A self-wake-up diagnosis method and system for battery management

Abstract

The present invention relates to the field of battery management technology, and in particular to a self-wake-up diagnostic method and system for battery management. The method comprises the following steps: monitoring key events of the battery in a dormant state through an asynchronous event response mechanism; establishing a battery self-wake-up model using a machine learning algorithm, monitoring and identifying abnormal behaviors in real time, recording events and data, and automatically waking up the battery for diagnosis and generating analysis results when the battery state exceeds a reference value; generating a diagnostic report based on the analysis results of the abnormal wake-up event; and providing recommendations for preventive measures based on the diagnostic results. The present invention realizes real-time monitoring, abnormality detection and diagnostic analysis of the battery state, providing a comprehensive and effective solution for battery management, and improving the stability, safety and reliability of the battery.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a self-awakening diagnosis method and system for battery management. Background Art

[0002] With the continuous development of battery technology, battery management is a crucial aspect, especially for mobile devices and portable electronic products. The performance and life of the battery are crucial to the stability of the device and the user experience. However, since the battery is affected by various factors during use, such as temperature changes, charge and discharge cycles, etc., the battery may have a series of problems, such as capacity decay, increased self-discharge, and decreased charging efficiency. At present, there are still the following problems in battery management: the existing technology is often unable to monitor potential problems inside the battery in real time, resulting in problems being detected only after they occur, causing irreversible damage to battery performance; the existing system lacks an effective mechanism to wake up the battery and restore it to normal working state. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a self-wake-up diagnosis method and system for battery management.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] In one aspect, a self-wake-up diagnostic method for battery management comprises the following steps:

[0006] Through the asynchronous event response mechanism, monitor the key events of the battery in the dormant state;

[0007] Establish a battery self-wake-up model. When the battery status is detected to exceed the reference value, the battery is automatically awakened for diagnosis and analysis results are generated.

[0008] Generate a diagnostic report based on the analysis results of abnormal wakeup events;

[0009] Provide recommendations for preventive measures based on diagnostic findings;

[0010] The establishment of a battery self-awakening model, when the battery status is detected to exceed a reference value, automatically awakens the battery for diagnosis, includes the following steps:

[0011] Monitor the battery's charge state through sensors and obtain real-time battery parameter data;

[0012] Analyze the battery performance under normal working conditions and establish a benchmark value;

[0013] Based on the monitored parameter data, a battery self-wake-up model is established using machine learning algorithms;

[0014] Based on the battery self-wake-up model, the battery status is monitored in real time to identify battery behavior patterns that are inconsistent with the baseline value;

[0015] When abnormal patterns are detected, corresponding events and data are recorded;

[0016] The method of establishing a battery self-wake-up model based on the monitored parameter data and using a machine learning algorithm includes the following steps:

[0017] Acquiring the parameter data and performing a preprocessing operation on the parameter data;

[0018] Extract key features related to the battery self-wake-up behavior; the key features include the battery's voltage, temperature, discharge rate, charging state, number of charging times and cycles, and historical fault data;

[0019] Divide the dataset into training and testing sets;

[0020] Use the long short-term memory network algorithm and gradient boosting tree to build a battery self-wake-up model;

[0021] Use the training set to train the battery self-wake-up model, and use the test set to evaluate the performance of the model;

[0022] Integrate the trained battery self-wake-up model into the real-time monitoring system.

[0023] Furthermore, the key events include the voltage, temperature, charge state, self-discharge rate and short circuit and short circuit events of the battery.

[0024] Furthermore, the analysis of the battery performance under normal working conditions and establishment of a reference value comprises the following steps:

[0025] Collect parameter data of the battery under normal working conditions;

[0026] extracting key features from the collected data; the key features including charge / discharge rate, battery temperature changes, and voltage fluctuations;

[0027] Model the collected data using statistical analysis or linear regression algorithms;

[0028] Build a baseline model that describes normal battery behavior;

[0029] Verify the established benchmark model and use some data for model validation;

[0030] Based on the established model, determine the normal range and threshold of battery performance;

[0031] Based on the results of model validation and the determined thresholds, benchmark values ​​describing normal battery performance are established.

