Distributed photovoltaic data acquisition and processing method

Abstract

The invention provides a distributed photovoltaic data acquisition and processing method, and belongs to the technical field of photovoltaic monitoring, and the method comprises the steps: carrying out the detection of the change condition of a physical layer of each photovoltaic panel in a region through detection waves; judging the state of the photovoltaic panel in the initial calibration stage, and predicting the power generation condition of the photovoltaic panel in the efficient power generation stage by combining the state of the photovoltaic panel represented in the initial calibration stage with the change condition of the current external environment; in the high-efficiency power generation stage, actual output is compared with prediction processing, the reason for difference of the photovoltaic panels is analyzed in combination with historical data, and finally, the all-day operation condition of the photovoltaic panels is determined in the state judgment stage, and uploading and feedback of information of the photovoltaic panels are completed. According to the distributed photovoltaic data acquisition and processing method provided by the invention, the state of each photovoltaic panel in the whole day can be identified, the result is more accurate, differential monitoring of each photovoltaic panel is realized, and the process of digitalization of a photovoltaic system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic monitoring, and more specifically, relates to a method for collecting and processing distributed photovoltaic data. Background Art

[0002] The power generation efficiency of photovoltaic panels is affected by external weather conditions and other factors, and the angle of the photovoltaic panels themselves and the dust on the surface also restrict the total output. For these reasons, existing methods need to predict the total output of photovoltaic panels in a region based on technologies such as neural networks. However, it should be noted that there are certain differences between each photovoltaic panel, which are not only limited to the spatial angle and the amount of solar energy received, but also include differences in internal circuits and the conversion efficiency of photovoltaic panels. This leads to the inability of existing methods to accurately and effectively identify the status of photovoltaic panels, unable to achieve differential monitoring of photovoltaic panels, and the digital level of the entire photovoltaic system is relatively low. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for collecting and processing distributed photovoltaic data, aiming to solve the problems of inability to accurately and effectively identify the status of photovoltaic panels, inability to achieve differential monitoring of photovoltaic panels, and relatively low digital level of the entire photovoltaic system.

[0004] To achieve the above object, the technical solution adopted by the present invention is: providing a method for collecting and processing distributed photovoltaic data, including:

[0005] Detecting the physical changes of each photovoltaic panel in the region through detection waves; dividing the whole day into an initial calibration stage, a high-efficiency power generation stage, and a status determination stage in sequence; determining the status of the photovoltaic panel in the initial calibration stage, and predicting the power generation situation of the photovoltaic panel in the high-efficiency power generation stage by combining the status of the photovoltaic panel represented by the initial calibration stage with the changes in the current external environment; comparing the actual output with the predicted output in the high-efficiency power generation stage and analyzing the reasons for the differences of the photovoltaic panel in combination with historical data, and finally determining the operation situation of the photovoltaic panel throughout the day and completing the upload and feedback of the information of each photovoltaic panel in the status determination stage.

[0006] In a possible implementation manner, the detecting the physical changes of each photovoltaic panel in the region through detection waves includes:

[0007] Setting up detection points, clarifying the positions of multiple photovoltaic panels in the region and numbering them; the detection points emitting detection waves to identify the changes in the spatial angles of each photovoltaic panel at adjacent time points.

[0008] In a possible implementation, the detection point emits a detection wave to identify the change in the spatial angle of each photovoltaic panel at adjacent time points, including:

[0009] Combining image processing technology and combining the detection wave to identify the outer frames and panels of each photovoltaic panel;

[0010] Based on the time and angle after the detection wave returns, identify the change in the spatial angle of the photovoltaic panel within adjacent time periods.

[0011] In a possible implementation, the detection point emits a detection wave to identify the change in the spatial angle of each photovoltaic panel at adjacent time points further includes:

[0012] Based on the amount of energy loss when the detection wave returns and the position where the detection wave returns, determine the thickness and distribution of dust on the panel of the photovoltaic panel.

