Power distribution system and method of modular power supply

Abstract

The invention discloses a power distribution system and method for a modular power supply, and aims to optimize power distribution through an intelligent means, improve the operation efficiency and stability of the system, and control a unit to check the states of all power supply modules, and evaluate the required total power output according to the load demand. Then, based on the capability of each module and a redundancy planning principle, an appropriate module combination is selected to meet the load requirement, and necessary voltage or current adjustment is carried out to ensure stable power supply. In the operation process, the control system continuously monitors the state and the overall performance of each module, and once a fault or an abnormal condition is detected, measures such as automatic switching to a standby module are taken immediately, so that the continuity and the safety of the system are ensured. In addition, through analysis and learning of historical data, the system can continuously optimize a power distribution strategy, and efficiency and reliability are further improved. And the ability of the system to deal with emergency situations is also enhanced.

Description

Technical Field

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

[0002] In traditional single large power supply systems, once a failure occurs, it is often necessary to shut down for maintenance, affecting the operation of the entire system. Traditional power supplies may not be able to efficiently cope with load fluctuations, resulting in energy waste. Under different load conditions, the efficiency of the power module fluctuates greatly, and it is impossible to achieve optimal energy utilization, resulting in energy waste. And due to the uneven distribution of loads, some power modules may be in a high-load or high-temperature working state for a long time, increasing the risk of overheating and failure. With the development of technology and the growth of business needs, power demand may increase rapidly. Traditional fixed-configuration power supply systems are difficult to adapt to these changes quickly, and a failure in a single power point may cause the entire system to fail. Modular design improves the system's fault tolerance through redundant configuration (i.e. setting up additional backup modules), ensuring continuous power supply even if some modules fail, enhancing overall stability. Summary of the invention

[0003] A method for distributing power of a modular power supply comprises the following steps; S1. System initialization and module status detection: When the system starts, the power management unit (PMU) identifies all connected power modules, obtains the rated power, conversion efficiency, load capacity and working status parameters of each module, and preliminarily allocates power according to the rated power and current load requirements of each module to ensure that the power supply at startup meets the minimum system requirements; S2. Real-time load monitoring: The total load demand of the entire system, including the power consumption of each load device, is monitored in real time through sensors. When the system load demand changes, the management unit will automatically detect the power change through algorithms and update the allocation strategy in time to ensure the best working efficiency of the modules under dynamic load conditions. S3. Module efficiency priority allocation: According to the current conversion efficiency of each power module, the system will sort the efficiency of each module in real time, give priority to the modules with higher conversion efficiency, gradually allocate power, and gradually enable high-efficiency modules for power output according to load requirements. When the high-efficiency modules cannot meet the demand, the low-efficiency modules will be enabled; S4. Load balancing and redundancy management: The system will continuously monitor the load of each module to ensure load balance between power modules, and avoid some modules being in a high-load state for a long time, resulting in overheating and overload. Redundant modules: To improve reliability, some modules can be used as redundant modules, which will be automatically enabled when the system load is too large or the module fails to ensure the continuity of power supply; S5. Overload and fault protection: The system continuously monitors the load of each module. When a module is found to be close to overload and exceeds its rated power, the management unit automatically adjusts the load distribution and transfers part of the load to other modules to avoid module damage. When a module fails or its efficiency decreases, the system automatically isolates it, stops distributing power to the module, and activates the backup module to maintain normal power supply. S6. Dynamic adjustment and optimization: The system continuously optimizes the power distribution strategy according to the working status, temperature, and efficiency parameters of the power modules to ensure that all modules maintain the best operating status under different load conditions. When the load fluctuates greatly, the system can automatically adapt by adjusting the working status of each module (such as starting and stopping or adjusting power output) to ensure efficient operation; S7. Intelligent shutdown and energy-saving mode: When the system load is low, the management unit will automatically shut down some modules and enter energy-saving mode, leaving only a small number of high-efficiency modules running to reduce unnecessary energy consumption. When the load demand increases, the system will automatically start more modules to balance the power supply, while avoiding all modules from running at full load for a long time, thus extending the service life of the equipment.