[0032] Furthermore, the use of the long short-term memory network algorithm and the gradient boosting tree to construct a battery self-wake-up model specifically includes the following steps:

[0033] Serialize battery parameter data into time series input;

[0034] Extract statistical features of time series data;

[0035] Determine the sequence length based on the expected timing pattern of battery behavior;

[0036] Use the extracted time series statistical features as input for training the model;

[0037] Adjust the size of the sliding window to better capture the temporal pattern of battery behavior;

[0038] Use K-fold cross validation to ensure the performance of the model under different data distributions;

[0039] Build a battery self-wake-up model by learning timing patterns.

[0040] Furthermore, the formula for determining the sequence length is specifically:

[0041] ,

[0042] Among them, N represents the sequence length, X represents the input sequence, Y represents the target sequence, and X t represents a sequence of battery parameter data from time steps tN to t-1, y t Label representing the battery behavior at time step t.

[0043] Furthermore, the battery behavior mode specifically includes voltage abnormality, temperature abnormality and charge and discharge rate abnormality.

[0044] Furthermore, the establishment of a battery self-wake-up model automatically wakes up the battery for diagnosis when it is monitored that the battery status exceeds a baseline value. It also includes performing diagnostic analysis based on the self-wake-up model and generating a diagnostic report. The diagnostic report includes the timestamp of the abnormal event, the detected abnormal pattern, the cause analysis and the recommended repair measures.

[0045] On the other hand, a self-wake-up diagnostic system for battery management includes: a sensor module, a data collection module, an abnormality detection module, a self-wake-up trigger module, a fault diagnosis module and a decision module; the sensor module, the data collection module, the abnormality detection module, the self-wake-up trigger module, the fault diagnosis module and the decision module are wirelessly connected;

[0046] The sensor module is used to communicate with battery-related sensors to obtain real-time battery parameter data;

[0047] The data collection module is used to collect, process and filter the data provided by the sensor interface module;

[0048] The abnormality detection module is used to construct a battery self-wake-up model using a machine learning algorithm, estimate the current state of the battery, and detect abnormal conditions of the battery;

[0049] The self-wake-up trigger module is used to trigger the self-wake-up of the battery when an abnormal event is detected through a self-wake-up model;

[0050] The fault diagnosis module is used to perform fault diagnosis through data analysis;

[0051] The decision module is used to make decisions according to the results of the fault diagnosis module, and the decisions include alarming, taking repair measures and adjusting the battery management strategy.

[0052] The beneficial effect of the present invention is that the present invention uses sensors to monitor the battery charge state in real time and obtain real-time parameter data, and combines machine learning algorithms to establish a self-wake-up model, so that the system can promptly identify abnormal battery status. This helps to improve the real-time understanding of battery performance, so that measures can be taken more promptly. By analyzing the battery performance under normal working conditions and establishing a baseline value, the normal working state of the battery can be judged more accurately, thereby ensuring that the self-wake-up model is established on a sufficient basis and reducing the possibility of false alarms and missed alarms.

[0053] By introducing an asynchronous event response mechanism, the present invention can monitor key events when the battery is in a dormant state, improve the system's sensitivity to key events, and thus respond to changes in the battery state more promptly. When an abnormal mode is detected, the system can record the corresponding events and data to provide detailed information for subsequent analysis. This helps to gain a deeper understanding of the causes of the abnormal mode and improves the efficiency of troubleshooting.

[0054] The present invention uses a self-wake-up model. When the battery status is detected to exceed the reference value, the system can automatically wake up the battery for diagnosis and generate a detailed diagnostic report. This helps to reduce the need for manual intervention and improve the automation level of battery management. Based on the analysis results of abnormal wake-up events, the system can generate targeted diagnostic reports and provide recommendations for preventive measures. This enables users to take appropriate measures before problems occur, extend battery life, and improve system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a self-wake-up diagnosis method for battery management in the present invention.

[0056] Figure 2 It is a flowchart of step S2 provided in an embodiment of the present invention.

[0057] Figure 3 It is a module schematic diagram of a self-wake-up diagnostic system for battery management in the present invention. DETAILED DESCRIPTION

[0058] See also Figure 1-3 As shown, the present invention relates to a self-wake-up diagnosis method and system for battery management.

