Modular power supply efficiency intelligent management method

Abstract

The invention discloses a modular power supply efficiency intelligent management method, and aims to record the working state, power output and conversion efficiency of a power supply module in real time through a sensor and monitoring software, carry out system energy efficiency analysis regularly, and can significantly improve the overall performance of a power supply system. Firstly, according to the scheme, by optimizing power distribution and fault early warning, the energy efficiency can be effectively improved, energy waste is reduced, and the operation cost is reduced. Meanwhile, the system can dynamically adjust the load of each module, prolong the service life of equipment, reduce the equipment fault risk through preventive maintenance, and improve the stability and reliability of the system. In addition, an energy efficiency analysis report of the system provides a data-driven decision basis for a manager, helps to discover the potential of efficiency improvement, and guides the future optimization direction. And by reducing energy consumption, the power supply system can also reduce carbon emission, promote sustainable development of enterprises and improve environmental protection benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical management, and in particular to a modular power supply efficiency intelligent management method. Background Art

[0002] In the power management system, inefficient operation, excessive energy consumption and potential failure hazards are common problems. These problems not only increase the operating costs of enterprises, but may also cause system instability, affecting business continuity and security. In particular, in traditional power management systems, the lack of real-time monitoring and effective data analysis of power modules leads to low operating efficiency of power modules under different load conditions. Due to frequent load changes, power modules often fail to dynamically adjust according to actual needs, resulting in the inability to maintain optimal efficiency at low or high loads, which in turn causes a lot of energy waste.

[0003] In addition, traditional systems often rely on regular or passive maintenance methods, making it difficult to detect and resolve potential equipment problems in a timely manner. Without real-time monitoring of key indicators such as power, temperature, and efficiency, modules may experience performance degradation or even damage due to overload or poor heat dissipation, leading to sudden failures. This not only shortens the life of the equipment, but may also trigger a chain reaction, affecting the stability and security of the entire system, and ultimately leading to serious consequences such as system downtime, production interruption, or data loss.

[0004] Especially in some industries with high requirements for power supply reliability, such as data centers, telecommunications base stations, manufacturing, etc., the failure or inefficient operation of the power supply system may bring huge economic losses and negative impacts. Therefore, how to improve the efficiency, reliability and safety of the power supply system through advanced monitoring technology and data analysis has become a key issue that needs to be solved in the field of modern power management. Summary of the invention

[0005] A modular power efficiency intelligent management method comprises the following steps: S1. Demand analysis and module configuration: Detect the current load demand of the system, determine the power range required by the power module, and dynamically select and configure the appropriate power module according to the load demand to reduce power redundancy and avoid unnecessary energy consumption; S2. Intelligent control of power modules: Automatically enable or disable certain power modules based on load changes. For example, disable some power modules when the load is low, and gradually enable additional modules when the load is high to ensure the best system efficiency. According to the load distribution, adjust the output ratio of each module to achieve reasonable power distribution and avoid overloading or underloading of a single module. S3. Efficiency optimization algorithm: Use artificial intelligence or rule-based algorithms to monitor the working efficiency of each power module in real time and make adjustments based on the data. For example, high-efficiency modules are used first and low-efficiency modules are shut down. Through historical data and real-time load change trends, future load fluctuations are predicted, and the operating status of the modules is adjusted in advance to reduce power conversion losses. S4. Parallel connection and redundancy management of power modules: When multiple power modules work in parallel, the power distribution between modules is adjusted through intelligent algorithms to avoid overload or performance bottleneck of a single module, while ensuring that each module operates in the highest efficiency range. Redundant module management: When necessary, certain redundant modules are configured to improve system reliability. When the load fluctuates, the redundant modules can automatically connect or exit work to ensure stable power supply. S5. Fault diagnosis and self-healing: Real-time monitoring of the power module, detection of temperature, voltage, current and other parameters, timely detection of abnormalities and fault diagnosis. When a fault occurs, the system can automatically isolate the problem module and reconfigure other normal modules to continue working to ensure system stability. S6. Energy efficiency monitoring and data analysis: Through sensors and monitoring software, the working status, power output and conversion efficiency of each module are recorded in real time, and detailed energy efficiency analysis reports are provided. Based on the collected data, system energy efficiency analysis is performed regularly to find potential optimization space and automatically adjust module configuration or operation strategy; S7. Remote control and upgrade: Remote management and control: Through the cloud or remote platform, the power module can be managed and regulated in real time, the power operation status can be monitored, and the configuration and start and stop modules can be adjusted remotely.

