An intelligent tracking closed-loop control system for slope photovoltaic panels

Abstract

The application provides a kind of intelligent tracking closed loop control system for side slope photovoltaic panel, it is related to data processing technical field, the method includes: by installing the light intensity sensor and two-axis angle sensor on the edge of photovoltaic panel, with preset sampling frequency real-time acquisition photovoltaic panel surface light intensity data and two-axis angle data of photovoltaic panel support;Wherein, two reference mark points are fixedly arranged at the diagonal position of photovoltaic panel surface;According to the position relationship of two reference mark points, establish plane coordinate system;Photovoltaic panel surface is divided into multiple area units based on plane coordinate system, obtain a data correction coefficient by analyzing the relative position distribution of each area unit;According to the data correction coefficient, the collected light intensity data and two-axis angle data are standardized, to obtain optimized light intensity data and optimized two-axis angle data.The application realizes high-precision adaptive tracking of side slope photovoltaic panel to the sun azimuth.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an intelligent tracking closed-loop control system for photovoltaic panels on slopes. Background Technology

[0002] As the global photovoltaic industry expands into idle slope terrains such as mountains and hills, building photovoltaic power stations on slope land has become an important way to alleviate the scarcity of land resources. Dual-axis solar tracking systems have become one of the core devices of slope photovoltaic power stations because they can adjust the angle of photovoltaic panels in real time to match the sun's position and improve the efficiency of light capture. However, the undulating slope terrain, the support structure being easily deformed by wind and temperature changes over a long period of time, and the uneven distribution of light due to local shadows caused by mountain shading and dust covering the surface of photovoltaic panels pose challenges to the precise control of traditional dual-axis tracking technology.

[0003] Taking a mountain slope photovoltaic power station as an example, the power station adopts a traditional dual-axis tracking system, which is designed based on a fixed plane coordinate system. It only collects data through a light intensity sensor installed at a single point in the center of the photovoltaic panel and a dual-axis angle sensor at the bottom of the support. During operation in a slope area with a certain slope, the support will undergo slight deformation due to the settlement of the support foundation during the rainy season. In addition, some photovoltaic panels will be covered by local shadows cast by adjacent mountains during certain periods. The traditional system cannot identify the differences in light distribution on the surface of the photovoltaic panel and the impact of support deformation on angle measurement. It can only adjust the angle based on the preset solar trajectory of the local latitude and longitude. In the end, there is a significant deviation between the actual pitch angle and horizontal rotation angle of the photovoltaic panel and the real-time position of the sun. The daily power generation is lower than that of photovoltaic power stations with the same configuration on flat land. The traditional dual-axis tracking technology has the defects of not building a dynamic calibration mechanism for slope terrain, not being able to compensate for measurement errors caused by uneven light and support deformation, and relying on a fixed coordinate system, resulting in low tracking accuracy and weak anti-interference ability. It is difficult to adapt to the complex environment of the slope and restricts the further improvement of the power generation efficiency of the slope photovoltaic power station. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent tracking closed-loop control system for slope photovoltaic panels, so as to realize high-precision adaptive tracking of the solar azimuth of the slope photovoltaic panels.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a smart tracking closed-loop control method for photovoltaic panels on slopes is provided, the method comprising:

[0007] Illuminance data and dual-axis angle data of the photovoltaic panel support are collected in real time at a preset sampling frequency using illuminance sensors and dual-axis angle sensors installed on the edge of the photovoltaic panel. Two reference markers are fixedly set at diagonal positions on the surface of the photovoltaic panel.

[0008] A planar coordinate system is established based on the positional relationship between two reference points; the surface of the photovoltaic panel is divided into multiple regional units based on the planar coordinate system, and a data correction coefficient is obtained by analyzing the relative positional distribution of each regional unit; the collected illuminance data and dual-axis angle data are standardized based on the data correction coefficient to obtain optimized illuminance data and optimized dual-axis angle data.

[0009] The optimized dual-axis angle data is subjected to benchmark calibration. The distance change and direction deviation between the two points are calculated by using the real-time coordinates and initial coordinates of the two optical reference marks. Based on the distance change and direction deviation between the two points, the amplitude and direction of the support displacement are quantified to obtain the calibrated dual-axis angle data.

[0010] The calibrated dual-axis angle data and adjusted illuminance data are compared with the solar trajectory prediction data calculated based on local latitude, longitude and seasonal solar terms to calculate the angle deviation and illuminance deviation between the current angle of the photovoltaic panel and the predicted solar position.

[0011] Based on the angle deviation and illuminance deviation, the drive bracket motor dynamically adjusts the pitch angle and horizontal rotation angle of the photovoltaic panel to match the real-time sun position.

[0012] Furthermore, illuminance data of the photovoltaic panel surface and dual-axis angle data of the photovoltaic panel support are collected in real time at a preset sampling frequency using illuminance sensors and dual-axis angle sensors installed on the edge of the photovoltaic panel; wherein, two reference marker points are fixedly set at diagonal positions on the photovoltaic panel surface, including:

[0013] The raw illuminance data and raw dual-axis angle data, which are synchronously collected by the illuminance sensor and the dual-axis angle sensor, are packaged into a data packet of a unified format.

[0014] Receive data packets uploaded by the acquisition device. The data packets contain image data acquired based on two optical reference marks set at diagonal positions on the surface of the photovoltaic panel.

[0015] The image data is processed to identify and extract the pixel coordinates of two optical reference markers in the image;

[0016] Based on pixel coordinates, determine the real-time position information of the two optical reference markers.

[0017] Furthermore, a planar coordinate system is established based on the positional relationship between the two reference points; the surface of the photovoltaic panel is divided into multiple regional units based on the planar coordinate system, and a data correction coefficient is obtained by analyzing the relative positional distribution of each regional unit, including:

[0018] A dynamic planar coordinate system is established on the surface of the photovoltaic panel based on the real-time position information of two optical reference markers determined from the image data.

[0019] In a dynamic planar coordinate system, the surface of the photovoltaic panel is virtually divided into multiple regions of equal area;

[0020] Based on the relative position of each region unit in the dynamic plane coordinate system, different weights are assigned to the sensor data from different region units; by calculating the spatial distribution relationship of the weights, a data correction coefficient is obtained to compensate for the uneven distribution of light on the surface of the photovoltaic panel.

[0021] Furthermore, the collected illuminance data and biaxial angle data are standardized according to the data correction coefficients to obtain optimized illuminance data and optimized biaxial angle data, including:

[0022] Receive data correction coefficients, as well as raw illuminance data and dual-axis angle data from each region unit;

[0023] The data correction coefficient is applied to the corresponding original illuminance data, and the measurement bias caused by local shadows or stains is eliminated by the weighted average algorithm to obtain the optimized illuminance data.

[0024] By combining the data correction coefficient with the original dual-axis angle data, the angle measurement error caused by uneven photovoltaic panel surface or sensor installation error is compensated, resulting in optimized dual-axis angle data.