[0059] Example 1

[0060] A self-wake-up diagnostic method for battery management, comprising the following steps:

[0061] S1: Monitor key events of the battery in sleep state through asynchronous event response mechanism;

[0062] The asynchronous event response mechanism includes maintaining monitoring of specific events in a dormant state, wherein the specific events include battery state changes and abnormal mode detection.

[0063] It should be noted that the main function of the asynchronous event response mechanism is to allow the system to monitor and respond to key events while the battery is in a dormant state. By allowing the battery to remain in a dormant state when full monitoring is not necessary, the asynchronous event response mechanism helps reduce the energy consumption of the battery. This is very important for extending battery life and optimizing battery use. Although the battery is in a dormant state, the asynchronous event response mechanism ensures that the system can wake up the battery immediately when a predefined key event or condition (such as the battery voltage drops below a certain threshold) is detected. By continuously listening to key events, the mechanism enhances the system's monitoring capabilities, enabling it to more effectively identify abnormal conditions or behavior patterns that require immediate attention, thereby achieving more sophisticated and timely battery management. The asynchronous event response mechanism helps improve the overall reliability and efficiency of the battery management system. By waking up the battery for inspection only when necessary, interference with battery performance can be reduced, while ensuring that the battery can receive necessary maintenance and diagnosis at critical moments.

[0064] S2: Establish a battery self-wake-up model. When the battery status is detected to exceed the reference value, the battery is automatically awakened for diagnosis and the analysis results are generated.

[0065] S21: monitor the charge state of the battery through sensors and obtain real-time parameter data of the battery to evaluate the performance of the battery under normal operating conditions;

[0066] Specifically, appropriate sensors, such as current sensors and voltage sensors, are selected and connected to the battery. A microcontroller or embedded system is used to read the output of the sensor to obtain real-time parameter data such as the battery's current, voltage, and temperature.

[0067] It should be noted that the purpose of collecting parameter data under normal operating conditions is to establish a baseline value for battery performance and a battery self-wake-up model. This process assumes that the battery is active, that is, "already awake", so that data can be collected and analyzed. The purpose is to enable the system to automatically identify battery behavior patterns that deviate from the normal range in the future, which may indicate potential problems with the battery.

[0068] Therefore, during the modeling and data collection phase, the battery is awake. This is done to ensure that accurate and representative battery performance data is collected. This data is then used to build a model that can monitor the battery status in real time during daily use and perform deeper diagnostic analysis through the self-awakening model when an abnormal state is detected.

[0069] S22: Analyze the battery performance under normal working conditions and establish a benchmark value;

[0070] Wherein, step S22 specifically includes the following steps:

[0071] S221: Collect parameter data of the battery under normal working conditions;

[0072] S222: extracting key features from the collected data; the key features include charging / discharging rate, battery temperature change, and voltage fluctuation;

[0073] S223: Use statistical analysis or linear regression algorithms to model the collected data;

[0074] S224: Establish a baseline model describing normal battery behavior;

[0075] S225: verify the established benchmark model and use part of the data to perform model verification;

[0076] S226: Determine a normal range and threshold of battery performance based on the established baseline model;

[0077] S227: Establish a benchmark value describing normal battery performance based on the model validation results and the determined threshold value.

[0078] Specifically, the collected data is preprocessed, including noise removal, outlier processing and data cleaning; key features are extracted from the data, such as charging / discharging rate, battery temperature changes, voltage fluctuations, etc.; statistical methods or simple models such as linear regression are used to preliminarily model the data to understand the correlation and possible patterns between features; linear regression is used to establish a baseline model that describes the normal behavior of the battery; based on the normal results and abnormal conditions output by the model, thresholds are set to determine the normal range. The thresholds are the upper and lower limits of a specific parameter or the threshold of the rate of change. The thresholds can be set based on the performance distribution predicted by the baseline model and business needs to determine whether the battery exhibits abnormal behavior. Combined with the results of model verification and the determined thresholds, a baseline value describing the normal performance of the battery is established. The baseline value will become a reference standard for subsequent battery performance monitoring and anomaly detection; as time goes by and more data is collected, the baseline value is regularly updated and maintained; the accuracy of the baseline value is regularly evaluated and adjusted according to actual conditions to ensure that the baseline value always reflects the normal performance range of the battery.

