Cooling control method, device and equipment of photovoltaic module and storage medium

Abstract

The invention relates to a cooling control method for a photovoltaic module and related equipment, and the method comprises the following steps: collecting the real-time temperature data of the photovoltaic module, and carrying out the classified statistics, and obtaining the temperature data of a partition; calling a preset safe temperature threshold value, calculating a temperature deviation value of each subarea, and determining an overtemperature subarea; calculating an overtemperature coverage proportion according to the overtemperature partition, and performing product operation on the overtemperature coverage proportion and a temperature deviation value to obtain a cooling demand level; a preset heat dissipation strategy library is matched according to the grade, and a corresponding cooling mode combination is obtained; corresponding cooling equipment is driven to operate through the combination, the temperature of the module is continuously regulated and controlled, the temperature deviation is maintained within a safety threshold range, and the technical problem that under the high-temperature environment or the long-time strong light condition, the surface temperature of the photovoltaic module can be remarkably increased, and consequently the photoelectric conversion efficiency is reduced is solved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic module technology, and in particular to a method, apparatus, equipment and storage medium for cooling control of photovoltaic modules. Background Technology

[0002] As the global energy structure accelerates its transformation towards cleaner and lower-carbon energy, photovoltaic (PV) power generation, as a crucial component of renewable energy, has seen its installed capacity grow rapidly. However, in actual operation, the power generation efficiency of PV modules is closely related to their operating temperature; typically, for every 1°C increase in temperature, the output power decreases by 0.3% to 0.5%. Under high-temperature environments or prolonged exposure to strong sunlight, the surface temperature of PV modules can rise significantly, leading not only to reduced photoelectric conversion efficiency but also accelerated material aging, affecting module lifespan and system reliability. Therefore, effectively controlling the operating temperature of PV modules has become a critical issue for improving the overall performance and economic efficiency of PV power generation systems. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method for cooling control of photovoltaic modules, comprising the following steps: Real-time temperature data of the photovoltaic module is collected, and the real-time temperature data is classified and statistically analyzed to obtain zone temperature data; A preset safe temperature threshold is retrieved, and the temperature data of each zone is calculated to obtain the temperature deviation value. Based on the temperature deviation value, the over-temperature zone of the photovoltaic module is determined. The over-temperature coverage ratio in the photovoltaic module is determined by the over-temperature zoning, and the cooling requirement level is obtained by multiplying the over-temperature coverage ratio with the temperature deviation value. Based on the cooling demand level, a preset heat dissipation strategy library is matched to obtain the corresponding cooling method combination, and the corresponding cooling equipment is driven to continuously cool the photovoltaic module based on the cooling method combination, so as to keep the temperature deviation within the safe temperature threshold range.

[0004] Furthermore, real-time temperature data of the photovoltaic module is collected, and the real-time temperature data is classified and statistically analyzed to obtain zoned temperature data, including: The surface of the photovoltaic module is divided into grid sampling points, and temperature sensors are installed based on the grid sampling points to obtain a sensor array; The photovoltaic module is scanned and temperature data is acquired based on the sensor array to obtain real-time temperature data. The real-time temperature data is then subjected to mean filtering to obtain an effective temperature value. Based on the effective temperature value, temperature mapping is performed on various parts of the photovoltaic module to obtain a temperature distribution map. The temperature distribution map is then divided into regions according to the cell array, backsheet, and frame of the photovoltaic module to obtain zoned temperature data.

[0005] Furthermore, the step of retrieving a preset safe temperature threshold and calculating the difference between the temperature data of each zone to obtain a temperature deviation value includes: Retrieve preset safety temperature thresholds, which include battery cell array safety thresholds, backplane safety thresholds, and frame safety thresholds, and each safety threshold corresponds to a different partition type identifier; The partition temperature data is type-identified to obtain partition type information, and the partition type information is matched with the partition type identifier to obtain the target threshold corresponding to each partition; The temperature difference between the partitioned temperature data and the corresponding target threshold is calculated to obtain the partitioned temperature difference value. The partitioned temperature difference value is then grouped according to the partition type to obtain temperature deviation values ​​including the battery cell temperature difference group, the back panel temperature difference group, and the frame temperature difference group.

[0006] Furthermore, determining the over-temperature zone of the photovoltaic module based on the temperature deviation value includes: Based on the temperature deviation value, grid block statistics are performed to obtain a temperature difference distribution table. The values ​​in the temperature difference distribution table are then cumulatively sorted to obtain a positive or negative temperature difference sequence. The positive or negative temperature difference sequence includes temperature data in each partition that exceeds the safe temperature threshold and temperature data in each partition that is below the safe temperature threshold. Based on the positive temperature difference sequence, surface hotspots of the photovoltaic module are marked to obtain a hotspot distribution map, and boundary detection is performed on the hotspot distribution map to obtain the outline of abnormal areas. The surface of the photovoltaic module is divided into zones based on the outline of the abnormal region to obtain the over-temperature zone.

[0007] Furthermore, determining the over-temperature coverage ratio in the photovoltaic module through the over-temperature zoning includes: The boundaries of the overheated zones are delineated to obtain an overheated region outline map, and the area of ​​the overheated region outline map is calculated to obtain the actual area value of each overheated zone. Based on the actual area value, the proportion of the overall area of ​​the photovoltaic module is calculated to obtain the area proportion data of each over-temperature zone, and the area proportion data is integrated to obtain the initial value of the total proportion of the over-temperature zone. The initial value of the total proportion of the overheated area is checked for overlap between adjacent zones. If there is overlap between adjacent overheated zones, the area of ​​the overlapping part is deducted and the proportion is recalculated to obtain the corrected overheated coverage ratio.

[0008] Furthermore, based on the cooling requirement level and a preset heat dissipation strategy library, a corresponding cooling method combination is obtained, including: Based on the cooling requirement level, query the corresponding candidate strategy in the heat dissipation strategy library; The heat dissipation efficiency of the candidate strategies is calculated to obtain an efficiency data table, and energy consumption statistics are performed on each strategy based on the efficiency data table to obtain energy consumption indicators. Based on the energy consumption index, the candidate strategies are prioritized to obtain a strategy ranking table. The top N strategies in the strategy ranking table are then combined and matched to obtain a cooling method combination of the corresponding level, where N is a preset threshold.

[0009] Furthermore, based on the aforementioned cooling method combination, the corresponding cooling equipment is driven to continuously cool the photovoltaic modules to ensure that the temperature deviation remains within the safe temperature threshold range, including: The cooling equipment is matched for each cooling method in the cooling method combination to obtain a cooling equipment list, and the location of the overheating zone is associated based on the cooling equipment in the cooling equipment list to obtain an equipment-zone correspondence table. Based on the cooling demand level, parameters are set for each cooling device in the device-zone correspondence table to obtain device control parameters. Then, the linkage timing of each device control parameter is checked to obtain a linkage control instruction set. The corresponding cooling equipment is driven to operate by the linkage control instruction set, and temperature change data of the overheating zone is collected in real time during operation. The temperature change data is compared with the safe temperature threshold to adjust the equipment control parameters and continuously drive the corresponding cooling equipment to operate until the temperature deviation of the overheating zone is within the safe temperature threshold range.