[0004] Further, a method for distributing power of a modular power supply, In step S2, when the system load demand changes, the management unit will automatically detect the power change through the algorithm and update the allocation strategy in time. The specific steps are as follows; S21. Monitoring load demand changes: The management unit continuously collects system load demand data through sensors and monitoring modules. The data includes current, voltage, power, and temperature parameters. The real-time data is analyzed through filtering algorithms to detect power changes in the system: sudden increase or decrease in load; S22. Power change analysis: Based on historical data and current changes, calculate the rate and magnitude of power changes to determine whether it is a short-term fluctuation or a long-term change. Analyze historical load data to predict the next possible load demand and make strategic adjustments in advance. S23. Calculation of power regulation strategy: According to the change of power demand, the management unit calculates a new power balance plan to match system supply with demand, reduce energy consumption and overload risks, and use dynamic programming optimization algorithms to find the best balance point between multiple objectives (such as minimum power consumption and maximum efficiency); S24. Allocation strategy update: According to the new power regulation strategy, the management unit reallocates the resources in the system and reallocates the power supply to different loads. It responds to the change of power demand by adjusting the working mode of the equipment: frequency regulation, load switching, and backup power activation. In the case of large changes in system power demand or emergencies, the management unit may activate the backup system or reduce some non-critical tasks to ensure the stable operation of the system. S25. Feedback control and continuous optimization: Continuously monitor the operation of the system, analyze the effectiveness of the allocation strategy, and adjust the strategy again when new power changes or instability are detected. Adopt an adaptive control algorithm and automatically optimize the next adjustment strategy based on feedback information after each adjustment, thereby improving the system's response speed and adaptability.

[0005] Further, a method for distributing power of a modular power supply, In step S6, the system uses a machine learning algorithm to continuously optimize the power distribution strategy based on the working status, temperature, and efficiency parameters of the power module. S61. Data collection and preprocessing: The system collects various parameter data of the power module through sensors, including: working status: such as current, voltage, power output, temperature: temperature data of the power module and key components, efficiency parameters: conversion efficiency of each module under different loads, remove abnormal data and noise, fill in missing data, ensure the data quality of input machine learning models, extract key features (such as working status, temperature fluctuations, load changes, etc.), and standardize or normalize these features to make the data more suitable for model training; S62. Model training: Use historical data (previous power allocation records, module temperature changes and their corresponding efficiencies) to train the random forest model so that it can learn the relationship between input features and target outputs (such as optimal power allocation strategies). Through cross-validation and hyperparameter tuning, the prediction accuracy and generalization ability of the model can be improved. S63. Strategy optimization and prediction: Through machine learning models, predict the trend of power demand changes in the future, prepare optimization strategies in advance, and generate the optimal power distribution strategy based on the temperature, current load and efficiency parameters of the power modules. For example, determine which power modules have the highest efficiency under different load conditions and give priority to these modules, adjust the working status of the power modules or switch different modules to reduce energy consumption or avoid overheating, and use machine learning algorithms combined with optimization algorithms (such as genetic algorithms or particle swarm algorithms) to find a balance between multiple optimization goals (such as energy efficiency, temperature control, and load balancing); S64. Real-time strategy adjustment and execution: Based on the prediction and optimization results of the model, the system will dynamically adjust the allocation strategy of the power modules, including: redistributing the load according to the efficiency and temperature status of each power module to ensure efficient operation; automatically switching to the backup module to prevent overheating when the temperature of some power modules is too high; allocating the load to different power modules to avoid a certain module from being overloaded for a long time, resulting in increased losses; converting the new allocation strategy into specific control instructions and issuing them to the power modules for adjustment; S65. Feedback mechanism and model update: Continuously monitor the working status, temperature and efficiency changes of the power module after adjusting the strategy, evaluate the effect of the new strategy, and regularly retrain or update the machine learning model based on actual operating data and feedback results to enable it to maintain higher accuracy in the face of new changes. Through feedback and adaptive mechanisms, continuously optimize its power distribution strategy to improve overall energy efficiency and stability.