[0079] It should be noted that the benchmark model is constructed by a linear regression algorithm, and the formula is as follows:

[0080] ,

[0081] Among them, Y represents the performance benchmark indicator of the battery, which is the battery life, discharge rate or other indicators to measure battery performance; X1, X2, X n Indicates factors that affect battery performance, such as temperature, number of charges, and current strength; It represents the expected value of Y when all independent variables are 0; , , is the slope coefficient, which indicates the expected change in Y for each unit increase in the corresponding independent variable; It represents the error term, and each additional unit can be temperature, number of charges, and current and voltage intensity.

[0082] S23: Based on the monitored parameter data, a battery self-wake-up model is established using a machine learning algorithm;

[0083] Wherein, step S23 specifically includes the following steps:

[0084] S231: Acquire the parameter data and perform preprocessing operations on the parameter data;

[0085] S232: extracting key features related to the battery self-awakening behavior; the key features include the battery voltage, temperature, discharge rate, charging state, charging times and cycles, and historical fault data;

[0086] S233: Divide the data set into a training set and a test set;

[0087] S234: Use long short-term memory network algorithm and gradient boosting tree to build a battery self-wake-up model;

[0088] Wherein, step S234 specifically includes the following steps:

[0089] Serialize battery parameter data into time series input;

[0090] Extract statistical features of time series data;

[0091] Determine the sequence length based on the expected timing pattern of battery behavior;

[0092] Specifically, the formula for determining the sequence length is:

[0093]

[0094] Among them, N represents the sequence length, X represents the input sequence, Y represents the target sequence, and X t represents a sequence of battery parameter data from time steps tN to t-1, y t Label representing the battery behavior at time step t.

[0095] Use the extracted time series statistical features as input for training the battery self-wake-up model;

[0096] Specifically, the processed battery parameter data is divided according to the determined sequence length to form multiple sample sequences, ensuring that each sample sequence contains the data features within the required time window size; the required statistical features are extracted for each sample sequence, including mean, standard deviation, maximum value, minimum value, etc., and the extracted features are combined in the order of the time series to construct an input data set, and a feature vector is formed for the statistical features within each time window; a label is assigned to each sample sequence to represent the type of battery behavior corresponding to the sequence, such as charging, discharging, sleep, etc., ensuring that the correspondence between the label and each sample sequence is accurate, so as to train the model for supervised learning; the constructed input data set is divided into a training set and a test set, usually divided according to a certain ratio, the training set is used for model training, and the test set is used for model verification and evaluation; the constructed sample sequences and labels are input into the model for training, and the model is trained for the selected algorithms such as LSTM and gradient boosting tree. The model learns the relationship between the features and labels in the input data to learn the timing pattern of battery behavior.

[0097] Adjust the size of the sliding window to better capture the temporal pattern of battery behavior;

[0098] Specifically, for different battery behavior patterns, try using sliding windows of different sizes, and adjust the window size according to the characteristics of the behavior pattern and the expected timing pattern to cover possible key features; use sliding windows of different sizes to slide and intercept the sequence data to obtain multiple subsequences of different lengths, ensuring that the step size and window size of the sliding window can fully cover the data and capture the important timing characteristics of battery behavior.

[0099] Use K-fold cross validation to ensure the performance of the model under different data distributions;

[0100] Specifically, the dataset is divided into K subsets, one of which is used as the validation set each time, and the remaining K-1 subsets are used as training sets. This is repeated K times, with a different subset used as the validation set each time, and K model evaluation results are finally obtained. Each model is evaluated using the validation set, and the performance indicators of the model on different validation sets are recorded. The performance indicators of the K validations are summarized, and the average performance is calculated as an estimate of the overall performance of the model. During the cross-validation process, the model parameters can be tuned according to different model performances.

[0101] Build a battery self-wake-up model by learning timing patterns.

[0102] Specifically, the LSTM algorithm extracts features from time series data (such as battery voltage, temperature, discharge rate, etc.). The formula involved is as follows:

[0103] Forget Gate: ;

[0104] Input Gate: ;

[0105] Output Gate: ;

[0106] in, represents the input feature vector at time step t; Represents the cell state at the previous time step; , , represents the activation value of the forget gate, input gate, and output gate at time step t; W and b are the weight matrix and bias term.

