Intelligent diagnosis method for charging abnormity of modular power supply based on edge calculation

Abstract

The invention discloses a modular power supply charging abnormity intelligent diagnosis method based on edge calculation, and the method achieves the function separation and fault isolation through the modular design of a power supply system. Data such as voltage, current, power and temperature are collected in real time, preliminary data processing is carried out through edge computing equipment, and invalid data transmission is reduced. And analyzing the data on the edge side by using an isolated forest algorithm, establishing an anomaly detection model, and when the detected data exceeds a preset threshold, determining that the data is abnormal. The system has real-time monitoring and alarming functions and can respond to and record abnormal events in time. By collecting abnormal events and processing strategies thereof, an algorithm model is continuously optimized, and the diagnosis accuracy is improved. A feedback mechanism is established, system parameters are adjusted according to diagnosis results, closed-loop management is formed, and the system is more suitable for the actual operation environment. According to the method, the intelligent level and the operation safety of the power supply charging system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of power supply charging analysis technology, and more specifically to a modular power supply charging anomaly intelligent diagnosis method based on edge computing. Background Art

[0002] Traditional power system fault detection and diagnosis methods have many limitations, which become more obvious in the face of the growing complexity and scale of modern power systems. Fault detection in traditional power systems relies on regularly scheduled manual inspections. Although this method can detect some obvious faults, due to the long inspection cycle, it is often impossible to detect the fault as soon as it occurs, resulting in a longer impact time of the fault and even missing the best time for maintenance. As the scale of power systems continues to expand, the monolithic design makes the entire system more and more complex. When a part of the system fails, it becomes very difficult to locate the source of the fault and take effective measures to isolate the fault area, which increases the complexity of maintenance work. Traditional fault detection and diagnosis methods have exposed many shortcomings in the face of modern power systems, and it is urgent to make up for these shortcomings through technological innovation. The introduction of a modular power charging abnormality intelligent diagnosis method based on edge computing can effectively solve the above problems, improve the real-time performance of fault detection, reduce the difficulty of maintenance, and enhance the flexibility of diagnostic methods. Summary of the invention

[0003] A modular power supply charging abnormality intelligent diagnosis method based on edge computing, comprising the following steps: S1. Modular design: A modular structure is used when designing the power system, with each module responsible for a specific function (such as charging, monitoring, etc.), which makes it easier to identify and isolate the source of the fault; S2. Data collection: Deploy sensors at key nodes of the power charging system, such as voltage sensors, current sensors, temperature sensors, etc., to monitor the power status in real time. The status includes voltage (Voltage): the output voltage and input voltage of the power module, current (Current): the output current and input current of the power module, power (Power): the output power and input power of the power module, temperature (Temperature): the operating temperature of the power module. The data collection points should cover all important electrical parameters to fully understand the working status of the power supply and transmit them through edge devices; S3. Edge computing processing: Use edge computing devices (such as embedded controllers or small servers) to process data directly near where the data is generated. Edge computing devices perform preliminary data cleaning and filtering to remove invalid values ​​and noise, including data standardization, normalization, formatting and analysis tasks, reducing the burden of network transmission; S4. Anomaly detection algorithm: The isolation forest algorithm is used to analyze the data uploaded by the edge device, and the detection model is trained on the historical data, and the anomaly detection formula is integrated: , where the standard deviation , represents the standard deviation of each parameter of the power state, the mean , represents the mean value of each parameter of the power state, abnormal detection, when Threshold, then are marked as anomalies, based on the model distinguishing data under normal operating conditions from anomalies; S5. Real-time monitoring and alarm: Monitor various indicators during the charging process in real time and set thresholds to trigger warnings and alarms. When an abnormality is detected, the edge computing device responds according to predefined rules and intelligent decision-making mechanisms, including adjusting charging parameters, sending alarms to maintenance personnel, recording abnormal events and further analyzing them. S6. Data analysis and optimization: Collect and store all abnormal events and their processing results for subsequent analysis, use big data analysis technology to optimize algorithm models, and improve the system's self-learning ability and diagnostic accuracy; S7. Feedback mechanism: Establish a closed-loop feedback system that allows diagnostic results and system operations to be recorded for improving future diagnostic logic and technology. Feedback information can also be used to adjust the parameter settings of the power system to make it more suitable for the actual working environment.