[0025] Furthermore, the optimized dual-axis angle data undergoes benchmark calibration. Using the real-time coordinates and initial coordinates of two optical reference markers, the distance change and directional deviation between the two points are calculated. Based on the distance change and directional deviation between the two points, the amplitude and direction of the support displacement are quantified to obtain the calibrated dual-axis angle data, including:

[0026] The optimized dual-axis angle data is subjected to benchmark calibration based on the initial coordinate data and real-time coordinate data of two optical reference marks;

[0027] Based on the comparison between the initial coordinate data and the real-time coordinate data, the distance change and orientation deflection angle between the two optical reference marks are calculated.

[0028] Establish an angle calibration parameter matrix based on the distance change value and the direction deflection angle;

[0029] The angle calibration parameter matrix is ​​applied to the optimized dual-axis angle data, and the measurement error caused by the deformation of the support is eliminated by coordinate transformation to obtain the calibrated dual-axis angle data.

[0030] Furthermore, the calibrated dual-axis angle data and adjusted illuminance data are compared with the solar trajectory prediction data calculated based on local latitude, longitude, and seasonal solar terms. The angular deviation and illuminance deviation between the current angle of the photovoltaic panel and the predicted solar azimuth are calculated, including:

[0031] Based on calibrated dual-axis angle data, optimized illuminance data, and solar trajectory prediction data generated based on local latitude and longitude, real-time time, and astronomical algorithms, the solar trajectory prediction data includes theoretical solar azimuth angle, elevation angle, and theoretical illuminance.

[0032] The calibrated dual-axis angle data is compared with the theoretical solar azimuth and elevation angles in the solar trajectory prediction data to calculate the angular deviation between the current pitch and horizontal rotation angles of the photovoltaic panel and the theoretical position of the sun.

[0033] The optimized illuminance data is compared with the theoretical illuminance in the solar trajectory prediction data. Combined with the current time and weather conditions, the illuminance deviation between the actual illuminance and the expected value is calculated.

[0034] Furthermore, based on the angular and illuminance deviations, the drive bracket motor dynamically adjusts the pitch and horizontal rotation angles of the photovoltaic panels to match the real-time solar azimuth, including:

[0035] The angle deviation value and illuminance deviation value are received and combined with the preset control strategy to obtain control commands for adjusting the tilt angle and horizontal rotation angle of the photovoltaic panel;

[0036] The control commands are converted into corresponding motor drive signals and sent to the support motor controller at the slope site via the communication network to obtain the motor drive signals;

[0037] Based on the motor drive signal, the drive bracket motor performs corresponding angular displacement operations to dynamically adjust the spatial attitude of the photovoltaic panel and obtain the adjusted spatial attitude of the photovoltaic panel.

[0038] Based on the adjusted spatial attitude of the photovoltaic panel, the matching status between the actual angle of the photovoltaic panel and the real-time position of the sun is confirmed, and the closed-loop control of the current cycle is completed.

[0039] Secondly, an intelligent tracking closed-loop control system for photovoltaic panels on slopes includes:

[0040] The acquisition module is used to collect illuminance data of the photovoltaic panel surface and dual-axis angle data of the photovoltaic panel support in real time at a preset sampling frequency using illuminance sensors and dual-axis angle sensors installed on the edge of the photovoltaic panel; wherein, two reference marker points are fixedly set at diagonal positions on the surface of the photovoltaic panel;

[0041] The optimization module is used to establish a planar coordinate system based on the positional relationship between two reference points; the surface of the photovoltaic panel is divided into multiple regional units based on the planar coordinate system, and a data correction coefficient is obtained by analyzing the relative positional distribution of each regional unit; the collected illuminance data and dual-axis angle data are standardized according to the data correction coefficient to obtain optimized illuminance data and optimized dual-axis angle data.

[0042] The calibration module is used to perform benchmark calibration on the optimized dual-axis angle data. It calculates the distance change and direction deviation between two points using the real-time coordinates and initial coordinates of two optical reference markers. Based on the distance change and direction deviation between the two points, it quantifies the amplitude and direction of the support displacement to obtain the calibrated dual-axis angle data. The calibrated dual-axis angle data and the adjusted illuminance data are compared with the solar trajectory prediction data calculated based on local latitude and longitude and seasonal solar terms to calculate the angle deviation and illuminance deviation between the current angle of the photovoltaic panel and the predicted solar azimuth.

[0043] The processing module is used to drive the bracket motor to dynamically adjust the pitch and horizontal rotation angles of the photovoltaic panels based on the angle deviation and illuminance deviation, so as to match the real-time sun position.

[0044] Thirdly, a computing device includes:

[0045] One or more processors;

[0046] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0047] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0048] The above-described solution of the present invention has at least the following beneficial effects:

[0049] This method employs a dynamic planar coordinate system constructed by fixing reference points at the diagonal positions of the photovoltaic panels. It divides the area into units and calculates data correction coefficients to standardize the collected illuminance and dual-axis angle data. An angle calibration parameter matrix is ​​established by comparing the real-time coordinates of optical reference marks with the initial coordinates to eliminate support deformation errors. Simultaneously, the calibrated data is compared with solar trajectory prediction data based on local latitude, longitude, and seasonal solar terms to calculate angle and illuminance deviations. A closed-loop control mechanism is used to drive the support motor to dynamically adjust the photovoltaic panel's pitch and horizontal rotation angles. This overcomes the technical problems of traditional dual-axis tracking, which relies on a fixed coordinate system, making it unsuitable for slope terrain, unable to compensate for measurement errors caused by uneven illumination and support deformation, and exhibiting low tracking accuracy and weak anti-interference capabilities. Ultimately, it achieves high-precision adaptive tracking of the solar azimuth for slope photovoltaic panels, effectively improving the photovoltaic panels' light capture efficiency and ensuring stable operation and enhanced power generation performance of slope photovoltaic power stations in complex terrain and environments. Attached Figure Description

[0050] Figure 1 This is a schematic flowchart of an intelligent tracking closed-loop control method for photovoltaic panels on slopes, provided by an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of an intelligent tracking closed-loop control system for photovoltaic panels on slopes, provided by an embodiment of the present invention. Detailed Implementation

[0052] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0053] like Figure 1 As shown, an embodiment of the present invention proposes an intelligent tracking closed-loop control method for photovoltaic panels on slopes, the method comprising the following steps:

[0054] Step 1: Using a illuminance sensor and a dual-axis angle sensor installed on the edge of the photovoltaic panel, illuminance data of the photovoltaic panel surface and dual-axis angle data of the photovoltaic panel support are collected in real time at a preset sampling frequency; wherein, two reference marker points are fixedly set at diagonal positions on the photovoltaic panel surface;

[0055] Step 2: Establish a planar coordinate system based on the positional relationship between the two reference points; divide the photovoltaic panel surface into multiple regional units based on the planar coordinate system, and obtain a data correction coefficient by analyzing the relative positional distribution of each regional unit; standardize the collected illuminance data and dual-axis angle data according to the data correction coefficient to obtain optimized illuminance data and optimized dual-axis angle data.