[0013] In a possible implementation, predicting the power generation situation of the photovoltaic panel in the high-efficiency power generation stage by combining the state of the photovoltaic panel characterized by the initial calibration stage with the changes in the current external environment includes:

[0014] Within a time interval where the light intensity and angle meet the requirements, summarize and fit the output curve of the photovoltaic panel, and infer the power generation efficiency of the photovoltaic panel in the high-efficiency power generation stage by combining the output curve and the change trend of the current day's external environment.

[0015] In a possible implementation, inferring the power generation efficiency of the photovoltaic panel in the high-efficiency power generation stage by combining the output curve and the change trend of the current day's external environment includes:

[0016] Sequentially and intermittently, starting from the current time point, combine the changes in the external environment parameters in a subsequent period of time, predict the power generation efficiency of the photovoltaic panel, and finally summarize.

[0017] In a possible implementation, analyzing the difference between the actual power generation result and the predicted result of the photovoltaic panel in the high-efficiency power generation stage by combining the comparison result and the output situation in the current state determination stage with the initial calibration stage and historical data includes:

[0018] Combining historical experience and on-site inspection to judge the reasons for the difference in the output results of each photovoltaic panel. The reasons include dust, abnormal swing, sintering of internal materials, unstable circuit connection, light energy being blocked, aging and discoloration of the surface cover plate, impurities inside, and internal deformation;

[0019] Identify the problems that occur in the photovoltaic panel, mark and feedback them; analyze the impact of each problem on the final power generation.

[0020] In a possible implementation, identify the problems that occur in the photovoltaic panel, mark and feedback them; analyze the impact of each problem on the final power generation, including:

[0021] In the state determination stage, formulate the maintenance methods and approaches for each photovoltaic panel, and predict the improvement degree of energy conversion and the effect on economic improvement after the maintenance of the photovoltaic panel.

[0022] In a possible implementation, in the high-efficiency power generation stage, compare the actual output with the predicted output and analyze the reasons for the differences in the photovoltaic panel in combination with historical data, including:

[0023] In the high-efficiency power generation stage, record the physical state of the photovoltaic panel and the current external environment parameters, record the amount of solar energy received by the photovoltaic panel and the power generation efficiency, and finally generate the corresponding characteristic curve.

[0024] Evaluate the state changes of the photovoltaic panel through the characteristic curves at different times.

[0025] In a possible implementation, finally, in the state determination stage, determine the operation conditions of the photovoltaic panel throughout the day and complete the upload and feedback of the information of each photovoltaic panel, including:

[0026] Create a model of the photovoltaic panel, and real-time feedback the physical level changes of the photovoltaic panel to the model; create a file for each photovoltaic panel, and combine the currently detected external environment conditions and power generation conditions to realize the remote visual monitoring of the photovoltaic panel in the model through real-time simulation.

[0027] The beneficial effects of the distributed photovoltaic data acquisition and processing method provided by the present invention are as follows: Compared with the prior art, in the distributed photovoltaic data acquisition and processing method of the present invention, first, the physical level changes of each photovoltaic panel in the area are detected by a detection wave; the whole day is sequentially divided into an initial calibration stage, a high-efficiency power generation stage, and a state determination stage. The state of the photovoltaic panel is determined in the initial calibration stage.

[0028] When the photovoltaic panel starts to generate a large amount of electric energy, predict the power generation situation of the photovoltaic panel in the high-efficiency power generation stage by combining the state of the photovoltaic panel characterized by the initial calibration stage with the changes in the current external environment; in the high-efficiency power generation stage, compare the actual output with the predicted output and analyze the reasons for the differences in the photovoltaic panel in combination with historical data. Through the recording and prediction of the physical level changes of the photovoltaic panel and the power generation situation, the present application can finally identify the state of each photovoltaic panel throughout the day, the result is more accurate, and the differential monitoring of each photovoltaic panel is realized, which improves the digitalization process of the photovoltaic system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0030] Figure 1 It is a flowchart of the distributed photovoltaic data acquisition and processing method provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the following further details the present invention with reference to the drawings and embodiments. 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.