[0006] A power distribution system for a modular power supply, the power distribution system for a modular power supply is used to implement any one of the power distribution methods for a modular power supply; the power distribution system for a modular power supply comprises: a data collection module, a management unit module, a communication module, a backup power supply and redundancy module, a user interface and monitoring module, and a feedback and self-learning module; The data collection module collects various parameters of the power module and the system in real time through sensors, collects data for system analysis and decision-making, and monitors the following items: power module status: output power, current, voltage; temperature sensor: monitors the power module, key components and ambient temperature; load sensor: monitors the power demand of the system load equipment and provides real-time feedback on load changes; Management unit module: responsible for the central control and decision-making of the system, performs data processing, strategy optimization and distribution control, collects data from sensors, performs preprocessing (such as cleaning and normalization), uses machine learning algorithms for model training and load prediction, generates power distribution strategies, combines real-time status data, performs multi-objective optimization (such as efficiency, temperature, load balance), and dynamically adjusts the output and working status of the power module according to the optimization strategy; Communication module: provides communication channels between modules to ensure the transmission of data and control signals, high-speed, low-latency data transmission, ensure system real-time performance, support two-way communication, and can upload sensor data and issue control instructions; Backup power supply and redundant modules: provide system redundancy and emergency power support to ensure that the system can still operate stably when the power module fails or is overloaded. The redundant module is usually in standby mode and automatically takes over the load when the main power module fails. Some modules support fast switching or automatic recovery to ensure uninterrupted system power supply. User interface and monitoring module: used to monitor system status, adjust configuration and perform fault diagnosis, display the output power, temperature, efficiency and other parameters of the power module in real time, and allow users to manually intervene in the system's power distribution strategy, such as adjusting priority or power threshold; Feedback and self-learning module: Based on the system's feedback mechanism and adaptive control algorithm, it continuously optimizes the power distribution strategy, collects the system's operating status and regulation results, and feeds them back to the management unit. Through historical data and operating results, it adjusts the prediction and strategy generation capabilities of the machine learning model.

[0007] Beneficial effects of the present invention: Through real-time monitoring and machine learning algorithms, the system can dynamically adjust power distribution according to load demand, ensure that each power module works at optimal efficiency, and reduce energy waste. The modular design allows hot-swappable power modules and has a redundant backup mechanism. When a power module fails, the backup module can quickly take over to ensure the continuity of power supply and avoid power interruptions. The sensor module monitors the temperature and load status of the power module in real time, and the management unit adjusts and controls in time according to the monitoring data to balance the load distribution, reduce the risk of long-term overload or overheating of the power module, and extend the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a method for distributing power of a modular power supply; DETAILED DESCRIPTION