[0056] Step 3: Perform benchmark calibration on the optimized dual-axis angle data. Calculate the distance change and direction deviation between the two points using the real-time coordinates and initial coordinates of the two optical reference markers. Based on the distance change and direction deviation between the two points, quantify the amplitude and direction of the support displacement to obtain the calibrated dual-axis angle data.

[0057] Step 4: Compare the calibrated dual-axis angle data and adjusted illuminance data with the solar trajectory prediction data calculated based on local latitude and longitude and seasonal solar terms, and calculate the angle deviation and illuminance deviation between the current angle of the photovoltaic panel and the predicted solar position.

[0058] Step 5: Based on the angle deviation and illuminance deviation, drive the bracket motor to dynamically adjust the pitch angle and horizontal rotation angle of the photovoltaic panel to match the real-time sun position.

[0059] In this embodiment of the invention, by employing a method of installing illuminance sensors and dual-axis angle sensors at the edge of the photovoltaic panel to collect data in real time at a preset sampling frequency, setting reference markers at the diagonal positions of the photovoltaic panel and establishing a planar coordinate system based on these markers, dividing the area into units based on the planar coordinate system, and obtaining data correction coefficients by analyzing the relative position distribution of the units to standardize the illuminance and dual-axis angle data, and then using optical reference markers to calculate distance changes and direction deviations based on real-time coordinates and initial coordinates to perform benchmark calibration on the dual-axis angle data, comparing the calibrated data with solar trajectory prediction data based on local latitude, longitude, and seasonal solar terms to calculate angle and illuminance deviations, and finally driving the support motor to adjust the pitch and horizontal rotation angles of the photovoltaic panel, this invention overcomes the technical problems of traditional slope photovoltaic tracking methods, such as the lack of a dynamic adaptation mechanism, difficulty in compensating for measurement errors caused by uneven illumination and support displacement, and low accuracy in matching the photovoltaic panel angle with the solar azimuth. This achieves dynamic and accurate tracking of the solar azimuth by the slope photovoltaic panel, effectively improving the photovoltaic panel's light capture efficiency.

[0060] In a preferred embodiment of the present invention, step 1 above may include:

[0061] Step 1.1: The raw illuminance data and raw dual-axis angle data synchronously collected by the illuminance sensor and the dual-axis angle sensor are encapsulated into a unified format data packet. Specifically, this involves: first, performing time synchronization calibration on the illuminance sensor and the dual-axis angle sensor installed at the edge of the photovoltaic panel to ensure that both start data acquisition simultaneously according to a preset sampling frequency. This guarantees that each acquisition operation can obtain the raw illuminance data of the photovoltaic panel surface and the raw dual-axis angle data of the photovoltaic panel support at the same time point. Then, the two types of raw data collected synchronously each time are integrated. According to a pre-set data structure standard, the acquisition time, sensor model, and illuminance value of the raw illuminance data, as well as the acquisition time, sensor model, pitch angle value, and horizontal rotation angle value of the raw dual-axis angle data, are all included in the same data framework and finally encapsulated into a unified format data packet.

[0062] Step 1.2: Receive the data packet uploaded by the acquisition device. The data packet contains image data acquired based on two optical reference marks positioned diagonally on the surface of the photovoltaic panel. Specifically, after fixing the two optical reference marks at diagonal positions on the photovoltaic panel surface, an image acquisition device is installed on the photovoltaic panel. The sampling frequency of the image acquisition device is synchronized with that of the illuminance sensor and the dual-axis angle sensor, so that the image acquisition device can capture images of the two optical reference marks on the photovoltaic panel surface at the same time as the illuminance sensor and the dual-axis angle sensor acquire data, thereby obtaining image data containing complete images of the two optical reference marks. Subsequently, the acquisition device integrates the original illuminance data, original dual-axis angle data, and corresponding image data generated each time into a complete data packet, which is transmitted to the main control unit of the intelligent tracking closed-loop control of the slope photovoltaic panel through a pre-established wired or wireless communication link. The main control unit receives the data packet uploaded by the acquisition device according to the preset data packet reception protocol, and performs a preliminary check on the integrity of the data packet to confirm whether the data packet contains all the necessary original data and image data. If any data is missing, a retransmission command is triggered to ensure that the received data packet information is complete.

[0063] Step 1.3 involves processing the image data to identify and extract the pixel coordinates of the two optical reference marks in the image. Specifically, this includes: preprocessing the image data in the received data packet; firstly, using a filtering algorithm to remove noise interference from the image; then, using brightness adjustment and contrast enhancement techniques to improve the visual effect of the image, ensuring that the two optical reference marks are clearly presented in the image and distinguished from other background areas on the photovoltaic panel surface; next, the image processing module runs an image recognition program to locate the specific positions of the two optical reference marks one by one in the preprocessed image based on their preset shape, color, and other features, eliminating interference from irrelevant elements such as stains and scratches on the photovoltaic panel surface; finally, based on the pixel coordinate system parameters of the image itself, determining the pixel coordinates corresponding to the center position of each optical reference mark, accurately reading and recording the horizontal and vertical pixel values ​​of the two optical reference marks in the image, and completing the pixel coordinate extraction.

[0064] Step 1.4: Based on pixel coordinates, determine the real-time position information of the two optical reference marks. This includes: pre-measuring and recording the relative installation parameters between the image acquisition device and the photovoltaic panel surface, including the installation height of the image acquisition device, the angle between the lens and the photovoltaic panel surface, and the lens focal length. Based on these parameters, establish a transformation model between the image pixel coordinate system and the actual physical coordinate system of the photovoltaic panel, clarifying the corresponding calculation relationship between pixel coordinates and physical coordinates. Then, convert the extracted pixel coordinates of the two optical reference marks into the actual physical position coordinates on the photovoltaic panel surface through calculation, obtaining the specific position values ​​of the two optical reference marks with a fixed point on the photovoltaic panel surface as a reference. Finally, verify the rationality of the converted physical position coordinates by comparing whether the position change of the same optical reference mark obtained from two consecutive acquisitions is within a reasonable range. If it exceeds the reasonable range, re-examine the transformation model parameters or image data processing process to ensure that the final determined real-time position information of the two optical reference marks reflects the actual position on the photovoltaic panel surface.

[0065] In this embodiment of the invention, by employing a technique that allows the illuminance sensor and the dual-axis angle sensor to synchronously acquire raw data and encapsulate it into a unified format data packet, receiving a data packet containing image data of the photovoltaic panel's diagonal optical reference mark, processing the image data to extract the reference mark's pixel coordinates, and then determining the real-time position information of the reference mark based on the pixel coordinates, the technical effect of overcoming the poor correlation caused by the asynchronous sensor data and the deviation caused by the lack of image support in the acquisition of the reference mark's position during the traditional data acquisition process is achieved. This ensures the synchronization and integrity of the acquired data, enables accurate determination of the real-time position of the optical reference mark, and improves the accuracy of the data acquisition stage of the entire control method.

