Distributed photovoltaic data processing method and system

Abstract

The invention discloses a distributed photovoltaic data processing method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining historical data generated during photovoltaic use through a monitoring point, building an influence analysis model based on a deep neural network model, carrying out the theoretical influence value prediction, and carrying out the calculation of an object distribution information graph through an object distribution information graph. Carrying out object numerical calculation of photovoltaic external influence; according to the method, data scanning is performed on the surface of the photovoltaic panel, the influence analysis model is established for external influence factors, the light transmittance is predicted according to the influence analysis model, and theoretical light transmittance of different dust thicknesses is generated, so that the influence of dust on light transmission of the photovoltaic panel can be accurately measured; and meanwhile, according to the pre-estimated value of the constructed influence analysis model, in combination with thermal imaging, re-analysis and comparison are performed, and a compensation coefficient is calculated according to light transmittance comparison to correct the electric quantity, so that the electric quantity calculation and pre-estimation are more practical, more accurate electric energy production is obtained, and reasonable use of energy data generated by distributed photovoltaic power generation is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a distributed photovoltaic data processing method and system. Background Art

[0002] Photovoltaic data refers to various data related to the performance of a photovoltaic power station, power generation prediction, and system maintenance collected during the operation of a photovoltaic power generation system. The data acquisition module mainly consists of a data collector, a meteorological sensor, a photovoltaic array sensor, and a communication terminal device, including but not limited to the voltage, current, and power data of photovoltaic panels, meteorological data (light intensity, temperature, wind speed, etc.), grid operation data, equipment basic information data, equipment actual operation data, historical power generation data, equipment maintenance and fault data, etc. Distributed photovoltaics are applied to industrial factories, commercial buildings, residential houses, agricultural facilities, public buildings, etc.;

[0003] In the prior art, when obtaining photovoltaic data, the output of the photovoltaic is statistically analyzed through sensors to obtain the total photovoltaic power generation data. Since the photovoltaic is affected by external factors during use, the obtained data is inaccurate. The inaccurate photovoltaic data makes it impossible to accurately grasp the real-time situation of photovoltaic power generation, resulting in a large error in the obtained photovoltaic data and unable to truly reflect the actual data state of the photovoltaic panel;

[0004] In view of the above technical defects, a solution is proposed herein. Summary of the Invention

[0005] The purpose of the present invention is to construct an impact analysis based on photovoltaic historical data, analyze the impact of external factors on the photovoltaic panel, calculate a compensation coefficient through comparison according to the light transmittance, and correct the power generation prediction, so that the power supply value during photovoltaic power generation is more accurate.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A distributed photovoltaic data processing method, comprising the following steps:

[0007] Step 1: Process and store the power generation data generated by the photovoltaic obtained through the sensor monitoring points, and simultaneously store the photovoltaic image data obtained through the lidar monitoring points, and obtain imaging data by monitoring the photovoltaic panel with an infrared scanner device;

[0008] Step 2: Conduct data analysis based on the power generation data and the image data. Calculate the photovoltaic data difference by selecting the power generation data and the photovoltaic standard data, obtain the external factors outside the photovoltaic panel by selecting the image data, conduct a correlation analysis based on the external factors, the photovoltaic panel area, and the photovoltaic panel characteristics, establish an impact analysis model based on a deep neural network model, and predict the theoretical impact value according to the impact analysis model;

[0009] Step 3: Obtain the temperature distribution on the surface of the photovoltaic panel based on the imaging data. Identify the normal and abnormal temperature regions according to the temperature distribution, and make differential markings to obtain the surface temperature layout diagram of the photovoltaic panel. Convert the temperature layout diagram into an object distribution information diagram, and perform numerical calculations on the objects outside the photovoltaic panel that have an impact on the abnormal temperature region based on different temperature data;

[0010] Step 4: Generate the theoretical external prediction impact value according to the impact analysis model. Obtain the actual object numerical values on the surface of the photovoltaic panel based on the imaging data. Perform a differential analysis on the theoretical impact value and the actual object numerical values to obtain the compensation coefficient between the two;

[0011] Step 5: Perform real-time correction on the power generation data of the photovoltaic according to the compensation coefficient to obtain the corrected photovoltaic power generation data analysis form, and send it to the data processing control terminal.

