A battery early warning management method and device, electronic equipment and storage medium

Abstract

The application discloses a battery early warning management method and device, electronic equipment and storage medium. The method comprises the following steps: when an access signal of a target battery is received, a battery management identifier of the target battery is generated; the battery management identifier is bound to basic information of the target battery; full-cycle information and environmental information of the target battery are obtained based on the battery management identifier; data fusion and feature extraction are performed on the full-cycle information to obtain fused feature information; the basic information, the environmental information and the fused feature information are input into a trained prediction model to obtain early warning level information of the target battery; and a corresponding processing strategy is executed based on the early warning level information. In the embodiment of the application, by integrating charging, battery replacement and energy storage full-cycle information and environmental information, combined with multi-source data fusion and feature extraction, the limitation of single-scene data fragmentation is broken, comprehensive and accurate data support is provided for early warning judgment, and the probability of early warning false alarm and missed alarm is greatly reduced.

Description

Technical Field

[0001] This application relates to the field of battery safety monitoring technology, and in particular to a battery early warning management method, device, electronic device and storage medium. Background Technology

[0002] With the rapid integration and development of the new energy vehicle industry and the energy storage industry, the safe and stable operation of batteries, as the core energy carrier, is directly related to the reliability and safety of the entire new energy system. Currently, battery early warning management technology has become the key to ensuring battery safety and improving battery utilization efficiency. However, existing battery early warning management solutions still have many technical shortcomings and are difficult to meet current early warning needs.

[0003] In existing technologies, battery early warning systems are mostly implemented for single application scenarios, resulting in fragmented battery operation data from different stages. This makes it impossible to form a complete picture of the battery's entire life cycle operation, which in turn leads to a lack of comprehensive data support for early warning judgments and makes it easy for false alarms and missed alarms to occur. Summary of the Invention

[0004] To address existing technical problems, this invention provides a battery early warning management method, device, electronic device, and storage medium. By establishing a unique battery identification mechanism, it achieves precise binding of full-cycle data with target batteries through battery management identifiers. It integrates full-cycle information on charging, battery swapping, and energy storage, as well as environmental information. By combining multi-source data fusion and feature extraction, it breaks through the limitations of fragmented data in a single scenario, providing comprehensive and accurate data support for early warning judgment and significantly reducing the probability of false alarms and missed alarms.

[0005] In a first aspect, embodiments of this application provide a battery early warning management method, the method comprising: When an access signal for the target battery is received, a battery management identifier for the target battery is generated; Bind the battery management identifier to the basic information of the target battery; Based on the battery management identifier, obtain the full-cycle information and environmental information of the target battery; the full-cycle information represents the relevant information of the target battery throughout the entire cycle of charging, battery swapping and energy storage; Data fusion and feature extraction are performed on the full-cycle information to obtain fused feature information; The basic information, the environmental information, and the fused feature information are input into the trained prediction model to obtain the warning level information of the target battery; The corresponding processing strategy is executed based on the warning level information.

[0006] In an optional embodiment, generating the battery management identifier of the target battery when an access signal of the target battery is received includes: Receive the access signal of the target battery; the access signal carries the battery physical identifier and access scenario identifier of the target battery; If the battery physical identifier indicates that the target battery is accessing the database for the first time, the battery management identifier is generated based on the preset encoding rules and the access scenario identifier; the encoding rules include the manufacturer encoding segment, the access time encoding segment, and the random check code segment.

[0007] In one optional embodiment, binding the battery management identifier to the basic information of the target battery includes: Obtain the basic information of the target battery; The basic information is subjected to standardized cleaning to obtain cleaned basic information; the standardized cleaning process includes at least one of the following: abnormal data removal, missing parameter completion, and conversion of unstructured information into structured code; Establish a mapping relationship between the battery management identifier and the cleaned basic information, and store the mapping relationship and the cleaned basic information in the database; If the target battery update information is received, the corresponding cleaned basic information in the database is updated based on the battery management identifier and the mapping relationship.

