Intelligent regulation and control method and device for solar cell panel

Abstract

The invention relates to the field of solar cell panel control, in particular to an intelligent regulation and control method and device for a solar cell panel. The method comprises the following steps: collecting sun illumination state information in real time based on an illumination sensing array; threshold judgment is carried out based on the sun illumination state information, a solar cell panel is activated, and an energy storage working mode is entered; performing dynamic pose adjustment on the cell panel based on the energy storage working mode, and activating a power generation execution state; dynamically adjusting the duty ratio of the battery module based on the power generation execution state, and generating a maximum power tracking strategy; feedforward presetting processing is carried out based on the maximum power tracking strategy, and automatic solar cell panel control is carried out. According to the invention, efficient, safe and stable solar cell panel control is realized, and the use efficiency of energy is further optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of solar panel control, and in particular to an intelligent regulation method and device for a solar panel. BACKGROUND

[0002] In the practical application of solar panels, the volatility of external factors such as light intensity, temperature, weather changes, and obstructions directly affects the output power and overall power generation efficiency of the panels. Traditional solar panel regulation methods mainly rely on maximum power point tracking (MPPT) technology, aiming to dynamically adjust the working state of the panels to operate at the maximum power output point. However, traditional MPPT methods mainly adjust the operating point based on instantaneous light conditions, ignoring the hysteresis of light changes and the influence of long-term factors such as panel temperature and aging. In addition, existing regulation methods often cannot adapt to changing environmental conditions in real time, especially in complex situations such as sudden weather changes or overcast days, obstructions, etc. The regulation effect of traditional MPPT technology is limited. With the continuous expansion of photovoltaic system scale and the deep integration between photovoltaic power generation and the power grid, the regulation demand of solar panels is no longer limited to the optimization of a single panel, but involves the coordinated control of the entire photovoltaic system. Traditional photovoltaic system regulation methods often lack intelligent decision-making capabilities and system-level optimization strategies, making it difficult to maintain high efficiency of the system under various environmental conditions. In addition, as the use time of solar panels gradually increases, the aging phenomenon will also affect the performance of the photovoltaic system, and traditional regulation methods are difficult to dynamically adjust according to the actual health status of the panels, resulting in a gradual decline in system efficiency. SUMMARY

[0003] To solve the above technical problems, the present application provides an intelligent regulation method and device for a solar panel to solve at least one of the above technical problems.

[0004] To achieve the above purpose, the present application provides an intelligent regulation method for a solar panel, comprising the following steps: Step S1: Real-time acquisition of sunlight state information based on a light sensing array; threshold judgment based on the sunlight state information, activation of the solar panel, and entry into an energy storage working mode; Step S2: Dynamic pose adjustment of the panel based on the energy storage working mode, activation of the power generation execution state; Step S3: Dynamic adjustment of the cell module duty cycle based on the power generation execution state, generation of a maximum power tracking strategy; Step S4: Feedforward pre-adjustment processing based on the maximum power tracking strategy, automatic solar panel control.

[0005] In the present specification, an intelligent regulation device for a solar cell panel is provided for performing an intelligent regulation method for a solar cell panel as described above, comprising: An illumination threshold value calculation unit is configured to collect solar illumination state information in real time based on an illumination sensing array; perform threshold value judgment based on the solar illumination state information; activate the solar cell panel; and enter an energy storage working mode; A pose adjustment unit is configured to perform dynamic pose adjustment of the cell panel based on the energy storage working mode; and activate a power generation execution state; A duty cycle adjustment unit is configured to perform dynamic adjustment of the cell module duty cycle based on the power generation execution state; and generate a maximum power tracking strategy; A pre-adjustment control unit is configured to perform feedforward pre-adjustment processing based on the maximum power tracking strategy; and perform automatic solar cell panel control.

[0006] The beneficial effects of the present application are as follows: by real-time monitoring of solar intensity through the light sensing array, the light changes under different environmental conditions can be accurately captured, ensuring that the working state of the panel is dynamically adjusted according to external conditions. This is crucial for maximizing the use of solar energy. The threshold judgment system determines whether the solar illumination meets the panel activation conditions, avoiding wasting resources in insufficient light, and ensuring that the panel only enters the working mode when the light is sufficient, improving the energy use efficiency of the system. The panel automatically enters the energy storage mode when the conditions are met, without the need for manual intervention, which improves the response speed and operational convenience of the system, and provides a foundation for subsequent energy management. By dynamically adjusting the pose of the solar panel (such as the inclination angle and orientation), the light receiving efficiency can be maximized, ensuring that the panel is always oriented at the best angle and follows the sun's trajectory, improving the power generation efficiency. Dynamic adjustment can automatically respond to changes in light, whether it is direct sunlight or cloud cover, and can adjust the angle of the panel to maximize the energy collection capacity of the panel. This step helps the panel achieve the best energy conversion rate in energy storage mode, reduces energy loss, and improves the overall performance of the system. By dynamically adjusting the duty cycle of the battery module, accurate maximum power point tracking (MPPT) can be achieved. When the light intensity and environmental temperature change, the system can adjust the working state in real time to ensure that the panel always works at the maximum power output point, thereby improving the energy conversion rate. Dynamic adjustment of the duty cycle can cope with fluctuations in panel performance under different light conditions, ensuring maximum power generation, adapting to changes in different seasons and weather, and avoiding operation under non-optimal conditions. Through the maximum power tracking strategy, energy waste can be avoided, especially in complex weather conditions, ensuring that the system operates continuously and efficiently, reducing unnecessary energy loss. Through feedforward control, the system can predict and adjust in advance before the light conditions change, reducing response delay. This advance processing helps to improve the reaction speed of the panel and ensures that the panel optimizes its working state in a timely manner when the light changes. Fully automated control can complete the adjustment of the solar panel without human intervention, reducing the complexity of operation and improving the stability and reliability of the overall system. The automated system can adjust the working state of the panel in real time and accurately, further optimizing the energy use efficiency, reducing system operation errors and human errors, and improving the overall performance of the system. BRIEF DESCRIPTION OF DRAWINGS

[0007] Fig. 1 A step flowchart for an intelligent control method for a solar panel of the present application; Fig. 2 A detailed implementation step flowchart for step S1; Fig. 3 A detailed implementation step flowchart for step S2. DETAILED DESCRIPTION

[0008] It should be understood that the specific embodiments described herein are merely exemplary and not intended to limit the present application.

[0009] The present application provides an intelligent control method and device for a solar cell panel. The execution subject of the method and device includes but is not limited to the following: mechanical equipment, data processing platform, cloud server node, network upload device, etc. which can be regarded as a general computing node of the present application. The data processing platform includes but is not limited to the following: audio image management system, information management system, cloud data management system, at least one of which.

[0010] Referring to Figs. 1 to 3 The present application provides an intelligent control method for a solar cell panel, including the following steps: Step S1: Real-time acquisition of sunlight state information based on a light sensing array; threshold judgment based on the sunlight state information, activation of the solar cell panel, and entry into energy storage mode; Step S2: Dynamic pose adjustment of the cell panel based on the energy storage mode, activation of the power generation execution state; Step S3: Dynamic adjustment of the cell module duty cycle based on the power generation execution state, generation of a maximum power tracking strategy; Step S4: Feedforward pre-adjustment processing based on the maximum power tracking strategy, automatic solar cell panel control.

[0011] In the embodiment of the present application, referring to Fig. 1 The present application provides a step flowchart of an intelligent control method for a solar cell panel. In this example, the steps of the intelligent control method for the solar cell panel include: Step S1: Real-time acquisition of sunlight state information based on a light sensing array; threshold judgment based on the sunlight state information, activation of the solar cell panel, and entry into energy storage mode; In this embodiment, in the process of intelligent control of solar panels, first of all, it is necessary to use the light sensing array to collect real-time sunlight state information. The light sensing array is composed of multiple light sensors, which are distributed in different areas of the panel to obtain comprehensive sunlight data. The selection of light sensors is very critical, and usually high-sensitivity and fast-response light sensors (such as silicon photocells, photoresistors, etc.) are selected, which can accurately capture the changes of solar radiation under different time periods and different weather conditions. The collected light data is analyzed in real time by the control unit and compared with the preset light threshold. Threshold judgment is usually based on two key parameters: one is the light intensity, and the other is the light duration. For example, when the real-time light intensity exceeds the set minimum power generation threshold (such as 300 W / m²), and the intensity duration exceeds a certain threshold (such as 5 minutes), the system considers that the current light condition is sufficient to support the power generation work of the panel. At this time, the panel will enter the energy storage working mode and start to absorb and store energy. If the light intensity is lower than the set threshold, the system will enter standby mode to avoid consuming too much energy under low-efficiency conditions. Threshold judgment not only depends on light intensity, but also may consider external environmental factors, such as the influence of season, weather, etc. For example, in overcast or cloudy weather, the threshold can be appropriately lowered to ensure that the power generation function can still be started under conditions close to the minimum threshold. The accurate threshold judgment in this stage ensures that the solar panel works under the best conditions, avoids starting power generation under unsuitable light conditions, and improves work efficiency and system life.