[0004] Furthermore, a modular power supply charging abnormality intelligent diagnosis method based on edge computing is proposed. S41. Data collection and preparation: Sensor data: including real-time data of temperature, voltage, and current, historical normal operation data, and marked abnormal event data; Data preprocessing: removing noise and missing values, ensuring data quality, extracting useful features from raw data, and scaling data to the same range for model training; S42. Feature Engineering: Select the most useful features for anomaly detection based on data analysis, including time series features: rolling mean, standard deviation, statistical features: maximum, minimum, kurtosis, and use principal component analysis (PCA) technology to reduce feature dimensions and improve model efficiency; S43. Anomaly detection model construction: The data set is divided into a training set and a test set. The training set is used to train the anomaly detection model based on the isolation forest algorithm, and the anomaly detection formula is integrated: , where the standard deviation , represents the standard deviation of each parameter of the power state, the mean , represents the mean value of each parameter of the power state, abnormal detection, when Threshold, then The data is marked as abnormal, and the test set verifies the model performance. The evaluation indicators (such as precision, recall rate, F1-score) are used to evaluate the abnormality detection effect of the model, and the hyperparameters of the model are adjusted to optimize the detection performance. S44. Model deployment and real-time detection: Deploy the trained model to the edge device for real-time anomaly detection. Optimize the performance and efficiency of the model based on the computing power and resource limitations of the edge device. Receive sensor data in real time and perform anomaly detection through the model. When an anomaly is detected, trigger the alarm mechanism to notify relevant personnel and the system to take measures. S45. Continuous optimization and maintenance: Continuously collect new data, regularly update and retrain models to adapt to new data distribution and abnormal patterns, adjust models based on actual detection results and feedback, improve detection accuracy, monitor the detection performance of the model, ensure its stability in the production environment, analyze false positives and false negatives, and further optimize models and detection strategies.

[0005] Furthermore, a modular power supply charging abnormality intelligent diagnosis method based on edge computing is proposed. In step S5, the edge computing device responds according to predefined rules and intelligent decision-making mechanisms, as follows: S51. Response of the edge computing device: Use a predefined rule engine to make decisions based on monitoring data and thresholds: if the voltage exceeds the upper limit, the shutdown operation is executed; if the temperature exceeds the set range, the fan is started to cool down; S52. Intelligent decision-making: Automatically adjust system parameters and thresholds based on real-time data to optimize system performance and safety, automatically adjust power module settings based on decision results, perform operations: adjust voltage, current, activate protection mechanisms, record abnormal events and response measures, and generate reports for subsequent analysis and improvement; S53. System Integration: Design interfaces with other systems, alarm systems, control systems and data recording systems to ensure information flow and coordinated operations, and conduct comprehensive testing of the entire monitoring system to ensure the effectiveness of data collection, threshold setting, warning alarms and edge computing response mechanisms; S54.Maintenance and Optimization: Regularly inspect and maintain sensors and computing equipment to ensure their normal operation, and continuously optimize threshold settings, data processing, and response mechanisms based on actual operating conditions and user feedback.

