Electric power data wireless transmission system based on special environment

Abstract

The invention belongs to the technical field of data transmission, and provides a power data wireless transmission system based on a special environment, and the system comprises a system hardware module which comprises an environment observer, a solar power generation system, a wireless transmission system and a sensor, the environment observer, the solar power generation system, the wireless transmission system and the sensor form an observation unit, and an unmanned plane can inspect and clean fallen leaves; the data analysis module analyzes the state of the solar power generation system through an illumination compensation model and judges an abnormal reason according to current, voltage and power; the fallen leaf identification module judges the fallen leaf shielding condition through image processing and blue pixel analysis based on unmanned aerial vehicle point cloud data; the fallen leaf cleaning module automatically purges and cleans when detecting a fallen leaf accumulation signal; compared with the prior art, the system can guarantee continuous field observation, fast and accurate identification of fallen leaves, automatic cleaning, high integration level, no need of complex algorithm and model training, quantification of the accumulation degree of fallen leaves, and meeting of special environment observation requirements.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of data transmission, and particularly relates to a power data wireless transmission system based on a special environment. BACKGROUND

[0002] With the deepening of the research on the natural environment, the importance of carrying out field observation work in a special environment is increasingly highlighted. In the traditional field observation technology, a solar power generation system is usually used to meet the power demand of monitoring equipment.

[0003] However, in practical application, this scheme has obvious disadvantages. On the one hand, in a long observation period, fallen leaves often cover the solar power generation panel. Since the condition of the solar power generation panel cannot be grasped in time, the observation work is forced to be interrupted. On the other hand, the observation point is mostly located in the deep forest, and it is difficult for the technical personnel to carry out daily inspection, so that the problem is difficult to find and solve in time.

[0004] Therefore, the present application provides a power data wireless transmission system based on a special environment. SUMMARY

[0005] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background art.

[0006] The technical scheme adopted by the present application to solve its technical problems is: In a first aspect, the present application provides a power data wireless transmission system based on a special environment, comprising the following modules: System hardware module: hardware required for arranging an observation unit; Data analysis module: obtaining current and voltage data and processing and analyzing the same, judging whether the solar charging panel is abnormal, and generating a solar charging panel abnormal signal; Fallen leaf identification module: based on the solar charging panel abnormal signal, processing and analyzing the alarm information according to its coordinates, and judging whether the unmanned aerial vehicle has reached the specified position area; Fallen leaf identification module: after the unmanned aerial vehicle reaches the fault position, the solar charging panel is photographed, the blue pixels of the solar charging panel are extracted and accumulated, the accumulated blue pixels are analyzed, and a fallen leaf accumulation signal is generated; Fallen leaf cleaning module: based on the fallen leaf accumulation signal, the unmanned aerial vehicle blows the fallen leaves on the solar charging panel in real time.

[0007] In a second aspect, the present application provides a power data wireless transmission method based on a special environment, comprising the following steps: Step one: arranging the hardware required for the observation unit; Step two: acquire current voltage data and process and analyze it to determine whether the solar charging panel is abnormal and generate a solar charging panel abnormality signal; Step three: based on the solar charging panel abnormality signal, process and analyze the alarm information according to its coordinates to determine whether the unmanned aerial vehicle has reached the specified location area; Step four: after the unmanned aerial vehicle reaches the fault location, the solar charging panel is photographed, the blue pixels of the solar charging panel are extracted and accumulated, and the accumulated blue pixels are analyzed to generate a fallen leaf accumulation signal; Step five: based on the fallen leaf accumulation signal, the unmanned aerial vehicle blows the fallen leaves on the solar charging panel in real time.