[0066] In a preferred embodiment of the present invention, step 2 above may include:

[0067] Step 2.1: Based on the real-time position information of the two optical reference marks determined from the image data, establish a dynamic planar coordinate system for the photovoltaic panel surface. This includes: first, retrieving the real-time position information of the two determined optical reference marks from the previously processed image data to clarify the actual physical coordinates of these two marks on the photovoltaic panel surface; then, selecting one of the optical reference marks as the origin of the dynamic planar coordinate system; using the origin as a reference, setting the line connecting the two optical reference marks as the horizontal axis of the coordinate system; and then, based on the actual orientation of the photovoltaic panel surface, establishing a vertical axis perpendicular to the horizontal axis to form a preliminary planar coordinate system framework; subsequently, setting the length unit of the coordinate system based on the actual distance between the two optical reference marks to ensure that the scale of the coordinate system accurately corresponds to the physical dimensions of the photovoltaic panel surface; whenever the updated real-time position information of the two optical reference marks is obtained, readjusting the origin position, axis direction, and scale of the coordinate system according to the above method to ensure that the coordinate system always maintains consistency with the current actual posture of the photovoltaic panel, avoiding the decoupling of the coordinate system from the photovoltaic panel surface due to support deformation or slope topography.

[0068] Step 2.2: Under the dynamic plane coordinate system, the photovoltaic panel surface is virtually divided into multiple equal-area regional units. Specifically, this includes: obtaining the actual length and width of the photovoltaic panel surface using measuring tools to determine its overall physical area; marking the corresponding horizontal and vertical ranges of the photovoltaic panel surface in the coordinate system based on the established dynamic plane coordinate system to clarify the specific boundaries of the photovoltaic panel within the coordinate system; determining the total number of regional units to be divided based on the size of areas on the photovoltaic panel surface where uneven illumination may occur and the accuracy requirements of sensor data acquisition; ensuring that each regional unit can cover local areas where illumination differences may exist; and dividing the photovoltaic panel surface into several equal parts in the dynamic plane coordinate system according to the principle of uniform horizontal and vertical division, making the horizontal and vertical dividing lines intersect each other, thus virtually dividing the photovoltaic panel surface into multiple regularly shaped and completely equal-area regional units, and recording the specific coordinate range of each regional unit in the dynamic plane coordinate system.

[0069] Step 2.3: Based on the relative position of each regional unit in the dynamic plane coordinate system, assign different weights to the sensor data from different regional units. By calculating the spatial distribution relationship of the weights, obtain a data correction coefficient to compensate for the uneven distribution of light on the photovoltaic panel surface. Specifically, this includes: first, analyzing the relative position characteristics of different regional units in the dynamic plane coordinate system, and combining the actual operation of the slope photovoltaic panel to summarize the pattern of light influence on regional units at different locations. For example, regional units near the edge of the photovoltaic panel are easily affected by mountain shading or ambient light and shadow, resulting in poor light stability; regional units located in the middle of the photovoltaic panel have relatively stable light. Based on this pattern, formulate weight allocation rules, assigning higher weights to regional units with strong light stability and high data reliability, and assigning lower weights to regional units with weak light stability and easily interfered data. According to the rules, assign corresponding weight values ​​to the sensor data corresponding to each regional unit one by one. Then, count the weight values ​​of all regional units, analyze the spatial distribution relationship of these weight values ​​in the dynamic plane coordinate system, and observe whether the weight values ​​show regular changes with the regional position, such as a gradient distribution that gradually decreases from the center to the edge. Based on this spatial distribution relationship and the deviation of historically collected illuminance data, a data correction coefficient is determined through comprehensive calculation. The coefficient is adjusted according to the weight difference of different regional units to make targeted adjustments to the illuminance data of each region, thereby compensating for the uneven distribution of illuminance on the photovoltaic panel surface caused by factors such as local shadows and dust coverage.

[0070] In this embodiment of the invention, a dynamic planar coordinate system for the photovoltaic panel surface is established based on the real-time position information of two optical reference markers. Under the dynamic coordinate system, the photovoltaic panel surface is virtually divided into multiple equal-area regional units. Then, different weights are assigned to the sensor data of different regional units according to the relative position of each regional unit. By calculating the spatial distribution relationship of the weights, a data correction coefficient is obtained to compensate for the uneven illumination distribution on the photovoltaic panel surface. Therefore, this overcomes the technical problems of traditional slope photovoltaic tracking, which relies on a fixed planar coordinate system that cannot adapt to the attitude changes of the photovoltaic panel caused by slope terrain or support deformation, and cannot specifically handle the impact of uneven illumination distribution on sensor data caused by local shadows on the photovoltaic panel surface. Thus, it achieves the technical effect of enabling the established coordinate system to match the actual attitude of the photovoltaic panel in real time, accurately covering different illumination areas on the photovoltaic panel surface with the divided regional units, and specifically compensating for the impact of uneven illumination with the obtained data correction coefficient, thereby improving the effectiveness and accuracy of the original data.

[0071] In a preferred embodiment of the present invention, step 2 above may include:

[0072] Step 2.4 involves receiving data correction coefficients and raw illuminance and dual-axis angle data from each regional unit. Specifically, this includes: first, determining the data source; the data correction coefficients are derived from the calculation results of the spatial distribution relationship of regional unit weights in previous steps; and the raw illuminance and dual-axis angle data from each regional unit are generated by illuminance sensors and dual-axis angle sensors installed at the edge of the photovoltaic panel corresponding to each regional unit. A connection is established with the data calculation module and each sensor according to a preset communication protocol. The data correction coefficients and the raw illuminance and raw dual-axis angle data corresponding to each regional unit are received sequentially. During the reception process, each set of data is identified and recorded, clarifying the applicable scope of the data correction coefficients, and the regional unit number and acquisition time of each piece of raw data to ensure a clear correspondence between data. Simultaneously, the received data undergoes preliminary verification to check whether the data format conforms to preset standards and whether the data values ​​are within a reasonable range. If data format errors or abnormal values ​​are found, a re-reception command is immediately triggered until complete and compliant data is obtained.

[0073] Step 2.5: Apply the data correction coefficients to the corresponding original illuminance data. Use a weighted average algorithm to eliminate measurement biases caused by local shadows or stains, obtaining optimized illuminance data. Specifically, this includes: first, establishing data association by matching the received data correction coefficients with the original illuminance data of each region unit, ensuring that the original illuminance data of each region unit corresponds to its specific correction coefficient weight; then, starting the weighted average algorithm processing flow, multiplying the original illuminance data of each region unit by its corresponding correction coefficient weight to obtain the weighted illuminance data of the region unit; finally, statistically analyzing the illuminance data of all regions. The weighted illuminance data of the photovoltaic panel is summed, and the sum of the correction coefficient weights of all regional units is calculated. Then, the sum of the weighted illuminance data is divided by the sum of the correction coefficient weights to obtain the average illuminance value of the entire photovoltaic panel surface. This value is the optimized illuminance data after eliminating the influence of local shadows or stains. After the calculation is completed, the illuminance data before and after optimization are compared to verify whether the optimized data can more accurately reflect the overall lighting conditions of the photovoltaic panel surface. If the optimization effect does not meet the preset standard, the data matching relationship or the execution process of the weighted average algorithm is re-checked until the optimized illuminance data that meets the requirements is obtained.