[0006] Further, a modular power efficiency intelligent management method, In step S3, an artificial intelligence algorithm is used to monitor the working efficiency of each power module in real time and make adjustments based on the data. The specific steps are as follows; S31. System initialization and preparation: Ensure that all power modules are equipped with necessary sensors (such as current sensors, voltage sensors, temperature sensors, etc.) to monitor key parameters, establish a reliable communication network, and ensure that each module can transmit data to the central control system; S32. Data collection: The sensors installed on each power module continuously collect the data during operation, including but not limited to: input / output voltage and current, temperature, power factor, energy consumption, and clean and format the collected data to remove abnormal values ​​and fill in missing values ​​to ensure data quality; S33. Data storage and management: Store the processed data in the database, ensure data security, and take appropriate encryption measures to prevent data leakage; S34. Data analysis and modeling: Extract useful features from raw data to prepare for subsequent model training. Select a regression model to predict the model's future energy consumption according to the problem requirements, or use a classification model to identify abnormal conditions. Use historical data to train a regression model to predict the model, and continuously optimize until satisfactory performance indicators are achieved. Verify the accuracy and robustness of the model through cross-validation or other methods. S35. Real-time monitoring and decision-making: Deploy the trained model in the production environment so that it can receive real-time data and respond quickly. According to the output of the model, the working efficiency of the current power module is evaluated. When it is found that the working efficiency of a module is lower than the expected standard, the system can automatically adjust its working parameters (such as changing the output power, switching the working mode, etc.) to improve efficiency. S36. Continuous optimization and maintenance: Establish a feedback mechanism to input the adjusted effects as new training data to the model to help the model continuously learn and improve, retrain the model regularly to adapt to new operating conditions or equipment changes, and continuously monitor the performance of the entire system to ensure long-term stable operation; S37. User Interface and Reporting: Provides a user-friendly graphical interface to display the status, efficiency and other important information of the power modules, set threshold alarms, promptly notify relevant personnel when abnormal conditions are detected, and regularly generate detailed performance reports for management review and decision support.

[0007] Further, a modular power efficiency intelligent management method, When multiple power modules work in parallel in step S4, the power distribution between modules is adjusted through an intelligent algorithm to avoid overload and performance bottleneck of a single module. The specific steps are as follows; S41. System initialization and module detection: When the system starts, first identify and register all parallel power modules, and determine the capacity, rated power range, and efficiency curve key parameters of each module; S42. Real-time data collection: collect the voltage, current, temperature, and power output data of each module in real time to accurately understand the load conditions of each module, and monitor the changes in the overall load to ensure that the total power of the power module meets the system requirements; S43. Intelligent power allocation algorithm: pre-process the collected data, remove noise and outliers, and perform normalization Load balancing algorithm: Power allocation algorithm based on efficiency optimization: According to the efficiency curve of each power module, the optimal working point of each module under the current conditions is calculated, and the power output of the high-efficiency module is allocated first to reduce energy consumption; Load balancing algorithm: The output power of each module is dynamically adjusted through the algorithm to ensure that the load of each module is relatively even, avoiding overloading of a single module or being in an inefficient range for a long time; Dynamic priority algorithm: Set different load priorities for each module, start the modules with high efficiency first, and dynamically adjust the priority and output power according to load changes; S44. Power regulation and module collaboration: Based on the calculation results of the intelligent algorithm, the output power of each power module is dynamically adjusted to ensure load balance and efficient operation. According to the current load demand, some power modules are intelligently started and stopped. When the load is low, some modules are shut down to reduce energy consumption. When the load increases, more modules are gradually started to share the power output to avoid overloading of a single module. S45. Overload and performance bottleneck detection: Real-time monitoring of the working status of each power module. When a module is close to the rated load or exceeds the operating temperature, the system automatically adjusts the power output of the module to reduce its load, detects whether the power module is close to its efficiency limit or output limit, and makes timely adjustments through the power allocation algorithm to avoid bottlenecks that lead to a decrease in overall system efficiency. S46. Fault tolerance and redundancy mechanism: If a power module fails or has an abnormality, such as overtemperature or voltage fluctuation, the system will automatically isolate the faulty module and distribute its load to other normal modules to avoid system paralysis. Redundant modules are added to the design to be used when the system load increases or a module fails, ensuring the reliability and stability of the power system.