[0010] The present invention also provides a cooling control device for photovoltaic modules, comprising: The acquisition module is used to acquire real-time temperature data of the photovoltaic module and classify and statistically analyze the real-time temperature data to obtain zone temperature data. The calculation module is used to retrieve a preset safe temperature threshold, perform difference calculation on the temperature data of each zone to obtain a temperature deviation value, and determine the over-temperature zone of the photovoltaic module based on the temperature deviation value. The calculation module is used to determine the over-temperature coverage ratio in the photovoltaic module through the over-temperature zone, and to perform a product calculation based on the over-temperature coverage ratio and the temperature deviation value to obtain the cooling demand level. The cooling module is used to match a preset heat dissipation strategy library based on the cooling demand level to obtain a cooling method combination corresponding to the level, and drive the corresponding cooling equipment to continuously cool the photovoltaic module based on the cooling method combination, so as to keep the temperature deviation within the safe temperature threshold range.

[0011] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0012] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0013] This invention provides a method for cooling control of photovoltaic modules, comprising the following steps: collecting real-time temperature data of the photovoltaic module and classifying and statistically analyzing the real-time temperature data to obtain zoned temperature data; retrieving a preset safe temperature threshold, calculating the difference between the temperature data of each zone to obtain a temperature deviation value, and determining the over-temperature zone of the photovoltaic module based on the temperature deviation value; determining the over-temperature coverage ratio in the photovoltaic module through the over-temperature zone, and performing a product operation based on the over-temperature coverage ratio and the temperature deviation value to obtain a cooling demand level; and matching a preset heat dissipation strategy based on the cooling demand level. The system obtains corresponding cooling method combinations based on the library, and drives the corresponding cooling equipment to continuously cool the photovoltaic modules based on the cooling method combinations, so as to keep the temperature deviation within the safe temperature threshold range. This solves the technical problem that the surface temperature of photovoltaic modules can rise significantly under high temperature environment or long-term strong light conditions, resulting in a decrease in photoelectric conversion efficiency. It realizes the calculation of the difference between the preset safe temperature threshold and the temperature data of each zone to obtain the temperature deviation value, and then determines the over-temperature zone. This realizes the real-time assessment and spatial positioning of the thermal state of the module, and improves the system's response sensitivity and judgment accuracy to abnormal temperature rise. Attached Figure Description

[0014] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart illustrating the steps of the photovoltaic module cooling control method in an embodiment of the present invention; Figure 2 This is a structural block diagram of the cooling control device for photovoltaic modules in an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0015] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0017] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0018] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0019] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0020] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0021] Reference Figure 1 This invention provides a method for controlling the cooling of photovoltaic modules, comprising the following steps: Step S1: Collect real-time temperature data of the photovoltaic module, and classify and statistically analyze the real-time temperature data to obtain zone temperature data.

[0022] Specifically, real-time temperature data of the photovoltaic module is collected and classified statistically analyzed to obtain zoned temperature data. This step relies on arranging multiple temperature sensors on the surface or back of the photovoltaic module according to a preset spatial distribution pattern. These sensors continuously monitor temperature changes in different areas of the module and transmit the collected temperature values ​​to the central control unit or local data processing module at fixed time intervals (e.g., every 5 seconds or every 10 seconds). Since photovoltaic modules are typically rectangular, their surface can be divided into several logical temperature monitoring areas. For example, a standard-sized photovoltaic panel can be divided into three horizontal regions: upper, middle, and lower, or further subdivided into upper left, upper right, left middle, and right middle. There are six partitions in total, divided into lower left and lower right sections. Each partition corresponds to the data stream fed back by one or more temperature sensors. As real-time temperature data is continuously input, the system automatically categorizes the received data into the corresponding partition label based on the physical location of each sensor. For example, data collected by sensors located at the top of the component are uniformly classified into the "Upper Partition," data from the middle part into the "Middle Partition," and data from the bottom into the "Lower Partition." Subsequently, all real-time temperature sampling values ​​within each partition are classified and statistically processed. The statistical methods include, but are not limited to, calculating the arithmetic mean, weighted average (based on sensor location weight), maximum value, or median of all sensor readings within that partition, thereby eliminating instantaneous fluctuations or individual abnormal readings. To mitigate interference and ensure the representativeness and stability of the obtained data, for example, in the actual operation scenario of a photovoltaic power station, two temperature sensors are configured in the upper section of a photovoltaic module, measuring current temperatures of 68.3°C and 69.1°C respectively; the two sensors in the middle section measure 65.5°C and 66.0°C; and the lower section measures 64.2°C and 63.8°C. The system categorizes these six sets of real-time temperature data according to spatial location and calculates the average value of each section to obtain the temperature data for the upper section as 68.7°C, the middle section as 65.75°C, and the lower section as 64.0°C, thus forming a complete set of zoned temperature data. This process not only preserves the spatial distribution characteristics of temperature but also... The reliability and usability of the data are enhanced through classification and statistics. Furthermore, this classification and statistics process can be executed periodically, for example, updating the zone temperature data every minute to adapt to dynamic changes in external conditions such as light intensity and ambient wind speed. Throughout the process, the zone division principles, sensor layout schemes, and statistical algorithms are pre-stored in the control system and can be parameterized according to the size, power rating, or installation tilt angle of different types of photovoltaic modules, ensuring the applicability and consistency of the method in different application scenarios. Therefore, through the above methods, the transformation from raw temperature signals to structured zone temperature data is achieved, providing accurate and spatially resolvable basic data support for subsequent over-temperature judgment and cooling decisions based on zone characteristics.

[0023] Step S2: Retrieve the preset safe temperature threshold, calculate the difference between the temperature data of each zone to obtain the temperature deviation value, and determine the over-temperature zone of the photovoltaic module based on the temperature deviation value.

[0024] Specifically, the system retrieves a preset safe temperature threshold, calculates the difference between the temperature data of each zone to obtain a temperature deviation value, and further determines the overheating zone of the photovoltaic module based on the temperature deviation value. This step relies on the pre-stored safe temperature threshold in the control system. This threshold is set according to the material characteristics of the photovoltaic module, operating environment standards, and manufacturer-recommended parameters. For example, it is set to 65°C, meaning that if the temperature of any zone of the module exceeds this value, it is considered to have an overheating risk. After obtaining the zone temperature data of 68.7°C for the upper zone, 65.75°C for the middle zone, and 64.0°C for the lower zone, the system sequentially compares the temperature value of each zone with the safe temperature threshold and calculates the difference. Specifically, taking the upper zone as an example, its temperature deviation value is equal to 68.7 minus 65, resulting in + 3.7°C indicates that the temperature of this zone exceeds the safe threshold of 3.7 degrees. Similarly, the temperature deviation of the middle zone is 65.75 minus 65, resulting in +0.75°C, which also exceeds the threshold. The temperature deviation of the lower zone is 64.0 minus 65, resulting in -1.0°C, which is within the normal range. The above difference calculation process is automatically executed by the control module, using standardized arithmetic logic to ensure that the temperature deviation value of each zone is accurately generated and recorded in numerical form in the data structure of the current monitoring cycle. Subsequently, based on whether the obtained temperature deviation value is greater than zero, the system determines whether the corresponding zone constitutes an over-temperature zone. That is, as long as the temperature deviation value of a certain zone is greater than 0, the zone is marked as over-temperature. In the above example, the temperature deviation values ​​of the upper zone and the middle zone are +3.7°C and +0, respectively.Both zones have a temperature deviation of 75°C, which is greater than zero. Therefore, these two zones are identified as overheating zones. The lower zone, however, is not included in the overheating zone category because its temperature deviation is negative and does not exceed the safe temperature threshold. The entire judgment process is implemented through programmed logic. For example, a conditional statement "if>0 then mark as overheating" is embedded in the control algorithm to ensure that the judgment process is real-time, consistent, and repeatable. In addition, the identification results of overheating zones correspond to the original zone definition in a spatial identification manner. For example, the system marks "Upper zone: Yes", "Middle zone: Yes", and "Lower zone: No" in the data table, thus forming a clear distribution map of overheating areas. This map not only records which areas are in an overheating state but also retains their corresponding temperature deviation values, providing direct input for subsequent calculations of the overheating coverage ratio and cooling requirement level. Throughout the entire process, the retrieval of the safe temperature threshold and the execution of the difference calculation are all handled efficiently. The determination of temperature zones and their corresponding over-temperature zones are performed cyclically according to a fixed time sequence, for example, completing a full process every minute to ensure continuous monitoring of the component's thermal state. Simultaneously, the safe temperature threshold can be dynamically adjusted based on season, geographical location, or component aging, but its calling mechanism remains consistent: before each calculation, the current valid value is read from a preset parameter library to avoid human intervention or configuration errors. Therefore, through the above continuous operation, a mathematical conversion from zone temperature data to temperature deviation values ​​is achieved, and over-temperature zones are accurately delineated accordingly, providing crucial criterion support for subsequent intelligent cooling control based on spatial distribution characteristics.