[0008] In one optional embodiment, obtaining the full-cycle information and environmental information of the target battery based on the battery management identifier includes: The battery management identifier is sent to the charging device, battery swapping device, and energy storage device; Receive charging-related information, battery swapping-related information, energy storage-related information, and environmental information returned by the charging device, the battery swapping device, and the energy storage device based on the battery management identifier; Determine the current status data and historical status data from the charging-related information, the battery swapping-related information, and the energy storage-related information; Battery latent data is determined based on the aforementioned historical state data; The battery explicit data is determined based on the current state data; The full-cycle information is determined based on the battery's implicit data and explicit data.

[0009] In one optional embodiment, the step of performing data fusion and feature extraction on the full-cycle information to obtain fused feature information includes: Obtain the correlation parameters, timeliness parameters, and accuracy parameters of the battery's implicit data; The first fusion weight of the battery latent data is determined based on the correlation parameter, the timeliness parameter, and the accuracy parameter. The second fusion weight of the battery explicit data is determined based on the first fusion weight; Based on the first fusion weight, the second fusion weight, the battery implicit data, and the battery explicit data, a weighted fusion is performed to obtain full-cycle fusion information; Feature extraction is performed on the full-cycle fusion information to obtain the fusion feature information.

[0010] In an optional embodiment, the step of inputting the basic information, the environmental information, and the fused feature information into a trained prediction model to obtain the warning level information of the target battery includes: The basic information, the environmental information, and the fused feature information are converted into formats to obtain the converted input information. The converted input information is input into the trained prediction model for hierarchical weighted inference, and the failure risk score, potential failure type and failure inducing factors of the target battery are output; the trained prediction model is a time series prediction model built based on LSTM, Transformer or CNN-LSTM hybrid algorithm; The warning level information is determined based on the fault risk score, the potential fault type, the fault inducing factors, and the preset warning threshold.

[0011] In one optional embodiment, executing the corresponding processing strategy based on the warning level information includes: If the warning level information is Level 1, execute the first processing strategy; the first processing strategy includes pushing the prompt information marked with the battery management identifier to the terminal interaction layer, and obtaining the full life cycle information and environmental information corresponding to the battery management identifier at a first frequency; Alternatively, if the warning level information is level two, a second processing strategy is executed; the second processing strategy includes pushing warning information labeled with the battery management identifier and the fault-inducing factors to the terminal interaction layer, sending first restriction information to the charging device, battery swapping device, and energy storage device, and acquiring full-cycle information and environmental information corresponding to the battery management identifier at a second frequency; the first restriction information is used to instruct the charging device, battery swapping device, and energy storage device to limit charging and discharging power and activate preliminary protection measures, and simultaneously increase the data acquisition frequency; the second frequency is greater than the first frequency; Alternatively, if the warning level information is level three, a third processing strategy is executed; the third processing strategy includes pushing the emergency warning information marked with the battery management identifier to the terminal interaction layer, and sending second restriction information to the charging device, the battery swapping device, and the energy storage device; the second restriction information is used to instruct the charging device, the battery swapping device, and the energy storage device to disconnect the charging and discharging link, isolate the fault module, activate fire prevention actions, and prohibit the target battery from participating in the charging, swapping, and storage process.

[0012] Secondly, embodiments of this application provide a battery warning management device, the device comprising: The identifier generation module is used to generate a battery management identifier for the target battery when an access signal for the target battery is received. The identifier binding module is used to bind the battery management identifier to the basic information of the target battery; The acquisition module is used to acquire the full-cycle information and environmental information of the target battery based on the battery management identifier; the full-cycle information represents the relevant information of the target battery throughout the entire cycle of charging, battery swapping and energy storage. The fusion module is used to perform data fusion and feature extraction on the full-cycle information to obtain fused feature information; The prediction module is used to input the basic information, the environmental information and the fused feature information into the trained prediction model to obtain the warning level information of the target battery; The execution module is used to execute the corresponding processing strategy based on the warning level information.

[0013] Thirdly, embodiments of this application provide an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The processor loads and executes the at least one instruction, at least one program, code set, or instruction set to implement the battery warning management method of the first aspect.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement the battery warning management method of the first aspect.

[0015] Fifthly, embodiments of this application provide a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the battery warning management method of the first aspect.