[0008] The beneficial effects of the present application are as follows: by mounting the blowing equipment on the unmanned aerial vehicle, on the one hand, the fallen leaves accumulated on the solar panel can be cleaned up, solving the problem of abnormal power generation caused by fallen leaves covering and thus interrupting the observation period; on the other hand, equipment failures can be found in time to ensure stable and continuous observation work; By introducing the light condition and threshold value relationship model, histogram normalization, light correction, image cropping, color space conversion, etc., the blue pixels are screened and compared with the threshold range of the number of blue pixels under different light conditions, which can accurately determine whether the solar panel is blocked by fallen leaves and quantify the degree of fallen leaf accumulation. Compared with traditional robot vision recognition technology, the model does not need to be trained for a long time and complex algorithms are not needed, so the recognition speed is fast and the accuracy is high. BRIEF DESCRIPTION OF DRAWINGS

[0009] The present application will be further described below with reference to the accompanying drawings.

[0010] Figure 1 is a system module diagram of a special environment-based power data wireless transmission system according to the present application; Figure 2 is a step flowchart of a special environment-based power data wireless transmission method according to the present application. DETAILED DESCRIPTION

[0011] In order to make the technical means, creative features, purposes and effects achieved by the present application easy to understand, the present application will be further described below with reference to the specific embodiments.