[0025] Step S3: Determine the over-temperature coverage ratio in the photovoltaic module through the over-temperature zoning, and perform a product operation based on the over-temperature coverage ratio and the temperature deviation value to obtain the cooling demand level.

[0026] Specifically, the over-temperature coverage ratio in the photovoltaic module is determined through the over-temperature zoning, and the cooling demand level is obtained by multiplying the over-temperature coverage ratio with the temperature deviation value. This step is first based on the over-temperature zoning results identified in the previous step, combined with the total number of zoning or total area of ​​the photovoltaic module for ratio calculation. In the aforementioned application scenario, the photovoltaic module is divided into three logical regions: upper, middle, and lower. The upper and middle regions have been identified as over-temperature zones based on the temperature deviation value, while the lower region is not over-temperature. Based on this status, the system divides the number of over-temperature zones (2) by the total number of zones (3) to calculate an over-temperature coverage ratio of 66.7%. This ratio reflects the proportion of space occupied by the area on the current module surface that is in an over-temperature state, and is an important indicator for measuring the range of thermal anomalies. This calculation process is automatically executed by the control module, using the fixed formula "over-temperature coverage ratio = number of over-temperature zones / The system performs arithmetic operations on the "total number of zones" to ensure consistent results. Subsequently, it retrieves the temperature deviation values ​​corresponding to each overheated zone, including +3.7°C for the upper zone and +0.75°C for the middle zone. These deviation values ​​are then statistically processed to generate a comprehensive value representing the overall severity of overheating. The processing method can use the maximum value, average value, or weighted average. For example, the maximum temperature deviation value of 3.7°C can be used as the input for subsequent calculations, or the average value (3.7 + 0.75) / 2 = 2.225°C can be calculated for a more balanced assessment. After determining the temperature deviation value used for calculation, the system multiplies it by the previously obtained overheating coverage ratio. For example, multiplying the maximum deviation value of 3.7°C by 66.7% yields 2.4679, or multiplying the average deviation value of 2.225°C by 66.7% yields 1.4835, this product result is defined as the cooling demand level, used to characterize the overall cooling intensity required by the photovoltaic module. This product calculation process is solidified in the control algorithm as a mathematical expression, ensuring consistent calculation logic each time, and the result is directly mapped to a dimensionless or standardized numerical level, facilitating subsequent matching with the heat dissipation strategy library. Throughout the calculation process, all data originates from the output of previous steps, including the determination results of overheated zones, zone temperature data, and the difference results between safe temperature thresholds; the data link is complete and traceable. Furthermore, the calculation method for the overheated coverage ratio can be adjusted according to the actual sensor layout. For example, when the areas of each zone are uneven, the system can introduce an area weighting factor to include the area proportion of each overheated zone in the calculation. If the upper zone accounts for 40% of the total module area, the middle zone accounts for 30%, and the lower zone accounts for 30%, then the overheated coverage ratio is 40% + 30% = 70%, thus more accurately reflecting the actual heat load distribution; after the product operation, the resulting cooling demand level is normalized and divided into preset level ranges, for example, 0-1 is low level, 1-2 is medium level, and above 2 is high level, providing a quantitative basis for subsequent strategy matching; therefore, through the above continuous data processing flow, the conversion from over-temperature zoning to over-temperature coverage ratio is realized, and the product operation is completed in combination with the temperature deviation value, finally generating a comprehensive index that integrates spatial range and temperature severity, namely the cooling demand level. The generation process of this index strictly depends on the output data of the preceding steps, and each step can be programmed in the control system, with high executability and engineering applicability.

[0027] Step S4: Based on the cooling demand level, a preset heat dissipation strategy library is matched to obtain the corresponding cooling method combination, and the corresponding cooling equipment is driven to continuously cool the photovoltaic module based on the cooling method combination, so as to keep the temperature deviation within the safe temperature threshold range.

[0028] Specifically, based on the cooling demand level, a preset heat dissipation strategy library is matched to obtain the corresponding cooling method combination. Then, based on the cooling method combination, the corresponding cooling equipment is driven to continuously cool the photovoltaic modules, ensuring that the temperature deviation remains within the safe temperature threshold range. This step relies on a pre-configured heat dissipation strategy library in the control system. This strategy library is stored in the controller's storage unit in the form of a data table or rule set, containing multiple preset cooling demand level ranges and their corresponding cooling method combinations. For example, the cooling demand level is divided into 0-1 as the first level. Levels 1-2 represent the second level, and levels 2 and above represent the third level. Each level is associated with one or more specific cooling operation commands. In the aforementioned application scenario, the cooling requirement level has been calculated as 2.4679 through multiplication. This value is greater than 2 and falls into the third level range. Based on this, the system retrieves and calls up the cooling method combination bound to the third level from the heat dissipation strategy library, such as "start the high-pressure spray device + turn on the forced air cooling system + adjust the fan to high speed." This combination exists in the form of an instruction set, clearly indicating the type of equipment to be activated, the operating parameters, and the execution order. Subsequently, the control module will... This cooling method combination is parsed into specific control signals and sent to the corresponding cooling devices via a communication interface. For example, an open signal is sent to the solenoid valve to start the spray system, and a PWM signal is sent to the fan controller to adjust the fan speed to a preset high speed level, ensuring that all designated devices operate synchronously according to the combined strategy. Throughout the entire driving process, the status feedback signals of each cooling device are transmitted back to the control system in real time to confirm the execution of the commands. If a device fails to respond or malfunctions, an alarm mechanism is triggered, and an attempt is made to switch to a backup device or adjust the strategy combination. While continuously operating, the system maintains real-time temperature data acquisition of photovoltaic modules and cyclically executes zone temperature data calculation, temperature deviation value judgment, over-temperature coverage ratio update, and cooling demand level recalculation to form a closed-loop control process. For example, after two minutes of operation of spray and air cooling, the temperature data is re-acquired and it is found that the upper zone has dropped to 66.2°C, the middle zone to 65.3°C, and the lower zone to 64.5°C. At this time, only the upper zone remains over-temperature zone, the over-temperature coverage ratio has dropped to 33.3%, and the maximum temperature deviation value is 1.2°C. After multiplication, the cooling demand level becomes 0.4. If the temperature is below 1, the system immediately matches the corresponding cooling method combination for the first level from the heat dissipation strategy library, such as "maintain low-speed air cooling only," and sends a command to shut down the spray device and adjust the fan to low speed to avoid excessive cooling. This strategy matching process is implemented based on a lookup table method or conditional judgment logic, ensuring that the corresponding strategy is accurately retrieved every time the cooling demand level changes. In addition, each cooling method combination in the heat dissipation strategy library is optimized according to energy efficiency, response speed, and equipment lifespan. For example, low-level systems prioritize natural air cooling or low-power fans, while high-level systems activate water-intensive or high-power spray systems to achieve energy-saving operation. Therefore, through the above process, automatic matching from the cooling demand level to the cooling method combination is achieved, driving the corresponding equipment to perform coordinated cooling operations, continuously adjusting the component temperature until the temperature deviation value returns to the safe temperature threshold range, and maintaining stable system operation.