[0107] The gradient boosting tree receives the features output by the long short-term memory network algorithm as input, and further learns and maps them to the target variable of battery self-wake-up. The gradient boosting tree minimizes the loss function by iteratively adding decision trees, and each tree tries to correct the residual left by the previous tree.

[0108] The formula for the self-wake-up model output is as follows:

[0109] ; Where M represents the total number of trees, Represents the prediction result of the mth tree; Indicates the contribution of the output of the mth tree to the final prediction; Represents the output result of the self-wake-up model.

[0110] Through this combination, the self-wake-up model of the present application can first capture the long-term dependencies and dynamic changes in the time series data through the long short-term memory network algorithm, and then finely analyze the complex relationship between these features and the target variable (battery self-wake-up behavior) through the gradient boosting tree, thereby realizing the self-wake-up of the battery.

[0111] S235: Use the training set to train the battery self-wake-up model, and use the test set to evaluate the performance of the model;

[0112] S236: Integrate the trained battery self-wake-up model into the real-time monitoring system.

[0113] S24: Based on the battery self-wake-up model, monitor the battery status in real time and identify battery behavior patterns that are inconsistent with the reference value; the battery behavior patterns include voltage abnormalities, temperature abnormalities, and charge and discharge rate abnormalities.

[0114] S25: when an abnormal pattern is detected, recording corresponding events and data;

[0115] Specifically, a unique identifier is generated for each detected abnormal event, and key information such as the timestamp of the event, the type of abnormality, and the specific values ​​of the battery parameters are recorded; while recording the abnormal event, consider notifying relevant personnel or system administrators in a timely manner through the notification mechanism.

[0116] S3: Generate a diagnostic report based on the analysis results of the abnormal wake-up event;

[0117] Specifically, the system records detailed information about the system starting up due to abnormal wake-up, including timestamp, wake-up reason, battery parameter data, etc.; analyzes and compares the battery parameter data obtained after wake-up to confirm the abnormal mode and find the specific cause of the abnormality, and uses the stored baseline value and historical data to compare and analyze abnormal events to determine the nature and severity of the abnormality. Based on the diagnostic analysis results, a detailed diagnostic report is generated, including abnormal modes, possible causes, expected impacts, and recommended solutions; the report is presented as a chart, text description, or recommended operating steps so that the system operator or relevant personnel can understand and take corresponding measures.

[0118] S4: Provide recommendations for preventive measures based on the diagnosis.

[0119] Specifically, conduct a detailed analysis of the abnormal diagnosis results to determine the specific cause of the abnormality, possible factors and scope of influence. By comparing the battery parameter data, confirm whether the abnormality originates from the battery itself or is caused by external factors. Based on the abnormal cause and analysis results, formulate corresponding preventive measures and take technical measures, such as adjusting the working mode of the battery management system, optimizing the charging and discharging strategy, or strengthening the monitoring and control of the battery working environment. Based on the diagnostic results, evaluate the performance and working mode of the existing system, and check whether the system needs to be optimized or improved to improve the stability and safety of battery management. Updating the control algorithm, improving the accuracy of the sensor or enhancing the system monitoring capability may all be directions for improvement. It is recommended to formulate a regular maintenance plan, including inspection, calibration and maintenance of the battery. Continuously monitor the battery parameter data, establish a more accurate baseline value, and regularly update the baseline value to reflect changes in battery performance.

[0120] In this embodiment, the battery charge state and parameter data are monitored in real time by sensors, and a self-wake-up model is established in combination with a machine learning algorithm, which can monitor the battery status in real time and perform diagnosis when an abnormal pattern is detected. By analyzing the battery performance under normal working conditions, a baseline model and baseline value are established to ensure that the monitoring of battery performance is accurate and reliable. As time goes by and data accumulates, the baseline value can be regularly updated and maintained so that it can reflect changes in battery performance. Using machine learning algorithms such as long short-term memory networks and gradient boosting trees, a battery self-wake-up model is established to better capture the timing patterns of battery behavior and improve the accuracy and sensitivity of anomaly detection.

[0121] When an abnormal pattern is detected, detailed event information is recorded, including timestamp, abnormality type, and battery parameter data. Relevant personnel or system administrators are notified in a timely manner through a notification mechanism so that they can take appropriate measures quickly. The introduction of an adaptive threshold setting mechanism can dynamically adjust the baseline value or threshold to adapt to changes in battery performance, thereby improving the adaptability of the system.