[0009] A method for distributing power of a modular power supply comprises the following steps; S1. System initialization and module status detection: When the system starts, the power management unit (PMU) identifies all connected power modules, obtains the rated power, conversion efficiency, load capacity and working status parameters of each module, and preliminarily allocates power according to the rated power and current load requirements of each module to ensure that the power supply at startup meets the minimum system requirements; S2. Real-time load monitoring: The total load demand of the entire system, including the power consumption of each load device, is monitored in real time through sensors. When the system load demand changes, the management unit will automatically detect the power change through algorithms and update the allocation strategy in time to ensure the best working efficiency of the modules under dynamic load conditions. S3. Module efficiency priority allocation: According to the current conversion efficiency of each power module, the system will sort the efficiency of each module in real time, give priority to the modules with higher conversion efficiency, gradually allocate power, and gradually enable high-efficiency modules for power output according to load requirements. When the high-efficiency modules cannot meet the demand, the low-efficiency modules will be enabled; S4. Load balancing and redundancy management: The system will continuously monitor the load of each module to ensure load balance between power modules, and avoid some modules being in a high-load state for a long time, resulting in overheating and overload. Redundant modules: To improve reliability, some modules can be used as redundant modules, which will be automatically enabled when the system load is too large or the module fails to ensure the continuity of power supply; S5. Overload and fault protection: The system continuously monitors the load of each module. When a module is found to be close to overload and exceeds its rated power, the management unit automatically adjusts the load distribution and transfers part of the load to other modules to avoid module damage. When a module fails or its efficiency decreases, the system automatically isolates it, stops distributing power to the module, and activates the backup module to maintain normal power supply. S6. Dynamic adjustment and optimization: The system continuously optimizes the power distribution strategy according to the working status, temperature, and efficiency parameters of the power modules to ensure that all modules maintain the best operating status under different load conditions. When the load fluctuates greatly, the system can automatically adapt by adjusting the working status of each module (such as starting and stopping or adjusting power output) to ensure efficient operation; S7. Intelligent shutdown and energy-saving mode: When the system load is low, the management unit will automatically shut down some modules and enter energy-saving mode, leaving only a small number of high-efficiency modules running to reduce unnecessary energy consumption. When the load demand increases, the system will automatically start more modules to balance the power supply, while avoiding all modules from running at full load for a long time, thus extending the service life of the equipment.

[0010] Further, a method for distributing power of a modular power supply, In step S2, when the system load demand changes, the management unit will automatically detect the power change through the algorithm and update the allocation strategy in time. The specific steps are as follows; S21. Monitoring load demand changes: The management unit continuously collects system load demand data through sensors and monitoring modules. The data includes current, voltage, power, and temperature parameters. The real-time data is analyzed through filtering algorithms to detect power changes in the system: sudden increase or decrease in load; S22. Power change analysis: Based on historical data and current changes, calculate the rate and magnitude of power changes to determine whether it is a short-term fluctuation or a long-term change. Analyze historical load data to predict the next possible load demand and make strategic adjustments in advance. S23. Calculation of power regulation strategy: According to the change of power demand, the management unit calculates a new power balance plan to match system supply with demand, reduce energy consumption and overload risks, and use dynamic programming optimization algorithms to find the best balance point between multiple objectives (such as minimum power consumption and maximum efficiency); S24. Allocation strategy update: According to the new power regulation strategy, the management unit reallocates the resources in the system and reallocates the power supply to different loads. It responds to the change of power demand by adjusting the working mode of the equipment: frequency regulation, load switching, and backup power activation. In the case of large changes in system power demand or emergencies, the management unit may activate the backup system or reduce some non-critical tasks to ensure the stable operation of the system. S25. Feedback control and continuous optimization: Continuously monitor the operation of the system, analyze the effectiveness of the allocation strategy, and adjust the strategy again when new power changes or instability are detected. Adopt an adaptive control algorithm and automatically optimize the next adjustment strategy based on feedback information after each adjustment, thereby improving the system's response speed and adaptability.