[0012] Furthermore, the data processing control terminal is used to process and store the data generated during the use of the photovoltaic panel, including the electrical measurement time of the power generation data, the rated power of the photovoltaic panel, and the power generation data. It is classified and stored according to the time series, including arranging the execution order and time of the data processing tasks according to the preset rules or real-time requirements for the generated data, and presenting the results of data analysis and mining to the user in an intuitive manner;

[0013] Both the lidar and the infrared scanner are installed near the photovoltaic panel. Determine the measurement position and set the angle according to the installation position and the number of installations of the measurement area and the photovoltaic panel. When performing image scanning and acquisition, set a dynamic change time schedule to perform unrestricted acquisition of the surface of the photovoltaic panel, and set the number of acquisitions per day during the use of the photovoltaic panel.

[0014] Furthermore, when obtaining the external factors outside the photovoltaic panel by selecting image data:

[0015] Before performing 3D data scanning by the lidar, verify that the parameters of the lidar are correct. Verify that the lidar image parameter settings are correct by obtaining experimental images before scanning;

[0016] When constructing the coordinates of the obtained image, specify the coordinate range inside the image data within [0, 1]. When obtaining the external factors of the image, identify the coverage area in the image and perform segmentation and extraction of the coverage area as the analysis reference area.

[0017] Furthermore, establishing the impact analysis model specifically includes the following:

[0018] S100: Construct a sample set from historical image data, estimated dust thickness, and light transmittance, and divide it into a training set, a validation set, and a test set. Construct the preprocessing layer, analysis layer, and output layer of the impact analysis model;

[0019] The preprocessing layer obtains the historical image data after lidar scanning, preprocesses the image data, then segments and extracts external factors. Through the refraction and propagation of light, the propagation value from the photovoltaic panel to the lidar and the propagation value from the external factors to the lidar are obtained, and the estimated dust thickness value on the photovoltaic panel is obtained. The calculation process is as follows:

[0020] Preset T as the dust thickness, I as the intensity value received by the lidar, I1 as the intensity value received by the lidar when there is no dust on the surface of the photovoltaic panel, k as the conversion coefficient set between the intensity and the dust thickness, and as the standard light transmittance without dust, and we get

[0021] The analysis layer obtains the estimated value of the dust thickness table on the photovoltaic panel, combines the preset standard attenuation coefficient, and calculates the light transmittance according to the estimated dust thickness value. The obtained light transmittance L is the theoretical influence value. The calculation process is as follows:

[0022]

[0023] The input layer will output the obtained light transmittance as the predicted theoretical light transmittance of the influence analysis model;

[0024] S101. Organize the calculated data of different dust thicknesses and their corresponding theoretical light transmittances. Use the dust thickness as the abscissa and the theoretical light transmittance as the ordinate to plot a series of points in the plane rectangular coordinate system, and connect these points with a smooth curve to generate the theoretical light transmittance curve under different dust thicknesses.

[0025] Furthermore, in step three, the numerical calculation of the objects with external influence on the photovoltaic includes the following specifically:

[0026] S200. Monitor the photovoltaic panel by using an infrared thermal imaging device to obtain the temperature distribution image on the surface of the photovoltaic panel, and invert the actual dust distribution on the surface of the photovoltaic panel according to the temperature difference.

[0027] S200. Use image processing and data analysis based on historical imaging to process the infrared thermal imaging image. Use image processing technology to segment the photovoltaic panel from the entire infrared thermal imaging image to obtain an image containing only the photovoltaic panel area, obtain the surface temperature layout map of the photovoltaic panel, and convert the temperature layout map into an object distribution information map.

[0028] S200. Construct a pixel point coordinate system in the object distribution information map to obtain the specific temperature value of each pixel point. According to the temperature difference between the temperatures of the photovoltaic panel, establish a corresponding relationship model between the temperature and the dust thickness, and calculate the dust thickness at different positions of the photovoltaic panel according to the temperature data in the infrared thermal imaging image.

[0029] The calculation process is as follows: Set different dust layers as the set d = {d1, d2, d3, …, dn}, the normal temperature obtained by the photovoltaic panel as the set W = {W1, W2, W3, …, Wn}, and the temperature with dust as the set w = {w1, w2, w3, …, wn}.