[0032] Please refer to Figure 1 , and now the distributed photovoltaic data acquisition and processing method provided by the present invention will be described. The distributed photovoltaic data acquisition and processing method includes:

[0033] Detecting the physical-level change conditions of each photovoltaic panel in the area through a detection wave; dividing the whole day into an initial calibration stage, an efficient power generation stage, and a state determination stage in sequence; determining the state of the photovoltaic panel in the initial calibration stage, and predicting the power generation situation of the photovoltaic panel in the efficient power generation stage by combining the state of the photovoltaic panel represented by the initial calibration stage with the current external environmental changes; comparing the actual output with the predicted processing in the efficient power generation stage and analyzing the reasons for the differences in the photovoltaic panel in combination with historical data, and finally determining the operation situation of the photovoltaic panel throughout the day in the state determination stage and completing the upload and feedback of the information of each photovoltaic panel.

[0034] The beneficial effects of the distributed photovoltaic data acquisition and processing method provided by the present invention are as follows: Compared with the prior art, in the distributed photovoltaic data acquisition and processing method of the present invention, first, the physical-level change conditions of each photovoltaic panel in the area are detected through a detection wave; the whole day is divided into an initial calibration stage, an efficient power generation stage, and a state determination stage in sequence. The state of the photovoltaic panel is determined in the initial calibration stage.

[0035] When a large amount of electrical energy starts to be generated by the photovoltaic panels, the power generation situation of the photovoltaic panels in the high-efficiency power generation stage is predicted by combining the state of the photovoltaic panels characterized by the initial calibration stage with the changes in the current external environment; in the high-efficiency power generation stage, the actual output is compared with the prediction process and the reasons for the differences generated by the photovoltaic panels are analyzed in combination with historical data. Through the recording and prediction of the physical changes of the photovoltaic panels and the power generation situation, the present application can finally identify the state of each photovoltaic panel throughout the day, with more accurate results and realizing the differential monitoring of each photovoltaic panel, thus promoting the digitalization process of the photovoltaic system.

[0036] Looking at the current situations at home and abroad, traditional data collection and statistical methods can only collect and analyze a part of the system data for a specific problem, and it is difficult to manage and reuse the monitoring data, which cannot meet the requirements of distributed photovoltaic comprehensive monitoring. The main reason is that traditional photovoltaic monitoring methods directly upload all the data of all photovoltaic power generation units to the terminal of the monitoring system for unified analysis and processing. However, due to the high density of test points in the distributed photovoltaic power generation system, the data collection is too scattered and the number is huge, which will inevitably lead to the inability of the data processing capacity of the distributed photovoltaic data collection system to meet the requirements, and the power consumption of the collection terminal is relatively high, with poor economy.

[0037] In some embodiments of the distributed photovoltaic data collection and processing method provided in the present application, the detection of the physical changes of each photovoltaic panel in the area by the detection wave includes:

[0038] Set up detection points, clarify the respective positions of multiple photovoltaic panels in the area and number them; the detection points emit detection waves to identify the changes in the spatial angles of each photovoltaic panel at adjacent time points.

[0039] The distribution areas of distributed photovoltaic points are numerous and scattered disorderly. After grid connection, it will have an impact on the power flow, voltage distribution, power supply reliability, power quality, protection and control, etc. of the distribution network. It is very necessary to conduct in-depth research on the impact brought by photovoltaic grid connection, and to master the operation characteristic library of distributed photovoltaic under different scenarios on the premise of ensuring the safe, stable and economic operation of the power system.

[0040] Influenced by external weather environment and other factors, currently neural networks are usually used to predict the short-term output of distributed photovoltaic clusters. The historical time-series data of the distributed photovoltaic cluster output is used as the input of the neural network model, and the mutual relationship between the historical time-series data of the output is deeply mined through continuous iteration, and then the output data of the distributed photovoltaic cluster at future moments is mapped.

[0041] However, existing prediction methods highly rely on the PV historical output data in the previous time interval. The correlation of PV historical output data at different times is relatively weak, and the output correlation between different time points of distributed PV clusters is not considered from a global perspective. At the same time, the distances of different distributed PV clusters in the spatial structure are different, and it is difficult for existing prediction methods to uncover the mutual influence relationship between the outputs of different distributed PV clusters, resulting in incomplete extraction of the output characteristics of distributed PV clusters and affecting the regulation and management of the power grid on distributed PV clusters.