[0029] In a specific embodiment, real-time temperature data of the photovoltaic module is collected, and the real-time temperature data is classified and statistically analyzed to obtain zoned temperature data, including: The surface of the photovoltaic module is divided into grid sampling points, and temperature sensors are installed based on the grid sampling points to obtain a sensor array; The photovoltaic module is scanned and temperature data is acquired based on the sensor array to obtain real-time temperature data. The real-time temperature data is then subjected to mean filtering to obtain an effective temperature value. Based on the effective temperature value, temperature mapping is performed on various parts of the photovoltaic module to obtain a temperature distribution map. The temperature distribution map is then divided into regions according to the cell array, backsheet, and frame of the photovoltaic module to obtain zoned temperature data.

[0030] Specifically, real-time temperature data of the photovoltaic module is collected and classified statistically analyzed to obtain zoned temperature data. This step involves dividing the surface of the photovoltaic module into grid sampling points and installing temperature sensors based on these grid sampling points to obtain a sensor array. In practice, firstly, based on the physical dimensions and structural characteristics of the photovoltaic module, several evenly distributed grid sampling points are arranged on its surface. These sampling points form a matrix layout according to preset rows and columns. For example, a standard 60-cell photovoltaic module can be divided into 6 rows and 10 columns, totaling 60 grid sampling points. Each sampling point corresponds to the central area of ​​a single cell. Additional sampling points are also set in the center of the backsheet, at the four corners of the frame, and near the junction box to cover non-power-generating areas. Subsequently, miniature temperature sensors are fixedly installed at each grid sampling point. These sensors use high-temperature resistant and UV-resistant encapsulation materials and are connected via insulated wires or... The wireless module is connected to the central data acquisition unit, and all sensors together form a sensor array covering the entire surface of the module. When the system is running, it performs scanning temperature acquisition on the photovoltaic module based on the sensor array. That is, the control module reads the output signal of each sensor sequentially, and the acquisition cycle is set to complete a full array scan every 10 seconds, thereby obtaining a set of real-time temperature data containing the temperature values ​​of all sampling points. To eliminate the influence of instantaneous interference or measurement noise, the real-time temperature data is subjected to mean filtering. Specifically, the arithmetic mean is calculated from the sampling values ​​of the same sensor over multiple consecutive cycles (e.g., 5 times). For example, if the sensor at a certain cell location reads 67.2°C, 68.1°C, 66.9°C, 68.3°C, and 67.5°C in 5 consecutive acquisitions, then its effective temperature value is (67.2+68.1+66.9+68.3+67.5) / 5=67.6°C is used as the stable temperature representative for this sampling point in subsequent calculations. After extracting the effective temperature values ​​of all sampling points, temperature mapping is performed on various parts of the photovoltaic module based on these effective temperature values. This involves associating the effective temperature value of each grid point with its spatial coordinates, generating a two-dimensional heat map-like temperature distribution map in the computer interface or the memory of the control system. Different colors in the map represent different temperature ranges, intuitively reflecting the thermal state of the module surface. Next, the temperature distribution map is divided into regions according to the cell array, backsheet, and frame of the photovoltaic module. This division is based on the physical structure of the module: the cell array region includes the positions of all cell units directly involved in photoelectric conversion, usually occupying a large area in the middle of the module; the backsheet region refers to the central part of the back of the module, which is generally equipped with dedicated sensors to monitor internal temperature rise; the frame region includes the four corners and the middle section of the long side of the aluminum frame. Taking a rectangular photovoltaic module as an example, its temperature distribution... The graph is divided into three logical regions: the cell array region contains data from the first 60 grid points; the backsheet region uses the temperature value from its center point; and the border region integrates the average values ​​from six sensors (four corner points and two center points). Finally, by calculating the average or maximum value of all valid temperature values ​​in each region, the temperatures are determined to be 67.8°C for the cell array region, 66.5°C for the backsheet region, and 64.2°C for the border region. These values ​​constitute the partitioned temperature data, used for subsequent difference calculations with the safety temperature threshold. Throughout the process, the layout of the grid sampling points, the configuration of the sensor array, the scanning frequency, the filtering algorithm parameters, and the region division rules are all pre-stored in the system configuration file and can be flexibly accessed across different module models. Therefore, this method achieves a complete link from physical sampling to data processing to spatial classification, ensuring that the obtained partitioned temperature data accurately reflects the true thermal distribution of the photovoltaic modules under actual operating conditions.

[0031] In a specific embodiment, the step of retrieving a preset safe temperature threshold and calculating the difference between the temperature data of each zone to obtain a temperature deviation value includes: Retrieve preset safety temperature thresholds, which include battery cell array safety thresholds, backplane safety thresholds, and frame safety thresholds, and each safety threshold corresponds to a different partition type identifier; The partition temperature data is type-identified to obtain partition type information, and the partition type information is matched with the partition type identifier to obtain the target threshold corresponding to each partition; The temperature difference between the partitioned temperature data and the corresponding target threshold is calculated to obtain the partitioned temperature difference value. The partitioned temperature difference value is then grouped according to the partition type to obtain temperature deviation values ​​including the battery cell temperature difference group, the back panel temperature difference group, and the frame temperature difference group.

[0032] Specifically, the system retrieves preset safety temperature thresholds and calculates the difference between the temperature data of each zone to obtain the temperature deviation value. This step involves retrieving preset safety temperature thresholds, which include safety thresholds for the cell array, backsheet, and frame, each corresponding to a different zone type identifier. During system initialization, the control module reads a set of pre-configured safety temperature threshold parameters from the storage unit. These parameters are set according to the temperature resistance performance and operating specifications of different parts of the photovoltaic module. For example, the safety threshold for the cell array is set to 65°C because it directly affects the photoelectric conversion efficiency and long-term high temperatures can easily cause PID effects; the safety threshold for the backsheet is set to 70°C because the backsheet material is mostly polymer. The composite layer has a slightly higher heat resistance than the battery cells. The frame safety threshold is set at 80°C, but higher temperature rise is allowed because the aluminum frame has fast thermal conductivity, large heat capacity, and does not participate in power generation. Each safety threshold is bound to a unique partition type identifier, such as "CELL_ARRAY" for the battery cell array safety threshold, "BACKPLATE" for the backplane safety threshold, and "FRAME" for the frame safety threshold. These identifiers serve as data indexes for subsequent matching operations. When the system obtains the partition temperature data output from the previous steps, i.e., the battery cell array area temperature is 67.8°C, the backplane area temperature is 66.5°C, and the frame area temperature is 64.2°C, it immediately performs type identification on the partition temperature data to obtain partition type information. This process is achieved by decoding... The system analyzes the metadata fields in the data packets. Each partition's temperature data carries a type label for its source region during generation; for example, the data packet for the cell array region is labeled "CELL_ARRAY," the backplane region is labeled "BACKPLATE," and the border region is labeled "FRAME." Based on these labels, the system extracts the partition type information for each partition and matches it against pre-stored partition type identifiers to determine the appropriate safe temperature threshold for each partition. For instance, upon identifying "CELL_ARRAY," the system retrieves the matching cell array safe threshold of 65°C from the parameter table as the target threshold for that partition; similarly, "BACKPLATE" matches 70°C, and "FRAME" matches 70°C. The system matches the temperature difference of AME to 80°C. After matching, the system calculates the difference between the partition temperature data and the corresponding target threshold, specifically by performing subtraction: 67.8°C minus 65°C in the cell array area equals +2.8°C, 66.5°C minus 70°C in the backplane area equals -3.5°C, and 64.2°C minus 80°C in the border area equals -15.8°C. These results are the partition temperature difference values. Subsequently, the partition temperature difference values ​​are grouped according to partition type, that is, temperature difference values ​​with the same partition type identifier are grouped into the same data group. The system allocates the temperature difference values ​​to the corresponding storage area according to the type information of each partition, ultimately forming three independent data sets: the cell temperature difference group contains +2.8°C, the backplane temperature difference group contains -3.8°C, and so on.The temperature difference group includes a range of 5°C and -15.8°C. These grouped data constitute the overall structure of the temperature deviation value. Throughout the processing, all matching rules, threshold parameters, and grouping logic are embedded in the control program to ensure consistency in each execution. For example, in a given run, if the backplane temperature rises to 72.1°C, its partition type information remains "BACKPLATE," matching the target threshold of 70°C. The difference is calculated to be +2.1°C, which is then grouped into the backplane temperature difference group for subsequent determination of whether an over-temperature partition exists. Therefore, through the aforementioned continuous operations, precise retrieval of multiple safety threshold types and targeted matching of partition data are achieved, along with the calculation and classification of partition temperature differences, providing a structured data foundation for subsequent differentiated temperature control decisions based on different types of areas.