[0011] Further, a method for distributing power of a modular power supply, In step S6, the system uses a machine learning algorithm to continuously optimize the power distribution strategy based on the working status, temperature, and efficiency parameters of the power module. S61. Data collection and preprocessing: The system collects various parameter data of the power module through sensors, including: working status: such as current, voltage, power output, temperature: temperature data of the power module and key components, efficiency parameters: conversion efficiency of each module under different loads, remove abnormal data and noise, fill in missing data, ensure the data quality of input machine learning models, extract key features (such as working status, temperature fluctuations, load changes, etc.), and standardize or normalize these features to make the data more suitable for model training; S62. Model training: Use historical data (previous power allocation records, module temperature changes and their corresponding efficiencies) to train the random forest model so that it can learn the relationship between input features and target outputs (such as optimal power allocation strategies). Through cross-validation and hyperparameter tuning, the prediction accuracy and generalization ability of the model can be improved. S63. Strategy optimization and prediction: Through machine learning models, predict the trend of power demand changes in the future, prepare optimization strategies in advance, and generate the optimal power distribution strategy based on the temperature, current load and efficiency parameters of the power modules. For example, determine which power modules have the highest efficiency under different load conditions and give priority to these modules, adjust the working status of the power modules or switch different modules to reduce energy consumption or avoid overheating, and use machine learning algorithms combined with optimization algorithms (such as genetic algorithms or particle swarm algorithms) to find a balance between multiple optimization goals (such as energy efficiency, temperature control, and load balancing); S64. Real-time strategy adjustment and execution: Based on the prediction and optimization results of the model, the system will dynamically adjust the allocation strategy of the power modules, including: redistributing the load according to the efficiency and temperature status of each power module to ensure efficient operation; automatically switching to the backup module to prevent overheating when the temperature of some power modules is too high; allocating the load to different power modules to avoid a certain module from being overloaded for a long time, resulting in increased losses; converting the new allocation strategy into specific control instructions and issuing them to the power modules for adjustment; S65. Feedback mechanism and model update: Continuously monitor the working status, temperature and efficiency changes of the power module after adjusting the strategy, evaluate the effect of the new strategy, and regularly retrain or update the machine learning model based on actual operating data and feedback results to enable it to maintain higher accuracy in the face of new changes. Through feedback and adaptive mechanisms, continuously optimize its power distribution strategy to improve overall energy efficiency and stability.

[0012] A power distribution system for a modular power supply, the power distribution system for a modular power supply is used to implement any one of the power distribution methods for a modular power supply; the power distribution system for a modular power supply comprises: a data collection module, a management unit module, a communication module, a backup power supply and redundancy module, a user interface and monitoring module, and a feedback and self-learning module; The data collection module collects various parameters of the power module and the system in real time through sensors, collects data for system analysis and decision-making, and monitors the following items: power module status: output power, current, voltage; temperature sensor: monitors the power module, key components and ambient temperature; load sensor: monitors the power demand of the system load equipment and provides real-time feedback on load changes; Management unit module: responsible for the central control and decision-making of the system, performs data processing, strategy optimization and distribution control, collects data from sensors, performs preprocessing (such as cleaning and normalization), uses machine learning algorithms for model training and load prediction, generates power distribution strategies, combines real-time status data, performs multi-objective optimization (such as efficiency, temperature, load balance), and dynamically adjusts the output and working status of the power module according to the optimization strategy; Communication module: provides communication channels between modules to ensure the transmission of data and control signals, high-speed, low-latency data transmission, ensure system real-time performance, support two-way communication, and can upload sensor data and issue control instructions; Backup power supply and redundant modules: provide system redundancy and emergency power support to ensure that the system can still operate stably when the power module fails or is overloaded. The redundant module is usually in standby mode and automatically takes over the load when the main power module fails. Some modules support fast switching or automatic recovery to ensure uninterrupted system power supply. User interface and monitoring module: used to monitor system status, adjust configuration and perform fault diagnosis. It displays the output power, temperature, efficiency and other parameters of the power module in real time, allowing users to manually intervene in the system's power distribution strategy, such as adjusting priority or power threshold. It also issues an alarm when the system is abnormal (such as overtemperature or overload), and records fault logs for subsequent analysis. Feedback and self-learning module: Based on the system's feedback mechanism and adaptive control algorithm, it continuously optimizes the power distribution strategy, collects the system's operating status and regulation results, and feeds them back to the management unit. Through historical data and operating results, it adjusts the prediction and strategy generation capabilities of the machine learning model.