[0030] Construct the objective function relationship: Where Q is a constant representing the fixed heat flux rate per unit time, k d is the set dust thermal conductivity, A is the area of the corresponding measurement region, and W n - w n is the difference between the temperature of the measurement region in the infrared thermal imaging image and the reference temperature without dust;

[0031] Calculate the actual transmittance L0 as the actual object value: L0 = e -αd .

[0032] Furthermore, perform a difference analysis between the theoretical influence value and the actual object value, which specifically includes the following:

[0033] Obtain the difference analysis between the theoretical influence value and the actual object value:

[0034] C is the compensation coefficient between the two. If C > 0, it means the actual transmittance is higher than the theoretical prediction value; if C < 0, it indicates that the actual transmittance is lower than the theoretical prediction value;

[0035] Construct the corrected formula for power generation:

[0036] E0 = C * E1, where E0 is the corrected actual power, and E1 is the theoretical power generation.

[0037] A distributed photovoltaic data processing system includes a data acquisition module, a model construction module, a data analysis module, a comparative analysis module, and a data processing module;

[0038] The data acquisition module is used to process and store the power generation data generated by the photovoltaic through the sensor monitoring points, and at the same time store the photovoltaic image data obtained through the lidar monitoring points, and obtain the imaging data by monitoring the photovoltaic panel according to the infrared scanner device;

[0039] The model construction module is used to perform data analysis based on the power generation data and the image data, calculate the photovoltaic data difference by selecting the power generation data and the photovoltaic standard data, obtain the external factors outside the photovoltaic panel by selecting the image data, perform a correlation analysis based on the external factors, the photovoltaic panel area, and the photovoltaic panel characteristics, establish an impact analysis model based on the deep neural network model, and predict the theoretical impact value according to the impact analysis model;

[0040] The data analysis module is used to obtain the temperature distribution on the surface of the photovoltaic panel according to the imaging data, identify the normal and abnormal temperature areas based on the temperature distribution, and make differential markings to obtain the surface temperature layout diagram of the photovoltaic panel. The temperature layout diagram is converted into an object distribution information diagram, and the numerical calculation of the object outside the photovoltaic panel is carried out for the abnormal temperature area according to different temperature data;

[0041] The comparative analysis module is used to generate the theoretical external prediction influence value according to the influence analysis model, obtain the actual object numerical value on the surface of the photovoltaic panel according to the imaging data, carry out differential analysis on the theoretical influence value and the actual object numerical value, and obtain the compensation coefficient between the two;

[0042] The data processing module is used to correct the power generation data of the photovoltaic in real time according to the compensation coefficient, obtain the corrected photovoltaic power generation data analysis form, and send it to the data processing control terminal.

[0043] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are:

[0044] The distributed photovoltaic data processing method and system can accurately analyze the influence of dust on the light transmittance of the photovoltaic panel by scanning the surface of the photovoltaic panel for data, establishing an influence analysis model for external influencing factors, predicting the light transmittance according to the influence analysis model, and generating the theoretical light transmittance of different dust thicknesses. At the same time, based on the predicted value of the established influence analysis model and combined with thermal imaging, a re-analysis and comparison are carried out, and the compensation coefficient is calculated according to the light transmittance comparison to correct the power, making the power calculation estimate more in line with the actual situation, so as to obtain a more accurate power generation amount, which is helpful for the rational use of the energy data generated by distributed photovoltaics. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Shows the structural schematic diagram of the method steps of the present invention;

[0046] Figure 2 Shows the structural schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment 1:

[0049] As Figure 1 shown, a distributed photovoltaic data processing method includes the following steps:

[0050] Step 1: Obtain the power generation data generated by the photovoltaic through the sensor monitoring points, process and store it. At the same time, obtain the photovoltaic image data through the lidar monitoring points and store it. Obtain the imaging data by monitoring the photovoltaic panel with the infrared scanner device;

[0051] Step 2: Conduct data analysis based on the power generation data and the image data. Calculate the photovoltaic data difference by selecting the power generation data and the photovoltaic standard data. Obtain the external factors outside the photovoltaic panel by selecting the image data. Conduct correlation analysis based on the external factors, the area of the photovoltaic panel, and the characteristics of the photovoltaic panel. Establish an impact analysis model based on the deep neural network model, and predict the theoretical impact value according to the impact analysis model;

[0052] When conducting the calculation experiment, the external factor is set to dust;