[0006] Furthermore, a modular power supply charging abnormality intelligent diagnosis method based on edge computing is proposed. In step S6, all abnormal events and their processing methods are collected and stored, and the algorithm model is optimized using big data analysis technology, as follows: Abnormal charging current: too low current: slow charging speed, the battery cannot reach the expected charging level; too high current: may cause battery overheating, damage or even fire; Solution: Check the charger: make sure the output current of the charger meets the device requirements, check the battery status: if the battery is aged or damaged, you may need to replace the battery, adjust the charging parameters: make sure the charging algorithm and charging curve are set correctly; Abnormal charging voltage: Too high voltage may cause battery overheating or damage; too low voltage may cause low charging efficiency and incomplete battery charging; Solution: Check the charger output voltage: Make sure the charger voltage is stable within the design range, check the battery charging interface: Make sure the battery interface and the charger interface are in good contact, monitor the battery voltage: Use the battery management system (BMS) to monitor the battery voltage status to prevent overcharging or over-discharging; Abnormal charging temperature: Too high temperature may cause battery overheating, damage battery life or cause safety problems; too low temperature may reduce charging efficiency and slow down battery charging speed; Solution: Check the cooling system: ensure that the charger and battery system have proper cooling measures, use temperature sensors: monitor the temperature of the battery and charger during charging and automatically adjust the charging strategy, and environmental control: avoid charging under extreme temperature conditions; Abnormal charging interface: loose or damaged interface: resulting in unstable charging or failure to charge; poor contact: may cause charging interruption or unstable current; Solution: Check the interface and cable: Make sure the charging interface is not damaged and the cable is firmly connected. Clean the interface: Clean the charging interface regularly to avoid dust or dirt affecting the contact quality. Replace the cable or interface: If a hardware problem is found, replace the damaged parts in time. Abnormal charging time: Charging time is too long: It may indicate a problem with the charging device or battery. Charging time is too short: Charging is incomplete and the battery may not be fully charged. Solution: Check the charger and battery health status: Battery aging or charger failure may affect charging time. Optimize charging strategy: Adjust the charging algorithm to optimize charging time and efficiency. Abnormal battery self-discharge: Self-discharge rate is too high: The battery loses power too quickly when not in use, which may be a battery problem or internal short circuit; Solution: Detect the internal status of the battery: Check whether the battery has a short circuit or internal fault, use an appropriate battery management system (BMS): monitor the battery status and reduce self-discharge problems; Data analysis and optimization Data collection: Record abnormal events: Collect data on all abnormal charging events, including time, type, and processing results; Store processing results: Save the results and measures of each abnormal processing. Data mining: Analyze patterns and trends of abnormal events to identify common problems and potential risks; Predictive analysis: predict possible anomalies in the future based on historical data and take preventive measures in advance; Optimize charging algorithm: adjust the charging algorithm based on data analysis results to improve charging efficiency and safety; Improve system self-diagnosis capabilities: Use machine learning models to detect anomalies and automatically adjust charging strategies.

[0007] Beneficial effects of the present invention: Through real-time data collection and edge computing, abnormalities can be quickly detected at the early stage of a fault, reducing the time the fault affects the system. The edge computing device completes data cleaning and preliminary analysis locally, reducing the need to transmit a large amount of raw data to the central server and reducing network bandwidth costs. The real-time monitoring and alarm mechanism can promptly notify maintenance personnel of abnormal situations, speeding up maintenance response and reducing downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flowchart of an intelligent diagnosis method for modular power supply charging abnormality based on edge computing; DETAILED DESCRIPTION