[0012] Embodiment 1 As shown in Figure 1 , a special environment-based power data wireless transmission system according to the present application embodiment comprises: System hardware module: hardware required for arranging observation units; The arrangement environment observer includes: air quality monitoring equipment, water quality monitoring equipment, weather, video monitoring and other environment observation required equipment; a solar power generation system, the solar power generation system includes: a solar power generation subunit and an energy storage subunit, used to provide power demand of the observation unit; a wireless transmission system, the wireless transmission system includes 4G / 5G or satellite communication, used to transmit data of the observation equipment and the solar power generation system; a sensor arrangement, in the observation unit, equipped with corresponding sensors, used to obtain current size and charging voltage in the process of real-time charging of the solar charging panel in the charging process; The environment observer, the solar power generation system and the wireless transmission system jointly form an observation unit; a drone is used to check the condition of the environment observation equipment and the solar power system in real time; the bottom of the drone is centrally provided with a blowing equipment and an industrial camera and a laser radar; the blowing equipment is used to clean fallen leaves accumulated on the solar power generation panel; the industrial camera is used to obtain real-time image information of the solar power generation panel; and the laser radar is used to generate point cloud data of the drone; Before the observation unit is formally used, the drone pre-scans the coordinates where the observation unit is located to generate point cloud data and record point cloud coordinate information of all observers; and the point cloud coordinate information is used for subsequent inspection of the drone; A data analysis module: in the power supply system, corresponding sensors are arranged to obtain current size and charging voltage of the solar charging panel in the charging process; those skilled in the art should understand that the real-time charging current size and the charging voltage are both based on the working condition of the solar charging panel under the normal working condition; in this state, a key condition of the solar charging panel, i.e. sunlight irradiation condition, has been excluded; based on the real-time current and voltage data obtained by the sensor, the data is analyzed and processed to determine whether the state of the solar power generation system is in the normal working condition; Specifically, a light compensation model is introduced, and the obtained real-time charging current is compared and analyzed based on the light compensation model; the size of the theoretical current is calculated by using the light compensation model, and the specific formula is: The theoretical current is calculated, wherein, is the current under standard experimental conditions, referred to as the nominal current, and the value is 1000W / m 2 , 25℃; is the real-time light intensity, and the unit is: W / m 2 ; is the current temperature coefficient, wherein, single crystal silicon is about +0.05% / ℃, perovskite is about-0.2% / ℃, and the actual value can be appropriately adjusted according to the difference between single glass components and double glass components; The calculated theoretical current is compared and analyzed with the real-time current; If the measured current is less than or equal to This indicates that there is an abnormality in the solar charging panel, which may be caused by external environmental interference, such as rapid air flow and fast cloud movement. Further analysis is needed, and the system will generate an abnormal current signal. If the measured current is greater than This indicates that the solar charging panel is functioning normally and no action needs to be taken. Based on the generated abnormal current signal, the degree of voltage deviation is analyzed and evaluated in real time. First, the expected voltage is calculated, and the specific calculation formula is as follows: The expected voltage was calculated. ;in, This is the current charging voltage of the solar panel; The internal resistance of the solar panel in series can be found on the nameplate provided by the solar panel manufacturer. This is the actual charging current; Furthermore, the degree of voltage deviation is calculated using the following formula: ; Calculate the voltage deviation ;in, For the expected voltage, This is the actual charging voltage; The calculated voltage deviation is analyzed and processed; if If the voltage is greater than or equal to 20%, it indicates that there are obvious fallen leaves or other obstructions on the solar charging panel, which will generate an abnormal voltage signal. like If the voltage is less than 20%, it indicates that the cause of the low voltage of the solar charging panel is a sudden change in the external environment, such as dynamic changes in the shadow on the solar surface. Based on the generated voltage anomaly signal, the instantaneous power fluctuation is calculated and analyzed; Specifically, the power dynamic fluctuation index is calculated using a formula; the formula is as follows: The power dynamic fluctuation index FI is calculated; where N is the sampling period, the preset sampling period of this invention is N=60, 1 minute of data, and the sampling frequency is 1Hz; Pt is the instantaneous power; It is the moving average power; If FI is less than 40%, it is determined to be a sudden change in the external environment; if FI is greater than or equal to 40%, it is determined to be that there is a significant obstruction on the solar charging panel; and an abnormal signal for the solar charging panel is generated. Fallen leaf recognition module: Based on the abnormal signal of the solar charging panel, the system will work based on the point cloud data information pre-scanned by the drone; according to the alarm information corresponding to the point cloud coordinate position, with the help of the observer position data pre-stored in the point cloud coordinate, the drone can start the self-check process according to its current coordinate position. Specifically, in the early hardware arrangement stage, the data scanning of each observation point is performed by means of the unmanned aerial vehicle, and the coordinate data of the observation point is obtained; the coordinate data and the point cloud data generated synchronously by the unmanned aerial vehicle are under the same coordinate system, so that the data conversion operation is not