Thermal management method and system for modular power supply

Abstract

According to the thermal management method and system of the modular power supply, heating elements are identified, temperature sensors are installed to evaluate thermal resistance and thermal capacity under different load conditions, the thermal coupling effect between modules is evaluated, an isolation layer and a thermal bridge are designed to optimize heat flow, load distribution is dynamically adjusted, an intelligent control mechanism is implemented, temperature stability is ensured, and the reliability of the system is improved. An external environment is monitored, a heat dissipation strategy is automatically adjusted to adapt to different conditions, a temperature prediction model is established by using historical data, measures are taken in advance to prevent overheating, and the whole system comprises a temperature monitoring and data analysis module, a thermal coupling effect evaluation and optimization module, an intelligent load distribution and control module and an external environment monitoring and adaptation module. And the temperature prediction and prevention module ensures that the modular power supply can effectively dissipate heat under various working conditions, so that the reliability and efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of power supply thermal management, and in particular to a thermal management method and system for a modular power supply. Background Art

[0002] Modular power supplies are becoming increasingly popular in various industrial applications due to their flexibility and scalability. However, as the operating frequency and efficiency of power electronic devices continue to increase, heat generation has become an important factor limiting the performance of modular power supplies. Effective thermal management is essential to ensure system reliability and extend service life.

[0003] Most traditional power supply systems use fixed heat dissipation strategies, such as constant-speed fans or passive heat sinks, which leads to low heat dissipation efficiency under different load and environmental conditions. Traditional thermal management systems often do not have intelligent load distribution functions and cannot dynamically adjust the load according to the actual temperature of each module, causing some modules to fail prematurely due to overheating. There is thermal coupling between the components inside the modular power supply, that is, the heat generation of one component will directly affect the temperature of the adjacent components, resulting in poor overall thermal management. Many thermal management methods do not take into account changes in external environments such as temperature and humidity, which makes them perform inconsistently in different seasons and regions. Existing methods rarely have the ability to predict future temperature changes, so measures cannot be taken in advance to avoid potential overheating problems. Summary of the invention

[0004] The present invention aims to provide a thermal management method and system for a modular power supply.

[0005] The problem to be solved by the present invention is: to solve the overheating problem of modular power supplies under different load and environmental conditions, by real-time temperature monitoring, thermal characteristic evaluation, intelligent adjustment of load distribution, adaptation to environmental changes and prediction of temperature trends, to ensure the stability and reliability of the power supply system and avoid performance degradation or failure caused by overheating.

[0006] A thermal management method for a modular power supply, the technical solution adopted is as follows: S1: Identify the heating elements in the modular power supply, install temperature sensors at the heating elements, obtain real-time temperature data, analyze the temperature distribution of the power module under different load conditions, analyze the temperature data, and calculate the thermal resistance and thermal capacity under different working conditions including maximum load, minimum load and normal working state; S2: Evaluate the thermal coupling effect between modular power supplies based on the thermal resistance and thermal capacitance calculated in S1, design the isolation layer and thermal bridge between modules, optimize the heat flow distribution, and test the thermal coupling effect under different module configurations; S3: Monitor the internal temperature of the module, dynamically adjust the load distribution according to the temperature of each module, implement an intelligent load distribution mechanism, distribute more loads to modules with lower temperatures, adjust the input voltage, adjust the adaptive voltage to reduce heat generation, adopt a redundant design, and automatically alarm and take corresponding measures when it detects that the temperature of a module is too high or when an abnormal temperature change occurs; S4: Install temperature and humidity environmental sensors to monitor changes in the external environment, automatically adjust the heat dissipation strategy according to changes in the external environment, test the thermal management effect of the power module under different environmental conditions, and preset different thermal management modes according to seasonal changes and geographical regions; S5: Use historical temperature data and combine it with the module's internal load changes, ambient temperature, and ambient humidity to establish a temperature prediction model to predict the temperature change trend in the future. Based on the temperature prediction results, take measures in advance to prevent overheating, and continuously optimize the prediction model in combination with real-time load and environmental data.