[0012] Step S2: dynamically adjusting the pose of the panel based on the energy storage working mode, and activating the power generation execution state; In this embodiment, after the battery panel enters the energy storage mode, the next step is to adjust the dynamic pose to keep the battery panel at the optimal angle to maximize solar energy absorption. The key to this step is the adjustment of the azimuth and elevation angles of the solar panel to ensure that the panel is perpendicular to the direction of sunlight to increase the efficiency of light absorption. Dynamic pose adjustment usually relies on the servo motor system of the battery panel, which can accurately control the angle and direction of the panel. This process requires determining the optimal angle of the panel based on the position of the light sensor and the sun. The position of the sun is usually determined by the solar zenith angle and azimuth angle, which can be calculated by a pre-set algorithm (such as the solar position model in astronomy). By monitoring the light intensity and the position of the sun in real time, the system adjusts the azimuth and elevation angles of the panel to ensure that the solar panel is always facing the sun, maximizing energy absorption. Dynamic adjustment is not just a simple timing adjustment, but also requires dynamic optimization based on the change of the solar altitude angle and the geographical location, such as during sunrise and sunset, the panel's pose is automatically adjusted to maximize solar energy absorption. At this time, the battery panel is activated into the power generation execution state and starts energy conversion. During this process, the battery panel converts the collected light energy into electrical energy through the battery module and stores it in the battery energy storage unit. Through precise pose adjustment, the efficiency of the battery panel can be maximized.

[0013] Step S3: Based on the power generation execution state, the dynamic adjustment of the battery module duty cycle is performed to generate a maximum power tracking strategy. In this embodiment, after entering the power generation execution state, the working efficiency of the solar panel is not only affected by the light intensity and the pose angle, but also closely related to the working state of the battery module. The duty cycle of the battery module refers to the proportion of the actual working time of the solar panel within a given time, which directly affects the power output of the battery module. In order to ensure that the solar panel is always in the best power generation state, it is necessary to adjust the duty cycle dynamically to achieve the maximum power output. In this step, the Maximum Power Point Tracking (MPPT) strategy is adopted. The core idea of this strategy is to dynamically adjust the duty cycle of the solar panel to ensure that the power output of the solar panel is always close to the maximum power point (MPP). The Maximum Power Point Tracking algorithm monitors the voltage and current values of the battery module in real time, calculates the current power output, and adjusts the duty cycle according to the power trend to make the solar panel always work in the optimal state. The key to dynamically adjusting the duty cycle is the accurate control of the power output. Through rapid acquisition and analysis of the voltage, current and power values of the battery module, the system can judge the current maximum power point in real time and adjust the working mode. Common maximum power tracking algorithms include "Perturb and Observe" (P&O) and "Incremental Conductance" (IncCond), which dynamically adjust the working state of the battery module according to the relationship between the output power and the voltage, current, to achieve the maximum power point. This strategy not only ensures the efficient operation of the solar panel, but also optimizes the energy storage process of the battery module, ensuring that the system can maintain the highest working efficiency under various light conditions.

[0014] Step S4: Based on the maximum power tracking strategy, the feedforward pre-adjustment process is carried out, and the automatic solar panel control is carried out.

[0015] In this embodiment, historical light data and weather forecast information are used to predict the light intensity in the future. The prediction process usually uses statistical learning methods or prediction algorithms based on weather models. For example, the trend of light changes in the next few hours can be predicted by time series analysis methods, combined with existing light sensor data, to predict the fluctuation of future light intensity. Based on the prediction results, the system will adjust the power generation mode of the solar panel in advance. For example, if the future light intensity is expected to decrease, the system will lower the working power of the solar panel in advance to avoid power overload caused by insufficient light; on the contrary, when the light intensity increases, the system will increase the power output of the solar panel to ensure that it can quickly respond to changes in light and maximize power generation efficiency. The feedforward pre-adjustment process can effectively reduce the response time of the system, improve the adaptability of the solar panel to light changes, and optimize its working efficiency. At the same time, combined with the maximum power tracking strategy, this process ensures the efficient operation of the solar panel in complex light environments, improves the overall energy conversion efficiency, and ensures the long-term stability of the system.

[0016] In this embodiment, referring to Fig. 2 For a detailed implementation step flowchart of step S1, in this embodiment, the detailed implementation steps of step S1 include: Based on the light sensing array, real-time solar light state information is collected; The solar light state information is subjected to sensing error calculation and adaptive filtering processing to extract a standardized light information sequence; The standardized light information sequence is subjected to light intensity calculation to generate real-time light intensity; The real-time light intensity is subjected to periodic fluctuation analysis to generate light intensity fluctuation characteristics; Based on the light intensity fluctuation characteristics, the stable light intensity within the period is calculated to obtain an effective light intensity value; Based on a preset energy storage start threshold, the effective light intensity value is subjected to identification, and when the effective light intensity value is not less than the preset energy storage start threshold, the solar panel is activated and enters the energy storage working mode.

[0017] In this embodiment, in the solar panel intelligent control system, the first step is to standardize the collection and intensity calculation of the light signal, to ensure the accuracy and stability of the input data. The system usually arranges multiple light sensors (such as photodiode arrays or silicon light probes) on the surface of the panel, and collects light signals at a frequency of 1 to 10 times per second. Due to factors such as environmental noise, temperature drift and sensor sensitivity differences, the system first performs signal standardization processing after collection. This process includes zero point calibration, bias compensation, temperature correction and noise filtering, and the filtering method can use sliding average or Kalman filtering to suppress random interference. In the experiment, the voltage signal output by the sensor is converted into light intensity units (such as W / m² or lux), and a corresponding relationship is established according to the calibration data. To ensure the consistency of multi-point collection, spatial weighted average processing is also required, and the weight parameter is determined according to the sensor installation angle and incident angle distribution. Finally, the system generates a light intensity sequence in real time, representing the light intensity change trend at different time points. This sequence will serve as the basic data for the entire intelligent control system, used for subsequent light fluctuation analysis and energy storage control decisions, ensuring consistency and accuracy in subsequent calculations. After completing the collection and standardization of the real-time light intensity sequence, the system enters the light fluctuation feature extraction stage. Solar light changes with time have obvious periodicity, especially in the case of intermittent cloud cover, the light signal will show periodic fluctuations. The main task of this step is to identify the change rule of the light signal within a certain time window and extract its fluctuation feature index. The system usually selects an analysis period of 5 to 15 minutes, and calculates the mean, maximum, minimum, standard deviation and coefficient of variation of the light signal in this period. Through these parameters, it can be judged whether the light is stable. For example, when the coefficient of variation is less than 0.1, the light is usually considered relatively stable; when it is greater than 0.3, it means there is obvious fluctuation. In order to more accurately identify the periodic change, the system will also perform frequency analysis on the light signal to extract the main fluctuation period and fluctuation amplitude. Experiments show that when the main fluctuation period is between 20 to 60 seconds, it is usually related to intermittent cloud cover. Finally, the system forms a set of light fluctuation feature parameters, including average intensity, fluctuation period, fluctuation amplitude and stability index, which are used to judge whether the light reaches the effective power generation condition. These parameters will be updated continuously to realize real-time light dynamic monitoring and trend analysis.