[0008] Further, a modular power efficiency intelligent management method, In step S6, the working status, power output and conversion efficiency of each module are recorded in real time through sensors and monitoring software, and a detailed energy efficiency analysis report is provided. Based on the collected data, system energy efficiency analysis is performed regularly. The specific steps are as follows; S61. Real-time monitoring and data processing: Use monitoring software to display the real-time status of each module, including output power, conversion efficiency and temperature. Before data is stored, clean noise and abnormal values ​​to remove unreasonable data; S62. Conversion efficiency calculation: Calculate the real-time conversion efficiency of each module by collecting input power and output power. The formula is as follows: , and establish an efficiency model based on load, temperature, and time to predict the energy efficiency trend of each module and compare it with real-time data; S63. Energy efficiency analysis report generation: according to the set period (such as daily, weekly or monthly), the system operation data is summarized to generate an energy efficiency analysis report. Output power total: statistics on the total output power of each module in the specified time period. Conversion efficiency analysis: calculate the average conversion efficiency of each module and its change trend with load. Load distribution: statistics on the load distribution of each module, showing which modules run more under high load or low load. Temperature and environmental impact: analyze the relationship between the temperature change of the module and its energy efficiency, and point out the impact of temperature on energy efficiency. S64. Regular energy efficiency analysis and system optimization suggestions: Based on the collected data, regularly analyze the operating efficiency of the power system, identify long-term efficiency change trends and bottlenecks, analyze the load changes of the system at different times, identify peak and valley load times, optimize module start and stop and power allocation strategies, and the reasons for efficiency decline: by comparing historical data, identify which modules have decreased in efficiency over time and whether maintenance, cleaning or replacement of components are required; S65. Load distribution optimization: According to the long-term efficiency performance of the modules, the power distribution of each module is adjusted so that the modules with higher efficiency bear more load. Based on the temperature, efficiency decline and load conditions, maintenance suggestions are provided, such as cleaning the radiator and replacing aging components. If the system is often in a high-load state, it can be recommended to expand the capacity. If the system is often in a low-load state, it can be recommended to reduce the number of enabled modules to save energy. S66. Automated regulation and feedback: Based on monitoring and analysis results, the power output of the power module is adjusted in real time, high-efficiency modules are allocated first, the operating time of inefficient modules is reduced, and some modules are automatically started and stopped according to load demand and module energy efficiency performance to achieve intelligent regulation.

[0009] Beneficial effects of the present invention: Through intelligent algorithms and real-time monitoring, the system can dynamically adjust power distribution according to the operating status and conversion efficiency of each module to ensure that high-efficiency modules work first, thereby improving overall energy efficiency. Through monitoring and analysis, inefficient working modules can be discovered in a timely manner to avoid energy waste and minimize unnecessary energy consumption. The system can monitor and dynamically balance the loads of each module to prevent certain modules from being in a high-load state for a long time, thereby extending the service life of the equipment. Real-time monitoring of the temperature, load and conversion efficiency of the modules can identify potential equipment failures or performance degradation in advance, arrange maintenance in a timely manner, and avoid excessive wear or sudden failure of the equipment. The system can detect faults such as overheating, overload or efficiency degradation in real time, and automatically start redundant modules to maintain stable system operation and avoid system interruptions caused by failure of a single module. This intelligent management method can effectively reduce energy waste, extend equipment life, and maintain efficient and stable power supply when the load changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a flow chart of a modular power efficiency intelligent management method; DETAILED DESCRIPTION