[0122] When an abnormal wake-up event occurs, a detailed diagnostic report is generated, including abnormal mode, cause analysis and recommended repair measures. Providing preventive measures based on the diagnostic results helps improve system performance and reduce the occurrence of battery failures. Introducing low-power monitoring methods to ensure that key parameters can be monitored in real time even in sleep mode helps reduce the additional burden on the battery and improve the energy efficiency of the system. Through the asynchronous event response mechanism, key events are monitored in sleep mode, which can respond to battery status changes and abnormal mode detection more quickly, improving the real-time and responsiveness of the system.

[0123] Example 2

[0124] A self-wake-up diagnostic system for battery management, comprising: a sensor module, a data collection module, an abnormality detection module, a self-wake-up trigger module, a fault diagnosis module and a decision module; the sensor module, the data collection module, the abnormality detection module, the self-wake-up trigger module, the fault diagnosis module and the decision module are wirelessly connected;

[0125] The sensor module is used to communicate with battery-related sensors to obtain real-time battery parameter data;

[0126] The data collection module is used to collect, process and filter the data provided by the sensor interface module;

[0127] The abnormality detection module is used to construct a battery self-wake-up model using a machine learning algorithm, estimate the current state of the battery, and detect abnormal conditions of the battery;

[0128] The self-wake-up trigger module is used to trigger the self-wake-up of the battery when an abnormal event is detected through a self-wake-up model;

[0129] The fault diagnosis module is used to perform fault diagnosis through data analysis, and to perform fault diagnosis through methods such as data pattern recognition and statistical analysis to determine the type and cause of abnormal events;

[0130] The decision module is used to make decisions according to the results of the fault diagnosis module, and the decision includes generating an alarm notification, taking repair measures and adjusting the battery management strategy.

[0131] In this embodiment, the sensor module can communicate with the battery-related sensors to obtain the battery parameter data in real time. The data collection module processes and filters the data provided by the sensor to ensure that the system can obtain accurate battery status information in a timely manner. The abnormality detection module uses a machine learning algorithm to build a battery self-wake-up model to estimate the current state of the battery and detect abnormal conditions of the battery. The self-wake-up trigger module performs diagnosis through the self-wake-up model and triggers self-wake-up when an abnormal event is detected. It can respond to abnormal battery conditions in a timely manner and realize automatic wake-up and diagnosis.

[0132] The fault diagnosis module performs fault diagnosis through data analysis, pattern recognition and statistical analysis to determine the type and cause of abnormal events. The decision module makes decisions based on the results of the fault diagnosis module, including generating alarm notifications, taking repair measures and adjusting battery management strategies. This helps to take timely measures to solve battery problems and improve the efficiency and reliability of battery management.

[0133] The modules in the system are connected through wireless communication, which helps to realize information transmission and collaborative work between modules. Such a design helps to improve the flexibility and scalability of the system. By building a battery self-wake-up model through machine learning algorithms, the system can better adapt to the timing pattern of battery behavior and improve the accuracy of abnormality detection. This helps to detect potential battery problems in advance and reduce the risk of battery damage and failure. The decision-making module can make decisions in real time based on the results of fault diagnosis, including issuing alarm notifications, taking repair measures, and adjusting battery management strategies. Such real-time decisions and operations help reduce the adverse effects of battery failures on systems and equipment, and improve the real-time and flexibility of battery management.