[0018] After determining the light fluctuation characteristics, the system further calculates the stable light intensity in a period, i.e. the effective light intensity value. This step aims to filter out rapid fluctuations caused by short-term cloud shadows, reflections, wind-borne dust particles and other factors, and extract the stable light part that can sustainably provide energy. The system sets the corresponding time window (usually 5 to 10 minutes) according to the fluctuation period parameters obtained in the previous stage of analysis, and performs weighted average processing within the window. The weight coefficient is set according to the light fluctuation amplitude. When the fluctuation is large, the system increases the smoothing weight to enhance stability; when the light is smooth, it maintains a higher response sensitivity. During the calculation process, abnormal points that are higher or lower than the average level by a certain percentage within a short time are automatically removed to avoid interference from abnormal flashes or shadows. Experimental data show that after smoothing and abnormal point removal, the fluctuation range of the light signal can be reduced by 30% to 40%, and the effective light intensity obtained can better represent the actual power generation capacity. The system takes this effective light value as the core parameter for judging the start of energy storage, and stores the stable light records of each time period in the database for subsequent algorithm optimization and energy prediction model calling.

[0019] After calculating the effective light intensity value, the system enters the judgment and execution phase. The goal of this phase is to achieve intelligent start-stop control of the energy storage device through a pre-set energy storage start threshold. The threshold parameter is usually set in the system according to the type of battery panel, energy conversion efficiency and climate conditions. For example, for a monocrystalline silicon battery panel with a conversion efficiency of about 20%, the energy storage start threshold can be set to 350 to 400 W / m² under standard light conditions. When the system detects that the effective light intensity is greater than or equal to the threshold, it is judged that there are sufficient light energy conditions, and a control signal is automatically sent to start the energy storage module. After starting, the electrical energy is transmitted to the energy storage unit through the DC / DC boost conversion module, and the system monitors the voltage, current and temperature parameters in real time to prevent overload and overheating. When the effective light intensity is continuously below the threshold (such as continuously below 300 W / m² for 2 minutes), the system automatically closes the energy storage circuit and enters standby mode to avoid energy loss and excessive operation of the equipment. This identification process can be realized through fuzzy logic control or multi-threshold decision algorithm, so that the system has self-adaptive adjustment capability when the light conditions fluctuate. Experimental results show that through this threshold identification mechanism, the utilization rate of the energy storage system can be increased by about 15%, and stable and efficient energy management can be achieved in unstable light environments.

[0020] In this embodiment, refer to Fig. 3 For the detailed implementation step flowchart of step S2, in this embodiment, the detailed implementation steps of step S2 include: Based on the energy storage working mode, the solar light state information is decomposed into a three-dimensional space vector to obtain the solar direction angle feature and the light incidence angle feature; According to the solar direction angle feature and the light incidence angle feature, real-time solar position three-dimensional coordinate calculation is performed to obtain the solar position three-dimensional coordinate; According to the solar position three-dimensional coordinate, dynamic pose adjustment is performed on the battery panel to activate the power generation execution state.

[0021] In this embodiment, after entering the energy storage mode, the light state information needs to be spatially vector decomposed to extract the solar direction angle feature and the light incidence angle feature. The core of this process is to obtain the light intensity distribution information in different directions through the light sensing array, so as to calculate the direction feature of solar radiation in space. The method is to arrange multiple light collection nodes on the surface and periphery of the solar panel, each node has a different receiving surface angle, which is used to capture light signals from different directions at the same time. The collection frequency of each node is set between 1 to 5 Hz, and parameters such as light intensity, azimuth angle, elevation angle and time stamp are recorded. By comparing the light intensity distribution of different nodes at the same time, the dominant direction of light in space can be determined. In order to ensure the accuracy of direction recognition, the interference of environmental reflection, shielding and scattered light also needs to be considered, so the weighted average method is used to normalize each light vector. The weighting coefficient is determined according to the sensitivity coefficient and the installation angle of each node. Finally, two key parameters are obtained: the solar direction angle feature, including the azimuth angle (usually in the range of 0° to 360°) and the elevation angle (0° to 90°); and the light incidence angle feature, which describes the angle relationship between the light and the panel surface. Through this spatial vector decomposition process, directional basic data can be provided for subsequent solar position calculation and panel attitude adjustment, ensuring accurate and real-time updating of light direction feature recognition. After obtaining the solar direction angle and the light incidence angle, it is necessary to further convert them into the accurate position coordinates of the sun in three-dimensional space. This process establishes a three-dimensional coordinate model by combining the angle features with the geographical position, time and seasonal parameters. The method is as follows: first, determine the geographical coordinate parameters, including latitude, longitude and local time zone correction value; second, read the current time information (year, month, day, hour, minute), and calculate the relative position of the sun combined with the sun-earth relationship constant. The solar direction angle feature provides the horizontal rotation angle of the sun relative to the ground, while the light incidence angle feature reflects the angle between the sunray and the normal line of the panel surface. By performing spatial geometric transformation on the two features, the relative coordinate position of the sun in three-dimensional space can be calculated. In order to ensure accuracy, an optical encoder with an angle resolution less than 0.1° can be used, and a time correction factor can be introduced to eliminate the slight deviation caused by the earth's rotation and revolution. The obtained three-dimensional coordinates of the sun are represented in the form of a three-dimensional vector, which includes three main parameters: horizontal displacement, vertical height and relative position. In order to improve real-time performance, a rolling update method can be used to recalculate the sun position every 30 seconds, thereby realizing continuous tracking. The coordinate results can be used to determine the spatial position of the sun relative to the panel, providing a basis for dynamic pose adjustment of the panel, so that the panel always maintains the best incident angle state to maximize light energy capture.The method is: inputting three-dimensional coordinate information into the angle control system of the battery panel, and adjusting the pitch angle and rotation angle of the battery panel in real time through a servo motor and an angle adjusting mechanism. The adjustment range is usually set between 0° to 90° in the elevation direction and 0° to 180° in the azimuth direction, and the step precision of adjustment is controlled within 0.1°. In order to ensure smooth action, proportional-integral-derivative (PID) adjustment algorithm is used to correct the motor speed and angle feedback, so that the battery panel movement does not appear oscillation or over-adjustment phenomenon. When it is detected that the angle between the posture of the battery panel and the direction of the sun is less than the set threshold (generally 3° to 5°), the system determines that the positioning is completed and activates the power generation execution state. In the power generation execution state, the battery panel starts the maximum power tracking process, automatically adjusts the working point according to the current and voltage changes, so that the output power reaches the optimal level. When the shadow changes or the light intensity decreases, the pose adjustment is still continuously carried out to ensure continuous improvement of light utilization rate. During the whole process, the angle feedback sensor (such as gyroscope or attitude sensor) monitors the change of the posture of the battery panel in real time, and the feedback information is used to correct the next angle command, so as to realize adaptive dynamic tracking. Through this adjustment process, the battery panel can maintain high-efficiency power generation state under different time periods and different light conditions, so that the energy conversion efficiency in the energy storage mode is significantly improved.

[0022] In the embodiment, the specific steps of dynamically adjusting the posture of the battery panel according to the three-dimensional coordinates of the position of the sun and activating the power generation execution state are: Performing geometric vertical optimization analysis on the battery pack based on the three-dimensional coordinates of the position of the sun to obtain target position parameters; The target position parameters include east-west azimuth angle and up-down elevation angle; Identify the current actual posture parameters of the battery panel; Calculate the attitude deviation value according to the attitude deviation of the target position parameters based on the current actual posture parameters of the battery panel; Perform deviation compensation calculation based on the attitude deviation value to obtain a deviation compensation amount; Drive the servo motor to adjust the dynamic posture of the battery panel according to the deviation compensation amount, and activate the power generation execution state. In this embodiment, after obtaining the three-dimensional coordinates of the sun's position, the geometric posture of the battery pack needs to be vertically optimized to determine the ideal orientation of the solar panel at the current time. The goal of this process is to make the panel surface normal direction as close as possible to the direction of sunlight incidence, so as to achieve perpendicular incidence of light and improve light energy absorption. The method is to establish a geometric model of the battery pack according to the azimuth component, elevation component and light incidence direction vector in the sun position coordinates. The model includes the installation reference angle of the panel, the position of the pitch rotation axis, the rotation limit range and the structure support height, etc. By space vector transformation, the sun incidence direction and the panel normal vector are compared, and the orientation parameters in the ideal state are calculated. To ensure accuracy, the azimuth resolution is generally set within 0.1°, and the elevation resolution is controlled within 0.05°. Then, through an iterative optimization algorithm, the angle difference between the light incidence angle and the panel normal angle is minimized to determine the target position parameters corresponding to the ideal posture of the panel. The target parameters include two core elements: the azimuth angle in the east-west direction and the elevation angle in the up-down direction. Both of them determine the accurate orientation of the panel in three-dimensional space, so that the perpendicular incidence of light is optimized. The final target position parameters will be used as the reference input for subsequent posture deviation calculation and dynamic adjustment, ensuring that the panel always maintains the maximum light receiving state under different sun elevation and azimuth changes. In order to realize the dynamic calibration of the target position, it is necessary to identify the actual posture parameters of the panel, i.e. its current azimuth and elevation information. This process is completed through angle sensing devices and posture detection units installed on the battery pack bracket. The method is to install high-precision angle sensors such as photoelectric encoders or inertial measurement units on the rotation and pitch axes of the panel. The angle sensor is used to measure the rotation displacement of the panel, and the inertial measurement unit calculates the three-dimensional posture by combining accelerometer and gyroscope data. The data sampling frequency is usually set to 10 to 20 Hz to ensure the real-time performance of posture recognition. To prevent measurement drift caused by vibration and environmental interference, the signal is smoothed through low-pass filtering and Kalman fusion algorithm to obtain stable angle output results. The measured posture data includes the current azimuth, elevation and posture change rate parameters, which describe the current spatial posture state of the panel. By calibrating with the structure reference point, the zero consistency and angle correspondence of the posture measurement can be ensured. After identification, the actual posture parameters obtained will be used as feedback signals input to the subsequent deviation calculation stage, to compare with the target position parameters and provide quantitative basis for dynamic adjustment.