[0042] Cluster division of PV according to the output nature is a more feasible theory at present. Considering that the output characteristics of PV are related to meteorological conditions such as sunlight and system parameters such as controller parameters, the correlation between PV outputs naturally exists. However, existing research is mostly based on simple clustering of output curves, and cannot provide real-time feedback on the overall characteristics changes of PV due to faults, installations, and wiring changes, which will further lead to deviations in PV output prediction and affect the accuracy of system decision-making and evaluation.

[0043] In some embodiments of the distributed PV data acquisition and processing method provided in this application, detecting the detection wave emitted by the detection point to identify the change in the spatial angle of each PV panel at adjacent time points includes:

[0044] Combining image processing technology and combining the detection wave to identify the outer frame and panel of each PV panel.

[0045] Identifying the change in the spatial angle of the PV panel within adjacent time periods according to the time and angle after the detection wave returns.

[0046] The purpose of this application is to provide a method for monitoring the operating state of PV panels, and the ultimate goal is to achieve effective prediction of the data acquisition and output of PV panels. It should be noted that the laying area of PV panels is usually large, and multiple PV panels are usually installed in an area, and the positions and angles of each PV panel have certain differences. It is precisely because of this difference that even the output data of PV panels in the same area will have certain differences.

[0047] Existing methods mostly analyze and predict the parameters of PV panels through neural networks and big data artificial intelligence. Although this prediction is based on environmental temperature, humidity, and irradiance, it does not accurately consider the differences between individual PV panels. It is precisely for the above reasons that the final result cannot well reflect the output efficiency of PV, and the credibility of the final result is relatively low.

[0048] This application simulates the real-time attitude of PV panels in the current analysis system on the basis of the existing technology, and through multi-angle measurement, finally clarifies the environment where the PV panels are located, and finally realizes the accurate acquisition of data.

[0049] In some embodiments of the distributed photovoltaic data acquisition and processing method provided in this application, detecting the change in the spatial angle of each photovoltaic panel at adjacent time points by the detection wave emitted from the detection point further includes:

[0050] Determine the thickness and distribution of dust on the photovoltaic panel surface according to the amount of energy loss when the detection wave returns and the position where the detection wave returns.

[0051] To achieve the above technical effects, first, an observation point is set in the erection area of the photovoltaic panel. The observation point emits a detection wave to the photovoltaic panels in the area in real time. The detection wave determines the change in the spatial angle of each photovoltaic panel according to the time and angle of the detection wave reflected back, and then gives a real-time feedback.

[0052] Specifically, in the embodiment, first, a model of each photovoltaic panel is initially created. In this model, the angle of the actual photovoltaic panel can be adjusted in real time, and the adjustment amplitude is proportional to the actual situation. More importantly, after the detection wave returns and extracts the angle change of each photovoltaic panel, by comparing the feedback information with the spatial angle detected at the previous time point, the magnitude of the angle change can finally be identified by comparing the change in the spatial angle at adjacent time points. After the identification is completed, the corresponding angle adjustment needs to be made in the model. Creating the model can be used for subsequent data reference on the one hand and facilitate remote analysis and management on the other hand, because the change situation of the photovoltaic panel can be more intuitively shown through the model.

[0053] In some embodiments of the distributed photovoltaic data acquisition and processing method provided in this application, predicting the power generation situation of the photovoltaic panel in the high-efficiency power generation stage by combining the state of the photovoltaic panel characterized by the initial calibration stage with the current change of the external environment includes:

[0054] In the time interval when the light intensity and angle meet the requirements, summarize and fit the output curve of the photovoltaic panel, and infer the power generation efficiency of the photovoltaic panel in the high-efficiency power generation stage by combining the output curve and the change trend of the external environment on the same day.