[0033] In a specific embodiment, determining the over-temperature zone of the photovoltaic module based on the temperature deviation value includes: Based on the temperature deviation value, grid block statistics are performed to obtain a temperature difference distribution table. The values ​​in the temperature difference distribution table are then cumulatively sorted to obtain a positive or negative temperature difference sequence. The positive or negative temperature difference sequence includes temperature data in each partition that exceeds the safe temperature threshold and temperature data in each partition that is below the safe temperature threshold. Based on the positive temperature difference sequence, surface hotspots of the photovoltaic module are marked to obtain a hotspot distribution map, and boundary detection is performed on the hotspot distribution map to obtain the outline of abnormal areas. The surface of the photovoltaic module is divided into zones based on the outline of the abnormal region to obtain the over-temperature zone.

[0034] Specifically, determining the over-temperature zones of the photovoltaic module based on the temperature deviation values ​​includes performing grid-based block statistics based on the temperature deviation values ​​to obtain a temperature difference distribution table, and cumulatively sorting the values ​​in the temperature difference distribution table to obtain a positive or negative temperature difference sequence; wherein, the positive or negative temperature difference sequence includes temperature data of each zone exceeding the safe temperature threshold and temperature data of each zone below the safe temperature threshold; after the system obtains the temperature deviation values ​​of the cell temperature difference group, backsheet temperature difference group, and frame temperature difference group output by the previous steps, these data are first sorted according to... The original grid sampling point locations are reconstructed to form a two-dimensional data structure consistent with the spatial layout of the sensor array, namely a temperature difference distribution table. This table arranges the temperature difference values ​​of all sampling points in rows and columns. For example, a 6x10 cell array area corresponds to a 6×10 temperature difference matrix. Each cell is filled with the zone temperature difference value for that location. If the temperature at a certain point is 68.3°C, and the corresponding cell array safety threshold is 65°C, then the temperature difference value at that point is +3.3°C and is filled into the corresponding cell. The back panel area and the border area... The temperature difference values ​​are also inserted into the corresponding positions in the table according to their physical locations. After the temperature difference distribution table is constructed, the system performs cumulative sorting on all values ​​in the table, that is, extracts all non-zero or all positive and negative values ​​and arranges them in order of size to form a positive temperature difference sequence and a negative temperature difference sequence. The positive temperature difference sequence contains all temperature difference data above the safe temperature threshold, such as +3.3°C, +2.8°C, +1.5°C, etc., arranged from largest to smallest, and is used to identify overheated areas. The negative temperature difference sequence contains data below the threshold, such as -3.5°C, -15.8°C, etc. The values ​​are arranged from largest to smallest absolute value for analysis of low-temperature regions. In a specific operational scenario, the system identifies 12 sampling points in the solar cell array region with temperature differences greater than 0, whose positive temperature difference sequence is +3.3, +3.1, +2.9, +2.8, +2.6, +2.4, +2.2, +2.1, +1.9, +1.7, +1.6, and +1.5°C. Subsequently, based on this positive temperature difference sequence, surface hotspots are marked on the photovoltaic modules, i.e., those exceeding a certain set sensitivity threshold (e.g., +1) in the positive temperature difference sequence.All sampling points (0°C and above) are marked as "hot spots" on the temperature distribution map. For example, the 12 points mentioned above are all marked as red highlighted areas, forming a hot spot distribution map. This map visually shows the local locations of abnormal temperature rise on the component surface. Next, boundary detection is performed on the hot spot distribution map. Image processing algorithms such as Canny edge detection or contour tracking technology are used to identify the outer boundary of the hot spot area. The system determines the start and end positions of the hot spot area by analyzing the temperature difference gradient of adjacent pixels. For example, it is found that these 12 hot spots are concentrated in the 3x3 range of the upper left corner of the component, forming a continuous high-temperature cluster. After boundary detection, the geometric contour coordinates of this area are output, such as the rectangular range from the upper left corner (1,1) to the lower right corner (3,3). Finally, the surface of the photovoltaic module is divided into zones based on the contour of the abnormal area. Based on the spatial range enclosed by the outline, the original solar cell array area is further subdivided into multiple sub-zones. The area covered by the outline is redefined as a new logical zone, namely the overheat zone. For example, in the original 60 grid points, the nine solar cells in rows 1 to 3 and columns 1 to 3 are designated as the "upper left overheat zone," and the remaining areas are marked as normal zones. This division result is written to the system's partition management module as a partition identifier for subsequent calculation of the overheat coverage ratio. Throughout the process, the generation of the temperature difference distribution table, the sorting of positive and negative temperature difference sequences, hotspot marking, boundary detection, and partition division are all automatically executed through preset algorithms without manual intervention. Therefore, through the above continuous processing, accurate identification from temperature deviation values ​​to overheat zones is achieved, ensuring dynamic capture and spatial definition of locally overheated areas on the photovoltaic module surface.

[0035] In a specific embodiment, determining the over-temperature coverage ratio in the photovoltaic module through the over-temperature zoning includes: The boundaries of the overheated zones are delineated to obtain an overheated region outline map, and the area of ​​the overheated region outline map is calculated to obtain the actual area value of each overheated zone. Based on the actual area value, the proportion of the overall area of ​​the photovoltaic module is calculated to obtain the area proportion data of each over-temperature zone, and the area proportion data is integrated to obtain the initial value of the total proportion of the over-temperature zone. The initial value of the total proportion of the overheated area is checked for overlap between adjacent zones. If there is overlap between adjacent overheated zones, the area of ​​the overlapping part is deducted and the proportion is recalculated to obtain the corrected overheated coverage ratio.