[0023] After determining the target position parameters and the current actual attitude parameters, the attitude deviation between the two is calculated to evaluate the angle difference between the panel and the ideal incident direction. This is done by comparing the azimuth and elevation angles in both directions and calculating the angle deviation in the horizontal and vertical directions. To improve recognition accuracy, time synchronization processing is performed on the angle measurement results to ensure consistency in the calculation time. Inertia delay and installation error compensation of the mechanical structure are also considered in the deviation calculation process. For example, if there is a 0.3° mechanical bias in the pitch axis, this value will be corrected in the deviation calculation. The calculated attitude deviation value not only contains the angle difference, but also combines the change rate parameter to form a dynamic deviation vector, which describes the directionality and change trend of the deviation. If the azimuth deviation exceeds 2° or the elevation deviation exceeds 1°, it is judged as a state that needs to be corrected. This deviation value will be used in the next compensation calculation step as the core basis for servo control adjustment. Through real-time monitoring and continuous calculation, the panel can be corrected in time when the light changes or the bracket moves, ensuring that the panel is close to the vertical direction of the light at any time. After obtaining the attitude deviation value, it needs to be converted into a specific compensation control quantity to drive the actuator to adjust the position. The goal of deviation compensation calculation is to achieve a smooth and accurate adjustment process, avoiding light loss caused by over-adjustment or hysteresis. The method is as follows: first, determine the compensation strategy according to the size and change rate of the deviation value. When the deviation is small (less than 1°), use a linear fine-tuning strategy; when the deviation is large (more than 3°), use an accelerated compensation method. To ensure the dynamic stability of compensation, the proportional-integral-derivative adjustment method is usually used, which sets the proportional coefficient, integral coefficient, and derivative coefficient to make the adjustment process both responsive and stable. The compensation output is expressed as an angle change, corresponding to the azimuth and pitch directions. To avoid fluctuations caused by wind or mechanical inertia, the compensation amount also needs to be limited to prevent the angle from changing more than the set threshold (usually 5°) in a single adjustment. At the same time, self-learning parameters are introduced to correct the adjustment error in different time periods, making subsequent compensation more accurate. After this series of calculations, the deviation compensation amount obtained will be directly used to control the movement of the servo motor, achieving automatic adjustment of the panel angle and gradually approaching the ideal state.

[0024] After the completion of the deviation compensation calculation, the panel dynamic position adjustment phase is entered. The goal of this phase is to control the servo motor action according to the compensation amount, so that the azimuth angle and the elevation angle of the panel quickly approach the target position, thereby restoring the vertical state of light. The method is as follows: after the servo motor receives the compensation angle signal, it drives the azimuth axis and the pitch axis to rotate. The rotation speed of the motor is automatically adjusted according to the deviation amount. When the deviation is large, a high rotation speed is used for coarse adjustment, and when the target is approached, the speed is gradually reduced to prevent overshoot. The angle adjustment accuracy is generally controlled within 0.1° to ensure the alignment accuracy of the final attitude. During the motor movement, the attitude sensor continuously feeds back real-time angle information, which is compared with the target value to form a closed-loop control, ensuring that the adjustment process is smooth and accurate. When the panel attitude deviates from the target direction to within the set range (usually below 1°), it is determined that the attitude adjustment is completed, and the power generation execution state is activated. At this time, the panel enters the maximum power tracking working state and automatically optimizes the output power according to the real-time light conditions. If the light direction changes, the attitude detection and adjustment process will restart to achieve continuous dynamic alignment. Through this closed-loop control process, the panel can still maintain high-efficiency power generation state in complex light environment, keeping the energy conversion efficiency at the optimal level.

[0025] In the embodiment, the specific steps of step S3 are: Based on the power generation execution state, the dynamic running conditions of the solar cell module are detected in full parameters to generate a running monitoring log; Based on the running monitoring log, the real-time output voltage of the battery module is calculated; According to the real-time light intensity, the real-time output voltage of the battery module is adjusted in multiple frequencies to detect the power output change information of the battery module; The power output change information is identified for positive feedback characteristics, and a power-voltage dynamic response curve is constructed; According to the power-voltage dynamic response curve, the differential extreme value is calculated to obtain the output power peak point; According to the output power peak point, the duty cycle of the battery module is dynamically adjusted to generate a maximum power tracking strategy.

[0026] In this embodiment, after the solar cell module enters the power generation execution state, the dynamic operation condition needs to be detected for full parameters to ensure the safety and stability of the power generation process and provide data support for subsequent performance analysis. The method is to arrange multiple sets of detection sensor units on the output end and key electrical nodes of the battery module, including voltage sampling module, current sampling module, temperature sensing module and light sensing assembly. These sensor units work synchronously in high-frequency sampling mode, and the sampling interval is generally set between 0.5 to 1 second. The collected operating parameters include real-time output voltage, output current, module surface temperature, terminal temperature rise, light intensity and load change data. After filtering and digitizing all parameters, they are recorded as operation monitoring logs in chronological order. The log content includes time identifier, running state code, power output, environmental parameters and device response information. In order to ensure data integrity and continuity, monitoring data uses segmented cache and redundant recording method to prevent information loss. During the detection process, alarm thresholds are set for temperature changes, current fluctuations and other key indicators to identify abnormal conditions in real time. When the detection data meet the stability conditions, the log is automatically updated and generates a running snapshot that can be used for subsequent analysis. Through this full-parameter detection and recording process, the running state of the battery module can be continuously monitored, and accurate data sources are provided for real-time output voltage calculation and maximum power point tracking. After the operation monitoring log is generated, the voltage-related data in it needs to be analyzed and calculated to obtain the real-time output voltage of the battery module under the current light and load conditions. The method is as follows: first, extract the current, power and load impedance parameters from the log, and use their corresponding relationship to determine the instantaneous voltage change trend. After filtering the voltage sampling signal, the high-frequency components caused by switch action, environmental noise or electrical interference are removed to obtain the stable average voltage value. Then, according to the time series analysis method, the continuous sampling points are fitted and calculated to form the voltage change curve. The voltage change curve can reflect the dynamic response characteristics of the battery module under the influence of temperature and light fluctuation. In order to improve the accuracy, temperature compensation coefficient and light correction coefficient are introduced, when the module temperature rises or the light weakens, the output voltage will be adjusted accordingly. After each calculation result is updated, the system automatically refreshes the real-time voltage value, and the time interval is usually set between 1 to 2 seconds to ensure the real-time of the output data. The final real-time output voltage data is not only used for subsequent power calculation, but also used for diagnosing battery piece attenuation, connection loss and contact resistance abnormalities, etc., to provide real-time feedback basis for stable operation of the battery module.