[0009] A modular power supply charging abnormality intelligent diagnosis method based on edge computing, comprising the following steps: S1. Modular design: A modular structure is used when designing the power system, with each module responsible for a specific function (such as charging, monitoring, etc.), which makes it easier to identify and isolate the source of the fault; S2. Data collection: Deploy sensors at key nodes of the power charging system, such as voltage sensors, current sensors, temperature sensors, etc., to monitor the power status in real time. The status includes voltage (Voltage): the output voltage and input voltage of the power module, current (Current): the output current and input current of the power module, power (Power): the output power and input power of the power module, temperature (Temperature): the operating temperature of the power module. The data collection points should cover all important electrical parameters to fully understand the working status of the power supply and transmit them through edge devices; S3. Edge computing processing: Use edge computing devices (such as embedded controllers or small servers) to process data directly near where the data is generated. Edge computing devices perform preliminary data cleaning and filtering to remove invalid values ​​and noise, including data standardization, normalization, formatting and analysis tasks, reducing the burden of network transmission; S4. Anomaly detection algorithm: The isolation forest algorithm is used to analyze the data uploaded by the edge device, and the detection model is trained on the historical data, and the anomaly detection formula is integrated: , where the standard deviation , represents the standard deviation of each parameter of the power state, the mean , represents the mean value of each parameter of the power state, abnormal detection, when Threshold, then are marked as anomalies, based on the model distinguishing data under normal operating conditions from anomalies; S5. Real-time monitoring and alarm: Monitor various indicators during the charging process in real time and set thresholds to trigger warnings and alarms. When an abnormality is detected, the edge computing device responds according to predefined rules and intelligent decision-making mechanisms, including adjusting charging parameters, sending alarms to maintenance personnel, recording abnormal events and further analyzing them. S6. Data analysis and optimization: Collect and store all abnormal events and their processing results for subsequent analysis, use big data analysis technology to optimize algorithm models, and improve the system's self-learning ability and diagnostic accuracy; S7. Feedback mechanism: Establish a closed-loop feedback system that allows diagnostic results and system operations to be recorded for improving future diagnostic logic and technology. Feedback information can also be used to adjust the parameter settings of the power system to make it more suitable for the actual working environment.

[0010] Furthermore, a modular power supply charging abnormality intelligent diagnosis method based on edge computing is proposed. S41. Data collection and preparation: Sensor data: including real-time data of temperature, voltage, and current, historical normal operation data, and marked abnormal event data; Data preprocessing: removing noise and missing values, ensuring data quality, extracting useful features from raw data, and scaling data to the same range for model training; S42. Feature Engineering: Select the most useful features for anomaly detection based on data analysis, including time series features: rolling mean, standard deviation, statistical features: maximum, minimum, kurtosis, and use principal component analysis (PCA) technology to reduce feature dimensions and improve model efficiency; S43. Anomaly detection model construction: The data set is divided into a training set and a test set. The training set is used to train the anomaly detection model based on the isolation forest algorithm, and the anomaly detection formula is integrated: , where the standard deviation , represents the standard deviation of each parameter of the power state, the mean , represents the mean value of each parameter of the power state, abnormal detection, when Threshold, then The data is marked as abnormal, and the test set verifies the model performance. The evaluation indicators (such as precision, recall rate, F1-score) are used to evaluate the abnormality detection effect of the model, and the hyperparameters of the model are adjusted to optimize the detection performance. S44. Model deployment and real-time detection: Deploy the trained model to the edge device for real-time anomaly detection. Optimize the performance and efficiency of the model based on the computing power and resource limitations of the edge device. Receive sensor data in real time and perform anomaly detection through the model. When an anomaly is detected, trigger the alarm mechanism to notify relevant personnel and the system to take measures. S45. Continuous optimization and maintenance: Continuously collect new data, regularly update and retrain models to adapt to new data distribution and abnormal patterns, adjust models based on actual detection results and feedback, improve detection accuracy, monitor the detection performance of the model, ensure its stability in the production environment, analyze false positives and false negatives, and further optimize models and detection strategies.