[0036] Specifically, the overheating coverage ratio in the photovoltaic module is determined through the overheating zoning. This step includes delineating the boundaries of the overheating zones to obtain an overheating region outline map, and calculating the area of ​​the overheating region outline map to obtain the actual area value of each overheating zone. After the system completes the previous step of dividing the overheating zones based on the abnormal region outline, the spatial range of each overheating zone is first visually delineated on the module surface model in a geometric manner. For example, in a certain operating instance, the system identified two independent overheating regions: one located in the upper left of the cell array. One section consists of nine solar cells arranged in rows 1 to 3 and columns 1 to 3. The other section, located in the middle right, consists of four solar cells arranged in rows 4 to 5 and columns 7 to 8. Based on the physical dimensions of these solar cells (standard cell size is 156mm × 156mm) and their spacing, the system draws a closed polygonal outline in a two-dimensional coordinate system, forming an overheating region outline. This outline contains two separate rectangular regions, with the vertex coordinates of each rectangle calculated from the center position and side length of the corresponding solar cell. Subsequently, the area of ​​the overheating region outline is calculated, specifically by dividing each section into sections... The number of solar cells enclosed by the outline is multiplied by the effective light-receiving area of ​​each cell. For example, if the area of ​​each cell is approximately 0.0243 m² (156 mm × 156 mm = 24336 mm²), then the actual area of ​​the upper left corner overheated zone is 9 × 0.0243 = 0.2187 m², and the upper right center overheated zone is 4 × 0.0243 = 0.0972 m². These two values ​​are the actual area values ​​of each overheated zone and are recorded in the data structure for subsequent processing. Next, the proportion of the photovoltaic module's overall area is calculated based on these actual area values. The overall area of ​​the photovoltaic module is then calculated as a percentage of the total area. Based on its standard external dimensions, for example, the typical size of a 60-cell module is 1650mm × 992mm, with a total area of ​​1.63632m². The system divides the actual area of ​​each overheat zone by this total area to obtain the area percentage of the upper left zone as 0.2187 / 1.63632≈13.36% and the middle right zone as 0.0972 / 1.63632≈5.94%. Then, the area percentage data is integrated by adding the area percentages of all overheat zones to obtain the initial value of the total overheat area percentage as 13.36% + 5.94% = 19%.3%, this value reflects the initial coverage degree of the overheated area on the current component surface; next, the initial value of the total proportion of the overheated area is checked for overlap between adjacent partitions. This process is achieved by analyzing whether there is spatial intersection of the contour coordinates of each overheated partition. The system uses computer geometric algorithms to determine whether any two rectangular contours overlap. For example, it checks whether the projection intervals of the two rectangles on the X and Y axes intersect. If the right boundary of one overheated partition is greater than the left boundary of another partition, and its upper boundary is greater than its lower boundary, etc., are met at the same time, then it is determined that there is overlap. In the aforementioned example, since the two overheated partitions are located at the upper left and middle right respectively, they are completely separated in space and have no coordinate overlap, so it is determined that there is no overlap and no correction is needed; however, in another operating scenario, if the system identifies three overheated partitions, where the first is in rows 2-3 and columns 2-4, the second is in rows 3-4 and columns 3-5, and the third is in row 5 and column 6, then the system detects that the first and second partitions are in the 3rd row and column 6. There are shared solar cells in rows 3 and columns 4, meaning there is an overlapping area. The overlapping area is the area of ​​two solar cells (row 3, column 3 and row 3, column 4), with an area value of 2 × 0.0243 = 0.0486 m², corresponding to approximately 2.97%. At this point, the overlapping area needs to be deducted and the percentage recalculated. The system subtracts the recalculated 2.97% from the initial value of the total percentage of the overheated area. For example, if the original initial value is 25.1%, the corrected overheated coverage percentage is 22.13%. This correction process ensures the uniqueness and accuracy of the area statistics, avoiding overestimation caused by the intersection of adjacent partition boundaries. Throughout the process, boundary delineation, area calculation, percentage integration, and overlap verification are all automatically executed through a preset algorithm, relying on accurate component physical parameters and spatial coordinate mapping. Therefore, through the above continuous operations, accurate quantification from overheated partitions to overheated coverage percentages is achieved, providing a reliable spatial weighting basis for subsequent generation of cooling demand levels.

[0037] In a specific embodiment, based on the cooling requirement level and a preset heat dissipation strategy library, a corresponding cooling method combination is obtained, including: Based on the cooling requirement level, query the corresponding candidate strategy in the heat dissipation strategy library; The heat dissipation efficiency of the candidate strategies is calculated to obtain an efficiency data table, and energy consumption statistics are performed on each strategy based on the efficiency data table to obtain energy consumption indicators. Based on the energy consumption index, the candidate strategies are prioritized to obtain a strategy ranking table. The top N strategies in the strategy ranking table are then combined and matched to obtain a cooling method combination of the corresponding level, where N is a preset threshold.

[0038] Specifically, based on the cooling demand level, a preset heat dissipation strategy library is matched to obtain the corresponding cooling method combination. This step includes querying the corresponding candidate strategy in the heat dissipation strategy library based on the cooling demand level. After the system completes the preceding process and generates a cooling demand level value (e.g., in a certain running instance, the calculated cooling demand level is 2.4679), the system uses this value as the search key to perform range matching in the heat dissipation strategy library pre-stored in the controller. The heat dissipation strategy library contains multiple strategy entries divided into cooling demand level ranges. Each entry is associated with one or more executable cooling method combinations and their parameter configurations. For example, when... When the cooling demand level is between 2.0 and 3.0, the system categorizes this level as "high-level" and retrieves all applicable strategies for this range as candidate strategies. These candidate strategies may include: "activating high-pressure spray + turning on forced air cooling + high-speed fan operation", "activating only the phase change material cooling module", "combining backplate liquid cooling and edge-blown air duct operation", and several other alternatives. Subsequently, the system calculates the heat dissipation efficiency of the candidate strategies to obtain an efficiency data table. This process uses a built-in thermodynamic model to simulate the expected cooling effect of each strategy under the current environmental conditions. The model input includes the current zone temperature data, ambient temperature, wind speed, light intensity, and group temperature. The system calculates the rate of temperature decrease and maximum temperature difference convergence speed of the photovoltaic module surface under each candidate strategy, taking into account parameters such as the thermal conductivity of the component material. For example, under conditions of ambient temperature 38°C, wind speed 1.5m / s, and light intensity 950W / m², the system calculates these parameters. The results show that the combination of "high-pressure spraying + forced air cooling" can reduce the temperature of the cell array area by 8.2°C within 5 minutes; "phase change material cooling" alone is expected to reduce the temperature by 5.3°C; and "backsheet liquid cooling + edge blowing" is expected to reduce the temperature by 6.8°C. These values ​​constitute the core content of the efficiency data table. Next, based on the efficiency data table, the system performs energy consumption statistics for each strategy to obtain energy consumption indicators. The system then calls upon each strategy to reduce energy consumption... The database of power consumption parameters for temperature control equipment includes parameters such as a high-pressure water pump power of 120W, a fan power of 80W at high speed, a phase change material activation energy consumption of 200kJ / cycle, and a liquid-cooled circulation pump power of 90W. Combining the expected runtime and operating mode of each strategy, the total energy consumption is calculated. For example, if "high-pressure spray + forced air cooling" runs continuously for 5 minutes, the total energy consumption is (120+80)×300 seconds÷3600≈16.67Wh. The energy consumption for a single activation of "phase change material cooling" is approximately 55.6Wh (200kJ≈55.6Wh). The energy consumption for a 6-minute run of the "backplate liquid cooling + edge blowing" combination is (90+50)×360≈14Wh.0Wh, these energy consumption values ​​are recorded as energy consumption indicators and bound to the corresponding strategies; then, based on the energy consumption indicators, the candidate strategies are prioritized. The system uses a comprehensive scoring algorithm to calculate the heat dissipation efficiency and energy consumption indicators weighted, for example, setting the efficiency weight to 60% and the energy consumption weight to 40%, generating a comprehensive score for each strategy. "High-pressure spray + forced air cooling" scores 82 points because it cools the fastest but has high energy consumption; "backplate liquid cooling + edge blowing" has moderate efficiency and low energy consumption, scoring 88 points; "phase change material cooling" is energy-saving but has a slow response and limited cooling range, scoring 75 points. The system arranges the strategies from high to low according to the scores, forming a strategy ranking table, in which "backplate liquid cooling + edge blowing" ranks first, "high-pressure spray + forced air cooling" second, and "phase change material cooling" third; finally, the strategy ranking table is... The system combines and matches the top N strategies to obtain corresponding cooling method combinations, where N is a preset threshold value set according to the system's control precision requirements. For example, N=2 indicates that the two optimal strategies are selected for fusion or selective activation. In this example, the system extracts the top two strategies from the ranking table and further determines whether their equipment resources conflict. If "backplane liquid cooling" and "high-pressure spraying" share the same water pipeline, they cannot operate simultaneously. Therefore, the higher-scoring "backplane liquid cooling + edge purging" is selected as the final cooling method combination. If there is no conflict, they can be merged into a stronger composite strategy. The entire matching process is automatically completed by the control program, ensuring that cooling requirements are met while maintaining optimal energy efficiency. Therefore, through the above process, intelligent decision-making from cooling requirement level to optimal cooling method combination is achieved, ensuring high efficiency and economy in heat dissipation response.