[0027] The load resistance of the battery module or the duty cycle of the DC / DC conversion unit is adjusted by the control module to periodically change the module operating voltage within a certain range in a multi-frequency manner. The adjustment frequency is generally set between 0.5 Hz and 2 Hz, and the voltage fluctuation amplitude is controlled within ±5% of the rated value to avoid excessive disturbance to cause power loss. After each voltage adjustment is completed, the corresponding output current and power values are immediately recorded to form a set of power change data points. The light intensity sensing component simultaneously monitors the current irradiance level and stores it in correspondence with the power response data for analyzing the impact of light changes on power characteristics. To prevent transient fluctuations from interfering with the measurement results, the output signal is subjected to smoothing filter processing, and only the stable segment data is retained. Through this multi-frequency adjustment process, the power change trend curve of the battery module under different operating voltages can be obtained, laying the foundation for identifying the positive feedback characteristics of power output. This step is equivalent to simulating the working state at different voltage points under real-time conditions to dynamically capture the power change law, ensuring that the subsequent maximum power point analysis is more accurate and reliable. The power output data corresponding to different voltage points are arranged in chronological order to form a functional relationship between power and voltage. By analyzing the response speed and amplitude difference between the power rise and fall stages, the positive feedback and negative feedback regions of power change can be identified. In the positive feedback region, voltage rise will lead to synchronous power increase, showing an increase in energy conversion efficiency; while in the negative feedback region, further voltage rise will cause power to decrease. To accurately distinguish between the two regions, the power data will be subjected to segmented fitting processing, and the power change rate parameter will be calculated. The identified positive feedback feature points are used to determine the best working interval of the battery module. The constructed power-voltage dynamic response curve reflects the energy conversion characteristics of the battery module under the current light, temperature, and load conditions. The shape of the curve can be used to judge the health status and energy utilization efficiency of the module, for example, when the curve peak value is shifted or flattened, it means that the module may have problems such as decay or poor contact.

[0028] The power change trend of the response curve is analyzed by discrete differential analysis to calculate the growth rate of power with voltage change. As the voltage rises, the power initially shows an upward trend, and when the growth rate gradually decreases and approaches zero, it indicates that the maximum power point has been approached. Through continuous data smoothing and differential calculation, the peak position can still be accurately identified in the presence of noise. To ensure stability, a tolerance interval (generally a power change range of ±0.5%) is set near the peak point to prevent false peaks caused by light fluctuations. The final determined peak point includes the corresponding voltage value, power value and output current parameters, which together define the optimal working state of the battery module. This peak point is the core basis for subsequent power tracking strategies and can reflect the highest energy output capability of the solar cell in real-time operating environment. After identifying the power peak point, the duty cycle of the battery module needs to be dynamically adjusted to keep the operating voltage at the optimal state corresponding to the peak value, thereby achieving maximum power tracking. The method is to adjust the duty cycle of the DC / DC conversion module according to the voltage parameter of the peak point to lock the battery module output voltage near the maximum power point. During the adjustment process, an adaptive step-by-step method is used, and the step size is automatically corrected according to the power change trend. When the maximum power point shifts due to changes in light or temperature, the duty cycle will be fine-tuned based on real-time monitoring results to keep the battery module running in the optimal energy conversion interval. To avoid output fluctuations caused by frequent adjustments, a dynamic smoothing factor is set to make the adjustment process both sensitive and stable. The final generated maximum power tracking strategy not only includes the duty cycle adjustment rule, but also includes compensation logic under conditions of light, temperature and load changes, which can achieve continuous adaptive power optimization. This strategy ensures that the battery module can output power close to the theoretical maximum value under different weather and time conditions, increasing energy utilization by 10% to 20% and significantly improving overall power generation performance and stability.

[0029] In this embodiment, the specific steps of step S4 are: According to the solar illumination state information, time window division is performed to extract illumination information of multiple time windows; The illumination intensity change rate and acceleration of adjacent time windows of the illumination information are calculated to construct an illumination dynamic change curve; real-time time information is extracted; According to the real-time time information, a multi-step prediction of solar intensity change is performed on the illumination dynamic change curve to obtain an illumination prediction curve; Based on the illumination prediction curve, a feedforward pre-adjustment processing is performed on the maximum power tracking strategy to construct an intelligent pre-adjustment control strategy; A conventional steady-state shutdown scheme and an abnormal shutdown scheme are constructed; and the intelligent pre-adjustment control strategy is combined to perform automatic solar cell panel control.

[0030] In this embodiment, the continuously collected illumination data is processed in time sequence, and each time period is defined as a time window. The window length can be flexibly set according to the illumination change rate, generally 30 seconds to 2 minutes, in order to balance the time resolution and calculation stability. A certain overlap ratio (about 20% to 30%) is allowed between adjacent windows to maintain data continuity. Key parameters such as average illumination intensity, maximum value, minimum value, fluctuation amplitude, standard deviation, etc. are extracted in each time window to form an illumination feature set. In order to reduce the interference of mutations, the illumination signal needs to be processed by sliding average or low-pass filtering to remove high-frequency noise caused by cloud cover or reflection. The illumination information extracted in different time windows not only reflects the short-term fluctuation trend, but also reveals the rhythmic characteristics of the illumination change. Through such time division, the originally complex and continuous illumination data can be converted into multiple relatively independent time period units, laying a foundation for subsequent illumination dynamic modeling and change rate analysis. The average illumination intensity of each adjacent time window is compared, the change amplitude of the illumination intensity per unit time is calculated, and the change amount of the change rate, i.e. the illumination acceleration, is further calculated. The change rate reflects the illumination increase or decrease trend, and the acceleration reflects the intensity of the illumination fluctuation. In order to enhance the calculation accuracy, the time weighted moving average method is used to smooth the rate sequence, reducing the influence of short-term illumination mutations on the overall trend. Subsequently, the light intensity dynamic change curve is drawn according to the continuous rate and acceleration data, making the real-time change characteristics of the illumination intuitively present in the form of time sequence. The rising section of the curve represents the illumination enhancement stage, and the falling section represents the illumination weakening or shading stage. At the same time, the current real-time time information is extracted from the time synchronization module, including the hour, minute, second and date parameters, and matched with the light intensity change curve. This time information provides a time reference for the subsequent prediction model, to associate the illumination change trend with the sunlight cycle rule. Through this step, the change speed and trend direction of the illumination intensity can be clearly grasped, providing accurate dynamic input for light intensity prediction and power generation strategy pre-adjustment.

[0031] The light change curve is taken as an input sequence, combined with time parameters and periodic rules to establish a time-dependent prediction model. Multi-step prediction uses a hierarchical sliding time window method to generate short-term, medium-term and long-term light intensity prediction results according to different prediction time intervals (such as 30 seconds, 1 minute, 3 minutes). A weighting coefficient is introduced during the prediction process, with the recent light change trend as the main reference and the long-term data as the auxiliary correction term, so as to balance the response speed and prediction stability. Through this multi-step calculation, the change trend curve of future light intensity in different time periods can be obtained. The light intensity prediction curve usually presents a smooth change trajectory, with the peak representing the expected strong light period and the trough representing the possible shading or attenuation stage. In order to ensure the accuracy of the prediction, a seasonal correction coefficient and a geographical latitude parameter are also introduced to compensate for the change of the solar elevation angle and the sunshine duration. The final light intensity prediction curve not only reflects the short-term light change, but also can be used to identify the light fluctuation trend in advance, providing predictive data support for power regulation and achieving active response to future light conditions. The rising or falling trend of the future time period in the light intensity prediction curve is analyzed, and the control parameters are adjusted in advance according to the trend change. If the predicted light intensity increases, the duty cycle upper limit of the battery module is reduced in advance to avoid excessive energy accumulation leading to rapid voltage rise; if the predicted light intensity decreases, the duty cycle is increased to keep the battery panel at a higher voltage, improving the weak light utilization efficiency. To achieve dynamic response, the pre-adjustment strategy introduces a time advance factor, so that the control signal is adjusted about 5 to 10 seconds before the light change occurs. The pre-adjustment amplitude is set according to the light intensity change rate, the greater the change rate, the higher the pre-adjustment effort. The intelligent pre-adjustment control strategy also considers temperature, load and voltage fluctuation and other auxiliary parameters, and through multi-variable correlation correction, the strategy is more adaptable to different environmental conditions. This method is equivalent to adding a "forward-looking adjustment layer" on the basis of traditional maximum power tracking, which can optimize the control path in advance before the light fluctuates sharply, thereby reducing power fluctuations and improving system response speed and power generation stability. The final intelligent pre-adjustment control strategy can achieve active control of the power generation process, keeping the power output in the optimal state at all times.