[0053] Step 3: Obtain the temperature distribution on the surface of the photovoltaic panel according to the imaging data. Identify the normal and abnormal temperature regions according to the temperature distribution, and make differential markings to obtain the surface temperature layout diagram of the photovoltaic panel. Convert the temperature layout diagram into an object distribution information diagram, and perform numerical calculations of the objects with external impacts on the photovoltaic for the abnormal temperature region according to different temperature data;

[0054] Step 4: Generate the theoretical external prediction impact value according to the impact analysis model. Obtain the actual object numerical value on the surface of the photovoltaic panel according to the imaging data. Conduct a difference analysis between the theoretical impact value and the actual object numerical value to obtain the compensation coefficient between the two;

[0055] Step 5: Real-time correct the power generation data of the photovoltaic according to the compensation coefficient to obtain the corrected photovoltaic power generation data analysis form, and send it to the data processing control terminal.

[0056] The data processing control terminal is used to process and store the data generated during the use of the photovoltaic panel, including the electrical measurement time of the power generation data, the rated power of the photovoltaic panel, and the power generation data. Store it according to the time series, including arranging the execution order and time of the data processing tasks according to the preset rules or real-time requirements for the generated data, and presenting the results of data analysis and mining to the user in an intuitive manner;

[0057] The data processing control terminal will perform data cleaning and normalization processing on the generated data, and set the same standard for data verification.

[0058] Both the lidar and the infrared scanner are installed near the photovoltaic panels. According to the installation positions and quantities of the measurement area and the photovoltaic panels, the measurement positions are determined and the angles are set. When performing image scanning and acquisition, a dynamic change schedule is set to collect the surface of the photovoltaic panels without time limit, and the number of acquisitions per day during the use of the photovoltaic panels is set.

[0059] When obtaining external factors outside the photovoltaic panels by selecting image data:

[0060] Before performing 3D data scanning by the lidar, verify that the parameters of the lidar are correct. By setting up experimental image acquisition before scanning, verify that the image parameter settings of the lidar are correct;

[0061] When constructing the coordinates of the obtained image, specify the coordinate range inside the image data within [0, 1]. When obtaining external factors of the image, identify the coverage area in the image, perform segmentation and extraction of the coverage area, and use it as the analysis reference area.

[0062] Establishing the impact analysis model specifically includes the following:

[0063] S100. Combine historical image data, estimated dust thickness values, and light transmittance to form a sample set, divide it into a training set, a validation set, and a test set, and construct a preprocessing layer, an analysis layer, and an output layer of the impact analysis model;

[0064] The preprocessing layer obtains the historical image data after lidar scanning. After preprocessing the image data, segment and extract external factors. Through the refraction and propagation of light, obtain the propagation value from the photovoltaic panel to the lidar and the propagation value from the external factor to the lidar, and obtain the estimated dust thickness value on the photovoltaic panel. The calculation process is as follows:

[0065] Preset T as the dust thickness, I as the intensity value received by the lidar, I1 as the intensity value received by the lidar when there is no dust on the surface of the photovoltaic panel, k as the conversion coefficient set for intensity and dust thickness, and as the standard light transmittance without dust, to obtain

[0066] The analysis layer obtains the estimated dust thickness table value on the photovoltaic panel, combines the preset standard attenuation coefficient, and calculates the light transmittance based on the estimated dust thickness value to obtain the light transmittance L as the theoretical impact value. The calculation process is as follows:

[0067]

[0068] The input layer will output the obtained light transmittance as the predicted theoretical light transmittance of the impact analysis model;

[0069] S101. Organize the calculated data of different dust thicknesses and their corresponding theoretical light transmittance. Using the dust thickness as the abscissa and the theoretical light transmittance as the ordinate, plot a series of points in the plane rectangular coordinate system, and connect these points with a smooth curve to generate the theoretical light transmittance curve under different dust thicknesses;

[0070] Build a model and combine it with the thermal imaging dust distribution, and then calculate the compensation coefficient by comparing with the theoretical light transmittance to correct the power, so that the power prediction is closer to the actual situation. When formulating the power supply plan and energy scheduling strategy, it can provide more reliable data support and contribute to the stable operation of the power system.