required; The point cloud coordinates of the fault position are imported into the flight control system of the unmanned aerial vehicle or the ground control station as target position information; meanwhile, the positioning system and the point cloud generation device of the unmanned aerial vehicle are ensured to work normally and can obtain and transmit relevant data in real time; According to the coordinates of the fault point and the current position of the unmanned aerial vehicle, a reasonable flight route is planned, so that the unmanned aerial vehicle can safely and efficiently reach the fault point; the flight performance of the unmanned aerial vehicle, obstacles, flight safety and other factors should be considered in the route planning; The unmanned aerial vehicle flies according to the planned route, and the flight control system continuously adjusts the flight attitude and speed of the unmanned aerial vehicle according to the real-time position information provided by the positioning system, so as to ensure that the unmanned aerial vehicle flies along the predetermined route; The distance between the current position of the unmanned aerial vehicle and the fault position is calculated by using the distance calculation formula, so as to determine whether the unmanned aerial vehicle reaches the fault point; Specifically, the formula is: The position distance D is calculated; wherein the coordinate of the unmanned aerial vehicle P is the position coordinate data, and the coordinate of the fault information G is the coordinate data of the fault information G; The position distance D is compared with the position distance threshold value; in the present application, the position distance threshold value is set to 0.3 meters If the position distance D is greater than or equal to 0.3 meters, it indicates that the unmanned aerial vehicle does not reach the specified position area; If the position distance D is less than 0.3 meters, it indicates that the unmanned aerial vehicle reaches the specified position area; Leaf recognition module: based on the unmanned aerial vehicle reaching the fault position, the industrial camera on the unmanned aerial vehicle is used to take pictures of the solar charging panel and the main body of the observation device from multiple directions; The obtained real-time shooting photos are processed to determine the abnormal source of the fault signal; Specifically, the photos taken by the unmanned aerial vehicle are first processed to unify the size, and then a light condition and threshold value relationship model is introduced to analyze and judge the obtained photos; Since the main body of the solar power generation panel is blue, a linear relationship between the light condition and the threshold value of the number of blue pixels is constructed, and the formula is specifically: ; wherein x is the light condition, B is the regression coefficient, E is the error term, and A is other influence conditions; A and B are solved by the least square method; By constructing the linear relationship between the illumination condition and the threshold value of the number of blue pixels, the color deviation of the photos taken at different angles under different illumination conditions can be effectively eliminated. For each solar panel image taken, the histogram normalization method is used to adjust the gray histogram of the image to a standard distribution state. Specifically, the gray value is normalized to by using the formula: ; wherein, and are the maximum and minimum values of the original gray value, and are the maximum and minimum values mapped to the standard range. In this way, the influence of different illumination conditions on the color and brightness of the image is reduced, so that the images taken under different illumination conditions have similar visual characteristics. Using the illumination model constructed in the early stage, combined with the real-time recorded illumination parameters, the image is corrected for illumination. Specifically, based on the illumination model parameters B and the real-time illumination x, the pixel value P is compensated, and the specific formula is: , to obtain the compensated pixel value ; is the standard condition illumination value; A is the intercept term. The solar panel region in the image is restored to a standard illumination condition, further improving the consistency of the image. According to the pre-labeled solar panel boundary information, the collected image is cropped to remove the background region unrelated to the solar panel in the image, and only the solar panel part is retained; this can reduce the data volume for subsequent processing and improve the processing efficiency. Color space conversion: convert the cropped image from RGB color space to HSV color space, because HSV color space has better characteristics in color differentiation and is more conducive to extracting blue pixels. According to the value range of blue pixels in the HSV color space under different illumination conditions determined in the early stage, the part belonging to blue pixels in the converted image is selected. Specifically, the normalized calculation is performed on each pixel (R, G, B) ∈ [0, 255]. The hue H is calculated as: The saturation S is calculated as: The transparency V is calculated as: ; wherein, , For example, the pixels with H value in the range of 10-130, S value in the range of 50-255 and V value in the range of 50-255 are blue pixels; According to the real-time recorded light parameters, the threshold range of the number of blue pixels of the solar power generation panel under normal conditions under the current light condition is calculated through the light condition and threshold relationship model constructed in advance; The number of blue pixels is counted and compared with the threshold, and the number of blue pixels in the processed image is counted; Specifically, the blue pixels are screened, and the formula is: ; The total number of blue pixels is calculated by accumulation method; The number of blue pixels counted is compared with the threshold range of the number of blue pixels calculated; It should be noted that the threshold of the number of blue pixels is a reference value set by the technical personnel in the industry, which is used to distinguish whether there is fallen leaf accumulation on the solar power generation panel; If the number of blue pixels is lower than the lower limit of the threshold range of the number of blue pixels, it is judged that the solar power generation panel may be blocked by fallen leaves; and a fallen leaf accumulation signal is generated; If it is within the threshold range, it is considered that the solar power generation panel has no fallen