Photovoltaic module installation monitoring method and system for complex terrain

Abstract

The application relates to the technical field of data processing, and discloses a photovoltaic module installation monitoring method and system for complex terrains. The method comprises the following steps: collecting three-dimensional coordinate points of hilly terrain by a laser radar and a GPS device, and calculating a terrain slope influence factor; performing coordinate transformation on sensor data according to the terrain slope influence factor by a three-axis inertial measurement unit, and outputting a pitch angle; performing slope compensation calculation by reading the pitch angle and the terrain slope influence factor, obtaining a slope correction installation angle, and generating an adjustment instruction; performing extended Kalman filtering fusion on GPS positioning data and the pitch angle, and obtaining a fused positioning coordinate; and performing terrain grading quality assessment based on the slope correction installation angle and the fused positioning coordinate, and triggering an alarm when the quality assessment value is lower than a standard. The application improves the precision, reliability and intelligent level of photovoltaic module installation monitoring under complex terrain conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a photovoltaic module installation monitoring method and system for complex terrains. BACKGROUND

[0002] In the prior art, photovoltaic module installation monitoring mainly adopts traditional flat ground installation monitoring methods, which monitor the installation position and inclination angle of photovoltaic modules through GPS positioning and ordinary angle sensors. These methods can meet the basic installation accuracy requirements under flat terrain conditions, but face many technical challenges in complex terrain environments. The traditional monitoring method usually uses fixed monitoring parameters and unified quality evaluation standards, and lacks adaptive adjustment capability to terrain changes.

[0003] The main deficiencies of the prior art include: unable to effectively handle the influence of complex terrain on sensor measurement accuracy, resulting in significant decrease in monitoring accuracy under complex terrain conditions such as hills and slopes; lack of dynamic compensation mechanism for terrain slope changes, so that the original sensor data cannot accurately reflect the real installation state of the photovoltaic module; GPS positioning is easily affected by multipath interference and signal shielding in complex terrain, and single sensor data cannot guarantee positioning accuracy; the quality evaluation standard is single, and the focus difference of installation monitoring under different terrain conditions is not considered.

[0004] The traditional method cannot establish a quantitative correlation between the terrain slope influence factor and the sensor measurement data, resulting in a lack of scientific basis for slope compensation calculation; lacks an extended Kalman filter algorithm for multi-sensor data fusion, which cannot effectively eliminate the influence of GPS multipath interference on positioning accuracy; lacks a differentiated quality evaluation mechanism based on terrain classification, which cannot dynamically adjust the monitoring standards and weight distribution according to different slope conditions, thereby affecting the intelligent level of the entire installation quality control. SUMMARY

[0005] The present application provides a photovoltaic module installation monitoring method and system for complex terrains, which solves the technical problems of being unable to adaptively monitor according to complex terrain characteristics and lacking intelligent quality evaluation control in the prior art. The present application improves the accuracy, reliability and intelligent level of photovoltaic module installation monitoring under complex terrain conditions.

[0006] In a first aspect, the present application provides a photovoltaic module installation monitoring method for complex terrains, which comprises:

[0007] Step S1: The laser radar and GPS device collect three-dimensional coordinate points of the hilly terrain according to the grid density, calculate the slope angle and slope azimuth angle of each measurement point based on the collected coordinate point data, and generate a terrain slope influence factor;

[0008] Step S2: A triaxial inertial measurement unit is arranged at a predetermined position of the photovoltaic module. The original data of the sensor is subjected to coordinate transformation according to the terrain slope influence factor, and the pitch angle is output.

[0009] Step S3: The pitch angle and the terrain slope influence factor data are read, slope compensation calculation processing is performed, the slope correction installation angle of the photovoltaic module is obtained, and an angle adjustment instruction is generated when the angle deviation exceeds a preset threshold.

[0010] Step S4: The GPS positioning data and the pitch angle are subjected to extended Kalman filter fusion, the GPS signal is subjected to multipath interference compensation by using a position correction matrix, and a fused positioning coordinate is obtained.

[0011] Step S5: Terrain grading quality assessment calculation is performed based on the slope correction installation angle and the fused positioning coordinate, a quality assessment value is obtained, and an alarm is triggered and the installation program is stopped when the quality assessment value is lower than a preset standard.

[0012] In a second aspect, the application provides a photovoltaic module installation monitoring system for complex terrain, which comprises:

[0013] A generation module is configured to collect three-dimensional coordinate points of hilly terrain by laser radar and GPS equipment according to grid density, calculate the slope angle and slope azimuth angle of each measurement point based on the collected coordinate point data, and generate a terrain slope influence factor.

[0014] A transformation module is configured to arrange a triaxial inertial measurement unit at a predetermined position of the photovoltaic module, perform coordinate transformation on the original data of the sensor according to the terrain slope influence factor, and output the pitch angle.

[0015] A reading module is configured to read the pitch angle and the terrain slope influence factor data, perform slope compensation calculation processing, obtain the slope correction installation angle of the photovoltaic module, and generate an angle adjustment instruction when the angle deviation exceeds a preset threshold.

[0016] A fusion module is configured to perform extended Kalman filter fusion on the GPS positioning data and the pitch angle, perform multipath interference compensation on the GPS signal by using a position correction matrix, and obtain a fused positioning coordinate.

[0017] An evaluation module is configured to perform terrain grading quality assessment calculation based on the slope correction installation angle and the fused positioning coordinate, obtain a quality assessment value, and trigger an alarm and stop the installation program when the quality assessment value is lower than a preset standard.

[0018] In a third aspect, a photovoltaic module installation monitoring device for complex terrains is provided, comprising a memory and at least one processor, the memory having instructions stored therein; the at least one processor invoking the instructions in the memory to cause the photovoltaic module installation monitoring device for complex terrains to perform the above-mentioned photovoltaic module installation monitoring method for complex terrains.

[0019] In a fourth aspect, a computer-readable storage medium is provided, having instructions stored therein, which, when executed on a computer, cause the computer to perform the above-mentioned photovoltaic module installation monitoring method for complex terrains.