[0032] According to the battery panel operating state, light prediction results and power output level, two types of shutdown schemes are formulated. The conventional steady-state shutdown scheme is used for automatic shutdown when the light gradually decays and the power output is continuously lower than the set threshold. This scheme avoids frequent switching caused by short-term light fluctuations through a delay judgment method. When the power continues to be lower than the threshold for a certain time (for example, 5 minutes), the system slowly reduces the duty cycle and disconnects the energy storage loop to achieve smooth shutdown. The abnormal shutdown scheme is used for emergency disconnection when abnormal conditions such as high temperature, voltage surge, light mutation or structural vibration are detected. This scheme has a fast response channel that can cut off the output loop within milliseconds to prevent equipment damage. Both schemes are combined with intelligent pre-adjustment control strategies to achieve adaptive linkage between operation and shutdown. When the light re-increases or the external conditions return to normal, the system can automatically restart and return to the optimal power generation mode. Through the coordinated design of conventional shutdown and abnormal shutdown, solar cell panels can have both intelligent response capability and safety protection characteristics in automated operation, thereby achieving efficient energy regulation in all time periods, adaptively and unattended.

[0033] In this embodiment, the conventional steady-state shutdown scheme is specifically; Based on the operation monitoring log, the real-time energy storage amount of the battery module is calculated; According to the real-time energy storage amount of the battery module, the precise idle energy storage calculation is performed to obtain the energy storage surplus percentage; The charging power and energy storage amount change correlation analysis is performed on the operation monitoring log to obtain the real-time charging rate; Based on the real-time charging rate, the required remaining charging time prediction is performed on the energy storage surplus percentage to obtain the prediction result; According to the prediction result, the power reduction analysis is performed in stages, and the duty cycle is adjusted step by step to build the conventional steady-state shutdown scheme.

[0034] In this embodiment, according to the voltage, current, and other parameters recorded in the operation monitoring log, the stored energy is calculated in real time through the battery module's charge and discharge characteristics model (such as the functional relationship between open-circuit voltage, battery internal resistance, and temperature, etc.). Specifically, the battery's energy storage can be estimated by the following steps: First, obtain the power output value from the real-time voltage and current data, and then calculate the energy storage level of the battery at the current time through integration. For example, the energy change in each time step can be obtained by multiplying the power value (i.e., the product of voltage and current) by the time interval, and the result is the change value of real-time energy storage. This process needs to consider the charging and discharging efficiency of the battery, which is usually higher during charging than during discharging, so the calculation of energy storage needs to be corrected according to the charging and discharging characteristics of the battery. Through this method, the real-time energy storage value of the battery module at each time can be obtained, forming a time-varying energy storage state data. First, calculate the maximum energy storage value according to the rated energy storage capacity of the battery module (such as the rated voltage and maximum storable energy). On this basis, the real-time energy storage and the maximum energy storage value are calculated by ratio, and the energy storage remaining percentage is obtained. For example, if the current energy storage is 200Wh and the maximum energy storage capacity is 1000Wh, then the energy storage remaining percentage is 20%. The calculation of energy storage remaining percentage needs to be corrected under different working conditions to adapt to the influence of environmental temperature, load fluctuation, and battery aging, etc. In addition, to ensure the accuracy of the calculation results, the energy storage remaining also needs to consider the loss under different loads and charging strategies, for example, the battery may have a certain efficiency decline during charging, which needs to be reflected in the percentage value of energy storage remaining. Through real-time monitoring and regular correction, accurate energy storage remaining percentage can be obtained, which provides basic data support for subsequent charging strategy adjustment.

[0035] The charging power (i.e., the product of the battery current and voltage) and the change in stored energy data are extracted from the operation monitoring log and paired. According to the relationship between the change in stored energy and the charging power in a continuous time interval, the charging rate in each time interval is calculated. For example, if the stored energy increases by ΔE (Wh) in a time interval Δt, and the average charging power during this period is P (W), the charging rate reflects the speed of battery charging and directly affects the efficiency during the storage process. To enhance accuracy, the charging rate at different current levels needs to be statistically analyzed and corrected, especially the nonlinear change of current during battery charging, which needs to be dynamically corrected through experimental parameters. Further, the charging rate of the battery is not only affected by the current charging power, but also related to the internal resistance, temperature, and remaining storage capacity of the battery, so the interaction of these factors needs to be considered when analyzing. Finally, through the calculation of the charging rate, key data support can be provided for subsequent charging time prediction and power regulation. According to the remaining charging time and the current charging state, different power reduction strategies are designed. When the battery's stored energy approaches full charge, the power output is gradually reduced in stages to avoid sudden load fluctuations. During the reduction process, the duty cycle of the battery charging power (i.e., the proportion of current during the battery charging and discharging process) is adjusted to gradually reduce the power output. For example, when the remaining charging time is more than 80%, the duty cycle is reduced from the current 90% to 60%; when the charging time is 50%, the duty cycle is further reduced to 40%; when the charging is close to completion, the duty cycle is finally reduced to less than 10%, entering the standby or stop charging state. This reduction strategy can smoothly transition through multiple stages, avoiding device damage or energy waste caused by sudden power changes. At the same time, adjustments need to be made according to the battery's charging efficiency curve, temperature rise during charging, and battery health status to ensure optimal matching of charging power and battery load during charging. Through this phased power reduction analysis and duty cycle adjustment scheme, the battery module can always be in an optimal working state during charging and smoothly transition to a shutdown or standby state after charging is complete, building a regular steady-state shutdown scheme.

[0036] In this embodiment, the specific steps of the intelligent pre-adjustment control strategy for automatic solar cell panel control are as follows: Setting abnormal shutdown conditions, and building an abnormal shutdown scheme based on the abnormal shutdown conditions; According to the abnormal shutdown scheme and the regular steady-state shutdown scheme, a multi-condition logical fusion is performed to build an intelligent shutdown strategy; Automatically control the solar cell panel through the intelligent shutdown strategy and the intelligent pre-adjustment control strategy.

[0037] In this embodiment, first determine the abnormal factors that may affect the normal operation of the battery panel. Common abnormal factors include: over-temperature protection, voltage too high or too low, abnormal charging current, excessive load fluctuation, excessive vibration, mechanical failure or external interference, etc. Each abnormal factor needs to set specific threshold or judgment condition. For example, when the surface temperature of the battery panel exceeds the set safety threshold (such as 65°C), or the output voltage of the battery module exceeds the maximum working voltage (such as 70V), these may cause the battery panel to be damaged, so the abnormal closing mechanism should be triggered. In order to achieve accurate abnormal closing, first set the trigger threshold of each abnormal condition according to the working characteristics of the battery panel and the battery module. For example, if the battery module current exceeds the maximum safety current (such as 10A), or the voltage drops to a certain lower limit (such as 36V), the system should immediately determine it as an abnormality. For over-temperature conditions, dynamic thresholds may also need to be set in combination with environmental temperature and battery panel working load. For mechanical failure, real-time vibration can be monitored using an acceleration sensor, and if the vibration frequency exceeds the set value, it may indicate that some internal components are loose or damaged. All these conditions are designed by parameterization and combined with real-time monitoring data to build an abnormal closing scheme, so that the battery panel can quickly respond when an accident occurs and avoid further damage.

[0038] In practical applications, the closing of the battery panel is not only affected by a single factor, but also determined by multiple factors. Therefore, it is crucial to integrate multi-condition logic to build an intelligent closing strategy based on the abnormal closing scheme and the conventional steady-state closing scheme. The approach is as follows: first, combine the abnormal closing conditions with the conventional steady-state closing conditions to set a set of hierarchical judgment logic. For example, under normal circumstances, the battery panel can smoothly shut down through the conventional steady-state closing scheme; while in abnormal situations, the abnormal closing scheme is triggered first, and the priority of the closing strategy is adjusted according to the severity of each type of abnormality.