[0007] Furthermore, the calculation of thermal resistance and thermal capacity under different working conditions including maximum load, minimum load and normal working state in S1 includes: S11: Maximum load is defined as the maximum output power and current level of the modular power supply, minimum load is defined as the minimum output power and current level of the modular power supply, normal working state is defined as the average output power and current level of the modular power supply, calculate the power loss of each heating element ; S12: Based on the technical specifications of the components, the theoretical thermal resistance of each heating component is calculated. The temperature change of the heating component is recorded by a temperature sensor under maximum load, minimum load and normal working conditions, and the thermal resistance is calculated in combination with the ambient temperature. The difference between the temperature of the heating component and the ambient temperature is calculated. , thermal resistance , the heat input of the modular power supply under maximum load, minimum load and normal working conditions , heat capacity .

[0008] Furthermore, in S2, the thermal coupling effect between modular power supplies is evaluated based on the thermal resistance and thermal capacitance calculated in S1, and the heat flow distribution is optimized, including: S21: Use the thermal resistance and thermal capacitance data calculated in S1 to evaluate the thermal coupling effect between modular power supplies, and use the thermal resistance and thermal capacitance data to establish a simplified thermal network model; S22: The simplified thermal network model simulates the flow of heat by a network consisting of nodes and edges. The nodes represent each heating element of the modular power supply, and the edges represent the heat transfer paths between these heating elements. Indicates that each node uses heat capacity express; S23: Use the established thermal network model to simulate the heat flux distribution under different configurations, and design the isolation layer and thermal bridge between modules according to the simulated heat flux distribution to optimize the heat flux distribution.

[0009] Furthermore, in S3, load distribution is dynamically adjusted according to the temperature of each module, and an intelligent load distribution mechanism is implemented, including: S31: De-noising and filtering the collected temperature data. The filtering adopts sliding average filtering with a filter window size of 5. The average and standard deviation of the past 10 data points are calculated. If the deviation between the current data point and the average value exceeds 2 times the standard deviation, the data point is abnormal and deleted. S32: Define a set of temperature thresholds to determine the temperature status of the module and define the normal operating temperature range , Warning temperature and critical temperature ; S33: Intelligent load distribution mechanism receives the temperature of each module , and outputs a load distribution vector ,in Indicates that the i The load ratio of each module; S34: For each module i , calculate its temperature state , , set the total load to , initialize the load vector is a zero vector, for each module i According to its temperature state Load distribution ratio , ,in is the indicator function, when , then it returns 1, otherwise it returns 0; S35: When there is a module temperature Greater than , then the load ratio of this module Set it to 0 and redistribute the load to other modules with lower temperature, update the load vector to reflect the new load distribution.

[0010] Furthermore, the heat dissipation strategy is automatically adjusted in S4 according to changes in the external environment, and different thermal management modes are preset according to seasonal changes and different geographical regions, including: S41: A low temperature and low humidity environment is defined as a temperature below 20°C and a humidity below 50%. A medium temperature and medium humidity environment is defined as a temperature between 20°C and 30°C and a humidity between 50% and 70%. A high temperature and high humidity environment is defined as a temperature greater than or equal to 30°C and a humidity greater than or equal to 70%. S42: A low-speed fan cooling strategy is used for low-temperature and low-humidity environments; a medium-speed fan cooling strategy is used for medium-temperature and medium-humidity environments; and a high-speed fan cooling strategy is used for high-temperature and high-humidity environments. S43: In winter, the ambient temperature and humidity are low, and a low-speed fan cooling strategy is adopted. In summer, the ambient temperature and humidity are high, and a high-speed fan cooling strategy is adopted. In spring and autumn, the ambient temperature and humidity are moderate, and a medium-speed fan cooling strategy is adopted.

[0011] Furthermore, in S5, the temperature prediction model is established by using the historical temperature data in combination with the load change inside the module, the ambient temperature and the ambient humidity, including: S51: Collect temperature records of the electric modular source in the past period of time, load records in the past period of time, and temperature and humidity records of the external environment; S52: Temperature prediction model is , ,in For some time in the future The predicted temperature after is the temperature change, is the power loss of the modular power supply under current load, is the cooling power, is the influence coefficient of power loss, ambient temperature change, and ambient humidity on temperature change; S53: Continuously collect real-time temperature, load, ambient temperature and humidity data, use the temperature prediction model function to regularly predict temperature changes in the future, compare the predicted temperature with the safety threshold temperature, continue monitoring when the predicted temperature is lower than the safety threshold temperature, and take measures in advance to prevent overheating when the predicted temperature reaches 90% of the safety threshold temperature.