[0011] Furthermore, a modular power supply charging abnormality intelligent diagnosis method based on edge computing is proposed. In step S5, the edge computing device responds according to predefined rules and intelligent decision-making mechanisms, as follows: S51. Response of the edge computing device: Use a predefined rule engine to make decisions based on monitoring data and thresholds: if the voltage exceeds the upper limit, the shutdown operation is executed; if the temperature exceeds the set range, the fan is started to cool down; S52. Intelligent decision-making: Automatically adjust system parameters and thresholds based on real-time data to optimize system performance and safety, automatically adjust power module settings based on decision results, perform operations: adjust voltage, current, activate protection mechanisms, record abnormal events and response measures, and generate reports for subsequent analysis and improvement; S53. System Integration: Design interfaces with other systems, alarm systems, control systems and data recording systems to ensure information flow and coordinated operations, and conduct comprehensive testing of the entire monitoring system to ensure the effectiveness of data collection, threshold setting, warning alarms and edge computing response mechanisms; S54.Maintenance and Optimization: Regularly inspect and maintain sensors and computing equipment to ensure their normal operation, and continuously optimize threshold settings, data processing, and response mechanisms based on actual operating conditions and user feedback.

[0012] Furthermore, a modular power supply charging abnormality intelligent diagnosis method based on edge computing is proposed. In step S6, all abnormal events and their processing methods are collected and stored, and the algorithm model is optimized using big data analysis technology, as follows: Abnormal charging current: too low current: slow charging speed, the battery cannot reach the expected charging level; too high current: may cause battery overheating, damage or even fire; Solution: Check the charger: make sure the output current of the charger meets the device requirements, check the battery status: if the battery is aged or damaged, you may need to replace the battery, adjust the charging parameters: make sure the charging algorithm and charging curve are set correctly; Abnormal charging voltage: Too high voltage may cause battery overheating or damage; too low voltage may cause low charging efficiency and incomplete battery charging; Solution: Check the charger output voltage: Make sure the charger voltage is stable within the design range, check the battery charging interface: Make sure the battery interface and the charger interface are in good contact, monitor the battery voltage: Use the battery management system (BMS) to monitor the battery voltage status to prevent overcharging or over-discharging; Abnormal charging temperature: Too high temperature may cause battery overheating, damage battery life or cause safety problems; too low temperature may reduce charging efficiency and slow down battery charging speed; Solution: Check the cooling system: ensure that the charger and battery system have proper cooling measures, use temperature sensors: monitor the temperature of the battery and charger during charging and automatically adjust the charging strategy, and environmental control: avoid charging under extreme temperature conditions; Abnormal charging interface: loose or damaged interface: resulting in unstable charging or failure to charge; poor contact: may cause charging interruption or unstable current; Solution: Check the interface and cable: Make sure the charging interface is not damaged and the cable is firmly connected. Clean the interface: Clean the charging interface regularly to avoid dust or dirt affecting the contact quality. Replace the cable or interface: If a hardware problem is found, replace the damaged parts in time. Abnormal charging time: Charging time is too long: It may indicate a problem with the charging device or battery. Charging time is too short: Charging is incomplete and the battery may not be fully charged. Solution: Check the charger and battery health status: Battery aging or charger failure may affect charging time. Optimize charging strategy: Adjust the charging algorithm to optimize charging time and efficiency. Abnormal battery self-discharge: Self-discharge rate is too high: The battery loses power too quickly when not in use, which may be a battery problem or internal short circuit; Solution: Detect the internal status of the battery: Check whether the battery has a short circuit or internal fault, use an appropriate battery management system (BMS): monitor the battery status and reduce self-discharge problems; Data analysis and optimization Data collection: Record abnormal events: Collect data on all abnormal charging events, including time, type, and processing results; Store processing results: Save the results and measures of each abnormal processing. Data mining: Analyze patterns and trends of abnormal events to identify common problems and potential risks; Predictive analysis: predict possible anomalies in the future based on historical data and take preventive measures in advance; Optimize charging algorithm: adjust the charging algorithm based on data analysis results to improve charging efficiency and safety; Improve system self-diagnosis capabilities: Use machine learning models to detect anomalies and automatically adjust charging strategies.