[0020] In the technical scheme provided in the present application, the three-dimensional coordinate points of the hilly terrain are collected according to the grid density by the laser radar and the GPS device, and the terrain slope influence factor is generated, effectively solving the technical problem that the traditional monitoring method cannot quantify the influence of terrain complexity on installation monitoring, and establishing a quantitative correlation between terrain features and monitoring accuracy. The technical feature of the three-axis inertial measurement unit is to perform coordinate transformation on the sensor raw data according to the terrain slope influence factor to output the pitch angle, realizing adaptive matching of the sensor measurement coordinate system and the terrain inclination condition, and effectively eliminating the systematic error caused by the terrain slope on the angle measurement. The technical scheme of obtaining the slope correction installation angle by reading the pitch angle and terrain slope influence factor data for slope compensation calculation and processing, innovatively establishes a dynamic angle correction mechanism based on terrain features, automatically generates an angle adjustment instruction when the angle deviation exceeds the preset threshold, and significantly improves the monitoring accuracy and intelligent adjustment capability of the photovoltaic module installation angle under complex terrain conditions. The technical feature of performing extended Kalman filtering fusion on the GPS positioning data and the pitch angle data, and using a position correction matrix to compensate for multipath interference of the GPS signal to obtain a fused positioning coordinate, effectively solves the problem of insufficient accuracy of a single sensor in complex terrain, and significantly improves the positioning accuracy and reliability through the multi-sensor data fusion and interference compensation mechanism.

[0021] The slope compensation algorithm of the application realizes intelligent perception and self-adaptive adjustment of the terrain changes by establishing a mathematical correlation model of the terrain slope influence factor and the sensor data, and the core contribution of the algorithm feature is to change the static monitoring method into a dynamic self-adaptive monitoring method. The application of extended Kalman filtering algorithm in complex terrain multi-sensor data fusion effectively handles the state estimation problem of nonlinear system through state prediction and update iteration operation, and the algorithm feature makes the GPS positioning data and inertial sensor data complementary to each other, which significantly improves the positioning accuracy and system robustness. The terrain grading quality evaluation algorithm realizes intelligent quality control for different slope conditions through weight coefficient distribution and weighted calculation processing, triggers an alarm and stops the installation program when the quality evaluation value is lower than the preset standard, and effectively guarantees the quality level and safety of the complex terrain photovoltaic component installation. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0023] Figure 1 An embodiment schematic diagram of the photovoltaic component installation monitoring method for complex terrain in the embodiment of the application;

[0024] Figure 2 An embodiment schematic diagram of the photovoltaic component installation monitoring system for complex terrain in the embodiment of the application;

[0025] Figure 3 A structural schematic block diagram of the photovoltaic component installation monitoring device for complex terrain in the embodiment of the application. DETAILED DESCRIPTION

[0026] The embodiment of the present application provides a photovoltaic module installation monitoring method and system for complex terrain. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] For ease of understanding, the specific process of the embodiment of the present application is described below. Please refer to Figure 1 One embodiment of the photovoltaic module installation monitoring method for complex terrain in the embodiment of the present application comprises the following steps.

[0028] Step S1: The laser radar and GPS device collect three-dimensional coordinate points of the hilly terrain according to the grid density, calculate the slope angle and slope azimuth angle of each measurement point based on the collected coordinate point data, and generate a terrain slope influence factor;

[0029] Step S2: The three-axis inertial measurement unit is arranged at the predetermined position of the photovoltaic module, the sensor raw data is subjected to coordinate transformation according to the terrain slope influence factor, and the pitch angle is output;

[0030] Step S3: The pitch angle and the terrain slope influence factor data are read, slope compensation calculation processing is performed, the slope correction installation angle of the photovoltaic module is obtained, and an angle adjustment instruction is generated when the angle deviation exceeds the preset threshold value;

[0031] Step S4: The GPS positioning data and the pitch angle are subjected to extended Kalman filtering fusion, the position correction matrix is used for multipath interference compensation of the GPS signal, and the fused positioning coordinates are obtained;

[0032] Step S5: The terrain grading quality evaluation calculation is performed based on the slope correction installation angle and the fused positioning coordinates, and the quality evaluation value is obtained, and an alarm is triggered and the installation program is stopped when the quality evaluation value is lower than the preset standard.

[0033] It can be understood that the execution subject of the present application can be a photovoltaic module installation monitoring system for complex terrain, and can also be a terminal or a server, and the specific place is not limited. The embodiment of the present application takes the server as the execution subject for example.

[0034] Specifically, the laser radar and GPS device collect three-dimensional coordinate points of the hilly terrain in a 5-meter x 5-meter grid density. The laser radar emits laser pulses and receives reflected signals to measure distance data, and the GPS device records the longitude and latitude coordinates of each measurement point at the same time. Timestamp synchronization registration processing ensures that the laser radar ranging data and GPS coordinate data correspond completely in the time dimension, forming a terrain coordinate point set containing X, Y, Z three-dimensional coordinate information. Two-way differential gradient calculation is a numerical differential operation on adjacent coordinate points in the X direction and the Y direction, respectively, to obtain the elevation change rate, i.e. the slope change rate parameter representing the inclination degree of the terrain at that point, and the slope direction change rate parameter representing the direction change of the terrain inclination. Weighted average filtering processing uses the slope and slope data of multiple adjacent measurement points for weighted average calculation to eliminate measurement noise and abnormal value influence. Composite trigonometric function transformation is to input the slope angle and slope azimuth angle data into a composite operation formula containing sine, cosine and tangent functions, and to calculate the terrain slope influence factor through nonlinear mapping. The factor comprehensively reflects the complexity of the terrain and the degree of influence on the installation of photovoltaic modules.

[0035] The three-axis inertial measurement unit includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, which are arranged on a standard bracket 0.5 meters below the predetermined installation position of the photovoltaic module. The coordinate transformation matrix construction operation calculates a three-axis calibration transformation matrix for the current terrain slope condition according to the terrain slope influence factor. This matrix is used to convert the original coordinate system measured by the sensor into a corrected coordinate system that adapts to the terrain slope. The three-axis calibration transformation matrix operation is a matrix multiplication operation of the acceleration and angular velocity raw data with the transformation matrix to obtain sensor data converted by the coordinate system. The attitude solving process uses a quaternion algorithm to convert the corrected acceleration and angular velocity data into Euler angle representation to calculate the pitch angle, roll angle, and yaw angle values that describe the sensor attitude. The Kalman filter stability processing uses a Kalman filter algorithm to perform time series filtering on the attitude angle data to eliminate sensor drift and random noise, and outputs stable pitch angle, roll angle, and yaw angle.