[0012] Further, a thermal management system of a modular power supply is used to implement any one of the thermal management methods of a modular power supply, the thermal management system of the modular power supply comprising: a temperature monitoring and data analysis module, a thermal coupling effect evaluation and optimization module, an intelligent load distribution and control module, an external environment monitoring and adaptation module, and a temperature prediction and prevention module; Temperature monitoring and data analysis module: monitors the temperature of heating components and analyzes the temperature distribution of the power module under different load conditions; Thermal coupling effect evaluation and optimization module: evaluates the thermal coupling effect between modules and designs the isolation layer and thermal bridge structure to optimize the heat flow distribution; Intelligent load distribution and control module: monitors the internal temperature of the module, dynamically adjusts the load distribution according to the temperature, and implements an intelligent load distribution mechanism; External environment monitoring and adaptation module: monitors changes in the external environment and automatically adjusts the heat dissipation strategy according to environmental changes; Temperature prediction and prevention module: Use historical temperature data to establish a prediction model and take measures in advance to prevent overheating.

[0013] The beneficial effects of the present invention are as follows: by installing temperature sensors on key heating components and monitoring temperature data in real time, it is ensured that the working status of the power module is understood in a timely manner, and the analysis of temperature distribution under different load conditions is helpful to accurately evaluate thermal performance and ensure that the system can operate stably under various working conditions. The establishment of a simplified thermal network model can intuitively understand the flow of heat in the system, thereby optimizing the module layout and thermal management strategy; Dynamically adjusting load distribution can not only improve the overall efficiency of the system, but also avoid local overheating problems and extend the service life of the power module. The automatic alarm and response mechanism in case of abnormal temperature changes improves the safety of the system and prevents failure or damage caused by overheating. Automatically adjust the heat dissipation strategy according to changes in external environmental conditions, so that the system can maintain the best working state in different seasons and regions. The preset thermal management mode can be flexibly switched according to geographical location and seasonal characteristics, enhancing the adaptability and reliability of the system; Using historical data to establish a predictive model can help the system respond to potential overheating risks in advance and improve the system's proactive management capabilities. Continuous optimization of the predictive model can gradually improve the prediction accuracy, thereby more effectively preventing the occurrence of overheating problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a thermal management method for a modular power supply; Figure 2 The figure is a module diagram of a thermal management system for a modular power supply. DETAILED DESCRIPTION

[0015] The present invention is further clearly and completely described below, but the protection scope of the present invention is not limited thereto.

[0016] A thermal management method for a modular power supply, the technical solution adopted is as follows: S1: Identify the heating elements in the modular power supply, install temperature sensors at the heating elements, obtain real-time temperature data, analyze the temperature distribution of the power module under different load conditions, analyze the temperature data, and calculate the thermal resistance and thermal capacity under different working conditions including maximum load, minimum load and normal working state; S2: Evaluate the thermal coupling effect between modular power supplies based on the thermal resistance and thermal capacitance calculated in S1, design the isolation layer and thermal bridge between modules, optimize the heat flow distribution, and test the thermal coupling effect under different module configurations; S3: Monitor the internal temperature of the module, dynamically adjust the load distribution according to the temperature of each module, implement an intelligent load distribution mechanism, distribute more loads to modules with lower temperatures, adjust the input voltage, adjust the adaptive voltage to reduce heat generation, adopt a redundant design, and automatically alarm and take corresponding measures when it detects that the temperature of a module is too high or when an abnormal temperature change occurs; S4: Install temperature and humidity environmental sensors to monitor changes in the external environment, automatically adjust the heat dissipation strategy according to changes in the external environment, test the thermal management effect of the power module under different environmental conditions, and preset different thermal management modes according to seasonal changes and geographical regions; S5: Use historical temperature data and combine it with the module's internal load changes, ambient temperature, and ambient humidity to establish a temperature prediction model to predict the temperature change trend in the future. Based on the temperature prediction results, take measures in advance to prevent overheating, and continuously optimize the prediction model in combination with real-time load and environmental data.

[0017] Furthermore, the calculation of thermal resistance and thermal capacity under different working conditions including maximum load, minimum load and normal working state in S1 includes: S11: Maximum load is defined as the maximum output power and current level of the modular power supply, minimum load is defined as the minimum output power and current level of the modular power supply, normal working state is defined as the average output power and current level of the modular power supply, calculate the power loss of each heating element ; S12: Based on the technical specifications of the components, the theoretical thermal resistance of each heating component is calculated. The temperature change of the heating component is recorded by a temperature sensor under maximum load, minimum load and normal working conditions, and the thermal resistance is calculated in combination with the ambient temperature. The difference between the temperature of the heating component and the ambient temperature is calculated. , thermal resistance , the heat input of the modular power supply under maximum load, minimum load and normal working conditions , heat capacity .