[0016] The battery early warning management method, device, electronic equipment, and storage medium provided in this application have the following technical effects: When an access signal for a target battery is received, a battery management identifier for the target battery is generated; the battery management identifier is bound to the basic information of the target battery; based on the battery management identifier, the full-cycle information and environmental information of the target battery are obtained; the full-cycle information represents the relevant information of the target battery throughout its charging, battery swapping, and energy storage cycles; data fusion and feature extraction are performed on the full-cycle information to obtain fused feature information; the basic information, the environmental information, and the fused feature information are input into a trained prediction model to obtain the warning level information of the target battery; and the corresponding processing strategy is executed based on the warning level information.

[0017] In this embodiment of the application, by establishing a unique battery identification mechanism, the battery management identifier enables precise binding of full-cycle data with the target battery, solving the problems of data confusion and unclear warning direction in the prior art when multiple batteries are running in collaboration, and realizing full-cycle traceability of a single battery.

[0018] Furthermore, by integrating information from the entire lifecycle of charging, battery swapping, and energy storage, as well as environmental information, and combining multi-source data fusion and feature extraction, the limitations of fragmented data in a single scenario are broken, providing comprehensive and accurate data support for early warning judgment and significantly reducing the probability of false alarms and missed alarms.

[0019] Furthermore, by using predictive models to accurately output early warning levels, and matching them with corresponding handling strategies, a closed-loop management system can be achieved for the entire process from battery access, status monitoring, early warning prediction to disposal execution. This transforms passive early warning into proactive prediction, improving the timeliness and relevance of battery early warnings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of an application environment provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a battery early warning management method provided in an embodiment of this application. Figure 1 ; Figure 3 This is a flowchart illustrating a battery early warning management method provided in an embodiment of this application. Figure 2 ; Figure 4 This is a flowchart illustrating a method for executing a processing strategy provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a battery early warning management device provided in an embodiment of this application; Figure 6 This is a hardware structure block diagram of a server for a battery early warning management method provided in an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0024] Please see Figure 1 , Figure 1 This is a schematic diagram of an application environment provided in an embodiment of this application. The battery early warning management system includes a server 101, a charging device 102, a battery swapping device 103, an energy storage device 104, and a target battery 105.

[0025] The server 101, as the core control unit, establishes communication connections with the charging device 102, the battery swapping device 103, and the energy storage device 104, enabling data transmission, reception, and processing. The charging device 102 provides charging services to the target battery 105 and collects relevant data during the charging process; the battery swapping device 103 performs the replacement operation of the target battery 105 and collects relevant data during the battery swapping process; the energy storage device 104 stores the electrical energy of the target battery 105 and collects relevant data and surrounding environmental information during the energy storage process.

[0026] The target battery 105 is the core object to be managed in advance. It can switch between charging equipment 102, battery swapping equipment 103 and energy storage equipment 104 to complete the entire cycle of charging, battery swapping and energy storage.

[0027] In this embodiment, when server 101 receives an access signal from target battery 105, it generates a battery management identifier for the target battery; binds the battery management identifier to the basic information of the target battery 105; obtains the full-cycle information and environmental information of the target battery 105 based on the battery management identifier; the full-cycle information represents the relevant information of the target battery 105 throughout the charging, battery swapping, and energy storage cycles; performs data fusion and feature extraction on the full-cycle information to obtain fused feature information; inputs the basic information, the environmental information, and the fused feature information into a trained prediction model to obtain the warning level information of the target battery 105; and executes a corresponding processing strategy based on the warning level information.

[0028] In this embodiment of the application, by establishing a unique battery identification mechanism, the battery management identifier enables precise binding of full-cycle data with the target battery, solving the problems of data confusion and unclear warning direction in the prior art when multiple batteries are running in collaboration, and realizing full-cycle traceability of a single battery.

[0029] Furthermore, by integrating information from the entire lifecycle of charging, battery swapping, and energy storage, as well as environmental information, and combining multi-source data fusion and feature extraction, the limitations of fragmented data in a single scenario are broken, providing comprehensive and accurate data support for early warning judgment and significantly reducing the probability of false alarms and missed alarms.