[0036] The slope compensation numerical operation mathematically operates the pitch angle data and the terrain slope influence factor to calculate the numerical difference between the actual measured installation angle of the photovoltaic module and the theoretical optimal installation angle. The numerical pairing matching establishes a one-to-one correspondence between the pitch angle data measured by the sensor and the terrain slope influence factor of the corresponding measurement position to form a slope-angle correlation data pair. The slope weight coefficient allocation processing allocates different weight coefficients to the pitch angle data according to different slope conditions, and the weight coefficient is higher when the slope is larger, and the pitch angle correction parameter with a weight identifier is obtained. The installation angle table lookup operation looks up the corresponding theoretical optimal installation angle value in the preset angle reference table according to the pitch angle correction parameter, and the reference table contains optimal installation angle recommended values under different terrain conditions. The numerical difference calculation subtracts the target installation angle reference value obtained by table lookup from the actual pitch angle data measured by the sensor to obtain the deviation amount of the current measurement angle and the target installation angle. The trigonometric function correction processing performs sine and cosine function operations on the deviation amount, considers the nonlinear influence of the terrain slope on the installation angle, and calculates the photovoltaic module installation angle value corrected by the terrain slope. The threshold comparison determines whether the slope correction installation angle value is greater than or less than the preset angle deviation threshold, and outputs an angle deviation out-of-limit state identifier when the deviation exceeds the threshold.

[0037] The time synchronization alignment processing ensures that the GPS positioning data and the pitch angle data are collected at the same time to form a position-pose fusion data set containing position coordinates and attitude angle information. The extended Kalman filter is a state estimation algorithm for nonlinear systems, which processes the fusion data set through state prediction and update iteration. The state prediction predicts the state value at the next time according to the system dynamic equation, and the update iteration uses the observation data to correct the predicted value to obtain the initial positioning coordinate value after filtering. The position correction matrix transformation calculates the position correction matrix according to the terrain slope condition, performs matrix transformation operation on the initial positioning coordinate value, eliminates the multipath interference of GPS signals caused by terrain shielding and reflection, and obtains the corrected positioning data. The coordinate system conversion converts the corrected positioning data from the WGS84 coordinate system of GPS to the local coordinate system of engineering application, and the accuracy verification processing verifies the positioning accuracy by comparing with the known control point coordinates, and finally outputs the fusion positioning coordinates meeting the accuracy requirements.

[0038] The data normalization preprocessing converts the slope correction installation angle and fusion positioning coordinate data into standardized values between 0 and 1, eliminates the influence of different data dimensions, and obtains quality evaluation input parameters. The topographic slope grading matching processing divides the evaluation area into three grades of 0-15 degrees, 15-30 degrees and 30-45 degrees according to the topographic slope, and different grades correspond to different evaluation standards and weight coefficients. The weighted calculation processing multiplies the installation angle accuracy and the position accuracy by the corresponding evaluation weight coefficients respectively and then adds them to obtain a quality evaluation score that comprehensively reflects the installation quality. The numerical comparison judgment compares the quality evaluation score with a preset quality standard threshold value, and when the score is lower than the threshold value, a state identifier of unqualified quality is output. The alarm triggering logic processing executes corresponding control logic according to the quality qualified state identifier, and when the quality is qualified, the installation program is continued, and when the quality is unqualified, an audible and light alarm is triggered and the installation equipment is automatically stopped.

[0039] In a specific embodiment, step S1 comprises:

[0040] The laser radar ranging data and the GPS coordinate data are time-stamped and synchronized for registration to obtain a set of terrain coordinate points;

[0041] The set of terrain coordinate points is subjected to bidirectional differential gradient calculation to obtain slope change rate and slope direction change rate parameters of each measurement point;

[0042] Based on the slope change rate and the slope direction change rate parameters, weighted average filtering processing is performed to obtain slope angle and slope aspect angle data;

[0043] The slope angle and slope aspect angle data are input into a composite trigonometric function transformation for non-linear mapping calculation to obtain a topographic slope influence factor.

[0044] Specifically, the timestamp synchronization registration process is a data matching process of accurately corresponding the laser radar ranging data and the GPS coordinate data according to the collection time. The laser radar records the transmission timestamp when transmitting each laser pulse and records the receiving timestamp when receiving the reflected signal. The distance data is calculated by the time difference. The GPS device records the timestamp and the latitude and longitude coordinates of each measurement point at the same time. The registration process matches the laser radar distance data and the GPS coordinate data corresponding to the same timestamp, eliminates the coordinate dislocation caused by the time deviation, and forms a terrain coordinate point set containing X coordinate, Y coordinate, Z coordinate and time information. Each coordinate point has complete three-dimensional spatial position information and time attribute, ensuring the accuracy of the data basis for subsequent calculation. The two-way difference gradient calculation is a mathematical processing process of numerical difference operation on adjacent measurement points in the terrain coordinate point set. The X direction slope change rate is calculated by dividing the elevation difference of the adjacent two points in the X direction by the horizontal distance difference. The slope change rate in the Y direction is calculated by the same method. The aspect change rate parameter is obtained by calculating the change of the azimuth angle between adjacent measurement points. Specifically, the aspect change degree of the measurement point is obtained by subtracting the azimuth angle of the line connecting the current point and the next point from the azimuth angle of the line connecting the current point and the previous point. The two-way difference means that the forward and backward gradient changes are considered at the same time, ensuring that the slope change rate and the aspect change rate parameter can accurately reflect the local change characteristics of the terrain at the point.

[0045] The weighted average filtering process is a data smoothing algorithm for eliminating measurement noise and abnormal value influence. A plurality of adjacent measurement points within a certain range around the current measurement point are selected. Different weight coefficients are assigned to the slope change rate and the aspect change rate parameter of each measurement point according to the distance. The closer the distance, the greater the weight. The farther the distance, the smaller the weight. The slope angle data after filtering is obtained by multiplying the slope change rate of each measurement point by the corresponding weight coefficient, summing and dividing by the total weight coefficient. The slope aspect angle data is obtained by processing the aspect change rate parameter in the same way. The filtering process effectively eliminates the data fluctuations caused by single-point measurement error and environmental interference, making the slope angle and slope aspect angle data more stable and reliable. The composite trigonometric function transformation is a mathematical transformation process of nonlinear mapping calculation of the slope angle and the slope aspect angle data. The transformation formula contains the composite operation of the sine function, the cosine function and the tangent function. The vertical component of the slope is calculated by inputting the slope angle into the sine function. The horizontal component of the aspect is calculated by inputting the slope aspect angle into the cosine function. The combined effect of the slope and the aspect is nonlinearly mapped by the tangent function to obtain a terrain slope influence factor that comprehensively reflects the complexity of the terrain. The numerical range of the factor is between 0 and 1. The larger the value, the more complex the terrain, and the more significant the influence on the installation monitoring of the photovoltaic module.