[0071] In step three, the numerical calculation of the objects affecting the outside of the photovoltaic includes the following specifically:

[0072] S200. By using an infrared thermal imaging device to monitor the photovoltaic panel, obtain the temperature distribution image on the surface of the photovoltaic panel, and invert the actual dust distribution on the surface of the photovoltaic panel according to the temperature difference;

[0073] S200. According to historical imaging, use image processing and data analysis to process the infrared thermal imaging image. Use image processing technology to segment the photovoltaic panel from the entire infrared thermal imaging image to obtain an image that only contains the area of the photovoltaic panel, obtain the surface temperature layout map of the photovoltaic panel, and convert the temperature layout map into an object distribution information map;

[0074] S200. Construct a pixel coordinate system in the object distribution information map to obtain the specific temperature value of each pixel point. According to the temperature difference between the temperatures of the photovoltaic panel, establish a corresponding relationship model between temperature and dust thickness, and calculate the dust thickness at different positions of the photovoltaic panel according to the temperature data in the infrared thermal imaging image.

[0075] The calculation process is as follows: Set different dust layers as the set d = {d1, d2, d3,..., dn}, the normal temperature obtained by the photovoltaic panel as the set W = {W1, W2, W3,..., Wn}, and the temperature with dust as the set w = {w1, w2, w3,..., wn}.

[0076] Construct the objective function relationship: where Q is a constant representing the fixed heat flux rate per unit time, k d is the set dust thermal conductivity, A is the area of the corresponding measurement region, W n - w n is the temperature difference between the measurement region temperature in the infrared thermal imaging image and the reference temperature without dust;

[0077] Calculate the actual light transmittance L0 as the actual object value: L0 = e -αd .

[0078] Perform a difference analysis between the theoretical influence value and the actual object value, which specifically includes the following:

[0079] Obtain the theoretical influence value and perform a difference analysis on the actual object value:

[0080] C is the compensation coefficient between the two. If C > 0, it means the actual light transmittance is higher than the theoretical predicted value; if C < 0, it means the actual light transmittance is lower than the theoretical predicted value;

[0081] Construct a correction formula for the power generation:

[0082] E0 = C * E1, where E0 is the corrected actual power, and E1 is the theoretical power generation. The corrected power can enable the power system dispatching department to better grasp the power generation capacity of the distributed photovoltaic power station, make power balance and allocation preparations in advance, ensure the stability and reliability of power supply, and avoid impacts on the power grid caused by the power generation fluctuations of the photovoltaic power station.

[0083] Example 2:

[0084] As Figure 2 shown, a distributed photovoltaic data processing system includes a data acquisition module, a model construction module, a data analysis module, a comparative analysis module, and a data processing module;

[0085] The data acquisition module is used to process and store the power generation data generated by the photovoltaic through the sensor monitoring points, and at the same time store the photovoltaic image data obtained through the lidar monitoring points, and obtain the imaging data by monitoring the photovoltaic panels according to the infrared scanner device;

[0086] The model construction module is used to perform data analysis based on the power generation data and the image data, calculate the photovoltaic data difference by selecting the power generation data and the photovoltaic standard data, obtain the external factors outside the photovoltaic panel by selecting the image data, perform correlation analysis based on the external factors, the photovoltaic panel area, and the photovoltaic panel characteristics, establish an impact analysis model based on the deep neural network model, and predict the theoretical influence value according to the impact analysis model;

[0087] The data analysis module is used to obtain the temperature distribution on the surface of the photovoltaic panel according to the imaging data, identify the normal and abnormal temperature regions according to the temperature distribution, and make differential markings to obtain the surface temperature layout map of the photovoltaic panel, convert the temperature layout map into an object distribution information map, and calculate the object value of the external influence on the photovoltaic for the abnormal temperature region according to different temperature data;

[0088] The comparative analysis module is used to generate the theoretical external predicted influence value according to the impact analysis model, obtain the actual object value on the surface of the photovoltaic panel according to the imaging data, perform a difference analysis between the theoretical influence value and the actual object value, and obtain the compensation coefficient between the two;

[0089] The data processing module is used to perform real-time correction on the power generation data of the photovoltaic according to the compensation coefficient, obtain the corrected photovoltaic power generation data analysis form, and send it to the data processing control end.

[0090] The setting of the interval and the size of the threshold is for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the number of base numbers set by those skilled in the art for each group of sample data; as long as the proportional relationship between the parameters and the quantified values is not affected.

[0091] The above formulas are all dimensionless and take their numerical calculations. The formula is a formula obtained by collecting a large amount of data for software simulation to approximate the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0092] In the two embodiments provided by the present application, it should be understood that the disclosed device and system can be implemented in other ways; for example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; another point, the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or module can be in an electrical, mechanical or other form.