Claims

1. A modular power supply charging abnormality intelligent diagnosis method based on edge computing, It is characterized in that The following steps are included: S1. Modular design: A modular structure is used when designing the power system, with each module responsible for a specific function: charging, monitoring, and identifying and isolating the source of the fault; S2. Data collection: Deploy sensors at each key node of the power charging system: voltage sensor, current sensor, temperature sensor, and monitor the power status parameters in real time. The parameters include voltage V: the output voltage and input voltage of the power module, current I: the output current and input current of the power module, power P: the output power and input power of the power module, and temperature T: the operating temperature of the power module. The data collection points cover all electrical parameters and are transmitted through edge devices. S3. Edge computing processing: Use edge computing devices: embedded controllers, small servers to process data directly near where the data is generated. Edge computing devices perform preliminary data cleaning and filtering to remove invalid values ​​and noise, including data standardization, normalization, formatting and analysis tasks, reducing the burden of network transmission; S4. Anomaly detection algorithm: The isolation forest algorithm is used to analyze the data uploaded by the edge device, and the detection model is trained on the historical data, and the anomaly detection formula is integrated: , where the standard deviation , represents the standard deviation of each parameter of the power state, the mean , represents the mean value of each parameter of the power state, abnormal detection, when Threshold, then are marked as anomalies, based on the model distinguishing data under normal operating conditions from anomalies; S5. Real-time monitoring and alarm: Monitor various indicators during the charging process in real time and set thresholds to trigger warnings. When an abnormality is detected, the edge computing device responds according to predefined rules and intelligent decision-making mechanisms, including adjusting charging parameters, sending alarms to maintenance personnel, and recording abnormal events for further analysis. S6. Data analysis and optimization: Collect and store all abnormal events and their processing methods, use big data analysis technology to optimize algorithm models, and improve the system's self-learning ability and diagnostic accuracy; S7. Feedback mechanism: Establish a closed-loop feedback system to allow diagnostic results and system operations to be recorded, improve future diagnostic logic and technology, and adjust the parameter settings of the power system based on the feedback information to make it more suitable for the actual working environment.

2. A modular power supply charging abnormality intelligent diagnosis method based on edge computing as claimed in claim 1, It is characterized in that S41. Data collection and preparation: Sensor data: including real-time data of temperature, voltage, and current, historical normal operation data, and marked abnormal event data; Data preprocessing: removing noise and missing values, ensuring data quality, extracting useful features from raw data, and scaling data to the same range for model training; S42. Feature Engineering: Select the most useful features for anomaly detection based on data analysis, including time series features: rolling mean, standard deviation, statistical features: maximum, minimum, kurtosis, and use principal component analysis (PCA) technology to reduce feature dimensions and improve model efficiency; S43. Anomaly detection model construction: The data set is divided into a training set and a test set. The training set is used to train the anomaly detection model based on the isolation forest algorithm, and the anomaly detection formula is integrated: , where the standard deviation , represents the standard deviation of each parameter of the power state, the mean , represents the mean value of each parameter of the power state, abnormal detection, when Threshold, then The model is marked as abnormal. The test set verifies the model performance. The evaluation indicators: precision, recall rate, F1-score are used to evaluate the abnormality detection effect of the model. The hyperparameters of the model are adjusted to optimize the detection performance. S44. Model deployment and real-time detection: Deploy the trained model to the edge device for real-time anomaly detection. Optimize the performance and efficiency of the model based on the computing power and resource limitations of the edge device. Receive sensor data in real time and perform anomaly detection through the model. When an anomaly is detected, trigger the alarm mechanism to notify relevant personnel and the system to take measures. S45. Continuous optimization and maintenance: Continuously collect new data, regularly update and retrain models to adapt to new data distribution and abnormal patterns, adjust models based on actual detection results and feedback, improve detection accuracy, monitor the detection performance of the model, ensure its stability in the production environment, analyze false positives and false negatives, and further optimize models and detection strategies.