[0018] Furthermore, in S2, the thermal coupling effect between modular power supplies is evaluated based on the thermal resistance and thermal capacitance calculated in S1, and the heat flow distribution is optimized, including: S21: Use the thermal resistance and thermal capacitance data calculated in S1 to evaluate the thermal coupling effect between modular power supplies, and use the thermal resistance and thermal capacitance data to establish a simplified thermal network model; S22: The simplified thermal network model simulates the flow of heat by a network consisting of nodes and edges. The nodes represent each heating element of the modular power supply, and the edges represent the heat transfer paths between these heating elements. Indicates that each node uses heat capacity express; S23: Use the established thermal network model to simulate the heat flux distribution under different configurations, and design the isolation layer and thermal bridge between modules according to the simulated heat flux distribution to optimize the heat flux distribution.

[0019] Furthermore, in S3, load distribution is dynamically adjusted according to the temperature of each module, and an intelligent load distribution mechanism is implemented, including: S31: De-noising and filtering the collected temperature data. The filtering adopts sliding average filtering with a filter window size of 5. The average and standard deviation of the past 10 data points are calculated. If the deviation between the current data point and the average value exceeds 2 times the standard deviation, the data point is abnormal and deleted. S32: Define a set of temperature thresholds to determine the temperature status of the module and define the normal operating temperature range , Warning temperature and critical temperature ; S33: Intelligent load distribution mechanism receives the temperature of each module , and outputs a load distribution vector ,in Indicates that the i The load ratio of each module; S34: For each module i , calculate its temperature state , , set the total load to , initialize the load vector is a zero vector, for each module i According to its temperature state Load distribution ratio , ,in is the indicator function, when , then it returns 1, otherwise it returns 0; S35: When there is a module temperature Greater than , then the load ratio of this module Set it to 0 and redistribute the load to other modules with lower temperature, update the load vector to reflect the new load distribution.

[0020] Furthermore, the heat dissipation strategy is automatically adjusted in S4 according to changes in the external environment, and different thermal management modes are preset according to seasonal changes and different geographical regions, including: S41: A low temperature and low humidity environment is defined as a temperature below 20°C and a humidity below 50%. A medium temperature and medium humidity environment is defined as a temperature between 20°C and 30°C and a humidity between 50% and 70%. A high temperature and high humidity environment is defined as a temperature greater than or equal to 30°C and a humidity greater than or equal to 70%. S42: A low-speed fan cooling strategy is used for low-temperature and low-humidity environments; a medium-speed fan cooling strategy is used for medium-temperature and medium-humidity environments; and a high-speed fan cooling strategy is used for high-temperature and high-humidity environments. S43: In winter, the ambient temperature and humidity are low, and a low-speed fan cooling strategy is adopted. In summer, the ambient temperature and humidity are high, and a high-speed fan cooling strategy is adopted. In spring and autumn, the ambient temperature and humidity are moderate, and a medium-speed fan cooling strategy is adopted.

[0021] Furthermore, in S5, the temperature prediction model is established by using the historical temperature data in combination with the load change inside the module, the ambient temperature and the ambient humidity, including: S51: Collect temperature records of the electric modular source in the past period of time, load records in the past period of time, and temperature and humidity records of the external environment; S52: Temperature prediction model is , ,in For some time in the future The predicted temperature after is the temperature change, is the power loss of the modular power supply under current load, is the cooling power, is the influence coefficient of power loss, ambient temperature change, and ambient humidity on temperature change; S53: Continuously collect real-time temperature, load, ambient temperature and humidity data, use the temperature prediction model function to regularly predict temperature changes in the future, compare the predicted temperature with the safety threshold temperature, continue monitoring when the predicted temperature is lower than the safety threshold temperature, and take measures in advance to prevent overheating when the predicted temperature reaches 90% of the safety threshold temperature.