[0074] Step 2.6 combines the data correction coefficient with the original dual-axis angle data to compensate for angle measurement errors caused by uneven photovoltaic panel surface or sensor installation errors, resulting in optimized dual-axis angle data. Specifically, this includes: firstly, analyzing the correlation logic between the data correction coefficient and the dual-axis angle measurement error. Since uneven photovoltaic panel surface or sensor installation errors can cause deviations in the angle measurement values ​​of different area units, and the data correction coefficient already reflects the positional characteristics and error impact of each area unit, it can be used as the basis for angle error compensation. Subsequently, for the original dual-axis angle data of each area unit, compensation calculation is performed in conjunction with the corresponding data correction coefficient. If the original dual-axis angle data of a certain area unit is too small due to a concave photovoltaic panel surface, or too large due to tilted sensor installation, the original dual-axis angle data is adjusted accordingly based on the weight ratio of the correction coefficient to offset the deviation caused by unevenness or installation errors. After compensating the angle data of a single region unit, the angle data of all region units after compensation are integrated. By averaging or taking the median, optimized dual-axis angle data that can represent the overall attitude of the photovoltaic panel are obtained. Finally, the optimized dual-axis angle data is compared with the actual physical attitude of the photovoltaic panel. For example, the actual angle of the photovoltaic panel can be obtained by manual measurement or auxiliary testing equipment to verify whether the optimized data accurately reflects the true angle of the photovoltaic panel and ensure that the angle measurement error is effectively compensated.

[0075] In this embodiment of the invention, the technical means of receiving data correction coefficients and original illuminance data and original dual-axis angle data from each regional unit, applying the data correction coefficients to the original illuminance data and processing them through a weighted average algorithm, and combining the data correction coefficients with the original dual-axis angle data, overcomes the technical problems of traditional dual-axis tracking technology, such as the inability to eliminate interference from local shadows or stains on illuminance measurement, the difficulty in compensating for angle measurement deviations caused by uneven photovoltaic panel surfaces or sensor installation errors, resulting in low accuracy of the original data and inability to support precise tracking. This achieves the technical effect of effectively eliminating measurement deviations in illuminance data and compensating for measurement errors in angle data, resulting in optimized illuminance data and dual-axis angle data, further improving the data reliability of the slope photovoltaic tracking system.

[0076] In a preferred embodiment of the present invention, step 3 above may include:

[0077] Step 3.1 involves performing benchmark calibration on the optimized dual-axis angle data, based on the initial and real-time coordinate data of two optical reference markers. Specifically, when the photovoltaic panel support is in an initial state with no deformation and the photovoltaic panel surface flat, the position data of the two optical reference markers in the dynamic plane coordinate system are collected and recorded. This data is then used as the initial coordinate data and stored in the system database as the benchmark for subsequent calibration. During the system's daily operation, after obtaining the optimized dual-axis angle data, the real-time position information of the two optical reference markers at the current moment is retrieved from the image and converted into corresponding real-time coordinate data. Subsequently, the benchmark calibration program is initiated, retrieving the initial coordinate data from the database and matching it with the real-time coordinate data. The initial coordinate data is then used as the reference standard for calibration of the optimized dual-axis angle data.

[0078] Step 3.2, based on the comparison of initial coordinate data and real-time coordinate data, calculate the distance change and directional deflection angle between the two optical reference marks. Specifically, this includes: extracting the initial coordinate data of the two optical reference marks, calculating the straight-line distance between the two marks in the initial state, and recording it as the initial distance value; simultaneously determining the initial direction of the line connecting the two marks based on the initial coordinates, for example, using the horizontal axis of the coordinate system as a reference, and recording the angle between the initial line connecting the line and the horizontal axis as the initial direction angle; then performing the same calculation on the real-time coordinate data to obtain the straight-line distance between the two marks in the real-time state and the angle between the real-time line connecting the line and the horizontal axis of the coordinate system; then calculating the distance change value by subtracting the initial distance value from the real-time distance value. If the result is positive, it indicates that the distance between the two marks has increased, and if it is negative, it indicates that the distance has decreased. The value directly reflects the degree of stretching or compression of the support due to deformation; next, calculating the directional deflection angle by subtracting the initial direction angle from the real-time direction angle. The resulting angle difference is the degree of directional deflection of the line connecting the two optical reference marks. The value can reflect the direction and amplitude of the torsion or tilt caused by the deformation of the support, thereby completely quantifying the amplitude and direction of the support displacement.

[0079] Step 3.3: Establish an angle calibration parameter matrix based on the distance change value and direction deflection angle. This includes: First, analyzing the correlation between the distance change value, direction deflection angle, and the dual-axis angle measurement error. For example, when the distance change value is positive and exceeds a preset threshold, it indicates tensile deformation of the support, which will lead to a smaller measured pitch angle in the dual-axis angle measurement. When the direction deflection angle is a specific angle, it will cause a corresponding angle deviation in the measured horizontal rotation angle. Based on these patterns, determine the types of angle calibration parameters, including distance compensation parameters for pitch angle and direction correction parameters for horizontal rotation angle. Then, set the value of the distance compensation parameter based on the magnitude of the distance change value; the larger the distance change, the larger the absolute value of the compensation parameter. Set the value of the direction correction parameter based on the magnitude and direction of the direction deflection angle; the larger the deflection angle, the larger the absolute value of the correction parameter, and the sign of the correction parameter varies depending on the deflection direction. Arrange these distance compensation parameters and direction correction parameters according to a preset matrix structure, such as rows corresponding to different angle types and columns corresponding to different calibration dimensions, ultimately forming the angle calibration parameter matrix.

[0080] Step 3.4: Apply the angle calibration parameter matrix to the optimized dual-axis angle data. Eliminate measurement errors caused by support deformation through coordinate transformation to obtain calibrated dual-axis angle data. Specifically, this includes: calling the established angle calibration parameter matrix; calculating the correlation between pitch angle data and distance compensation parameters based on the correspondence of different parameters in the matrix; adding or subtracting the corresponding distance compensation parameters from the optimized pitch angle data to eliminate the influence of support tension or compression deformation on pitch angle measurement; calculating the correlation between horizontal rotation angle data and direction correction parameters; adding or subtracting the corresponding direction correction parameters from the optimized horizontal rotation angle data to eliminate the influence of support torsion or tilt deformation on horizontal rotation angle measurement. During the calculation process, strictly follow the logic of coordinate transformation to ensure that each angle data can accurately match the corresponding calibration parameter, avoiding parameter misalignment that could lead to calibration failure. After the calculation is completed, a new set of dual-axis angle data is obtained, which is the calibrated dual-axis angle data.