[0046] In a specific embodiment, step S2 comprises:

[0047] The terrain slope influence factor is input into the coordinate transformation matrix construction operation to obtain a three-axis calibration transformation matrix.

[0048] The three-axis calibration transformation matrix operation is performed on the acceleration and angular velocity original data collected by the three-axis inertial measurement unit to obtain sensor data.

[0049] The attitude solving processing is performed based on the sensor data to obtain a pitch angle value.

[0050] The pitch angle value is subjected to Kalman filtering stability processing to obtain a pitch angle.

[0051] Specifically, the coordinate transformation matrix construction operation is a calculation process of converting the terrain slope influence factor into a mathematical transformation matrix. The input terrain slope influence factor value ranges from 0 to 1, representing a quantitative indicator of terrain complexity. The construction operation first multiplies the factor by a preset angle correction coefficient to obtain a slope correction angle, and then calculates the elements of the three-axis coordinate transformation matrix based on the correction angle. The transformation matrix is a three-row and three-column mathematical matrix. The first row and first column element of the matrix is equal to the cosine value of the slope correction angle, the first row and third column element is equal to the sine value of the slope correction angle, the third row and first column element is equal to the negative value of the sine value of the slope correction angle, and the third row and third column element is equal to the cosine value of the slope correction angle. The remaining elements are filled with corresponding values according to the standard rotation transformation matrix rule. Finally, a three-axis calibration transformation matrix specifically for the current terrain slope condition is obtained. This matrix can convert the sensor original coordinate system into a corrected coordinate system that adapts to the terrain inclination. The three-axis calibration transformation matrix operation is a matrix calculation process of performing mathematical multiplication operation on the sensor original data and the transformation matrix. The acceleration original data collected by the three-axis inertial measurement unit contains acceleration components in X-axis, Y-axis and Z-axis directions. The angular velocity original data also contains angular velocity components in three axial directions. The acceleration data is constructed into a three-row and one-column column vector, and is subjected to matrix multiplication operation with the three-axis calibration transformation matrix to obtain the acceleration data converted by the coordinate system. The angular velocity original data is processed in the same way to obtain the converted angular velocity data. The transformed sensor data has eliminated the influence of terrain slope on the measurement coordinate system, and can more accurately reflect the attitude information of the photovoltaic module relative to the real horizontal plane.

[0052] Attitude calculation processing is the algorithmic calculation process that converts accelerometer and angular velocity sensor data into attitude angles. The processing algorithm uses a complementary filtering method to fuse accelerometer and gyroscope data. The accelerometer calculates the static attitude angle through the direction of the gravity vector, but it is easily affected by motion acceleration interference. The gyroscope calculates dynamic attitude changes through angular velocity integration, but there is a problem of cumulative error. The complementary filtering algorithm weights and fuses the low-frequency signal from the accelerometer and the high-frequency signal from the gyroscope. The specific calculation process is to multiply the angle obtained by the gyroscope integration by the high-frequency weighting coefficient, multiply the angle calculated by the accelerometer by the low-frequency weighting coefficient, and add the two to obtain the fused attitude angle. The pitch angle, roll angle, and yaw angle are calculated by the arctangent function. Among them, the pitch angle represents the degree of tilt of the photovoltaic module around the horizontal axis, which is directly related to the monitoring accuracy of the installation angle of the photovoltaic module. Kalman filter stability processing is a recursive estimation algorithm that eliminates random noise and drift errors in attitude angle data. The Kalman filter includes two recursive calculation steps: state prediction and state update. State prediction predicts the pitch angle value at the next moment based on the system dynamic model. The prediction process considers the influence of angular velocity changes on the pitch angle and introduces process noise variance to describe system uncertainty. State update uses sensor observation data to correct the predicted value. The update process uses Kalman gain to balance the reliability of the predicted value and the observed value. When the sensor noise is large, the predicted value is trusted more, and when the system uncertainty is large, the observed value is trusted more. After multiple recursive iterations, a stable pitch angle output with noise and drift removed is obtained.

[0053] In one specific embodiment, step S3 includes:

[0054] The pitch angle and terrain slope influence factor are used to perform slope compensation numerical calculations to obtain the deviation between the current measurement angle and the target installation angle;

[0055] The deviation is corrected using trigonometric functions to obtain the slope correction installation angle value for the photovoltaic module;

[0056] Based on the slope correction installation angle value, a threshold comparison judgment is made to obtain the angle deviation exceeding the limit status indicator;

[0057] The angle deviation exceeding the limit status flag is logically processed to obtain the angle adjustment command.

[0058] Specifically, the slope compensation numerical operation is a process of calculating the angle deviation by mathematically calculating the pitch angle data and the terrain slope influence factor. The pitch angle represents the inclination degree of the photovoltaic module relative to the horizontal plane, and the terrain slope influence factor reflects the influence degree of the terrain complexity on the installation monitoring. The numerical operation first multiplies the pitch angle value and the terrain slope influence factor to obtain a slope correction coefficient, and then subtracts the slope correction coefficient from the pitch angle to obtain the current measurement angle in theory. At the same time, the target installation angle is calculated according to the design requirements of the photovoltaic module and the local solar angle. The target installation angle is the inclination angle that the photovoltaic module should have to achieve the best power generation efficiency. The current measurement angle and the target installation angle are subtracted to obtain the deviation amount representing the difference between the two. The positive deviation amount indicates that the actual installation angle is greater than the target angle, and the negative deviation amount indicates that the actual installation angle is less than the target angle. The absolute value of the deviation amount directly reflects the deviation degree of the installation precision of the photovoltaic module. The trigonometric function correction process is a calculation process of mathematically transforming the angle deviation to obtain the accurate installation angle. The correction process considers the nonlinear influence of the terrain slope on the angle measurement. The deviation amount is input into the sine function to calculate the vertical angle component, and input into the cosine function to calculate the horizontal angle component. The corrected angle value is obtained by dividing the vertical component by the horizontal component through the inverse tangent function. The correction process also needs to consider the weight adjustment of the terrain slope influence factor on the trigonometric function calculation. The weight adjustment is to multiply the terrain slope influence factor as a correction coefficient with the trigonometric function calculation result to obtain the photovoltaic module slope correction installation angle value considering the terrain influence. This value has eliminated the measurement error and calculation deviation caused by the terrain slope.