[0093] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent replacements or changes, and should be covered by the protection scope of the present invention.

Claims

1. A distributed photovoltaic data processing method, characterized in that, It includes the following steps: Step 1: Obtain the power generation data generated by the photovoltaic through the sensor monitoring points, process and store it. At the same time, obtain the photovoltaic image data through the lidar monitoring points and store it. Obtain the imaging data by monitoring the photovoltaic panels with the infrared scanner device; Step 2: Conduct data analysis based on the power generation data and the image data. Calculate the photovoltaic data difference by selecting the power generation data and the photovoltaic standard data. Obtain the external factors outside the photovoltaic panels by selecting the image data. Conduct correlation analysis based on the external factors, the area of the photovoltaic panels, and the characteristics of the photovoltaic panels. Establish an impact analysis model based on the deep neural network model, and predict the theoretical impact value according to the impact analysis model; Step 3: Obtain the temperature distribution on the surface of the photovoltaic panels according to the imaging data. Identify the normal and abnormal temperature regions according to the temperature distribution and make differential markings to obtain the surface temperature layout diagram of the photovoltaic panels. Convert the temperature layout diagram into an object distribution information diagram, and calculate the object values of the external impacts on the photovoltaic for the abnormal temperature regions according to different temperature data; Step 4: Generate the theoretical external predicted impact value according to the impact analysis model. Obtain the actual object values on the surface of the photovoltaic panels according to the imaging data. Conduct a differential analysis of the theoretical impact value and the actual object values to obtain the compensation coefficient between the two; Step 5: Correct the power generation data of the photovoltaic in real time according to the compensation coefficient to obtain the corrected photovoltaic power generation data analysis form and send it to the data processing control terminal.

2. The distributed photovoltaic data processing method according to claim 1, wherein In Step 1, the data processing control terminal is used to process and store the data generated during the use of the photovoltaic panels, including the electrical measurement time of the power generation data, the rated power of the photovoltaic panels, and the power generation data. Classify and store them according to the time series, including arranging the execution order and time of the data processing tasks according to the preset rules or real-time requirements for the generated data, and presenting the results of data analysis and mining to the user in an intuitive manner; Both the lidar and the infrared scanner are installed near the photovoltaic panels. Determine the measurement positions and set the angles according to the installation positions and the number of installations of the measurement area and the photovoltaic panels. When conducting image scanning and acquisition, set a dynamic change time schedule to collect the surface of the photovoltaic panels without time limit, and set the number of acquisitions per day during the use of the photovoltaic panels.

3. The distributed photovoltaic data processing method according to claim 1, wherein When obtaining the external factors outside the photovoltaic panels by selecting the image data: Before performing 3D data scanning with the lidar, verify that the parameters of the lidar are correct. Verify that the lidar image parameter settings are correct by obtaining experimental images before setting the scan; When constructing the coordinates of the obtained images, specify the coordinate range inside the image data within [0, 1]. When obtaining the external factors of the image, identify the coverage area in the image, perform segmentation and extraction of the coverage area as the analysis reference area.

4. The distributed photovoltaic data processing method according to claim 1, characterized in that, In Step 3, the establishment of the impact analysis model specifically includes the following: S100: Construct a sample set from the historical image data, the estimated dust thickness value, and the light transmittance, divide it into a training set, a validation set, and a test set, and construct a preprocessing layer, an analysis layer, and an output layer of the impact analysis model; The preprocessing layer obtains the historical image data after lidar scanning, preprocesses the image data, and then segments and extracts external factors. Through the refraction and propagation of light, the propagation value from the photovoltaic panel to the lidar and the propagation value from the external factors to the lidar are obtained, and the estimated dust thickness value on the photovoltaic panel is obtained. The calculation process is as follows: Preset T as the dust thickness, I as the intensity value received by the lidar, I1 as the intensity value received by the lidar when there is no dust on the surface of the photovoltaic panel, k as the conversion coefficient set for the intensity and the dust thickness, and as the standard light transmittance without dust, to obtain The analysis layer obtains the estimated value of the dust thickness table on the photovoltaic panel, combines the preset standard attenuation coefficient, and calculates the light transmittance according to the estimated dust thickness value to obtain the light transmittance L as the theoretical influence value. The calculation process is as follows: The input layer outputs the obtained light transmittance as the predicted theoretical light transmittance of the influence analysis model; S101. Organize the calculated data of different dust thicknesses and their corresponding theoretical light transmittances. Use the dust thickness as the abscissa and the theoretical light transmittance as the ordinate to plot a series of points in the plane rectangular coordinate system, and connect these points with a smooth curve to generate the theoretical light transmittance curve under different dust thicknesses.