[0039] The specific logic can comprehensively evaluate multiple conditions based on real-time data: for example, when the battery module voltage approaches the maximum voltage threshold, even if there is no temperature anomaly, the abnormal closing should still be started; if the temperature exceeds the set safety value, the temperature control closing should be executed first, even if the charging current and voltage are within the normal range. For more complex abnormal conditions, such as abnormal vibration monitoring data, fault closing may be triggered immediately, rather than waiting for temperature or current anomalies. This multi-condition logic fusion strategy can be divided into multiple priority levels, and each priority level adjusts the strategy according to the actual situation. For example, during charging, if the battery panel charging rate is too high and the battery temperature is also rising, the "temperature priority" closing should be executed first, and when the current exceeds the set range, the "current priority" closing should be executed. In this logic, the abnormal closing condition is used as an emergency response measure to ensure timely protection of the battery panel, and the conventional closing is used as a "safety escape" in daily operation.

[0040] On the basis of multi-condition logic fusion, the combination of intelligent shutdown strategy and intelligent pre-adjustment control strategy can realize the autonomous and intelligent regulation of solar panels in dynamic environment. The method is as follows: during the operation of the panel, the intelligent pre-adjustment control strategy optimizes the charging and discharging behavior of the panel in advance according to the real-time light intensity and power prediction, ensuring the maximum efficiency of power conversion and storage; and the intelligent shutdown strategy responds in time to the changes in external conditions (such as temperature, voltage, current, vibration, etc.) during operation, and automatically switches to the most appropriate shutdown mode when abnormal conditions or near shutdown conditions occur, ensuring the safety of the panel.

[0041] When the light intensity decreases sharply, the intelligent pre-adjustment control strategy will reduce the charging power in advance to avoid energy overload; and when the battery energy storage capacity approaches the maximum, the intelligent shutdown strategy will automatically start the shutdown mechanism to close the charging channel. Conversely, in the case of increased light or decreased power output, the intelligent pre-adjustment control strategy will adjust the charging power to ensure that the battery always maintains the best charging state.

[0042] The triggering of the intelligent shutdown strategy is often based on the dynamic input of the intelligent pre-adjustment control strategy, and this control interaction can flexibly adjust according to the immediate state of the panel. When over-temperature, over-voltage, over-current and other faults occur, the intelligent shutdown strategy can execute the most appropriate shutdown program first, such as quickly disconnecting the energy storage circuit or distributing the load; and in the normal working state, it can adjust the charging power, energy storage ratio, etc. of the panel to avoid efficiency loss caused by sharp fluctuations in light or unstable load.

[0043] In this embodiment, the specific steps for setting the abnormal shutdown condition and constructing an abnormal shutdown scheme based on the abnormal shutdown condition are as follows: The abnormal shutdown condition includes that the real-time light intensity is lower than the light power generation threshold and the wind speed exceeds the safety threshold. When the real-time light intensity is lower than the light power generation threshold, the light insufficient shutdown condition is triggered, and the solar panel is closed; Based on the wind speed sensor to collect the environmental wind speed; Define 15m / s as the safety threshold; When the environmental wind speed exceeds the safety threshold, the severe weather shutdown condition is triggered, and the solar panel is immediately closed.

[0044] In this embodiment, when the intelligent control of the solar panel is needed, the abnormal closing condition when the light intensity is lower than a certain specific value is first set. Generally, the power generation capacity of the solar panel is closely related to the light intensity. When the light intensity is lower than the set light power generation threshold, the power generation efficiency of the battery panel is obviously decreased, and it can not provide enough power, and even can cause the battery panel to enter the low efficiency working state due to insufficient light, and increase the energy waste. In the control logic of the battery panel, a light power generation threshold is set, for example, the light intensity lower than 300 W / m² is set as the light power generation threshold. The light intensity is monitored in real time. If the real-time light intensity detected by the light sensor is lower than the threshold, the system will judge that the current cannot effectively generate power, at this time the "insufficient light closing condition" is triggered, and the power generation process of the battery panel is automatically stopped. In order to ensure the accuracy of the triggering condition, the collection of light intensity needs to consider the variables such as the angle of the sun and the weather conditions, to ensure the accuracy and flexibility.

[0045] To avoid false triggering, a time window can be set. For example, if the light intensity is below the threshold for more than 5 minutes, it is determined that there is insufficient light, and the closing process is triggered. If it is a short-term light change, the closing operation can not be triggered, but the low-power mode is maintained, and the power generation state is automatically restored after the light intensity recovers. This process can achieve accurate insufficient light judgment through real-time acquisition of light sensor data and dynamic change algorithm, and effectively close the battery panel. Through the real-time monitoring of environmental wind speed by the wind speed sensor, a safety threshold is set. According to experience and experimental data, 15 m / s can be defined as the safety threshold of wind speed. When the real-time measured wind speed exceeds this threshold, the system will trigger the "severe weather closing condition", that is, automatically close the battery panel to prevent excessive wind from causing structural damage to the battery panel or equipment failure. The triggering of this condition requires ensuring the data acquisition accuracy of the wind speed sensor and the accuracy of the real-time wind speed information. In order to avoid the instantaneous fluctuation of wind speed data leading to false triggering, the system should process the wind speed data through time-weighted average. For example, if the wind speed exceeds 15 m / s for a period of time, the closing program is triggered, rather than simply relying on instantaneous data. This way effectively reduces the possibility of false operation. In actual operation, the collection frequency of the wind speed sensor needs to be high enough, for example, measuring once every second, to ensure that rapid changes in wind speed can be captured. When the wind speed exceeds the set threshold, the battery panel will immediately stop generating electricity and enter the protection mode to avoid damage to the equipment in severe weather conditions. By comparing the real-time light intensity with the preset threshold, if the light intensity is continuously below the threshold, the system stops generating electricity by controlling the current output of the battery panel. Specifically, in the control logic of the battery panel, a detection module is embedded, which periodically reads sensor data. If the value is below the set threshold for several consecutive times (such as reading 5 times every 30 seconds), the system will determine that there is insufficient light and trigger the closing command. In order to further optimize the response time, the control logic can also dynamically adjust according to the light change trend, for example, in periods of rapid light intensity change, the determination time window can be appropriately extended to reduce the risk of false triggering. When executing the closing operation, the charging and discharging circuits of the battery panel are automatically disconnected, and the battery panel enters standby state. When the light intensity recovers to above the threshold, the system will re-detect the light value and resume normal work. This process is realized through the cooperation of data acquisition and logic control module, ensuring that the battery panel can automatically shut down when working inefficiently, avoiding energy waste. When the environmental wind speed exceeds the set safety threshold (15 m / s), the system should immediately start the "severe weather closing condition" to close the battery panel to ensure equipment safety. The triggering of this condition requires the cooperation of the wind speed sensor and the control module. The wind speed sensor should be located in an open position in the area where the battery panel is located, and should have high precision and anti-interference ability to accurately measure the wind speed.

[0046] The wind speed sensor collects wind speed data in real time and transmits the data to the control module. The control module continuously compares the real-time wind speed with the set critical value (15 m / s). If the wind speed exceeds this value, the control module will immediately issue a shutdown command and start the protection mechanism of the solar panel. To avoid false triggering due to instantaneous fluctuations in wind speed, the wind speed data will be filtered and averaged over a time window. For example, if the average wind speed exceeds 15 m / s continuously for the past 10 seconds, the shutdown operation is triggered. In addition, the sampling frequency of wind speed data also needs to meet the real-time monitoring requirements. For example, the wind speed sensor collects data once every second, and when the wind speed reaches the critical value, the control module should respond quickly and trigger the protection measures to ensure that the solar panel is closed in time to avoid being affected by the wind. This shutdown mechanism when the wind speed exceeds the safety threshold can effectively prevent physical damage to the solar panel caused by high wind speed, such as panel displacement, falling or mechanical structure damage, thereby improving the service life and safety of the solar panel. Through the abnormal shutdown conditions of light intensity and wind speed, the safety of the solar panel is guaranteed. In actual operation, in addition to the independent insufficient light and excessive wind speed shutdown mechanism, the priority of these two conditions needs to be sorted. For example, when the light intensity is insufficient, first determine whether it is caused by cloud cover or other temporary factors, and if the light does not recover within a short period of time, the shutdown is executed; in the case of high wind speed, the shutdown operation should be performed immediately, because strong wind has greater physical damage to the equipment. In view of complex climate changes and uncertainties, the control system of the solar panel should have adaptive ability, which can dynamically adjust the shutdown strategy according to different environmental conditions. For example, in the case of high wind speed, even if the light intensity does not reach the threshold value, the shutdown can still be quickly responded to prevent damage to the equipment caused by wind. By considering the insufficient light and adverse weather conditions, intelligent control can protect the solar panel equipment in real time and accurately, providing the best safety protection under different environmental conditions. The implementation of these control strategies not only ensures the efficient operation of the solar panel, but also greatly reduces the risk of equipment failure and damage caused by environmental factors.