[0059] The threshold comparison judgment is a logical judgment process of comparing the slope-corrected installation angle value with a preset standard to determine whether it exceeds the allowed range. The preset threshold is determined in advance according to the technical specifications and quality requirements of photovoltaic module installation, and usually includes two boundary values of an upper threshold and a lower threshold. The upper threshold represents the maximum allowed installation angle deviation, and the lower threshold represents the minimum allowed installation angle deviation. The comparison judgment compares the slope-corrected installation angle value with the upper threshold and the lower threshold respectively. When the angle value exceeds the upper threshold, it is judged as positive overrun, and when the angle value is lower than the lower threshold, it is judged as negative overrun. When the angle value is between the upper and lower thresholds, it is judged as normal range. The comparison result generates a corresponding angle deviation overrun state identifier. The overrun state identifier is represented by a digital coding method. The positive overrun identifier is 1, the negative overrun identifier is -1, and the normal range identifier is 0. This identifier directly reflects the qualified state of the current photovoltaic module installation angle. The logical processing is a decision algorithm for generating corresponding control instructions according to the angle deviation overrun state identifier. The logical processing includes two sub-processes of condition judgment and instruction generation. The condition judgment reads the value of the overrun state identifier and executes the corresponding logical branch. When the identifier is 1, the positive adjustment logical branch is executed. When the identifier is -1, the negative adjustment logical branch is executed. When the identifier is 0, the keep logical branch is executed. The instruction generation generates specific angle adjustment instructions according to the execution result of the logical branch. The positive adjustment instruction includes the operation parameters and adjustment amplitude of reducing the installation angle. The negative adjustment instruction includes the operation parameters and adjustment amplitude of increasing the installation angle. The keep instruction indicates that the current installation angle meets the requirements and does not need to be adjusted. The adjustment amplitude is determined according to the size of the deviation and the weight of the terrain slope influence factor. The larger the deviation, the larger the adjustment amplitude. The larger the terrain slope influence factor, the higher the adjustment precision.

[0060] In a specific embodiment, the process of performing step of performing slope compensation value operation on the pitch angle and the terrain slope influence factor can specifically include the following steps:

[0061] The pitch angle and the terrain slope influence factor corresponding to the current position are matched in value to obtain a slope-angle correlation data pair;

[0062] The slope-angle correlation data pair is subjected to slope weight coefficient distribution processing to obtain a pitch angle correction parameter;

[0063] Based on the pitch angle correction parameter, a table lookup operation is performed on the installation angle to obtain a target installation angle reference value;

[0064] The pitch angle and the target installation angle reference value are subjected to value difference calculation to obtain the deviation of the current measured angle from the target installation angle.

[0065] Specifically, the numerical pair matching is a data association process of establishing a one-to-one correspondence between the pitch angle and the terrain slope influence factor. The pitch angle data is derived from the output results of the three-axis inertial measurement unit after coordinate transformation and Kalman filtering processing. The terrain slope influence factor is derived from the results of the composite trigonometric function transformation calculation of the terrain data collected by the laser radar and GPS equipment. The pair matching accurately corresponds the two groups of data according to the spatial position and time stamp of data collection, ensures that each pitch angle value corresponds to the terrain slope influence factor of the current measurement position, checks the integrity and validity of the data in the matching process, eliminates abnormal values and missing values, forms a slope-angle related data pair containing two numerical values of pitch angle and terrain slope influence factor, and the data pair establishes a direct mathematical association between the sensor measurement results and the terrain characteristics. The slope weight coefficient distribution processing is a calculation process of assigning corresponding weights to the pitch angle data according to the numerical value of the terrain slope influence factor. The weight coefficient reflects the influence degree of the terrain slope on the pitch angle measurement accuracy. The larger the terrain slope influence factor value, the more complex the terrain, the more significant the influence on angle measurement, and higher weight coefficient needs to be assigned for correction. The weight coefficient calculation adopts a linear mapping method, which maps the numerical value of the terrain slope influence factor from 0 to 1 to the weight coefficient range from 1.0 to 1.5. The specific calculation is to multiply the terrain slope influence factor by 0.5 and then add 1.0 to obtain the weight coefficient. After the weight coefficient distribution is completed, the pitch angle value is multiplied by the corresponding weight coefficient to obtain the pitch angle correction parameter considering the influence of terrain slope. The parameter has already contained the compensation effect of terrain complexity on angle measurement.

[0066] The installation angle lookup table operation is a retrieval process of searching for a corresponding target installation angle in a preset data table according to the pitch angle correction parameter. The data table contains optimal photovoltaic module installation angle recommended values under different terrain conditions and different pitch angle correction parameters. The table data is established based on photovoltaic power generation efficiency optimization theory and a large amount of measured data statistical analysis. The table is classified according to the numerical range of the pitch angle correction parameter. Each numerical interval corresponds to a recommended target installation angle reference value. The lookup table operation first determines the numerical interval to which the pitch angle correction parameter belongs, and then reads the target installation angle reference value corresponding to the interval. When the pitch angle correction parameter is not at the boundary of the standard interval, a linear interpolation method is used to calculate the accurate target installation angle reference value. The interpolation calculation is to multiply the position ratio of the pitch angle correction parameter between the two adjacent intervals by the difference value of the target angles of the two intervals, and then add the target angle of the smaller interval to obtain the target installation angle reference value for the current terrain condition and pitch angle state. The numerical difference calculation is a calculation process of mathematical subtraction operation between the actual measured pitch angle and the target installation angle reference value obtained by lookup table. Before calculation, it is necessary to ensure that the two values use the same angle unit and reference coordinate system. The result of difference calculation is the deviation amount of the current measured angle and the target installation angle. The positive and negative signs of the deviation amount represent the deviation direction. A positive value indicates that the actual angle is greater than the target angle and needs to be reduced and adjusted. A negative value indicates that the actual angle is less than the target angle and needs to be increased and adjusted. The absolute value of the deviation amount represents the adjustment amplitude requirement. The deviation amount value is directly used for trigonometric correction processing and threshold comparison judgment.