5. The distributed photovoltaic data processing method according to claim 1, wherein In step three, the specific object numerical calculation of the external influence on the photovoltaic includes the following: S200. Monitor the photovoltaic panel by using an infrared thermal imaging device to obtain the temperature distribution image on the surface of the photovoltaic panel, and invert the actual dust distribution on the surface of the photovoltaic panel according to the temperature difference. S200. According to historical imaging, use image processing and data analysis to process the infrared thermal imaging image. Use image processing technology to segment the photovoltaic panel from the entire infrared thermal imaging image to obtain an image containing only the photovoltaic panel area, obtain the surface temperature layout map of the photovoltaic panel, and convert the temperature layout map into an object distribution information map. S200. Construct a pixel coordinate system in the object distribution information map to obtain the specific temperature value of each pixel point. According to the temperature difference between the temperatures of the photovoltaic panel, establish a corresponding relationship model between temperature and dust thickness, and calculate the dust thickness at different positions of the photovoltaic panel according to the temperature data in the infrared thermal imaging image. The calculation process is as follows: Set different dust layers as the set d = {d1, d2, d3,... dn}, the normal temperature obtained by the photovoltaic panel as the set W = {W1, W2, W3,... Wn}, and the temperature with dust as the set w = {w1, w2, w3,... wn}. Construct the objective function relationship: where Q is a constant representing the fixed heat flux rate per unit time, k d is the set thermal conductivity of the dust, A is the area of the corresponding measurement region, W n -w n is the difference between the temperature of the measurement region in the infrared thermal imaging image and the reference temperature without dust; Calculate the actual light transmittance L0 as the actual object value: L0 = e -αd .

6. The distributed photovoltaic data processing method according to claim 1, wherein, Conduct a difference analysis between the theoretical influence value and the actual object value, specifically including the following: Obtain the theoretical influence value and the actual object value for differential analysis: C is the compensation coefficient between the two. If C > 0, it means that the actual light transmittance is higher than the theoretical predicted value; if C < 0, it means that the actual light transmittance is lower than the theoretical predicted value. Construct a corrected formula for power generation: E0 = C * E1, where E0 is the corrected actual power, and E1 is the theoretical power generation.

7. A distributed photovoltaic data processing system, characterized in that, It includes a data acquisition module, a model construction module, a data analysis module, a comparative analysis module, and a data processing module; The data acquisition module is used to process and store the power generation data generated by the photovoltaic through the sensor monitoring points, and at the same time store the photovoltaic image data obtained through the lidar monitoring points, and obtain the imaging data by monitoring the photovoltaic panel with an infrared scanner device; The model construction module is used to perform data analysis based on power generation data and image data. By selecting power generation data and photovoltaic standard data, it calculates the photovoltaic data difference. By selecting image data, it obtains the external factors outside the photovoltaic panel. It conducts correlation analysis based on the external factors, photovoltaic panel area, and photovoltaic panel characteristics, establishes an impact analysis model based on a deep neural network model, and predicts the theoretical impact value according to the impact analysis model; The data analysis module is used to obtain the temperature distribution on the surface of the photovoltaic panel according to the imaging data, identify the normal and abnormal temperature regions based on the temperature distribution, and make differential markings to obtain the surface temperature layout map of the photovoltaic panel. It converts the temperature layout map into an object distribution information map and calculates the object values of the external impact on the photovoltaic for the abnormal temperature region based on different temperature data; The comparison and analysis module is used to generate the theoretical external predicted impact value according to the impact analysis model, obtain the actual object values on the surface of the photovoltaic panel according to the imaging data, conduct a differential analysis of the theoretical impact value and the actual object values, and obtain the compensation coefficient between the two; The data processing module is used to perform real-time correction on the power generation data of the photovoltaic according to the compensation coefficient, obtain the corrected power generation data analysis sheet of the photovoltaic, and send it to the data processing control terminal.

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