unit corresponding to the detection node comprises: Acquire reference environmental data of a reference photovoltaic power generation unit, wherein the reference environmental data includes: irradiation intensity, ambient temperature and ambient humidity; Obtaining predicted power generation efficiency based on the reference environmental data and a pre-trained efficiency prediction model; The service life of the reference photovoltaic power generation unit is obtained, and the age depreciation coefficient is obtained based on the service life. The product of the age depreciation coefficient and the predicted power generation efficiency is calculated to obtain the reference power generation efficiency.

10. A photovoltaic power generation efficiency evaluation system based on drone detection data, characterized in that: The system comprises: An evaluation environment confirmation module is used to receive an efficiency evaluation instruction and confirm an efficiency evaluation environment based on the efficiency evaluation instruction, wherein the efficiency evaluation environment includes a detection drone, a reference photovoltaic power generation unit group and an efficiency evaluation system, wherein the efficiency evaluation system includes: a data collection unit, a data analysis unit, a data evaluation unit and a result feedback unit, wherein the reference photovoltaic power generation unit group includes a plurality of reference photovoltaic power generation units; A detection path fitting module is used to obtain a reference coordinate set using a reference photovoltaic power generation unit group, wherein the reference coordinate set includes a plurality of reference coordinates, and the reference coordinates correspond to the reference photovoltaic power generation units one by one, and obtain a fitting detection path sequence based on the reference coordinate set, wherein the fitting detection path sequence includes one or more fitting detection paths, and the fitting detection path includes a plurality of detection nodes, and the detection nodes include detection angles and detection coordinates; A photovoltaic image detection module is used to sequentially drive the detection drone using the fitting detection paths in the fitting detection path sequence, and to monitor the drone coordinates of the driving detection drone in real time, and to obtain a target photovoltaic image based on the detection angle corresponding to the detection node when the drone coordinates are the detection coordinates; A power generation efficiency evaluation module, used to obtain an evaluation loss factor using a target photovoltaic image, a data collection unit, a data analysis unit, and a data evaluation unit; Acquiring a reference power generation efficiency based on a reference photovoltaic power generation unit corresponding to the detection node; The product of the evaluation loss coefficient and the reference power generation efficiency is calculated to obtain the evaluation power generation efficiency, and the result feedback unit is used to send the evaluation power generation efficiency to the initiator of the efficiency evaluation instruction to realize the power generation efficiency evaluation of the reference photovoltaic power generation unit group.

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