leaf blockage or the blockage is not serious; and other fault signals are generated; at this time, further checking needs to be carried out manually; The fallen leaf cleaning module: based on the fallen leaf accumulation signal, the unmanned aerial vehicle blows the fallen leaves on the solar charging panel in real time, blows and detects whether there are residual fallen leaves according to the above detection method, until the fallen leaves are cleaned, and the unmanned aerial vehicle returns to the unmanned aerial vehicle control station; The technical scheme of the embodiment is: after the above process, the system compares the blue pixel value on the solar power generation panel, and judges whether the solar power generation panel is blocked by fallen leaves through threshold comparison analysis; compared with the traditional robot visual recognition technical scheme, the method can quickly identify the fallen leaf accumulation, without spending time to train the fallen leaf accumulation identification model, and without using complex algorithm; in addition, based on the threshold comparison, the method can also quantify the degree of fallen leaf accumulation; Embodiment 2 As Figure 2 shown, based on embodiment 1, the application also provides a special environment-based power data wireless transmission method, which comprises the following steps: Step 1: arrange the hardware required by the observation unit; The arrangement environment observer includes: air quality monitoring equipment, water quality monitoring equipment, weather, video monitoring and other environment observation required equipment; a solar power generation system, the solar power generation system includes: a solar power generation subunit and an energy storage subunit, used to provide power demand of the observation unit; a wireless transmission system, the wireless transmission system includes 4G / 5G or satellite communication, used to transmit data of the observation equipment and the solar power generation system; sensors are arranged in the observation unit, and corresponding sensors are equipped, used to obtain current size and charging voltage of the solar charging panel in the real-time charging process; The environment observer, the solar power generation system and the wireless transmission system jointly form an observation unit; a drone is used to check the condition of the environment observation equipment and the solar power generation system in real time; the bottom of the drone is centrally provided with a blowing equipment, an industrial camera and a laser radar; the blowing equipment is used to clean fallen leaves accumulated on the solar power generation panel; the industrial camera is used to obtain real-time image information of the solar power generation panel; and the laser radar is used to generate point cloud data of the drone; Before the observation unit is formally used, the drone pre-scans the coordinates where the observation unit is located, generates point cloud data, and records point cloud coordinate information of all observers; and is used for subsequent inspection of the drone; In step two, corresponding sensors are arranged in the power supply system, used to obtain current size and charging voltage of the solar charging panel in the charging process; based on the real-time current and voltage data obtained by the sensors, the data is analyzed and processed to determine whether the state of the solar power generation system is in a normal working state; Specifically, a light compensation model is introduced, and the obtained real-time charging current is compared and analyzed based on the light compensation model; the size of the theoretical current is calculated by using the light compensation model, and the specific formula is as follows: The theoretical current is calculated as ; wherein, is the current under standard experimental conditions, referred to as the nominal current, and the value is 1000W / m 2 , 25℃; is the real-time light intensity, and the unit is: W / m 2 ; is the current temperature coefficient, wherein, single crystal silicon is about +0.05% / ℃, perovskite is about-0.2% / ℃, and the actual value can be appropriately adjusted according to the difference between single glass components and double glass components; The calculated theoretical current is compared and analyzed with the real-time current; if the measured current is less than or equal to , it indicates that there is an abnormal condition of the solar charging panel, which may be caused by external environmental interference such as rapid air flow and rapid movement of clouds, and further analysis is required, and the system generates a current abnormal signal; If the measured current is greater than It is indicated that there is no abnormal situation of the solar charging panel, and no any processing is performed. The voltage deviation degree is analyzed and evaluated in real time. Firstly, the expected voltage is calculated, and the calculation formula is specifically: The expected voltage V is calculated as ; wherein, is the charging voltage of the current solar charging panel; is the internal resistance of the solar series assembly, which can be obtained according to the nameplate marked by the solar manufacturer; is the actual charging current; The voltage deviation degree is calculated, and the specific formula is: The voltage deviation degree VD is calculated as ; wherein, is the expected voltage V, is the actual charging voltage V; The calculated voltage deviation degree is analyzed and processed. If is greater than or equal to 20%, it is indicated that there is obvious fallen leaf blocking or other blocking on the solar charging panel, and a voltage abnormal signal is generated. If is less than 20%, it is indicated that the factor causing the voltage of the solar charging panel to be too low is the sudden change of the external environment, such as the dynamic change of the shadow on the surface of the solar panel. The instantaneous power fluctuation is calculated and analyzed. Specifically, the power dynamic fluctuation index is calculated by the formula; the formula is specifically: The power dynamic fluctuation index FI is calculated as ; wherein, N is the sampling period, and the sampling period of the present application is preset as N=60, 1 minute data, and the sampling frequency is 1 Hz; Pt is the instantaneous power; is the moving average power; If FI is less than 40%, it is determined that the sudden change of the external environment; If FI is greater than or equal to 40%, it is determined that there is obvious blocking on the solar charging panel; and a solar charging panel abnormal signal is generated. Step three: based on the solar charging panel abnormal signal, the