[0081] In this embodiment of the invention, the optimized dual-axis angle data is calibrated based on the initial coordinate data and real-time coordinate data of two optical reference marks. By comparing the initial coordinates and real-time coordinates, the distance change and directional deflection angle between the two optical reference marks are calculated. An angle calibration parameter matrix is ​​established based on the distance change and directional deflection angle. The angle calibration parameter matrix is ​​then applied to the optimized dual-axis angle data and coordinate transformation is used. Therefore, this overcomes the technical problem in traditional slope photovoltaic tracking technology that cannot compensate for the dual-axis angle measurement error caused by the deformation of the support due to the slope environment, which leads to the photovoltaic panel angle tracking deviating from the actual solar azimuth. This effectively eliminates the interference of support deformation on angle measurement, obtains calibrated dual-axis angle data, and improves the accuracy and stability of slope photovoltaic panel tracking of the solar azimuth.

[0082] In a preferred embodiment of the present invention, step 4 above may include:

[0083] Step 4.1: Based on the calibrated dual-axis angle data, optimized illuminance data, and solar trajectory prediction data generated based on local latitude and longitude, real-time time, and astronomical algorithms (including theoretical solar azimuth, altitude, and theoretical illuminance), specifically, this involves: retrieving the processed calibrated dual-axis angle data and the processed optimized illuminance data, ensuring that the acquisition time of these two types of data is consistent with the current processing time; then, by obtaining the local latitude and longitude information of the photovoltaic power station location, and obtaining real-time time information via a clock, including year, month, day, hour, minute, and second, the data is... The local latitude and longitude and real-time time are input into a preset astronomical algorithm. Based on the astronomical laws of the sun's movement, the algorithm calculates the theoretical position parameters and theoretical illumination parameters of the sun at the current moment. Specifically, these include the theoretical solar azimuth angle (the angle between the sun's projection on the horizontal plane and due north), the theoretical solar altitude angle (the angle between the sun and the horizontal plane), and the theoretical illumination intensity (the expected illumination value at that moment under unobstructed conditions). These parameters together constitute the solar trajectory prediction data. Finally, the calibrated dual-axis angle data, the optimized illumination data, and the generated solar trajectory prediction data are associated and stored to ensure that the three types of data correspond one-to-one in the time dimension.

[0084] Step 4.2: Compare the calibrated dual-axis angle data with the theoretical solar azimuth and elevation angles in the solar trajectory prediction data to calculate the angular deviation between the current pitch and horizontal rotation angles of the photovoltaic panel and the theoretical position of the sun. Specifically, this includes: first, clarifying the correspondence between the calibrated dual-axis angle data and the theoretical parameters in the solar trajectory prediction data. The calibrated dual-axis angle data includes the tracking angles corresponding to the solar elevation angle and the horizontal rotation angle corresponding to the solar azimuth angle of the current pitch angle of the photovoltaic panel. The theoretical solar elevation angle in the solar trajectory prediction data is the ideal pitch angle target value that the photovoltaic panel should achieve, and the theoretical solar azimuth angle is the ideal horizontal rotation angle target value that the photovoltaic panel should achieve. Then… The calibrated pitch angle data is compared with the theoretical solar altitude angle. The difference between the calibrated pitch angle and the theoretical solar altitude angle is the pitch angle deviation value. If the difference is positive, it indicates that the pitch angle of the photovoltaic panel is too high, and if it is negative, it indicates that it is too low. At the same time, the calibrated horizontal rotation angle data is compared with the theoretical solar azimuth angle. The difference between the calibrated horizontal rotation angle and the theoretical solar azimuth angle is the horizontal rotation angle deviation value. Similarly, the direction of deflection is determined by the sign. Finally, the pitch angle deviation value and the horizontal rotation angle deviation value are integrated to form a comprehensive angle deviation value between the current angle of the photovoltaic panel and the theoretical position of the sun, which fully reflects the degree of deviation between the angle of the photovoltaic panel and the theoretical position of the sun.

[0085] Step 4.3 compares the optimized illuminance data with the theoretical illuminance in the solar trajectory prediction data. Considering the current time and weather conditions, the illuminance deviation between the actual and expected values ​​is calculated. Specifically, this includes: initially comparing the optimized illuminance data with the theoretical illuminance in the solar trajectory prediction data, calculating the initial difference between the two (optimized illuminance data minus theoretical illuminance), and then analyzing the normal variation pattern of illuminance intensity in the current time. For example, illuminance intensity gradually increases in the morning and gradually decreases in the afternoon. If the initial difference does not conform to this pattern, it is necessary to determine whether... To detect abnormal deviations, real-time meteorological data, including cloud cover and visibility, is introduced. If the current weather is cloudy, the theoretical light intensity needs to be adjusted downward based on the cloud cover and then compared with the optimized light intensity data. If it is sunny, the original theoretical light intensity is used directly for comparison. Based on the time pattern and meteorological conditions, the theoretical light intensity is corrected, and the difference between it and the optimized light intensity data is recalculated to obtain the final light intensity deviation value. If the deviation value is positive, it means that the actual light intensity is stronger than expected; if it is negative, it means that it is weaker than expected. This accurately reflects the difference between the actual light intensity and the theoretical expectation, eliminating false deviations caused by normal environmental factors.

[0086] In this embodiment of the invention, based on calibrated dual-axis angle data, optimized illuminance data, and solar trajectory prediction data including theoretical solar azimuth, altitude, and theoretical illuminance generated by combining local latitude and longitude, real-time time, and astronomical algorithms, the calibrated dual-axis angle data is compared with the theoretical solar azimuth and altitude in the prediction data to calculate the angle deviation value. Simultaneously, the optimized illuminance data is compared with the theoretical illuminance in the prediction data, and the illuminance deviation value is calculated in conjunction with the current time and meteorological conditions. Therefore, this overcomes the technical problem of traditional dual-axis tracking technology, which only presets solar trajectory adjustments based on local latitude and longitude, failing to combine calibrated and optimized actual data with theoretical solar trajectory data, and unable to effectively calculate the angle deviation between the photovoltaic panel angle and the theoretical position of the sun, or the illuminance deviation between the actual illuminance and the expected value, resulting in a lack of accurate basis for subsequent angle adjustments. This achieves the technical effect of accurately quantifying angle and illuminance deviations, ensuring that the photovoltaic panel can accurately match the real-time position of the sun, and laying a key data foundation for improving light capture efficiency.

[0087] In a preferred embodiment of the present invention, step 5 above may include:

[0088] Step 5.1: Receive angle deviation and illuminance deviation values. Combined with a preset control strategy, obtain control commands for adjusting the photovoltaic panel's pitch and horizontal rotation angles. Specifically, this includes: receiving the output angle deviation and illuminance deviation values, performing timestamp matching on the data to ensure that the two types of deviation values ​​correspond to the same acquisition period. The preset control strategy needs to be formulated based on the slope environment characteristics. For example, when the absolute value of the angle deviation exceeds a set threshold, the adjustment direction is determined based on the sign of the deviation. If the deviation is positive, the corresponding angle needs to be decreased; if the deviation is negative, the corresponding angle needs to be increased. Simultaneously, the adjustment amplitude is set according to the magnitude of the deviation; the larger the deviation, the larger the single adjustment amplitude, but it does not exceed the motor's maximum adjustment range. If the illuminance deviation shows that the actual illuminance is significantly lower than the theoretical value, it is determined that there may be temporary shading. In this case, the adjustment frequency is appropriately reduced to reduce invalid operations. Based on these rules, the control decision module calculates the specific pitch and horizontal rotation adjustment amounts and integrates them into control commands that include the adjustment direction, amplitude, and execution time.