[0134] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A self-wake-up diagnostic method for battery management, characterized in that: The following steps are involved: Through the asynchronous event response mechanism, monitor the key events of the battery in the dormant state; Establish a battery self-wake-up model. When the battery status is detected to exceed the reference value, the battery is automatically awakened for diagnosis and analysis results are generated. Generate a diagnostic report based on the analysis results of abnormal wakeup events; Provide recommendations for preventive measures based on diagnostic findings; The establishment of a battery self-awakening model, when the battery status is detected to exceed a reference value, automatically awakens the battery for diagnosis, includes the following steps: Monitor the battery's charge state through sensors and obtain real-time battery parameter data; Analyze the battery performance under normal working conditions and establish a benchmark value; Based on the monitored parameter data, a battery self-wake-up model is established using machine learning algorithms; Based on the battery self-wake-up model, the battery status is monitored in real time to identify battery behavior patterns that are inconsistent with the baseline value; When abnormal patterns are detected, corresponding events and data are recorded; The method of establishing a battery self-wake-up model based on the monitored parameter data and using a machine learning algorithm includes the following steps: Acquiring the parameter data and performing a preprocessing operation on the parameter data; Extract key features related to the battery self-wake-up behavior; the key features include the battery's voltage, temperature, discharge rate, charging state, number of charging times and cycles, and historical fault data; Divide the dataset into training and testing sets; Use the long short-term memory network algorithm and gradient boosting tree to build a battery self-wake-up model; Use the training set to train the battery self-wake-up model, and use the test set to evaluate the performance of the model; Integrate the trained battery self-wake-up model into the real-time monitoring system.

2. A self-wake-up diagnosis method for battery management according to claim 1, characterized in that: The key events include the battery's voltage, temperature, state of charge, self-discharge rate, and short and short circuit events.

3. The self-wake-up diagnosis method for battery management according to claim 1, characterized in that: The step of analyzing the battery performance under normal working conditions and establishing a reference value comprises the following steps: Collect parameter data of the battery under normal working conditions; extracting key features from the collected data; the key features including charge / discharge rate, battery temperature changes, and voltage fluctuations; Model the collected data using statistical analysis or linear regression algorithms; Build a baseline model that describes normal battery behavior; Verify the established benchmark model and use some data for model validation; Based on the established model, determine the normal range and threshold of battery performance; Based on the results of model validation and the determined thresholds, benchmark values ​​describing normal battery performance are established.

4. A self-wake-up diagnosis method for battery management according to claim 1, characterized in that: The battery self-wake-up model is constructed by using the long short-term memory network algorithm and the gradient boosting tree, and specifically includes the following steps: Serialize battery parameter data into time series input; Extract statistical features of time series data; Determine the sequence length based on the expected timing pattern of battery behavior; Use the extracted time series statistical features as input for training the model; Adjust the size of the sliding window to better capture the temporal pattern of battery behavior; Use K-fold cross validation to ensure the performance of the model under different data distributions; Build a battery self-wake-up model by learning timing patterns.

5. A self-wake-up diagnosis method for battery management according to claim 4, characterized in that: The formula for determining the sequence length is specifically: , Among them, N represents the sequence length, X represents the input sequence, Y represents the target sequence, and X t represents a sequence of battery parameter data from time steps tN to t-1, y t Label representing the battery behavior at time step t.

6. A self-wake-up diagnosis method for battery management according to claim 1, characterized in that: The battery behavior mode specifically includes voltage abnormality, temperature abnormality and charge and discharge rate abnormality.

7. A self-wake-up diagnosis method for battery management according to claim 1, characterized in that: The battery self-wake-up model is established, and when the battery status is monitored to exceed a baseline value, the battery is automatically awakened for diagnosis. It also includes performing diagnostic analysis based on the self-wake-up model and generating a diagnostic report, wherein the diagnostic report includes a timestamp of the abnormal event, a detected abnormal pattern, a cause analysis, and suggested repair measures.

8. A self-wake-up diagnostic system for battery management, characterized in that: The system is applied to a self-wake-up diagnosis method for battery management as described in any one of claims 1 to 7, comprising: a sensor module, a data collection module, an abnormality detection module, a self-wake-up trigger module, a fault diagnosis module and a decision module; the sensor module, the data collection module, the abnormality detection module, the self-wake-up trigger module, the fault diagnosis module and the decision module are wirelessly connected; The sensor module is used to communicate with battery-related sensors to obtain real-time battery parameter data; The data collection module is used to collect, process and filter the data provided by the sensor interface module; The abnormality detection module is used to construct a battery self-wake-up model using a machine learning algorithm, estimate the current state of the battery, and detect abnormal conditions of the battery; The self-wake-up trigger module is used to trigger the self-wake-up of the battery when an abnormal event is detected through a self-wake-up model; The fault diagnosis module is used to perform fault diagnosis through data analysis; The decision module is used to make decisions according to the results of the fault diagnosis module, and the decisions include alarming, taking repair measures and adjusting the battery management strategy.

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