[0055] On the one hand, the detection wave can continuously measure the spatial angles of multiple photovoltaic panels in the area, and on the other hand, it can detect the dust degree on the surface of the photovoltaic panel. After the detection wave is emitted from the detection point, it will propagate in the air and then contact the frame of the photovoltaic panel and the front of the photovoltaic panel. By measuring the frame size, the position and angle of the current photovoltaic panel can be identified. When the detection wave contacts the dust on the front of the photovoltaic panel, since the dust has multiple reflection surfaces, that is, the energy of the detection wave itself will generate a certain loss after contacting the dust. After the lost detection wave returns to the observation point, by digitally extracting its energy value, the thickness of the dust can be determined.

[0056] What is more intuitive is that, since the emitted detection wave can contact the entire front of the photovoltaic panel, and precisely because of the above-mentioned characteristics, the detection wave can accurately judge the distribution of dust on the photovoltaic panel, that is, it can depict the distribution of dust on the photovoltaic panel.

[0057] In some embodiments of the distributed photovoltaic data collection and processing method provided in the present application, the power generation efficiency of the photovoltaic panel in the high-efficiency power generation stage is inferred by combining the output curve and the change trend of the external environment on the day, including:

[0058] The power generation efficiency of photovoltaic panels is predicted and finally summarized intermittently, taking the current time point as the starting point and combining the changes in external environmental parameters in the following period of time.

[0059] After the detection wave measures the angle and dust of the photovoltaic panels in the area, it is necessary to measure the temperature and other parameters of the photovoltaic panel surface through the sensors installed on the photovoltaic panels and the external detection devices. More importantly, it is necessary to measure the current and voltage output by the photovoltaic panels through relevant metering devices. It should be pointed out that the measurement of current and voltage needs to be clear for each photovoltaic panel, because only in the above case can each photovoltaic panel be monitored.

[0060] The embodiment is to first determine the temperature, humidity, wind direction, wind speed, light intensity and light angle of the current environment, and then determine the surface temperature of each photovoltaic panel, so as to clarify the current state of the photovoltaic panel. After these external conditions are determined, the information detected by the detection wave is superimposed on the current and voltage data of each photovoltaic panel to finally provide real-time feedback on the operation status of each photovoltaic panel.

[0061] In some embodiments of the distributed photovoltaic data collection and processing method provided in the present application, the difference between the actual power generation result and the predicted result of the photovoltaic panel in the high-efficiency power generation stage is analyzed based on the comparison results and the output situation in the current state determination stage combined with the initial calibration stage and historical data, including:

[0062] Combining historical experience and on-site inspections, we determine the reasons for the differences in output results of each photovoltaic panel, which include dust, abnormal swing, sintering of internal materials, unstable circuit connections, blocked light energy, aging and discoloration of the surface cover, internal impurities and internal deformation.

[0063] Identify the problems with the photovoltaic panels and mark and provide feedback; analyze the impact of each problem on the final power generation.

[0064] Some existing methods can relatively easily obtain some external parameter conditions of the photovoltaic panel. However, it should be noted that the power generation efficiency of the photovoltaic panel is different at different times of the day, which has a direct relationship with the light conditions and temperature in the external environment. This causes the power output of the photovoltaic panel to change synchronously with the external environment throughout the day. When collecting existing photovoltaic panel data, it is assumed that the external environment remains unchanged. However, the state of the photovoltaic panel is different at different time points of the day. By recording and analyzing the different power output effects of the photovoltaic panel at different time points, the state of the photovoltaic panel can ultimately be judged, and it can be more intuitively determined whether the current photovoltaic panel is in a normal state.

[0065] In some embodiments of the distributed photovoltaic data collection and processing method provided in this application, identify the problems that occur in the photovoltaic panel and mark and feedback them; analyze the impact of each problem on the final power generation, including:

[0066] In the state determination stage, formulate the maintenance methods and means for each photovoltaic panel, and predict the improvement degree of energy conversion and the effect on economic improvement after the maintenance of the photovoltaic panel.

[0067] For a more detailed description, first, after there is light and the photovoltaic panel starts to generate electricity, start real-time recording of the power generation state of the photovoltaic panel. In the embodiment, the whole day is divided into an initial calibration stage, a high-efficiency power generation stage, and a state determination stage.