[0039] In a specific embodiment, the photovoltaic module is continuously cooled by driving the corresponding cooling equipment based on the cooling method combination to ensure that the temperature deviation is within the safe temperature threshold range, including: The cooling equipment is matched for each cooling method in the cooling method combination to obtain a cooling equipment list, and the location of the overheating zone is associated based on the cooling equipment in the cooling equipment list to obtain an equipment-zone correspondence table. Based on the cooling demand level, parameters are set for each cooling device in the device-zone correspondence table to obtain device control parameters. Then, the linkage timing of each device control parameter is checked to obtain a linkage control instruction set. The corresponding cooling equipment is driven to operate by the linkage control instruction set, and temperature change data of the overheating zone is collected in real time during operation. The temperature change data is compared with the safe temperature threshold to adjust the equipment control parameters and continuously drive the corresponding cooling equipment to operate until the temperature deviation of the overheating zone is within the safe temperature threshold range.

[0040] Specifically, based on the cooling method combination, the corresponding cooling equipment continuously cools the photovoltaic modules to ensure that the temperature deviation remains within the safe temperature threshold range. This step involves matching each cooling method in the cooling method combination with corresponding cooling equipment to obtain a cooling equipment list. Then, based on the cooling equipment in the list, the system performs location association on the over-temperature zones to obtain a device-zone mapping table. After the system determines the final cooling method combination to be "backsheet liquid cooling + edge blowing," it first maps each cooling method in the combination to the physical equipment actually installed on the photovoltaic modules. For example, "backsheet liquid cooling" corresponds to the micro liquid cooling plate integrated on the back of the module and the circulating water pump connected to it, while "edge blowing" corresponds to the micro fan array installed at the four corners of the module's aluminum frame. The system retrieves the model, communication address, and control interface information of these devices from the equipment configuration database to form a mapping table including circulating water... A list of cooling devices with pump ID PUMP_01 and fan group ID FAN_GROUP_A is generated. Then, based on each cooling device in this list, and combined with the spatial location of the overheated zone identified in the previous steps, a driving relationship between the devices and the overheated area is established. For example, in the aforementioned operating scenario, the overheated zone is the area in rows 1-3 and columns 1-3 of the upper left corner of the cell array. This area is located in the upper half of the module and close to the upper edge of the frame. Based on this, the system determines that the two upper fans (FAN_01 and FAN_02) in the edge blowing have the strongest airflow coverage effect on the upper left overheated area. The backplane liquid cooling plate covers the entire back of the module and can uniformly cool the entire backplane area, including the upper left area. Therefore, PUMP_01 is associated with the upper left overheated area, and FAN_01 and FAN_02 are also associated with the same area, forming a device-zone correspondence table. The table records "PUMP_01". → Upper left overheat zone, “FAN_01 → Upper left overheat zone”, “FAN_02 → Upper left overheat zone” and other corresponding relationships; then, based on the cooling demand level, the parameters of each cooling device in the device-zone correspondence table are set to obtain the device control parameters. The system calls the preset parameter mapping rules according to the current cooling demand level 2.4679, which belongs to the high level range, and sets the circulating water pump to a high flow mode (such as flow rate 2).5L / min), set high-speed gears (e.g., 12V full voltage operation) for FAN_01 and FAN_02, and write these operating parameters into the equipment control commands; then, perform linkage timing verification on the control parameters of each device to ensure that multiple devices are coordinated in terms of startup order, operating rhythm, and shutdown logic, avoiding weakened cooling effect or equipment damage due to timing conflicts. For example, if the system detects that the liquid cooling system needs to start before the air cooling system to prevent dry burning, it generates a timing constraint of "start the water pump for 3 seconds and then start the fan". Combined with the ambient wind speed and temperature change trend to predict the running time, it is finally integrated into a set of linkage control commands containing device address, control parameters, execution order, and duration; drive the corresponding cooling equipment to operate through the linkage control command set. The control module sends a start command to PUMP_01, FAN_01, and FAN_02 through the CAN bus or wireless communication module. The equipment then starts to operate according to the set parameters. The liquid cooling system starts circulating coolant, and the fan generates directional airflow to blow on the component surface. During operation, the system continuously collects temperature change data from the overheated zone, acquiring the latest temperature value of each grid sampling point in the upper left area every 10 seconds. The average value is then calculated and compared with the battery array's safe threshold of 65°C. If the average temperature gradually decreases from 67.8°C to 66.2°C, and the temperature deviation decreases from +2.8°C to +1.2°C, it indicates effective cooling. If the temperature remains above the threshold, the system dynamically adjusts the equipment control parameters, such as increasing the fan speed to ultra-high speed or adding spray assistance. If the temperature has dropped to 64.9°C with a deviation of -0.1°C, a load reduction command is issued to reduce the water pump flow or shut down some fans. This adjustment process is repeated cyclically, continuously driving the corresponding cooling equipment until the temperature deviation of the overheated zone is within the safe temperature threshold range and remains stable for 5 minutes. Only then does the system determine that cooling is complete and enter standby mode. Therefore, through the above process, closed-loop control from strategy to execution is achieved, ensuring the accuracy and continuity of the cooling process.

[0041] The cooling control method for photovoltaic modules in the embodiments of the present invention has been described above. The cooling control device for photovoltaic modules in the embodiments of the present invention will be described below. Please refer to [link / reference]. Figure 2 One embodiment of the cooling control device for photovoltaic modules in this invention includes: The acquisition module 21 is used to acquire real-time temperature data of the photovoltaic module and classify and statistically analyze the real-time temperature data to obtain zone temperature data. The calculation module 22 is used to retrieve a preset safe temperature threshold, perform difference calculation on the temperature data of each zone to obtain a temperature deviation value, and determine the over-temperature zone of the photovoltaic module based on the temperature deviation value. The calculation module 23 is used to determine the over-temperature coverage ratio in the photovoltaic module through the over-temperature zone, and to perform a product calculation based on the over-temperature coverage ratio and the temperature deviation value to obtain the cooling demand level. The cooling module 24 is used to match a preset heat dissipation strategy library based on the cooling demand level to obtain a cooling method combination corresponding to the level, and drive the corresponding cooling device to continuously cool the photovoltaic module based on the cooling method combination, so as to keep the temperature deviation within the safe temperature threshold range.