[0067] In a specific embodiment, step S4 comprises:

[0068] The GPS positioning data is time-synchronized and aligned with the pitch angle to obtain a position-pose fusion data set.

[0069] The position-pose fusion data set is subjected to extended Kalman filter state prediction and update iteration operation to obtain a positioning coordinate initial value.

[0070] Based on the positioning coordinate initial value, a position correction matrix transformation calculation is performed to obtain corrected positioning data. The corrected positioning data is subjected to coordinate system conversion and precision verification processing to obtain a fused positioning coordinate.

[0071] Specifically, the time synchronization alignment process is a data synchronization process that accurately matches GPS positioning data and pitch angle data according to the collection timestamp. The GPS positioning data is derived from the real-time positioning output of the satellite navigation receiver and contains longitude, latitude, altitude, and timestamp information. The pitch angle data is derived from the output of the three-axis inertial measurement unit in step S2 after coordinate transformation and Kalman filtering processing. The synchronization alignment process first checks the timestamp format and accuracy of the two sets of data to ensure uniform time reference, and then pairs the GPS positioning data and pitch angle data collected at the same time according to the timestamp to form a position-attitude fusion data set containing position coordinates and attitude angle information. This data set establishes a direct correlation between the spatial position and installation attitude of the photovoltaic module, laying a data foundation for subsequent multi-sensor data fusion. The extended Kalman filter state prediction and update iteration operation is a recursive algorithm for processing state estimation of nonlinear systems. The extended Kalman filter processes the complex relationships in the position-attitude fusion data set by linearizing nonlinear functions. The state prediction step predicts the position coordinates at the next time step based on the system dynamic model, taking into account the effects of photovoltaic module motion state and attitude changes on position, while introducing a process noise covariance matrix to describe the uncertainty of the system. The update iteration step uses observation data to correct the prediction results, linearizes the nonlinear observation function by calculating the Jacobian matrix, then calculates the Kalman gain to weigh the credibility of the predicted value and the observed value. The iteration operation repeats the prediction and update process at each time step, gradually converging to the optimal state estimate value, and finally outputs the positioning coordinate initial value after multi-sensor fusion.

[0072] The position correction matrix transformation calculation is a mathematical transformation process for eliminating the influence of GPS signal multipath interference. The multipath interference refers to the deviation of signal propagation path caused by factors such as ground reflection and building obstruction in complex terrain, resulting in a decrease in positioning accuracy. The position correction matrix is a mathematical correction model established according to the terrain characteristics and signal propagation environment. The matrix elements are obtained by analyzing the multipath error characteristics under different terrain conditions. The transformation calculation performs matrix multiplication operation on the initial positioning coordinates and the position correction matrix, and performs mathematical compensation on the systematic errors in the GPS positioning data. The correction calculation particularly considers the signal shielding and reflection effects in complex terrain, and eliminates the adverse effects of these factors on positioning accuracy through matrix transformation, to obtain corrected positioning data compensated for multipath interference. The positioning accuracy of this data is significantly improved compared to the original GPS data. The coordinate system conversion and accuracy verification process is the final processing process for converting the corrected positioning data into the engineering application coordinate system and verifying the accuracy. The coordinate system conversion converts the WGS84 geocentric coordinate system used by GPS into the local coordinate system used by the photovoltaic power station engineering construction. The conversion process involves mathematical transformations such as coordinate origin translation, axis rotation and scaling, to ensure that the positioning data is consistent with the coordinate system of the engineering design drawings. The accuracy verification process evaluates the accuracy level of the fused positioning by comparing with the known control point coordinates. The verification process calculates the difference between the positioning result and the true value. When the accuracy meets the requirements of photovoltaic module installation monitoring, the final fused positioning coordinates are output. When the accuracy does not meet the requirements, the repositioning or error correction program is triggered.

[0073] In a specific embodiment, step S5 comprises:

[0074] The slope correction installation angle and the fused positioning coordinates are subjected to data normalization preprocessing to obtain quality evaluation input parameters.

[0075] The quality evaluation input parameters are subjected to terrain slope grading matching processing to obtain evaluation weight coefficients.

[0076] The installation angle accuracy and the position accuracy are subjected to weighted calculation processing based on the evaluation weight coefficients to obtain quality evaluation scores.

[0077] The quality evaluation scores are compared with the preset quality standard threshold value to obtain a quality qualified state identifier. The quality qualified state identifier is subjected to alarm triggering logic processing to obtain installation program control instructions and perform corresponding continue or stop operations.

[0078] Specifically, the data normalization preprocessing is a mathematical transformation process of converting data of different dimensions and numerical ranges into a unified standard, the slope correction installation angle is derived from the angle value in step S3 after slope compensation and trigonometric function correction processing, the unit is degree, the numerical range is usually between 0 to 90 degrees, the fusion positioning coordinate is derived from the coordinate data in step S4 after extended Kalman filtering and position correction, the unit is meter, the numerical range is determined according to the specific engineering coordinate system, the normalization preprocessing first determines the maximum and minimum boundary of each data, then uses the maximum and minimum value standardization method to convert the data to dimensionless value between 0 to 1, the specific calculation is to subtract the minimum value from the original value and divide by the difference between the maximum value and the minimum value, to get the standardized quality evaluation input parameter, which eliminates the dimensional difference between different data types, and lays a unified data foundation for subsequent comprehensive evaluation calculation. The topographic slope grading matching processing is a classification algorithm that differentiates the quality evaluation standard according to the complexity of the terrain, the grading matching divides the complex terrain into three levels according to the terrain slope influence characteristics mentioned in the disclosure, 0 to 15 degrees for gentle slope level, 15 to 30 degrees for medium slope level, 30 to 45 degrees for steep slope level, each level corresponds to different quality evaluation weight distribution strategy, the matching processing reads the topographic slope data of the current position, judges which slope level the position belongs to through numerical comparison, then extracts the evaluation weight coefficient of the corresponding level from the preset weight coefficient database, the weight coefficient of gentle slope level focuses on position accuracy, the weight coefficient of medium slope level balances angle and position accuracy, the weight coefficient of steep slope level focuses on angle accuracy, the distribution of weight coefficient reflects the focus and difficulty of photovoltaic module installation monitoring under different terrain conditions.