[0068] In the initial calibration stage, based on existing data and relevant experience, through the measurement of current external environment parameters and the integration and judgment of the relatively weak power generation of the current photovoltaic panel, the state of the photovoltaic panel is ultimately preliminarily determined. In the high-efficiency power generation stage, the state of the photovoltaic panel needs to be recorded and fed back in real time. In the state determination stage, at this time, the light intensity is weak. After a day of energy conversion, the change in the state of the photovoltaic panel can be finally determined through re-determination of the external environment.

[0069] In some embodiments of the distributed photovoltaic data collection and processing method provided in this application, in the high-efficiency power generation stage, compare the actual power output with the predicted processing and analyze the reasons for the differences in the photovoltaic panel in combination with historical data, including:

[0070] In the high-efficiency power generation stage, record the physical state of the photovoltaic panel and the current external environment parameters, record the amount of solar energy received by the photovoltaic panel and the power generation efficiency, and finally generate the corresponding characteristic curve.

[0071] Evaluate the state change of the photovoltaic panel through the characteristic curves at different times.

[0072] For a more detailed description, first, in the morning, the light intensity is relatively low and the photovoltaic panel receives less light energy. In this case, the initial state of the photovoltaic panel is checked. The embodiment is to determine the time point when the photovoltaic panel starts to generate voltage and current. Then, as the light intensity gradually increases, the light can shine more directly on the photovoltaic panel. By statistically analyzing the output of the photovoltaic panel in the next period of time, the output of the photovoltaic panel under stronger light intensity and different angles can be accurately predicted. By determining the state of each photovoltaic panel in the initial calibration stage, the output of the photovoltaic panel in the high-efficiency power generation stage can be inferred. More importantly, after the high-efficiency power generation stage, as the light intensity and angle decrease, the theoretical power generation and actual power generation of the photovoltaic panel can be compared, the gap between the two can be analyzed, and the gap can be refined. Finally, it is verified by the state determination stage.

[0073] In some embodiments of the distributed photovoltaic data acquisition and processing method provided in this application, finally, in the state determination stage, the operation conditions of the photovoltaic panel throughout the day are determined, and the information of each photovoltaic panel is uploaded and fed back, including:

[0074] Create a model of the photovoltaic panel and real-time feedback the physical changes of the photovoltaic panel to the model; create a file for each photovoltaic panel, and combine the currently detected external environmental conditions and power generation conditions to realize remote visual monitoring of the photovoltaic panel in the model through real-time simulation.

[0075] After clarifying the difference between the theoretical power generation and actual power generation of the photovoltaic panel, it is necessary to analyze the reasons for the difference by means of external sensor components such as detection waves emitted by cameras and detectors. The current reasons can be summarized as a series of problems such as dust, the photovoltaic panel swinging, the internal materials of the photovoltaic panel being sintered, the circuit connection of the photovoltaic panel being unstable, the relevant light energy being blocked, the cover plate on the surface of the photovoltaic panel aging and discoloring, impurities existing inside the photovoltaic panel, and deformation occurring inside the photovoltaic panel. Through relevant detection components and manual inspections, etc., the conditions of each photovoltaic panel are sorted out and the status is fed back together, and finally, precise control of the photovoltaic panel is achieved.

[0076] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed photovoltaic data collection and processing method, characterized in that: include: Detect physical changes of each photovoltaic panel in the area through detection waves; The whole day is divided into an initial calibration stage, a high-efficiency power generation stage and a state determination stage in sequence; the state of the photovoltaic panel is determined in the initial calibration stage, and the power generation of the photovoltaic panel in the high-efficiency power generation stage is predicted by combining the state of the photovoltaic panel represented in the initial calibration stage with the change of the current external environment; In the efficient power generation stage, the actual output is compared with the predicted output and the reasons for the differences in the photovoltaic panels are analyzed in combination with historical data. Finally, in the status determination stage, the operation status of the photovoltaic panels throughout the day is determined and the upload and feedback of the information of each photovoltaic panel is completed.