[0089] Step 5.2 involves converting the control commands into corresponding motor drive signals and transmitting them to the support motor controller at the slope site via the communication network. This process includes: receiving the control commands, parsing the adjustment parameters for the pitch and horizontal rotation angles, and converting the angle adjustment amounts into motor-recognizable drive parameters based on the support motor model and control protocol. For example, the angle value is converted into the pulse count of a stepper motor or the rotation angle signal of a servo motor, ensuring that the converted signal matches the input format of the motor controller. The converted motor drive signal is then encrypted and packaged using a communication network adapted to the slope terrain, such as an industrial Ethernet or wireless transmission network, and sent to the support motor controller at the slope site. Upon receiving the signal, the motor controller checks its integrity and format correctness. If correct, it stores the drive signal for later execution.

[0090] Step 5.3: Based on the motor drive signal, the drive bracket motor performs the corresponding angular displacement operation to dynamically adjust the spatial attitude of the photovoltaic panel, obtaining the adjusted spatial attitude of the photovoltaic panel. Specifically, the bracket motor controller sends start commands to the dual-axis motors controlling the pitch angle and horizontal rotation angle according to the stored motor drive signal. The pitch motor rotates clockwise or counterclockwise according to the pitch adjustment parameters in the drive signal, driving the photovoltaic panel to rotate around the horizontal axis to adjust the pitch angle. The horizontal rotation motor drives the photovoltaic panel to rotate around the vertical axis according to the horizontal adjustment parameters to adjust the horizontal rotation angle. During the adjustment process, the encoder built into the motor provides real-time feedback of the rotation angle. If the actual rotation amount deviates from the adjustment amount required by the drive signal by more than the set value, fine-tuning is triggered to ensure adjustment accuracy. After the adjustment is completed, the current pitch angle and horizontal rotation angle of the photovoltaic panel are collected by the dual-axis angle sensor and recorded as the adjusted spatial attitude data of the photovoltaic panel.

[0091] Step 5.4: Based on the adjusted spatial attitude of the photovoltaic panel, confirm the matching status between the actual angle of the photovoltaic panel and the real-time azimuth of the sun, and complete the closed-loop control of the current cycle. Specifically, this includes: acquiring the adjusted spatial attitude data of the photovoltaic panel, and calculating the real-time azimuth and altitude angles of the sun using astronomical algorithms in conjunction with the real-time time and local latitude and longitude. Compare the actual angle of the photovoltaic panel with the real-time azimuth and altitude angles of the sun, and calculate whether the deviation between the two is within the preset allowable range. If the deviation is within the allowable range, confirm that the actual angle of the photovoltaic panel and the real-time azimuth of the sun are well matched; if the deviation exceeds the range, record the deviation information as the adjustment basis for the next control cycle. After confirming the matching status, mark the end of the current control cycle, trigger the next round of data acquisition process, and form continuous closed-loop control.

[0092] In this embodiment of the invention, the angle deviation value and illuminance deviation value are received and combined with a preset control strategy to generate control commands to adjust the pitch angle and horizontal rotation angle of the photovoltaic panel. The control commands are converted into corresponding motor drive signals and sent to the slope support motor controller through the communication network. Based on the motor drive signals, the support motor is driven to perform angular displacement operation to adjust the spatial attitude of the photovoltaic panel. Then, the actual angle of the photovoltaic panel is confirmed to match the real-time azimuth of the sun to complete the closed-loop control of the current cycle. Therefore, this method overcomes the technical problems of traditional dual-axis tracking technology, which lacks a dynamic control command generation and execution mechanism based on actual deviation, lacks a closed-loop link for attitude matching confirmation, cannot correct the angle deviation of the photovoltaic panel in time, and cannot ensure accurate matching between the photovoltaic panel and the real-time azimuth of the sun. Thus, it achieves the dynamic adjustment of the spatial attitude of the photovoltaic panel, ensures that its actual angle and the real-time azimuth of the sun always maintain a high degree of matching, improves the solar tracking accuracy and light energy capture efficiency of the slope photovoltaic panel, and ensures the stability and reliability of the tracking system through closed-loop control.

[0093] like Figure 2 As shown, embodiments of the present invention also provide an intelligent tracking closed-loop control system for slope photovoltaic panels, comprising:

[0094] The acquisition module is used to collect illuminance data of the photovoltaic panel surface and dual-axis angle data of the photovoltaic panel support in real time at a preset sampling frequency using illuminance sensors and dual-axis angle sensors installed on the edge of the photovoltaic panel; wherein, two reference marker points are fixedly set at diagonal positions on the surface of the photovoltaic panel;

[0095] The optimization module is used to establish a planar coordinate system based on the positional relationship between two reference points; the surface of the photovoltaic panel is divided into multiple regional units based on the planar coordinate system, and a data correction coefficient is obtained by analyzing the relative positional distribution of each regional unit; the collected illuminance data and dual-axis angle data are standardized according to the data correction coefficient to obtain optimized illuminance data and optimized dual-axis angle data.

[0096] The calibration module is used to perform benchmark calibration on the optimized dual-axis angle data. It calculates the distance change and direction deviation between two points using the real-time coordinates and initial coordinates of two optical reference markers. Based on the distance change and direction deviation between the two points, it quantifies the amplitude and direction of the support displacement to obtain the calibrated dual-axis angle data. The calibrated dual-axis angle data and the adjusted illuminance data are compared with the solar trajectory prediction data calculated based on local latitude and longitude and seasonal solar terms to calculate the angle deviation and illuminance deviation between the current angle of the photovoltaic panel and the predicted solar azimuth.

[0097] The processing module is used to drive the bracket motor to dynamically adjust the pitch and horizontal rotation angles of the photovoltaic panels based on the angle deviation and illuminance deviation, so as to match the real-time sun position.