[0079] The weighted calculation processing is a calculation process of mathematically synthesizing the installation angle accuracy and the position accuracy according to weight coefficients. The installation angle accuracy is quantified by quantifying the deviation degree of the installation angle after slope correction from the theoretical optimal angle, and the position accuracy is quantified by quantifying the deviation degree of the positioning coordinates from the designed installation position. The weighted calculation first multiplies the normalized installation angle accuracy by the corresponding angle weight coefficient, multiplies the normalized position accuracy by the corresponding position weight coefficient, and then adds the two weighted results to obtain a comprehensive quality evaluation score. The score comprehensively reflects the overall quality level of the photovoltaic module installation under the current terrain condition. The higher the score, the better the installation quality, and the lower the score, the worse the installation quality. The weighted calculation process considers the importance difference of each accuracy index under different terrain conditions, and ensures the rationality and accuracy of the quality evaluation result. The numerical comparison judgment is a logical judgment process of comparing the quality evaluation score with a preset standard to determine the qualified state. The preset quality standard threshold is determined in advance according to the industry specifications and engineering requirements of photovoltaic module installation. Usually, two dividing points of qualified threshold and excellent threshold are set. The comparison judgment compares the quality evaluation score with the qualified threshold. When the score is higher than the qualified threshold, it is determined that the quality is qualified. When the score is lower than the qualified threshold, it is determined that the quality is unqualified. The judgment result generates a corresponding quality qualified state identifier. The qualified state identifier uses Boolean logic representation. The quality is qualified as true value, and the quality is unqualified as false value. The identifier directly determines the subsequent alarm triggering and control instruction generation.

[0080] The alarm triggering logic processing is a decision algorithm for executing corresponding control strategies according to the quality qualified state identifier. The logic processing includes three sub-processes of state judgment, alarm control and instruction generation. The state judgment reads the Boolean value of the quality qualified state identifier and executes the corresponding logical branch. When the identifier is true value, the normal continue logical branch is executed. When the identifier is false value, the abnormal stop logical branch is executed. The alarm control determines whether to trigger the sound and light alarm device according to the execution result of the logical branch. The normal continue branch does not trigger the alarm, and the abnormal stop branch triggers the alarm and continuously outputs the alarm signal. The instruction generation generates specific installation program control instructions according to the quality state and the alarm control result. The normal continue branch generates the control instruction of continuing installation. The instruction content includes keeping the current installation parameters and continuing to execute the installation task of the next photovoltaic module. The abnormal stop branch generates the control instruction of stopping installation. The instruction content includes immediately stopping all installation equipment running, keeping the current installation state and waiting for manual inspection processing. The control instruction is sent to the control unit of the installation equipment through the communication interface to execute the corresponding continue or stop operation.

[0081] The above describes the photovoltaic module installation monitoring method for complex terrain in the embodiments of the present application. The photovoltaic module installation monitoring system for complex terrain in the embodiments of the present application is described below. Please refer to Figure 2The embodiment of the photovoltaic module installation monitoring system for complex terrain in the application comprises:

[0082] The generating module is configured to collect three-dimensional coordinate points of the hilly terrain according to the grid density by the laser radar and the GPS device, calculate the slope angle and the slope azimuth angle of each measurement point based on the collected coordinate point data, and generate a terrain slope influence factor;

[0083] The transforming module is configured to arrange a three-axis inertial measurement unit at a predetermined position of the photovoltaic module, perform coordinate transformation on the original data of the sensor according to the terrain slope influence factor, and output the pitch angle;

[0084] The reading module is configured to read the pitch angle and the terrain slope influence factor data, perform slope compensation calculation processing, obtain the slope correction installation angle of the photovoltaic module, and generate an angle adjustment instruction when the angle deviation exceeds a preset threshold value;

[0085] The fusion module is configured to perform extended Kalman filtering fusion on the GPS positioning data and the pitch angle, perform multipath interference compensation on the GPS signal by using a position correction matrix, and obtain a fused positioning coordinate;

[0086] The evaluation module is configured to perform terrain grading quality evaluation calculation based on the slope correction installation angle and the fused positioning coordinate, obtain a quality evaluation value, and trigger an alarm and stop the installation program when the quality evaluation value is lower than a preset standard.

[0087] The above Figure 2 The photovoltaic module installation monitoring system for complex terrain in the embodiment of the application is described in detail from the perspective of a modular functional entity, and the photovoltaic module installation monitoring device for complex terrain in the embodiment of the application is described in detail from the perspective of hardware processing.

[0088] Reference Figure 3 The embodiment of the application also provides a photovoltaic module installation monitoring device for complex terrain, which can be a server, and the internal structure thereof can be as shown in Figure 3The processor of the computer is used to provide computing and control capabilities. The memory of the photovoltaic module installation monitoring device for complex terrains includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the photovoltaic module installation monitoring device for complex terrains is used to store the corresponding data in the embodiment. The network interface of the photovoltaic module installation monitoring device for complex terrains is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the above method.

[0089] Those skilled in the art can understand that, Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme, and does not constitute a limitation on the photovoltaic module installation monitoring device for complex terrains to which the scheme is applied.