system will work based on the point cloud data information obtained by the pre-scanning of the unmanned aerial vehicle; according to the alarm information corresponding to the point cloud coordinate position, with the help of the observer position data stored in the point cloud coordinate in advance, the unmanned aerial vehicle can start the self-checking process according to the current coordinate position thereof; According to the coordinates of the fault point and the current position of the unmanned aerial vehicle, a reasonable flight route is planned to enable the unmanned aerial vehicle to safely and efficiently reach the fault point; the route planning should take into account the flight performance of the unmanned aerial vehicle, obstacles, flight safety and other factors; The unmanned aerial vehicle flies according to the planned route, and the flight control system continuously adjusts the flight attitude and speed of the unmanned aerial vehicle according to the real-time position information provided by the positioning system to ensure that it flies along the predetermined route; The distance between the current position of the unmanned aerial vehicle and the fault position is calculated using a distance calculation formula to determine whether the unmanned aerial vehicle has reached the fault point; Step four: based on the unmanned aerial vehicle reaching the fault position, an industrial camera on the unmanned aerial vehicle is used to take multiple photos of the solar charging panel and the observation equipment main body; The real-time obtained photos are processed to determine the abnormal source of the fault signal; Specifically, the photos taken by the unmanned aerial vehicle are first processed to unify the size, and then a light condition and threshold value relationship model is introduced to analyze and judge the obtained photos; Since the main body of the solar panel is blue, a linear relationship between the light condition and the threshold value of the number of blue pixels is established, and the formula is as follows: ; Wherein, x is the light condition, B is the regression coefficient, E is the error term, and A is other influence conditions; A and B are solved by least squares method; By establishing a linear relationship between the light condition and the threshold value of the number of blue pixels, the color deviation of the photos taken at different angles under different light conditions can be effectively eliminated; For each solar panel image taken, a histogram normalization method is used to adjust the gray histogram of the image to a standard distribution state; In this way, the influence of different light conditions on the color and brightness of the image is reduced, so that the images taken under different light conditions have similar visual features; Using the light model established in the early stage, combined with the real-time recorded light parameters, the image is corrected for light; The solar panel area in the image is restored to a standard light condition, further improving the consistency of the image; According to the pre-labeled solar panel boundary information, the collected image is cropped to remove the background area unrelated to the solar panel and only keep the solar panel part; This can reduce the amount of data for subsequent processing and improve processing efficiency; Color space conversion: convert the cropped image from RGB color space to HSV color space, because HSV color space has better characteristics in color differentiation and is more conducive to extracting blue pixels; According to the numerical range of the blue pixel in the HSV color space under different light conditions determined in advance, the part belonging to the blue pixel in the converted image is screened out; According to the real-time recorded light parameters, the blue pixel threshold range of the solar panel under normal conditions under the current light condition is calculated through the light condition and threshold relationship model constructed in advance; The number of blue pixels is counted and compared with the threshold value, and the number of blue pixels in the processed image is counted; Specifically, the blue pixels are screened out, and the formula is: ; The total number of blue pixel values is calculated by accumulation method; The number of blue pixels obtained by counting is compared with the calculated blue pixel threshold range; It should be noted that the blue pixel threshold is a reference value set by the technical personnel in the industry, which is used to distinguish whether there is fallen leaf accumulation on the solar panel; If the number of blue pixels is lower than the lower limit of the blue pixel threshold range, it is judged that the solar panel may be blocked by fallen leaves; then a fallen leaf accumulation signal is generated; If it is within the threshold range, it is considered that the solar panel has no fallen leaf blockage or the blockage is not serious; then other fault signals are generated; at this time, manual further inspection is required; Step five: based on the fallen leaf accumulation signal, the unmanned aerial vehicle blows the fallen leaves on the solar charging panel in real time, blows and detects whether there is residual fallen leaf according to the above detection method, until the fallen leaves are cleaned up, and the unmanned aerial vehicle returns to the unmanned aerial vehicle control station; The basic principles, main features and advantages of the present application are shown and described. Those skilled in the art should understand that the present application is not limited to the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A wireless power data transmission system for special environments, used to monitor and transmit status data of solar panels in the field, characterized in that: Comprise: Data analysis module: obtain current and voltage data, and process and analyze them to determine whether the solar charging panel is abnormal, and generate a solar charging panel abnormal signal; Fallen leaf identification module: based on the solar charging panel abnormal signal, process and analyze the alarm information coordinates to determine whether the unmanned aerial vehicle has reached the specified location area; Fallen leaf identification module: after the unmanned aerial vehicle reaches the fault location, take a photo of the solar charging panel, extract the blue pixels of the solar charging panel and accumulate them, analyze the accumulated blue pixels and generate a fallen leaf accumulation signal; Fallen leaf cleaning module: based on the fallen leaf accumulation signal, the unmanned aerial vehicle real-time blows the fallen leaves on the solar charging panel.