[0098] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A smart tracking closed-loop control method for photovoltaic panels on slopes, characterized in that, The method includes: Illuminance data from the photovoltaic panel surface and dual-axis angle data from the photovoltaic panel support are collected in real time at a preset sampling frequency using illuminance sensors and dual-axis angle sensors installed at the edge of the photovoltaic panel. Two reference markers are fixedly set at diagonal positions on the photovoltaic panel surface, including: The raw illuminance data and raw dual-axis angle data, which are synchronously collected by the illuminance sensor and the dual-axis angle sensor, are packaged into a data packet of a unified format. Receive data packets uploaded by the acquisition device. The data packets contain image data acquired based on two optical reference marks set at diagonal positions on the surface of the photovoltaic panel. The image data is processed to identify and extract the pixel coordinates of two optical reference markers in the image; Based on pixel coordinates, determine the real-time position information of the two optical reference markers; A planar coordinate system is established based on the positional relationship between two reference points. The surface of the photovoltaic panel is divided into multiple regional units based on this coordinate system. A data correction coefficient is obtained by analyzing the relative positional distribution of each regional unit, including: A dynamic planar coordinate system is established on the surface of the photovoltaic panel based on the real-time position information of two optical reference markers determined from the image data. In a dynamic planar coordinate system, the surface of the photovoltaic panel is virtually divided into multiple regions of equal area; Based on the relative position of each region unit in the dynamic plane coordinate system, different weights are assigned to the sensor data from different region units; by calculating the spatial distribution relationship of the weights, a data correction coefficient is obtained to compensate for the uneven distribution of light on the photovoltaic panel surface. The collected illuminance data and dual-axis angle data are standardized according to the data correction coefficient to obtain optimized illuminance data and optimized dual-axis angle data, including: Receive data correction coefficients, as well as raw illuminance data and dual-axis angle data from each region unit; The data correction coefficient is applied to the corresponding original illuminance data, and the measurement bias caused by local shadows or stains is eliminated by the weighted average algorithm to obtain the optimized illuminance data. By combining the data correction coefficient with the original dual-axis angle data, the angle measurement error caused by uneven photovoltaic panel surface or sensor installation error is compensated, and the optimized dual-axis angle data is obtained. The optimized dual-axis angle data is subjected to benchmark calibration. The distance change and direction deviation between the two points are calculated by using the real-time coordinates and initial coordinates of the two optical reference marks. Based on the distance change and direction deviation between the two points, the amplitude and direction of the support displacement are quantified to obtain the calibrated dual-axis angle data. The calibrated dual-axis angle data and adjusted illuminance data are compared with the solar trajectory prediction data calculated based on local latitude, longitude and seasonal solar terms to calculate the angle deviation and illuminance deviation between the current angle of the photovoltaic panel and the predicted solar position. Based on the angle deviation and illuminance deviation, the drive bracket motor dynamically adjusts the pitch angle and horizontal rotation angle of the photovoltaic panel to match the real-time sun position.

2. The intelligent tracking closed-loop control method for photovoltaic panels on slopes according to claim 1, characterized in that, The optimized dual-axis angle data is subjected to benchmark calibration. The distance change and direction deviation between the two points are calculated by comparing the real-time coordinates and initial coordinates of the two optical reference marks. Based on the distance change and directional deviation between two points, the amplitude and direction of the support displacement are quantified to obtain calibrated biaxial angle data, including: The optimized dual-axis angle data is subjected to benchmark calibration based on the initial coordinate data and real-time coordinate data of two optical reference marks; Based on the comparison between the initial coordinate data and the real-time coordinate data, the distance change and orientation deflection angle between the two optical reference marks are calculated. Establish an angle calibration parameter matrix based on the distance change value and the direction deflection angle; The angle calibration parameter matrix is ​​applied to the optimized dual-axis angle data, and the measurement error caused by the deformation of the support is eliminated by coordinate transformation to obtain the calibrated dual-axis angle data.

3. The intelligent tracking closed-loop control method for photovoltaic panels on slopes according to claim 2, characterized in that, The calibrated dual-axis angle data and adjusted illuminance data are compared with solar trajectory prediction data calculated based on local latitude, longitude, and seasonal solar terms. The angular deviation and illuminance deviation between the current angle of the photovoltaic panel and the predicted solar azimuth are calculated, including: Based on calibrated dual-axis angle data, optimized illuminance data, and solar trajectory prediction data generated based on local latitude and longitude, real-time time, and astronomical algorithms, the solar trajectory prediction data includes theoretical solar azimuth angle, elevation angle, and theoretical illuminance. The calibrated dual-axis angle data is compared with the theoretical solar azimuth and elevation angles in the solar trajectory prediction data to calculate the angular deviation between the current pitch and horizontal rotation angles of the photovoltaic panel and the theoretical position of the sun. The optimized illuminance data is compared with the theoretical illuminance in the solar trajectory prediction data. Combined with the current time and weather conditions, the illuminance deviation between the actual illuminance and the expected value is calculated.

4. The intelligent tracking closed-loop control method for photovoltaic panels on slopes according to claim 3, characterized in that, Based on angular and illuminance deviations, the drive bracket motor dynamically adjusts the pitch and horizontal rotation angles of the photovoltaic panels to match the real-time solar azimuth, including: The angle deviation value and illuminance deviation value are received and combined with the preset control strategy to obtain control commands for adjusting the tilt angle and horizontal rotation angle of the photovoltaic panel; The control commands are converted into corresponding motor drive signals and sent to the support motor controller at the slope site via the communication network to obtain the motor drive signals; Based on the motor drive signal, the drive bracket motor performs corresponding angular displacement operations to dynamically adjust the spatial attitude of the photovoltaic panel and obtain the adjusted spatial attitude of the photovoltaic panel. Based on the adjusted spatial attitude of the photovoltaic panel, the matching status between the actual angle of the photovoltaic panel and the real-time position of the sun is confirmed, and the closed-loop control of the current cycle is completed.

5. An intelligent tracking closed-loop control system for photovoltaic panels on slopes, the system implementing the method as described in any one of claims 1 to 4, characterized in that, include: The acquisition module is used to collect illuminance data of the photovoltaic panel surface and dual-axis angle data of the photovoltaic panel support in real time at a preset sampling frequency using illuminance sensors and dual-axis angle sensors installed on the edge of the photovoltaic panel; wherein, two reference marker points are fixedly set at diagonal positions on the surface of the photovoltaic panel; The optimization module is used to establish a planar coordinate system based on the positional relationship between two reference points; the surface of the photovoltaic panel is divided into multiple regional units based on the planar coordinate system, and a data correction coefficient is obtained by analyzing the relative positional distribution of each regional unit; the collected illuminance data and dual-axis angle data are standardized according to the data correction coefficient to obtain optimized illuminance data and optimized dual-axis angle data. The calibration module is used to perform benchmark calibration on the optimized dual-axis angle data. It calculates the distance change and direction deviation between two points using the real-time coordinates and initial coordinates of two optical reference markers. Based on the distance change and direction deviation between the two points, it quantifies the amplitude and direction of the support displacement to obtain the calibrated dual-axis angle data. The calibrated dual-axis angle data and the adjusted illuminance data are compared with the solar trajectory prediction data calculated based on local latitude and longitude and seasonal solar terms to calculate the angle deviation and illuminance deviation between the current angle of the photovoltaic panel and the predicted solar azimuth. The processing module is used to drive the bracket motor to dynamically adjust the pitch and horizontal rotation angles of the photovoltaic panels based on the angle deviation and illuminance deviation, so as to match the real-time sun position.

6. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 4.

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