[0090] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium, and can also be a volatile computer readable storage medium. The computer readable storage medium stores instructions, which, when executed on a computer, cause the computer to perform the steps of the photovoltaic module installation monitoring method for complex terrains.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, system and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0092] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical scheme of the present application or the part that contributes to the prior art or the whole or part of the technical scheme can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a photovoltaic module installation monitoring device for complex terrains (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0093] The above examples are only used to illustrate the technical solutions of the present application, but not to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those ordinarily skilled in the art should understand: the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring the installation of photovoltaic modules in complex terrain, characterized in that, The method includes: Step S1: The lidar and GPS devices collect three-dimensional coordinate points of the hilly terrain according to the grid density. Based on the collected coordinate point data, the slope angle and slope azimuth angle of each measurement point are calculated to generate the terrain slope influence factor. Step S2: The three-axis inertial measurement unit is deployed at the predetermined position of the photovoltaic module, and the original sensor data is transformed according to the terrain slope influence factor to output the pitch angle; Step S3: Read the pitch angle and terrain slope influence factor data, perform slope compensation calculation processing to obtain the slope correction installation angle of the photovoltaic module, and generate an angle adjustment command when the angle deviation exceeds a preset threshold. This includes: performing slope compensation numerical calculation on the pitch angle and terrain slope influence factor to obtain the deviation between the current measured angle and the target installation angle, wherein the pitch angle is numerically matched with the terrain slope influence factor corresponding to the current position to obtain a slope-angle correlation data pair; the slope weight coefficient is assigned to the slope-angle correlation data pair to obtain the pitch angle correction parameter; an installation angle lookup table calculation is performed based on the pitch angle correction parameter to obtain the target installation angle reference value; and the numerical difference between the pitch angle and the target installation angle reference value is calculated to obtain the deviation between the current measured angle and the target installation angle. The deviation is corrected using a trigonometric function to obtain the slope correction installation angle value of the photovoltaic module; a threshold comparison is performed based on the slope correction installation angle value to obtain an angle deviation exceeding the limit status indicator; the angle deviation exceeding the limit status indicator is logically processed to obtain an angle adjustment command; Step S4: The GPS positioning data and the elevation angle are fused using an extended Kalman filter, and the GPS signal is compensated for multipath interference using a position correction matrix to obtain the fused positioning coordinates; Step S5: Based on the slope-corrected installation angle and fused positioning coordinates, perform terrain-level quality assessment calculations to obtain quality assessment values. When the quality assessment value is lower than the preset standard, trigger an alarm and stop the installation process.

2. The photovoltaic module installation monitoring method for complex terrain according to claim 1, characterized in that, Step S1 includes: The lidar ranging data and GPS coordinate data are time-stamped and registered to obtain a set of terrain coordinate points. A two-way differential gradient calculation is performed on the set of terrain coordinate points to obtain the slope change rate and slope aspect change rate parameters of each measurement point; Based on the slope change rate and slope aspect change rate parameters, a weighted average filtering process is performed to obtain slope angle and slope azimuth angle data; The slope angle and slope azimuth angle data are input into a composite trigonometric function transformation for nonlinear mapping calculation to obtain the terrain slope influence factor.

3. The photovoltaic module installation monitoring method for complex terrain according to claim 1, characterized in that, Step S2 includes: The terrain slope influencing factor is input into the coordinate transformation matrix to construct the operation, and a three-axis calibration transformation matrix is ​​obtained. The triaxial calibration transformation matrix operation is performed on the raw acceleration and angular velocity data collected by the triaxial inertial measurement unit to obtain sensor data; The attitude calculation is performed based on the sensor data to obtain the pitch angle value; The pitch angle value is then subjected to Kalman filtering for stability processing to obtain the pitch angle.

4. The photovoltaic module installation monitoring method for complex terrain according to claim 1, characterized in that, Step S4 includes: The GPS positioning data and the pitch angle are time-synchronized and aligned to obtain a position-attitude fusion dataset. Extended Kalman filter state prediction and update iteration operations are performed on the position-attitude fusion dataset to obtain the initial value of the positioning coordinates; Based on the initial positioning coordinates, a position correction matrix transformation is calculated to obtain corrected positioning data; the corrected positioning data is then subjected to coordinate system transformation and accuracy verification to obtain fused positioning coordinates.

5. The photovoltaic module installation monitoring method for complex terrain according to claim 1, characterized in that, Step S5 includes: The slope-corrected installation angle and the fused positioning coordinates are preprocessed using data normalization to obtain the quality assessment input parameters; The quality assessment input parameters are subjected to terrain slope classification matching processing to obtain assessment weight coefficients; Based on the aforementioned evaluation weighting coefficients, the installation angle accuracy and position accuracy are weighted and calculated to obtain a quality evaluation score. The quality assessment score is compared with a preset quality standard threshold to obtain a quality pass status indicator; based on the quality pass status indicator, alarm trigger logic is processed to obtain installation program control instructions and execute corresponding continue or stop operations.

6. A photovoltaic module installation monitoring system for complex terrain, characterized in that, For implementing the photovoltaic module installation monitoring method for complex terrain as described in any one of claims 1 to 5, the photovoltaic module installation monitoring system for complex terrain comprises: The generation module is used by lidar and GPS devices to collect three-dimensional coordinate points of hilly terrain according to grid density, calculate the slope angle and slope azimuth angle of each measurement point based on the collected coordinate point data, and generate terrain slope influence factors. The transformation module is used to deploy the three-axis inertial measurement unit at a predetermined position of the photovoltaic module, and to perform coordinate transformation on the original sensor data according to the terrain slope influence factor, and output the pitch angle. The reading module is used to read the pitch angle and terrain slope influence factor data, perform slope compensation calculation processing to obtain the slope correction installation angle of the photovoltaic module, and generate an angle adjustment command when the angle deviation exceeds a preset threshold. This includes: performing slope compensation numerical calculations on the pitch angle and terrain slope influence factor to obtain the deviation between the current measured angle and the target installation angle; matching the pitch angle with the terrain slope influence factor corresponding to the current position to obtain a slope-angle correlation data pair; assigning slope weight coefficients to the slope-angle correlation data pair to obtain pitch angle correction parameters; performing an installation angle lookup table calculation based on the pitch angle correction parameters to obtain the target installation angle reference value; and calculating the numerical difference between the pitch angle and the target installation angle reference value to obtain the deviation between the current measured angle and the target installation angle. The deviation is corrected using a trigonometric function to obtain the slope correction installation angle value of the photovoltaic module; a threshold comparison is performed based on the slope correction installation angle value to obtain an angle deviation exceeding the limit status indicator; the angle deviation exceeding the limit status indicator is logically processed to obtain an angle adjustment command; The fusion module is used to fuse GPS positioning data with the elevation angle using extended Kalman filtering, and to compensate for multipath interference of GPS signals using a position correction matrix to obtain fused positioning coordinates. The evaluation module is used to perform terrain-level quality assessment calculations based on the slope-corrected installation angle and fused positioning coordinates to obtain a quality assessment value. When the quality assessment value is lower than the preset standard, an alarm is triggered and the installation program is stopped.

7. A photovoltaic module installation monitoring device for complex terrain, characterized in that, The method includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the photovoltaic module installation monitoring method for complex terrain as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to execute the photovoltaic module installation monitoring method for complex terrain as described in any one of claims 1 to 5.

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