[0022] Further, a thermal management system of a modular power supply is used to implement any one of the thermal management methods of a modular power supply, the thermal management system of the modular power supply comprising: a temperature monitoring and data analysis module, a thermal coupling effect evaluation and optimization module, an intelligent load distribution and control module, an external environment monitoring and adaptation module, and a temperature prediction and prevention module; Temperature monitoring and data analysis module: monitors the temperature of heating components and analyzes the temperature distribution of the power module under different load conditions; Thermal coupling effect evaluation and optimization module: evaluates the thermal coupling effect between modules and designs the isolation layer and thermal bridge structure to optimize the heat flow distribution; Intelligent load distribution and control module: monitors the internal temperature of the module, dynamically adjusts the load distribution according to the temperature, and implements an intelligent load distribution mechanism; External environment monitoring and adaptation module: monitors changes in the external environment and automatically adjusts the heat dissipation strategy according to environmental changes; Temperature prediction and prevention module: Use historical temperature data to establish a prediction model and take measures in advance to prevent overheating.

[0023] The present invention provides a thermal management method and system for a modular power supply, which identifies heating elements and installs temperature sensors to evaluate thermal resistance and thermal capacity under different load conditions, evaluates thermal coupling effects between modules and designs isolation layers and thermal bridges to optimize heat flow, dynamically adjusts load distribution and implements intelligent control mechanisms to ensure temperature stability, monitors the external environment and automatically adjusts heat dissipation strategies to adapt to different conditions, uses historical data to establish a temperature prediction model, and takes measures in advance to prevent overheating. The entire system includes a temperature monitoring and data analysis module, a thermal coupling effect evaluation and optimization module, an intelligent load distribution and control module, an external environment monitoring and adaptation module, and a temperature prediction and prevention module, which ensure that the modular power supply can effectively dissipate heat under various working conditions and improve system reliability and efficiency.

Claims

1. A thermal management method for a modular power supply, characterized in that: include: S1: Identify the heating elements in the modular power supply, install temperature sensors at the heating elements, obtain real-time temperature data, analyze the temperature distribution of the power module under different load conditions, analyze the temperature data, and calculate the thermal resistance and thermal capacity under different working conditions including maximum load, minimum load and normal working state; S2: Evaluate the thermal coupling effect between modular power supplies based on the thermal resistance and thermal capacitance calculated in S1, design the isolation layer and thermal bridge between modules, optimize the heat flow distribution, and test the thermal coupling effect under different module configurations; S3: Monitor the internal temperature of the module, dynamically adjust the load distribution according to the temperature of each module, implement an intelligent load distribution mechanism, distribute more loads to modules with lower temperatures, adjust the input voltage, adjust the adaptive voltage to reduce heat generation, adopt a redundant design, and automatically alarm and take corresponding measures when it detects that the temperature of a module is too high or when an abnormal temperature change occurs; S4: Install temperature and humidity environmental sensors to monitor changes in the external environment, automatically adjust the heat dissipation strategy according to changes in the external environment, test the thermal management effect of the power module under different environmental conditions, and preset different thermal management modes according to seasonal changes and geographical regions; S5: Use historical temperature data and combine it with the module's internal load changes, ambient temperature, and ambient humidity to establish a temperature prediction model to predict the temperature change trend in the future. Based on the temperature prediction results, take measures in advance to prevent overheating, and continuously optimize the prediction model in combination with real-time load and environmental data.

2. A thermal management method for a modular power supply as claimed in claim 1, characterized in that: S1 calculates the thermal resistance and thermal capacity under different working conditions including maximum load, minimum load and normal working state, including: S11: Maximum load is defined as the maximum output power and current level of the modular power supply, minimum load is defined as the minimum output power and current level of the modular power supply, normal working state is defined as the average output power and current level of the modular power supply, calculate the power loss of each heating element ; S12: Based on the technical specifications of the components, the theoretical thermal resistance of each heating component is calculated. The temperature change of the heating component is recorded by a temperature sensor under maximum load, minimum load and normal working conditions, and the thermal resistance is calculated in combination with the ambient temperature. The difference between the temperature of the heating component and the ambient temperature is calculated. , thermal resistance , the heat input of the modular power supply under maximum load, minimum load and normal working conditions , heat capacity .

3. A thermal management method for a modular power supply as claimed in claim 1, characterized in that: In S2, the thermal coupling effect between modular power supplies is evaluated based on the thermal resistance and thermal capacitance calculated in S1, and the heat flow distribution is optimized, including: S21: Use the thermal resistance and thermal capacitance data calculated in S1 to evaluate the thermal coupling effect between modular power supplies, and use the thermal resistance and thermal capacitance data to establish a simplified thermal network model; S22: The simplified thermal network model simulates the flow of heat by a network consisting of nodes and edges. The nodes represent each heating element of the modular power supply, and the edges represent the heat transfer paths between these heating elements. Indicates that each node uses heat capacity express; S23: Use the established thermal network model to simulate the heat flux distribution under different configurations, and design the isolation layer and thermal bridge between modules according to the simulated heat flux distribution to optimize the heat flux distribution.