[0011] A modular power efficiency intelligent management method comprises the following steps: S1. Demand analysis and module configuration: Detect the current load demand of the system, determine the power range required by the power module, and dynamically select and configure the appropriate power module according to the load demand to reduce power redundancy and avoid unnecessary energy consumption; S2. Intelligent control of power modules: Automatically enable or disable certain power modules based on load changes. For example, disable some power modules when the load is low, and gradually enable additional modules when the load is high to ensure the best system efficiency. According to the load distribution, adjust the output ratio of each module to achieve reasonable power distribution and avoid overloading or underloading of a single module. S3. Efficiency optimization algorithm: Use artificial intelligence or rule-based algorithms to monitor the working efficiency of each power module in real time and make adjustments based on the data. For example, high-efficiency modules are used first and low-efficiency modules are shut down. Through historical data and real-time load change trends, future load fluctuations are predicted, and the operating status of the modules is adjusted in advance to reduce power conversion losses. S4. Parallel connection and redundancy management of power modules: When multiple power modules work in parallel, the power distribution between modules is adjusted through intelligent algorithms to avoid overload or performance bottleneck of a single module, while ensuring that each module operates in the highest efficiency range. Redundant module management: When necessary, certain redundant modules are configured to improve system reliability. When the load fluctuates, the redundant modules can automatically connect or exit work to ensure stable power supply. S5. Fault diagnosis and self-healing: Real-time monitoring of the power module, detection of temperature, voltage, current and other parameters, timely detection of abnormalities and fault diagnosis. When a fault occurs, the system can automatically isolate the problem module and reconfigure other normal modules to continue working to ensure system stability. S6. Energy efficiency monitoring and data analysis: Through sensors and monitoring software, the working status, power output and conversion efficiency of each module are recorded in real time, and detailed energy efficiency analysis reports are provided. Based on the collected data, system energy efficiency analysis is performed regularly to find potential optimization space and automatically adjust module configuration or operation strategy; S7. Remote control and upgrade: Remote management and control: Through the cloud or remote platform, the power module can be managed and regulated in real time, the power operation status can be monitored, and the configuration and start and stop modules can be adjusted remotely.

[0012] Further, a modular power efficiency intelligent management method, In step S3, an artificial intelligence algorithm is used to monitor the working efficiency of each power module in real time and make adjustments based on the data. The specific steps are as follows; S31. System initialization and preparation: Ensure that all power modules are equipped with necessary sensors (such as current sensors, voltage sensors, temperature sensors, etc.) to monitor key parameters, establish a reliable communication network, and ensure that each module can transmit data to the central control system; S32. Data collection: The sensors installed on each power module continuously collect the data during operation, including but not limited to: input / output voltage and current, temperature, power factor, energy consumption, clean and format the collected data, remove outliers, fill in missing values, etc., to ensure data quality; S33. Data storage and management: Store the processed data in the database, ensure data security, and take appropriate encryption measures to prevent data leakage; S34. Data analysis and modeling: Extract useful features from raw data to prepare for subsequent model training. Select a regression model to predict the model's future energy consumption according to the problem requirements, or use a classification model to identify abnormal conditions. Use historical data to train a regression model to predict the model, and continuously optimize until satisfactory performance indicators are achieved. Verify the accuracy and robustness of the model through cross-validation or other methods. S35. Real-time monitoring and decision-making: Deploy the trained model in the production environment so that it can receive real-time data and respond quickly. According to the output of the model, the working efficiency of the current power module is evaluated. When it is found that the working efficiency of a module is lower than the expected standard, the system can automatically adjust its working parameters (such as changing the output power, switching the working mode, etc.) to improve efficiency. S36. Continuous optimization and maintenance: Establish a feedback mechanism to input the adjusted effects as new training data to the model to help the model continuously learn and improve, retrain the model regularly to adapt to new operating conditions or equipment changes, and continuously monitor the performance of the entire system to ensure long-term stable operation; S37. User Interface and Reporting: Provides a user-friendly graphical interface to display the status, efficiency and other important information of the power modules, set threshold alarms, promptly notify relevant personnel when abnormal conditions are detected, and regularly generate detailed performance reports for management review and decision support.