Claims

1. A method for distributing power of a modular power supply, characterized in that: The steps include: S1. System initialization and module status detection: When the system starts, the power management unit identifies all connected power modules, obtains the rated power, conversion efficiency, load capacity and working status parameters of each module, and preliminarily allocates power according to the rated power of each module and the current load demand; S2. Real-time load monitoring: The total load demand of the entire system, including the power consumption of each load device, is monitored in real time through sensors. When the system load demand changes, the management unit will automatically detect the power change through algorithms and update the allocation strategy in a timely manner; S3. Module efficiency priority allocation: According to the current conversion efficiency of each power module, the system will sort the efficiency of each module in real time, give priority to the modules with higher conversion efficiency, gradually allocate power, and gradually enable high-efficiency modules for power output according to load requirements. When the high-efficiency modules cannot meet the demand, the low-efficiency modules will be enabled; S4. Load balancing and redundancy management: The system will continuously monitor the load of each module to ensure load balance between power modules, and avoid some modules being in a high-load state for a long time, resulting in overheating and overload. To improve reliability, redundant modules are set up and automatically enabled when the system load is too large or a module fails; S5. Overload and fault protection: The system continuously monitors the load of each module. When a module is found to be close to overload and exceeds its rated power, the management unit automatically adjusts the load distribution and transfers part of the load to other modules to avoid module damage. When a module fails and its efficiency decreases, the system automatically isolates it, stops distributing power to the module, and activates the backup module to maintain normal power supply. S6. Dynamic adjustment and optimization: The system uses machine learning algorithms to continuously optimize the power distribution strategy based on the working status, temperature, and efficiency parameters of the power modules. All modules maintain the best operating status under different load conditions. When the load fluctuates greatly, the system can automatically adapt by adjusting the working status of each module. S7. Intelligent shutdown and energy-saving mode: When the system load is low, the management unit will automatically shut down some modules and enter energy-saving mode, leaving only a small number of high-efficiency modules running to reduce unnecessary energy consumption. When the load demand increases, the system will automatically start more modules to balance the power supply and avoid all modules running at full load for a long time, thus extending the service life of the equipment.

2. A method for distributing power of a modular power supply as claimed in claim 1, characterized in that: In step S2, when the system load demand changes, the management unit will automatically detect the power change through the algorithm and update the allocation strategy in time. The specific steps are as follows; S21. Monitoring load demand changes: The management unit continuously collects system load demand data through sensors and monitoring modules. The data includes current, voltage, power, and temperature parameters. The real-time data is analyzed through filtering algorithms to detect power changes in the system: sudden increase or decrease in load; S22. Power change analysis: Based on historical data and current changes, calculate the rate and magnitude of power changes to determine whether it is a short-term fluctuation or a long-term change. Analyze historical load data to predict the next possible load demand and make strategic adjustments in advance. S23. Calculation of power regulation strategy: According to the change of power demand, the management unit calculates a new power balance plan to match system supply with demand, reduce energy consumption and overload risks, and use dynamic programming optimization algorithm to find a balance between multiple goals: minimum power consumption and maximum efficiency; S24. Allocation strategy update: According to the new power regulation strategy, the management unit reallocates the resources in the system and reallocates the power supply to different loads. It responds to the change of power demand by adjusting the working mode of the equipment: frequency regulation, load switching, and backup power activation. In the case of large changes in system power demand and emergencies, the backup system is activated and some non-critical tasks are unloaded. S25. Feedback control and continuous optimization: Continuously monitor the operation of the system, analyze the effectiveness of the allocation strategy, detect new power changes and instabilities, adjust the strategy again, and use an adaptive control algorithm to automatically optimize the next adjustment strategy based on feedback information after each adjustment, thereby improving the system's response speed and adaptability.