4. A thermal management method for a modular power supply as claimed in claim 1, characterized in that: In S3, load distribution is dynamically adjusted according to the temperature of each module, and an intelligent load distribution mechanism is implemented, including: S31: De-noising and filtering the collected temperature data. The filtering adopts sliding average filtering with a filter window size of 5. The average and standard deviation of the past 10 data points are calculated. If the deviation between the current data point and the average value exceeds 2 times the standard deviation, the data point is abnormal and deleted. S32: Define a set of temperature thresholds to determine the temperature status of the module and define the normal operating temperature range , Warning temperature and critical temperature ; S33: Intelligent load distribution mechanism receives the temperature of each module , and outputs a load distribution vector ,in Indicates that the i The load ratio of each module; S34: For each module i , calculate its temperature state , , set the total load to , initialize the load vector is a zero vector, for each module i According to its temperature state Load distribution ratio , ,in is the indicator function, when , then it returns 1, otherwise it returns 0; S35: When there is a module temperature Greater than , then the load ratio of this module Set it to 0 and redistribute the load to other modules with lower temperature, update the load vector to reflect the new load distribution.

5. The thermal management method of a modular power supply according to claim 1, characterized in that: The S4 automatically adjusts the heat dissipation strategy according to changes in the external environment, and presets different thermal management modes according to seasonal changes and geographical regions, including: S41: A low temperature and low humidity environment is defined as a temperature below 20°C and a humidity below 50%. A medium temperature and medium humidity environment is defined as a temperature between 20°C and 30°C and a humidity between 50% and 70%. A high temperature and high humidity environment is defined as a temperature greater than or equal to 30°C and a humidity greater than or equal to 70%. S42: A low-speed fan cooling strategy is used for low-temperature and low-humidity environments; a medium-speed fan cooling strategy is used for medium-temperature and medium-humidity environments; and a high-speed fan cooling strategy is used for high-temperature and high-humidity environments. S43: In winter, the ambient temperature and humidity are low, and a low-speed fan cooling strategy is adopted. In summer, the ambient temperature and humidity are high, and a high-speed fan cooling strategy is adopted. In spring and autumn, the ambient temperature and humidity are moderate, and a medium-speed fan cooling strategy is adopted.

6. A thermal management method for a modular power supply as claimed in claim 1, characterized in that: In S5, the temperature prediction model is established by using the historical temperature data and combining the internal load change of the module, the ambient temperature and the ambient humidity, including: S51: Collect temperature records of the electric modular source in the past period of time, load records in the past period of time, and temperature and humidity records of the external environment; S52: Temperature prediction model is , ,in For some time in the future The predicted temperature after is the temperature change, is the power loss of the modular power supply under current load, is the cooling power, is the influence coefficient of power loss, ambient temperature change, and ambient humidity on temperature change; S53: Continuously collect real-time temperature, load, ambient temperature and humidity data, use the temperature prediction model function to regularly predict temperature changes in the future, compare the predicted temperature with the safety threshold temperature, continue monitoring when the predicted temperature is lower than the safety threshold temperature, and take measures in advance to prevent overheating when the predicted temperature reaches 90% of the safety threshold temperature.

7. A thermal management system for a modular power supply, characterized in that: A thermal management method for a modular power supply according to any one of claims 1 to 6, wherein the thermal management system of the modular power supply comprises: a temperature monitoring and data analysis module, a thermal coupling effect evaluation and optimization module, an intelligent load distribution and control module, an external environment monitoring and adaptation module, and a temperature prediction and prevention module; Temperature monitoring and data analysis module: monitors the temperature of heating components and analyzes the temperature distribution of the power module under different load conditions; Thermal coupling effect evaluation and optimization module: evaluates the thermal coupling effect between modules and designs the isolation layer and thermal bridge structure to optimize the heat flow distribution; Intelligent load distribution and control module: monitors the internal temperature of the module, dynamically adjusts the load distribution according to the temperature, and implements an intelligent load distribution mechanism; External environment monitoring and adaptation module: monitors changes in the external environment and automatically adjusts the heat dissipation strategy according to environmental changes; Temperature prediction and prevention module: Use historical temperature data to establish a prediction model and take measures in advance to prevent overheating.

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