[0013] Further, a modular power efficiency intelligent management method, When multiple power modules work in parallel in step S4, the power distribution between modules is adjusted through an intelligent algorithm to avoid overload and performance bottleneck of a single module. The specific steps are as follows; S41. System initialization and module detection: When the system starts, first identify and register all parallel power modules, and determine the capacity, rated power range, and efficiency curve key parameters of each module; S42. Real-time data collection: collect the voltage, current, temperature, and power output data of each module in real time to accurately understand the load conditions of each module, and monitor the changes in the overall load to ensure that the total power of the power module meets the system requirements; S43. Intelligent power allocation algorithm: pre-process the collected data, remove noise and outliers, and perform normalization Load balancing algorithm: Power allocation algorithm based on efficiency optimization: According to the efficiency curve of each power module, the optimal working point of each module under the current conditions is calculated, and the power output of the high-efficiency module is allocated first to reduce energy consumption; Load balancing algorithm: The output power of each module is dynamically adjusted through the algorithm to ensure that the load of each module is relatively even, avoiding overloading of a single module or being in an inefficient range for a long time; Dynamic priority algorithm: Set different load priorities for each module, start the modules with high efficiency first, and dynamically adjust the priority and output power according to load changes; S44. Power regulation and module collaboration: Based on the calculation results of the intelligent algorithm, the output power of each power module is dynamically adjusted to ensure load balance and efficient operation. According to the current load demand, some power modules are intelligently started and stopped. When the load is low, some modules are shut down to reduce energy consumption. When the load increases, more modules are gradually started to share the power output to avoid overloading of a single module. S45. Overload and performance bottleneck detection: Real-time monitoring of the working status of each power module. When a module is close to the rated load or exceeds the operating temperature, the system automatically adjusts the power output of the module to reduce its load, detects whether the power module is close to its efficiency limit or output limit, and makes timely adjustments through the power allocation algorithm to avoid bottlenecks that lead to a decrease in overall system efficiency. S46. Fault tolerance and redundancy mechanism: If a power module fails or has an abnormality, such as overtemperature or voltage fluctuation, the system will automatically isolate the faulty module and distribute its load to other normal modules to avoid system paralysis. Redundant modules are added to the design to be used when the system load increases or a module fails, ensuring the reliability and stability of the power system.

[0014] Further, a modular power efficiency intelligent management method, In step S6, the working status, power output and conversion efficiency of each module are recorded in real time through sensors and monitoring software, and a detailed energy efficiency analysis report is provided. Based on the collected data, system energy efficiency analysis is performed regularly. The specific steps are as follows; S61. Real-time monitoring and data processing: Use monitoring software to display the real-time status of each module, including output power, conversion efficiency and temperature. Before data is stored, clean noise and abnormal values ​​to remove unreasonable data; S62. Conversion efficiency calculation: Calculate the real-time conversion efficiency of each module by collecting input power and output power. The formula is as follows: , and establish an efficiency model based on load, temperature, and time to predict the energy efficiency trend of each module and compare it with real-time data; S63. Energy efficiency analysis report generation: according to the set period (such as daily, weekly or monthly), the system operation data is summarized to generate an energy efficiency analysis report. Output power total: statistics on the total output power of each module in the specified time period. Conversion efficiency analysis: calculate the average conversion efficiency of each module and its change trend with load. Load distribution: statistics on the load distribution of each module, showing which modules run more under high load or low load. Temperature and environmental impact: analyze the relationship between the temperature change of the module and its energy efficiency, and point out the impact of temperature on energy efficiency. S64. Regular energy efficiency analysis and system optimization suggestions: Based on the collected data, regularly analyze the operating efficiency of the power system, identify long-term efficiency change trends and bottlenecks, analyze the load changes of the system at different times, identify peak and valley load times, optimize module start and stop and power allocation strategies, and the reasons for efficiency decline: by comparing historical data, identify which modules have decreased in efficiency over time and whether maintenance, cleaning or replacement of components are required; S65. Load distribution optimization: According to the long-term efficiency performance of the modules, the power distribution of each module is adjusted so that the modules with higher efficiency bear more load. Based on the temperature, efficiency decline and load conditions, maintenance suggestions are provided, such as cleaning the radiator and replacing aging components. If the system is often in a high-load state, it can be recommended to expand the capacity. If the system is often in a low-load state, it can be recommended to reduce the number of enabled modules to save energy. S66. Automated regulation and feedback: Based on monitoring and analysis results, the power output of the power module is adjusted in real time, high-efficiency modules are allocated first, the operating time of inefficient modules is reduced, and some modules are automatically started and stopped according to load demand and module energy efficiency performance to achieve intelligent regulation.