[0042] In this embodiment, the specific implementation of each unit in the above device embodiment is described in the above method embodiment, and will not be repeated here.

[0043] like Figure 3 As shown in the diagram, this embodiment of the invention provides a structural schematic block diagram of a computer device, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the above-described method for cooling photovoltaic modules.

[0044] It is evident that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented in this device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0045] Furthermore, this application also discloses a computer program product or computer program stored in a computer-readable storage medium. A processor of a computer device can read the computer program from the computer-readable storage medium and execute the computer program, causing the computer device to perform the aforementioned photovoltaic module cooling control method. Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0046] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method of temperature control of a photovoltaic assembly, characterized in that, The method comprises the following steps: Collecting real-time temperature data of the photovoltaic module, and performing classified statistics on the real-time temperature data to obtain partition temperature data; Retrieving a preset safe temperature threshold, performing difference calculation on each partition temperature data to obtain a temperature deviation value, and determining an over-temperature partition of the photovoltaic module based on the temperature deviation value; Determining an over-temperature coverage ratio in the photovoltaic module through the over-temperature partition, and performing product operation on the over-temperature coverage ratio and the temperature deviation value to obtain a cooling demand level; Matching a preset heat dissipation strategy library based on the cooling demand level to obtain a cooling mode combination corresponding to the level, and driving a corresponding cooling device to continuously cool the photovoltaic module based on the cooling mode combination to realize that the temperature deviation is within the range of the safe temperature threshold.

2. The temperature reduction control method of a photovoltaic module according to claim 1, wherein Collecting real-time temperature data of the photovoltaic module, and performing classified statistics on the real-time temperature data to obtain partition temperature data, comprising: Dividing the surface of the photovoltaic module into grid sampling points, and installing temperature sensors based on the grid sampling points to obtain a sensor array; Performing scanning temperature collection on the photovoltaic module based on the sensor array to obtain real-time temperature data, and performing mean filtering processing on the real-time temperature data to obtain effective temperature values; Performing temperature mapping on each part of the photovoltaic module based on the effective temperature values to obtain a temperature distribution map, and dividing the temperature distribution map into regions according to the cell array, the back plate and the frame in the photovoltaic module to obtain partition temperature data.

3. The method of claim 1, wherein the temperature of the photovoltaic module is controlled by adjusting the amount of air supplied to the photovoltaic module. The retrieving a preset safe temperature threshold, performing difference calculation on each partition temperature data to obtain a temperature deviation value, comprises: Retrieving a preset safe temperature threshold, wherein the safe temperature threshold comprises a cell array safe threshold, a back plate safe threshold and a frame safe threshold, and each safe threshold corresponds to a different partition type identifier; Performing type identification on the partition temperature data to obtain partition type information, and matching the partition type information with the partition type identifier to obtain a target threshold corresponding to each partition; Performing difference calculation on the partition temperature data and the corresponding target threshold to obtain a partition temperature difference value, and grouping the partition temperature difference value according to the partition type to obtain temperature deviation values including a cell temperature difference group, a back plate temperature difference group and a frame temperature difference group.

4. The temperature reduction control method of a photovoltaic module according to claim 3, wherein Determining an over-temperature partition of the photovoltaic module based on the temperature deviation value, comprising: Performing grid block statistics based on the temperature deviation value to obtain a temperature difference distribution table, and performing cumulative sorting on the values in the temperature difference distribution table to obtain a positive or negative temperature difference sequence; wherein the positive or negative temperature difference sequence comprises each partition temperature data exceeding the safe temperature threshold and each partition temperature data being lower than the safe temperature threshold; Performing surface hotspot marking on the photovoltaic module based on the positive temperature difference sequence to obtain a hotspot distribution map, and performing boundary detection on the hotspot distribution map to obtain an abnormal area contour; Dividing the surface of the photovoltaic module into partitions based on the abnormal area contour to obtain an over-temperature partition.

5. The method of claim 1, wherein the temperature of the photovoltaic module is controlled by adjusting the amount of air flow through the photovoltaic module. Determining an over-temperature coverage ratio in the photovoltaic module through the over-temperature partition, comprising: The super-temperature partition is outlined to obtain a super-temperature area contour map, and the super-temperature area contour map is subjected to area calculation to obtain actual area values of the super-temperature partitions; Based on the actual area values, the overall area of the photovoltaic module is subjected to proportion calculation to obtain area proportion data of the super-temperature partitions, and the area proportion data is integrated to obtain a super-temperature area total proportion initial value; The super-temperature area total proportion initial value is subjected to adjacent partition overlap verification, if there is an adjacent super-temperature partition overlap, the overlapping area is deducted and the proportion is recalculated to obtain a corrected super-temperature coverage ratio.

6. The method of claim 1, wherein the temperature of the photovoltaic module is controlled by adjusting the amount of air flow through the photovoltaic module. Based on the cooling demand level, a preset heat dissipation strategy library is matched to obtain a corresponding cooling mode combination, including: Based on the cooling demand level, a corresponding candidate strategy is queried in the heat dissipation strategy library; The candidate strategy is subjected to heat dissipation efficiency calculation to obtain an efficiency data table, and based on the efficiency data table, the energy consumption of each strategy is counted to obtain an energy consumption index; Based on the energy consumption index, the candidate strategies are prioritized to obtain a strategy sorting table, and the first N strategies in the strategy sorting table are combined and matched to obtain a corresponding cooling mode combination of the cooling demand level, where N is a preset threshold.

7. The method of claim 1, wherein the temperature of the photovoltaic module is controlled by adjusting the amount of air flow through the photovoltaic module. Based on the cooling mode combination, the corresponding cooling equipment is driven to continuously cool the photovoltaic module to realize that the temperature deviation is within the range of the safety temperature threshold, including: Each cooling mode in the cooling mode combination is matched with a cooling equipment to obtain a cooling equipment list, and based on the cooling equipment in the cooling equipment list, the super-temperature partitions are associated with positions to obtain an equipment-partition correspondence table; Based on the cooling demand level, each cooling equipment in the equipment-partition correspondence table is parameterized to obtain equipment control parameters, and the equipment control parameters are subjected to linkage timing verification to obtain a linkage control instruction set; The corresponding cooling equipment is driven to operate through the linkage control instruction set, and temperature change data of the super-temperature partitions are collected in real time during operation, and based on the temperature change data and the safety temperature threshold, the equipment control parameters are adjusted to continuously drive the corresponding cooling equipment to operate until the temperature deviation of the super-temperature partitions is within the range of the safety temperature threshold.

8. A cooling control device of a photovoltaic module, characterized by, Including: The acquisition module is used for acquiring real-time temperature data of the photovoltaic module, and classifying and counting the real-time temperature data to obtain partition temperature data; The calculation module is used for calling a preset safety temperature threshold, calculating the difference of each partition temperature data to obtain a temperature deviation value, and determining a super-temperature partition of the photovoltaic module based on the temperature deviation value; The operation module is used for determining a super-temperature coverage ratio in the photovoltaic module through the super-temperature partition, and performing product operation on the super-temperature coverage ratio and the temperature deviation value to obtain a cooling demand level; The cooling module is used for matching a preset heat dissipation strategy library based on the cooling demand level to obtain a corresponding cooling mode combination, and driving the corresponding cooling equipment to continuously cool the photovoltaic module based on the cooling mode combination to realize that the temperature deviation is within the range of the safety temperature threshold. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8. The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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