2. The system according to claim 1, wherein the system is based on wireless transmission of power data in a special environment. The judgment process of whether the solar charging panel is abnormal is: Based on the real-time current and voltage data obtained by the sensor, the data is analyzed and processed to determine whether the state of the solar power generation system is in a normal working state.

3. The system according to claim 2, wherein the system is characterized by: The specific process of processing the real-time charging current is: Introduce the light compensation model, compare and analyze the obtained real-time charging current with the light compensation model; and calculate the size of the theoretical current using the light compensation model.

4. The system according to claim 2, wherein the system is characterized by: The specific process of analyzing the real-time charging current is: If the measured current is less than or equal to 0.85 times the theoretical current, it indicates that the solar charging panel has an abnormal condition, and a current abnormal signal is generated; If the measured current is greater than 0.85 times the theoretical current, it indicates that the solar charging panel does not have an abnormal condition.

5. The system according to claim 2, wherein the system is based on wireless transmission of power data in a special environment. The specific process of processing the real-time charging voltage is: The real-time analysis evaluates the voltage deviation, and the expected voltage is calculated first, and the calculation formula is specifically: The expected voltage is calculated as ; wherein, is the charging voltage of the current solar charging panel; is the internal resistance of the solar series assembly, is the actual charging current; The voltage deviation degree is calculated by using the formula: The voltage deviation degree is calculated by using the formula: Wherein, is the expected voltage, is the actual charging voltage; the calculated voltage deviation degree is analyzed and processed.

6. The power data wireless transmission system based on special environment according to claim 5, characterized in that: The specific process of analyzing the voltage deviation is: If the voltage deviation is greater than or equal to 20%, it indicates that there is obvious fallen leaf blocking or other blocking on the solar charging panel, and a voltage abnormal signal is generated; If the voltage deviation is less than 20%, it indicates that the factor causing the low voltage of the solar charging panel is the sudden change of the external environment, based on the generated voltage abnormal signal, the instantaneous power fluctuation is calculated and analyzed.

7. The system according to claim 6, wherein the system is based on wireless transmission of power data in a special environment. The specific process of calculating the instantaneous power fluctuation is: the power dynamic fluctuation index is calculated using the formula; If the power dynamic fluctuation index is less than 40%, it is determined to be a sudden change of the external environment; If the power dynamic fluctuation index is greater than or equal to 40%, it is determined that there is obvious blocking on the solar charging panel; then a solar charging panel abnormal signal is generated.

8. The system according to claim 1, wherein the system is based on wireless transmission of power data in a special environment. The specific process of determining whether the unmanned aerial vehicle has reached the specified location area is: calculate the position distance; If the position distance is greater than or equal to 0.3 meters, it indicates that the unmanned aerial vehicle has not reached the specified location area; If the position distance is less than 0.3 meters, it indicates that the unmanned aerial vehicle has reached the specified location area.

9. The system according to claim 1, wherein the system is based on wireless transmission of power data in a special environment. The specific process of analyzing the accumulated blue pixels and generating the fallen leaf accumulation signal is: If the number of blue pixels is below the lower limit of the threshold range of the number of blue pixels, it is determined that the solar panel may have fallen leaf blocking; then a fallen leaf accumulation signal is generated; If the number of blue pixels is within the threshold range, it is considered that the solar panel has no fallen leaf blocking or the blocking is not serious; then other fault signals are generated; at this time, manual further inspection is required.

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