Claims

1. A modular power efficiency intelligent management method, characterized in that: The steps include: S1. Demand analysis and module configuration: Detect the current load demand of the system, determine the power range required by the power module, and dynamically select and configure the appropriate power module according to the load demand to reduce power redundancy and avoid unnecessary energy consumption; S2. Intelligent control of power modules: Based on load changes, some power modules are automatically enabled or disabled. Some power modules are disabled when the load is low, and additional modules are gradually enabled when the load is high. The output ratio of each module is adjusted according to the load distribution to achieve reasonable power distribution and avoid overload or underload of a single module. S3. Efficiency optimization algorithm: Using artificial intelligence algorithms, the working efficiency of each power module is monitored in real time, and adjustments are made based on the data, giving priority to high-efficiency modules and shutting down inefficient modules. Through historical data and real-time load change trends, future load fluctuations are predicted, and the operating status of the modules is adjusted in advance to reduce power conversion losses. S4. Parallel connection and redundancy management of power modules: When multiple power modules work in parallel, the power distribution between modules is adjusted through intelligent algorithms to avoid overload and performance bottleneck of a single module, while ensuring that each module operates in the highest efficiency range. Redundant module management: Configure certain redundant modules to improve system reliability. When the load fluctuates, the redundant modules automatically connect and exit the work; S5. Fault diagnosis and self-healing: Real-time monitoring of the power module, detection of temperature, voltage, and current parameters, timely detection of abnormalities and fault diagnosis. When a fault occurs, the system automatically isolates the problem module and reconfigures other normal modules to continue working; S6. Energy efficiency monitoring and data analysis: Through sensors and monitoring software, the working status, power output and conversion efficiency of each module are recorded in real time, and detailed energy efficiency analysis reports are provided. Based on the collected data, system energy efficiency analysis is performed regularly to find potential optimization space and automatically adjust module configuration and operation strategy; S7. Remote control and upgrade: Remote management and control: Through the cloud or remote platform, the power module can be managed and regulated in real time, the power operation status can be monitored, and the configuration and start and stop modules can be adjusted remotely.

2. A modular power efficiency intelligent management method as claimed in claim 1, characterized in that: In step S3, an artificial intelligence algorithm is used to monitor the working efficiency of each power module in real time and make adjustments based on the data. The specific steps are as follows; S31. System initialization and preparation: Ensure that all power modules are equipped with necessary sensors: current sensors, voltage sensors, temperature sensors monitor key parameters, establish a communication network, and each module can transmit data to the central control system; S32. Data collection: The sensors installed on each power module continuously collect the data during operation, including input / output voltage and current, temperature, power factor, and energy consumption. The collected data is cleaned and formatted to remove abnormal values ​​and fill in missing values. S33. Data storage and management: Store the processed data in the database and take encryption measures to prevent data leakage; S34. Data analysis and modeling: Extract useful features from raw data, select a regression model to predict the model's future energy consumption according to the problem requirements, or use a classification model to identify abnormal conditions, use historical data to train the regression model to predict the model, and continuously optimize until satisfactory performance indicators are achieved, and verify the accuracy and robustness of the model through cross-validation or other methods; S35. Real-time monitoring and decision-making: Deploy the trained model in the production environment so that it can receive real-time data and respond quickly. According to the output of the model, the working efficiency of the current power module is evaluated. When the working efficiency of a module is found to be lower than the expected standard, the system automatically adjusts its working parameters: change the output power, switch the working mode; S36. Continuous optimization and maintenance: Establish a feedback mechanism to input the adjusted effects as new training data to the model to help the model continuously learn and improve, retrain the model regularly to adapt to new operating conditions and equipment changes, and continuously monitor the performance of the entire system; S37. User Interface and Reporting: Provides a user-friendly graphical interface to display the status, efficiency and other important information of the power modules, set threshold alarms, promptly notify relevant personnel when abnormal conditions are detected, and regularly generate detailed performance reports for management review and decision support.