2. The distributed photovoltaic data collection and processing method according to claim 1, characterized in that: The detection of physical level changes of each photovoltaic panel in the area by using detection waves includes: Detection points are set up to identify the respective positions of the plurality of photovoltaic panels in the area and to number them; the detection points emit detection waves to identify changes in the spatial angles of the photovoltaic panels at adjacent time points.

3. The distributed photovoltaic data collection and processing method according to claim 2, characterized in that: The detection point sends out detection waves to identify the changes in the spatial angles of each photovoltaic panel at adjacent time points, including: Combining image processing technology with the detection wave to identify the outer frame and panel of each photovoltaic panel; The change of the spatial angle of the photovoltaic panel in adjacent time periods is identified according to the time and angle after the detection wave returns.

4. The distributed photovoltaic data collection and processing method according to claim 2, characterized in that: The detection point sends out detection waves to identify the changes in the spatial angles of the photovoltaic panels at adjacent time points, and further includes: The thickness and distribution of dust on the photovoltaic panel are determined according to the amount of energy loss when the detection wave returns and the position of the detection wave when it returns.

5. The distributed photovoltaic data collection and processing method according to claim 1, characterized in that: The predicting of the power generation of the photovoltaic panel in the high-efficiency power generation stage by combining the state of the photovoltaic panel represented in the initial calibration stage with the change of the current external environment includes: In the time interval when the light intensity and angle meet the requirements, the output curve of the photovoltaic panel is summarized and fitted, and the power generation efficiency of the photovoltaic panel in the high-efficiency power generation stage is inferred by combining the output curve and the changing trend of the external environment on that day.

6. The distributed photovoltaic data collection and processing method according to claim 5, characterized in that: The power generation efficiency of the photovoltaic panel in the high-efficiency power generation stage is inferred by combining the output curve and the change trend of the external environment on that day, including: The power generation efficiency of the photovoltaic panel is predicted and finally summarized by taking the current time point as the starting point and combining the changes of the external environmental parameters in the following period of time.

7. The distributed photovoltaic data collection and processing method according to claim 1, characterized in that: The analysis of the difference between the actual power generation result and the predicted result of the photovoltaic panel in the high-efficiency power generation stage according to the comparison result and the output condition of the current state determination stage combined with the initial calibration stage and historical data includes: The reasons for the differences in the output results of the photovoltaic panels are determined based on historical experience and on-site inspections. The reasons include dust, abnormal swing, sintering of internal materials, unstable circuit connection, light energy being blocked, aging and discoloration of the surface cover, impurities inside, and internal deformation; Identify the problems with the photovoltaic panels and mark and provide feedback; analyze the impact of each problem on the final power generation.

8. The distributed photovoltaic data collection and processing method according to claim 7, characterized in that: The problems with the photovoltaic panels are identified and marked and feedback is provided; Analysis of the impact of various issues on the final power generation includes: In the state determination stage, maintenance modes and methods are formulated for each photovoltaic panel, and the degree of improvement in energy conversion and the effect on economic improvement of the photovoltaic panel after maintenance is completed are predicted.

9. The distributed photovoltaic data collection and processing method according to claim 1, characterized in that: The reasons for comparing the actual output with the predicted output during the high-efficiency power generation stage and analyzing the differences in the photovoltaic panels in combination with historical data include: In the high-efficiency power generation stage, the physical state of the photovoltaic panel and the current external environmental parameters are recorded, and the amount of solar energy received by the photovoltaic panel and the power generation efficiency are recorded to finally generate a corresponding characteristic curve; The state change of the photovoltaic panel is evaluated by the characteristic curves at different times.

10. The distributed photovoltaic data collection and processing method according to claim 1, characterized in that: The final step of determining the operation status of the photovoltaic panels throughout the day and completing the uploading and feedback of the information of each photovoltaic panel in the state determination stage includes: Create a model of the photovoltaic panel and feed back the physical changes of the photovoltaic panel to the model in real time; create a file for each photovoltaic panel, combine the currently detected external environment and power generation conditions to simulate in real time to achieve remote visual monitoring of the photovoltaic panel in the model.

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