[0030] Furthermore, by using predictive models to accurately output early warning levels, and matching them with corresponding handling strategies, a closed-loop management system can be achieved for the entire process from battery access, status monitoring, early warning prediction to disposal execution. This transforms passive early warning into proactive prediction, improving the timeliness and relevance of battery early warnings.

[0031] The following describes a specific embodiment of a battery early warning management method according to this application. Figure 2 This is a flowchart illustrating a battery early warning management method provided in an embodiment of this application. Figure 1 This specification provides method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual system or server products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown in the embodiments or drawings... Figure 2 As shown, it may include: S201: When an access signal for the target battery is received, a battery management identifier for the target battery is generated.

[0032] S202: Bind the battery management identifier to the basic information of the target battery.

[0033] S203: Obtain the full-cycle information and environmental information of the target battery based on the battery management identifier; the full-cycle information represents the relevant information of the target battery throughout the entire cycle of charging, battery swapping and energy storage.

[0034] S204: Perform data fusion and feature extraction on the full-cycle information to obtain fused feature information.

[0035] S205: Input the basic information, the environmental information, and the fused feature information into the trained prediction model to obtain the warning level information of the target battery.

[0036] S206: Execute the corresponding processing strategy based on the warning level information.

[0037] Figure 3 This is a flowchart illustrating a battery early warning management method provided in an embodiment of this application. Figure 2 The method may include: S301: When an access signal for the target battery is received, a battery management identifier for the target battery is generated.

[0038] When a target battery is connected to any of the following devices: charging equipment, battery swapping equipment, or energy storage equipment, the device will send an access signal to the server. After receiving the access signal, the server will trigger the generation of a battery management identifier to achieve unique identification of the target battery, providing a foundation for subsequent full-cycle data association and traceability.

[0039] In an optional embodiment, generating the battery management identifier of the target battery when an access signal of the target battery is received includes: S3011: Receive the access signal of the target battery.

[0040] In this embodiment, the access signal carries the target battery's physical identifier and access scenario identifier. The battery physical identifier is a unique identifier that comes with the target battery at the factory, such as a serial number or QR code, used for initial battery identification. The access scenario identifier indicates the current access device type of the target battery, such as the identifier code corresponding to "charging device," "battery swapping device," or "energy storage device," facilitating server differentiation of the access scenario.

[0041] S3012: If the battery physical identifier indicates that the target battery is accessing the database for the first time, the battery management identifier is generated based on the preset encoding rules and the access scenario identifier.

[0042] The encoding rules include a manufacturer encoding segment, an access time encoding segment, and a random check code segment.

[0043] In this embodiment, the server compares the received battery physical identifier with the battery physical identifiers already stored in the database. If no matching record is found, the target battery is determined to be accessing for the first time. At this time, a battery management identifier is generated according to a preset encoding rule. For example, it consists of a 2-bit manufacturer code segment, a 14-bit access time code segment (year, month, day, hour, minute, second) corresponding to the access time, a 1-bit access scenario identifier segment to distinguish between charging, battery swapping, and energy storage scenarios, and a 3-bit random check code segment. This ensures that the generated battery management identifier is globally unique and can quickly identify the battery manufacturer, access time, and access scenario through encoding.

[0044] S3013: The battery physical identifier indicates that the target battery is not accessing the database for the first time, and the battery management identifier is determined based on the battery physical identifier.

[0045] In this embodiment, if the server finds a record in the database that matches the received battery physical identifier, it determines that the target battery is not being accessed for the first time. At this time, the battery management identifier corresponding to the target battery that has been stored in the database is retrieved through the battery physical identifier. There is no need to generate it repeatedly, ensuring that the same target battery uses a unique battery management identifier throughout the entire cycle, thus ensuring the continuity of data association.

[0046] S302: Bind the battery management identifier to the basic information of the target battery.

[0047] After generating the battery management identifier, the server will uniquely bind the identifier to the basic information of the target battery, establishing a unique mapping relationship. This allows the basic characteristic information of the target battery to be quickly retrieved through the battery management identifier, providing a basis for subsequent early warning prediction and strategy matching.

[0048] In one optional embodiment, binding the battery management identifier to the basic information of the target battery includes: S3021: Obtain the basic information of the target battery.