[0047] In the present embodiment, an intelligent control device for a solar panel is provided for performing the intelligent control method for a solar panel as described above, comprising: A light threshold calculation unit is configured to collect solar light state information in real time based on a light sensing array, and perform threshold judgment based on the solar light state information to activate the solar panel and enter an energy storage working mode. A pose adjustment unit is configured to perform dynamic pose adjustment of the solar panel based on the energy storage working mode to activate a power generation execution state. A duty cycle adjustment unit is configured to perform dynamic adjustment of the duty cycle of the battery module based on the power generation execution state to generate a maximum power tracking strategy. Pre-adjusting control unit is used for feedforward pre-adjusting processing based on maximum power tracking strategy, and automatic solar cell panel control is performed.

[0048] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the application being defined by the appended claims and not by the above description, and all changes falling within the meaning and range of equivalency of the elements of the patent file are therefore intended to be embraced within the present application.

[0049] The foregoing merely illustrates the principles of the application and applies only to the particular cases described and illustrated herein. Numerous modifications in the embodiments described herein will be readily apparent to those skilled in the art, and the principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Accordingly, the application is not to be restricted based on the specific RTM illustrated and described, but is instead to be accorded the full scope consistent with the claims, the principles and scope of equivalence, and the latitude of modifications as defined within the specification and claims.

Claims

1. A smart control method for solar panels, characterized in that, Includes the following steps: Step S1: Real-time acquisition of sunlight status information based on the light sensor array; Based on the solar illumination status information, a threshold judgment is made to activate the solar panel and enter the energy storage working mode. Step S2: Based on the energy storage working mode, dynamically adjust the position and orientation of the battery panel to activate the power generation execution state; Step S3: Dynamically adjust the duty cycle of the battery module based on the power generation execution status to generate a maximum power point tracking strategy; Step S4: Perform feedforward pre-adjustment processing based on the maximum power point tracking strategy to achieve automated solar panel control.

2. The intelligent control method for solar panels according to claim 1, characterized in that, The specific steps of step S1 are as follows: Real-time acquisition of solar illumination status information based on a light sensor array; The solar illumination status information is subjected to sensing error calculation and adaptive filtering to extract a standardized illumination information sequence; Calculate the illumination intensity from the standardized illumination information sequence to generate real-time illumination intensity; Periodic fluctuation analysis of real-time light intensity is performed to generate light intensity fluctuation characteristics; The effective illuminance value is obtained by calculating the stable illuminance within a period based on the characteristics of illuminance fluctuation. The effective light intensity value is identified by benchmarking based on a preset energy storage start-up threshold. When the effective light intensity value is not less than the preset energy storage start-up threshold, the solar panel is activated and the energy storage working mode is entered.

3. The intelligent control method for solar panels according to claim 1, characterized in that, The specific steps of step S2 are as follows: Based on the energy storage working mode, the solar illumination state information is decomposed into three-dimensional spatial vectors to obtain the solar direction angle features and the illumination incident angle features. The real-time three-dimensional coordinates of the sun's position are calculated based on the characteristics of the sun's direction angle and the characteristics of the incident angle of sunlight. The solar panels are dynamically positioned and adjusted according to the three-dimensional coordinates of the sun's position to activate the power generation process.

4. The intelligent control method for solar panels according to claim 3, characterized in that, The specific steps for dynamically adjusting the pose of the solar panel based on the three-dimensional coordinates of the sun's position to activate the power generation execution state are as follows: Based on the three-dimensional coordinates of the sun's position, a geometric vertical optimization analysis of the battery pack is performed to obtain the target position parameters; The target position parameters include the east-west azimuth angle and the vertical elevation angle; Identify the current actual attitude parameters of the solar panel; The attitude deviation is calculated based on the current actual attitude parameters of the solar panel to obtain the attitude deviation value; The deviation compensation amount is obtained by calculating the deviation compensation based on the attitude deviation value. The servo motor is driven by the deviation compensation amount to dynamically adjust the position of the solar panel and activate the power generation execution state.

5. The intelligent control method for solar panels according to claim 1, characterized in that, Step S3 is as follows: Based on the power generation execution status, perform full parameter detection on the dynamic operating conditions of the solar cell module and generate an operation monitoring log; Calculate the real-time output voltage of the battery module based on the operation monitoring log; The real-time output voltage of the battery module is adjusted at multiple frequencies based on the real-time light intensity, and the power output change information of the battery module is detected. The positive feedback characteristics of the power output change information are identified to construct a power-voltage dynamic response curve; The peak output power point is obtained by calculating the differential extrema based on the power-voltage dynamic response curve; The battery module duty cycle is dynamically adjusted based on the peak output power to generate a maximum power point tracking strategy.

6. The intelligent control method for solar panels according to claim 1, characterized in that, The specific steps of step S4 are as follows: Based on the solar illumination status information, a time-series window is divided, and illumination information from multiple time windows is extracted. Calculate the rate of change and acceleration of light intensity in adjacent time windows of the illumination information, construct a dynamic curve of light intensity change, and extract real-time time information; Based on the real-time information, the solar intensity dynamic change curve is predicted in multiple steps to obtain the solar intensity prediction curve. Based on the light intensity prediction curve, the maximum power point tracking strategy is fed forward and pre-tuned to construct an intelligent pre-tuning control strategy. Construct routine steady-state shutdown and abnormal shutdown schemes; combine intelligent pre-adjustment control strategies to achieve automated control of solar panels.

7. The intelligent control method for solar panels according to claim 6, characterized in that, The conventional steady-state shutdown scheme is specifically as follows: Calculate the real-time energy storage of the battery module based on the operation monitoring logs; Based on the real-time energy storage of the battery module, a precise idle energy storage calculation is performed to obtain the percentage of remaining energy storage. A correlation analysis of charging power and energy storage changes was performed on the operation monitoring logs to obtain the real-time charging rate; Based on the real-time charging rate, the required remaining charging time is predicted according to the percentage of energy storage capacity, and the prediction results are obtained. Based on the prediction results, a phased power reduction analysis was conducted, and the duty cycle was gradually adjusted to construct a normalized steady-state shutdown scheme.

8. The intelligent control method for solar panels according to claim 6, characterized in that, The specific steps for automated solar panel control using a smart pre-adjustment control strategy are as follows: Set abnormal shutdown conditions, and construct an abnormal shutdown scheme based on the abnormal shutdown conditions; A smart shutdown strategy is constructed by fusing multiple conditions based on abnormal shutdown schemes and normal steady-state shutdown schemes. Automated control of solar panels is achieved through intelligent shutdown and intelligent pre-adjustment control strategies.

9. The intelligent control method for solar panels according to claim 1, characterized in that, The specific steps for setting abnormal shutdown conditions and constructing an abnormal shutdown scheme based on these conditions are as follows: The abnormal shutdown conditions include real-time light intensity being lower than the photovoltaic power generation threshold and ambient wind speed exceeding the safety threshold. When the real-time light intensity is lower than the solar power generation threshold, the insufficient light shutdown condition is triggered, and the solar panels are turned off. Based on wind speed sensor to collect ambient wind speed; A wind speed of 15 m / s is defined as the safety threshold. When the ambient wind speed exceeds the safety threshold, the severe weather shutdown condition is triggered, and the solar panels are immediately shut down.

10. A smart control device for solar panels, characterized in that, The method for performing the intelligent control method for solar panels as described in claim 1 includes: The illumination threshold calculation unit is used to collect real-time solar illumination status information based on the illumination sensor array; and to perform threshold judgment based on the solar illumination status information to activate the solar panel and enter the energy storage working mode. The posture adjustment unit is used to dynamically adjust the posture of the battery panel based on the energy storage working mode and activate the power generation execution state. The duty cycle adjustment unit is used to dynamically adjust the duty cycle of the battery module based on the power generation execution status and generate a maximum power point tracking strategy. The pre-adjustment control unit is used for feedforward pre-adjustment processing based on the maximum power point tracking strategy to automate the control of solar panels.

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