3. The method for distributing power of a modular power supply according to claim 1, characterized in that: In step S6, the system uses a machine learning algorithm to continuously optimize the power distribution strategy based on the working status, temperature, and efficiency parameters of the power module. S61. Data collection and preprocessing: The system collects various parameter data of the power module through sensors, including: working status: such as current, voltage, power output, temperature: temperature data of the power module and key components, efficiency parameters: conversion efficiency of each module under different loads, removes abnormal data and noise, fills in missing data, extracts key features: working status, temperature fluctuation, load change, and standardizes and normalizes the above features; S62. Model training: Use historical data: power distribution records, module temperature changes and their corresponding efficiencies to train the random forest model and obtain the optimal power distribution strategy. Through cross-validation and hyperparameter tuning, improve the prediction accuracy and generalization ability of the model. S63. Strategy optimization and prediction: Through machine learning models, the trend of power demand changes in the future is predicted, and optimization strategies are prepared in advance. According to the temperature, current load and efficiency parameters of the power module, the model generates the optimal power distribution strategy. The distribution strategy includes: determining which power module has the highest efficiency under different load conditions and giving priority to these modules, adjusting the working state of the power module or switching different modules to reduce energy consumption or avoid overheating, and using machine learning algorithms combined with genetic optimization algorithms to find a balance between multiple optimization goals: energy efficiency, temperature control, and load balance; S64. Real-time strategy adjustment and execution: Based on the prediction and optimization results of the model, the system will dynamically adjust the allocation strategy of the power modules, including: redistributing the load according to the efficiency and temperature status of each power module to ensure efficient operation; automatically switching to the backup module to prevent overheating when the temperature of some power modules is too high; allocating the load to different power modules to avoid a certain module from being overloaded for a long time, resulting in increased losses; converting the new allocation strategy into specific control instructions and issuing them to the power modules for adjustment; S65. Feedback mechanism and model update: Continuously monitor the working status, temperature and efficiency changes of the power module after adjusting the strategy, evaluate the effect of the new strategy, and regularly retrain or update the machine learning model based on actual operating data and feedback results to enable it to maintain higher accuracy in the face of new changes. Through feedback and adaptive mechanisms, continuously optimize its power distribution strategy to improve overall energy efficiency and stability.

4. A power distribution system for a modular power supply, characterized in that: The power distribution system of a modular power supply is used to implement a power distribution method of a modular power supply as described in any one of claims 1 to 3; The power distribution system of the modular power supply comprises: a data collection module, a management unit module, a communication module, a backup power supply and redundancy module, a user interface and monitoring module, and a feedback and self-learning module; The data collection module collects various parameters of the power module and the system in real time through sensors, collects data for system analysis and decision-making, and monitors the following items: power module status: output power, current, voltage; temperature sensor: monitors the power module, key components and ambient temperature; load sensor: monitors the power demand of the system load equipment and provides real-time feedback on load changes; Management unit module: responsible for the central control and decision-making of the system, performs data processing, strategy optimization and distribution control, collects data from sensors and performs preprocessing, uses machine learning algorithms for model training and load forecasting, generates power distribution strategies, combines real-time status data, and performs multi-objective optimization; Communication module: provides communication channels between modules, high-speed, low-latency data transmission, ensures system real-time performance, supports two-way communication, and can upload sensor data and issue control instructions; Backup power supply and redundant module: Provide system redundancy and emergency power support to ensure that the system can still operate stably when the power module fails or is overloaded. The redundant module is usually in standby mode. User interface and monitoring module: used to monitor system status, adjust configuration and perform fault diagnosis. It displays the output power, temperature, efficiency and other parameters of the power module in real time, allows users to manually intervene in the system's power distribution strategy, issues an alarm when the system is abnormal, and records fault logs. Feedback and self-learning module: Based on the system's feedback mechanism and adaptive control algorithm, it continuously optimizes the power distribution strategy, collects the system's operating status and regulation results, and feeds them back to the management unit. Through historical data and operating results, it adjusts the prediction and strategy generation capabilities of the machine learning model.

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