3. A modular power efficiency intelligent management method as claimed in claim 1, characterized in that: When multiple power modules work in parallel in step S4, the power distribution between modules is adjusted through an intelligent algorithm to avoid overload and performance bottleneck of a single module. The specific steps are as follows; S41. System initialization and module detection: When the system starts, first identify and register all parallel power modules, and determine the capacity, rated power range, and efficiency curve key parameters of each module; S42. Real-time data collection: collect the voltage, current, temperature, and power output data of each module in real time to accurately understand the load conditions of each module, and monitor the changes in the overall load to ensure that the total power of the power module meets the system requirements; S43. Intelligent power allocation algorithm: pre-process the collected data, remove noise and outliers, and perform normalization Load balancing algorithm: Power allocation algorithm based on efficiency optimization: According to the efficiency curve of each power module, the optimal working point of each module under the current conditions is calculated, and the power output of the high-efficiency module is allocated first to reduce energy consumption; Load balancing algorithm: The output power of each module is dynamically adjusted through the algorithm to ensure that the load of each module is relatively even, avoiding overloading of a single module or being in an inefficient range for a long time; Dynamic priority algorithm: Set different load priorities for each module, start the modules with high efficiency first, and dynamically adjust the priority and output power according to load changes; S44. Power regulation and module collaboration: Based on the calculation results of the intelligent algorithm, the output power of each power module is dynamically adjusted to ensure load balance and efficient operation. According to the current load demand, some power modules are intelligently started and stopped. When the load is low, some modules are shut down to reduce energy consumption. When the load increases, more modules are gradually started to share the power output to avoid overloading of a single module. S45. Overload and performance bottleneck detection: Real-time monitoring of the working status of each power module. When a module is close to the rated load or exceeds the operating temperature, the system automatically adjusts the power output of the module to reduce its load, detects whether the power module is close to its efficiency limit or output limit, and makes timely adjustments through the power allocation algorithm to avoid bottlenecks that lead to a decrease in overall system efficiency. S46. Fault tolerance and redundancy mechanism: If a power module fails or has an abnormality, such as overtemperature or voltage fluctuation, the system will automatically isolate the faulty module and distribute its load to other normal modules to avoid system paralysis. Redundant modules are added to the design to be used when the system load increases or a module fails, ensuring the reliability and stability of the power system.

4. A modular power efficiency intelligent management method as claimed in claim 1, characterized in that: In step S6, the working status, power output and conversion efficiency of each module are recorded in real time through sensors and monitoring software, and a detailed energy efficiency analysis report is provided. Based on the collected data, system energy efficiency analysis is performed regularly. The specific steps are as follows; S61. Real-time monitoring and data processing: Use monitoring software to display the real-time status of each module, including output power, conversion efficiency and temperature. Before data is stored, clean noise and abnormal values ​​to remove unreasonable data; S62. Conversion efficiency calculation: Calculate the real-time conversion efficiency of each module by collecting input power and output power. The formula is as follows: , and establish an efficiency model based on load, temperature, and time to predict the energy efficiency trend of each module and compare it with real-time data; S63. Energy efficiency analysis report generation: according to the set cycle, the system operation data is summarized to generate an energy efficiency analysis report, the total output power of each module in the specified time period is counted, and the average conversion efficiency of each module and its change trend with load are calculated. Load distribution: the load distribution of each module is counted, the relationship between the temperature change of the module and its energy efficiency is analyzed, and the impact of temperature on energy efficiency is pointed out; S64. Regular energy efficiency analysis and system optimization suggestions: Based on the collected data, regularly analyze the operating efficiency of the power system, identify long-term efficiency change trends and bottlenecks, analyze the load changes of the system at different times, identify peak and valley load times, optimize module start and stop and power allocation strategies, and by comparing historical data, identify which modules have decreased in efficiency over time and need maintenance, cleaning, or replacement of components; S65. Load distribution optimization: According to the long-term efficiency performance of the modules, the power distribution of each module is adjusted so that the modules with higher efficiency bear more loads. Maintenance suggestions are provided based on temperature, efficiency drop and load conditions. S66. Automated regulation and feedback: Based on monitoring and analysis results, the power output of the power module is adjusted in real time, high-efficiency modules are allocated first, the operating time of inefficient modules is reduced, and some modules are automatically started and stopped according to load demand and module energy efficiency performance to achieve intelligent regulation.

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