3. A modular power supply charging abnormality intelligent diagnosis method based on edge computing as claimed in claim 1, It is characterized in that In step S5, the edge computing device responds according to predefined rules and intelligent decision-making mechanisms, as follows: S51. Response of the edge computing device: Use a predefined rule engine to make decisions based on monitoring data and thresholds: if the voltage exceeds the upper limit, the shutdown operation is executed; if the temperature exceeds the set range, the fan is started to cool down; S52. Intelligent decision-making: Automatically adjust system parameters and thresholds based on real-time data to optimize system performance and safety, automatically adjust power module settings based on decision results, perform operations: adjust voltage, current, activate protection mechanisms, record abnormal events and response measures, and generate reports for subsequent analysis and improvement; S53. System Integration: Design interfaces with other systems, alarm systems, control systems and data recording systems to ensure information flow and coordinated operations, and conduct comprehensive testing of the entire monitoring system to ensure the effectiveness of data collection, threshold setting, warning alarms and edge computing response mechanisms; S54.Maintenance and Optimization: Regularly inspect and maintain sensors and computing equipment to ensure their normal operation, and continuously optimize threshold settings, data processing, and response mechanisms based on actual operating conditions and user feedback.

4. A modular power supply charging abnormality intelligent diagnosis method based on edge computing as claimed in claim 1, It is characterized in that In step S6, all abnormal events and their processing methods are collected and stored, and the algorithm model is optimized using big data analysis technology, as follows: Abnormal charging current: too low current: slow charging speed, the battery cannot reach the expected charging level; too high current: causing battery overheating, damage or even fire; Solution: Check the charger: make sure the output current of the charger meets the device requirements, check the battery status: the battery is aging or damaged and needs to be replaced, adjust the charging parameters: make sure the charging algorithm and charging curve are set correctly; Abnormal charging voltage: Too high voltage: causes battery overheating and damage; too low voltage: low charging efficiency and incomplete battery charging; Solution: Check the charger output voltage: Check the battery charging interface: Make sure the battery interface and the charger interface are in good contact, monitor the battery voltage: Use the battery management system BMS to monitor the battery voltage status to prevent overcharging and over-discharging; Abnormal charging temperature: Too high temperature: causes battery overheating, damages battery life and causes safety issues; too low temperature: reduces charging efficiency and slows down battery charging speed; Solution: Check the heat dissipation system: The charger and battery system have heat dissipation measures. Use temperature sensors: Monitor the temperature of the battery and charger during charging and automatically adjust the charging strategy. Environmental control: Avoid charging under extreme temperature conditions. Abnormal conditions of the charging interface: loose or damaged interface: resulting in unstable charging; poor contact: may cause charging interruption and unstable current; Solution: Check the interface and cable, make sure the cable is firmly connected. Clean the interface: Clean the charging interface regularly to prevent dust and dirt from affecting the contact quality. If hardware problems are found, replace damaged parts in time. Abnormal charging time: Charging time is too long: It indicates that there is a problem with the charging equipment; Charging time is too short: Charging is incomplete and the battery is not fully charged; Solution: Check the charger and battery health status: Battery aging and charger failure may affect charging time. Optimize charging strategy: Adjust the charging algorithm to optimize charging time and efficiency. Abnormal battery self-discharge: Self-discharge rate is too high: The battery loses power too quickly when not in use, which may be a battery problem or internal short circuit; Solution: Check the internal status of the battery: Check whether the battery has short circuit or internal fault, use appropriate battery management system BMS: monitor the battery status and reduce self-discharge problems; Data analysis and optimization: Data collection: Record abnormal events: Collect data on all abnormal charging events, including time, type, and processing results; Store processing results: Save the results and measures of each abnormal processing. Data mining: Analyze patterns and trends of abnormal events to identify common problems and potential risks; Predictive analysis: predict possible anomalies in the future based on historical data and take preventive measures in advance; Optimize charging algorithm: adjust the charging algorithm based on data analysis results to improve charging efficiency and safety; Improve system self-diagnosis capabilities: Use machine learning models to detect anomalies and automatically adjust charging strategies.

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