[0049] In this embodiment, the basic information is the core inherent information of the target battery, specifically including battery model, production batch, rated capacity, rated voltage, manufacturer, factory testing parameters, warranty period, etc., which can be obtained by retrieving the manufacturer's database through the battery physical identifier of the target battery, or supplemented by the collection terminal of the access device.

[0050] S3022: Perform standardized cleaning processing on the basic information to obtain cleaned basic information.

[0051] In one possible embodiment, the standardized cleaning process includes at least one of the following: abnormal data removal, missing parameter completion, and conversion of unstructured information into structured codes. For example, removing factory inspection parameters that exceed reasonable ranges, completing missing warranty period information, and converting unstructured manufacturer names such as "XX manufacturer" into corresponding manufacturer codes, i.e., structured codes, ensures the standardization, completeness, and consistency of basic information, and avoids the accuracy of subsequent early warning judgments being affected by chaotic basic information.

[0052] S3023: Establish a mapping relationship between the battery management identifier and the cleaned basic information, and store the mapping relationship and the cleaned basic information in the database.

[0053] In this embodiment, the server establishes a one-to-one mapping relationship to ensure that one battery management identifier corresponds to the basic information of only one target battery. At the same time, the mapping relationship and the cleaned basic information are synchronously stored in the database for easy retrieval and retrieval in the future.

[0054] S3024: If the update information of the target battery is received, update the corresponding cleaned basic information in the database based on the battery management identifier and the mapping relationship.

[0055] When there are situations such as warranty period extension, factory parameter correction, or manufacturer information update, the basic information of the target battery changes. After receiving the updated information, the server retrieves the corresponding mapping relationship through the battery management identifier, and then updates the basic information bound to the identifier in the database to ensure that the basic information is consistent with the actual situation of the target battery and to ensure the accuracy of subsequent early warning management.

[0056] S303: Obtain the full life cycle information and environmental information of the target battery based on the battery management identifier.

[0057] In this embodiment of the application, the full-cycle information represents the relevant information of the target battery throughout the entire cycle of charging, battery swapping, and energy storage.

[0058] After the battery management identifier is bound to basic information, the server uses the identifier as the core index to link charging equipment, battery swapping equipment, and energy storage equipment, and comprehensively collects the target battery's operating data and surrounding environmental information throughout the entire life cycle of charging, battery swapping, and energy storage. This achieves accurate correlation and comprehensive coverage of data throughout the entire life cycle, breaking the limitations of data fragmentation in a single scenario.

[0059] In one optional embodiment, obtaining the full-cycle information and environmental information of the target battery based on the battery management identifier includes: S3031: Send the battery management identifier to the charging device, the battery swapping device, and the energy storage device.

[0060] The server synchronously sends the battery management identifier of the target battery to the charging device, battery swapping device, and energy storage device associated with the target battery. After receiving the identifier, each device completes the identity matching with the target battery, confirms the uniqueness of the battery subject to be collected, and avoids data confusion.

[0061] S3032: Receive the charging-related information, the battery swapping-related information, the energy storage-related information, and the environmental information returned by the charging device, the battery swapping device, and the energy storage device based on the battery management identifier.

[0062] Each device collects corresponding data based on the battery management identifier and returns it to the server.

[0063] The charging equipment collects and returns charging-related information, such as charging voltage, charging current, and charging temperature.

[0064] The battery swapping equipment collects and returns relevant information about battery swapping, such as the remaining battery power before and after the swap, and the swapping time.

[0065] Energy storage devices collect and return energy storage-related information, such as energy storage power and module balance status.

[0066] The environmental information of the target battery's location is sent by the corresponding device, such as ambient temperature, humidity, and grid voltage fluctuations. If the target battery is at the charging device, the environmental information is sent by the charging device.

[0067] All returned data carries this battery management identifier to ensure that the data accurately corresponds to the target battery.

[0068] S3033: Determine the current state data and historical state data from the charging-related information, the battery swapping-related information, and the energy storage-related information.

[0069] In this embodiment of the application, after receiving all returned data, the server uses the battery management identifier as the core index and classifies and sorts the charging, battery swapping, and energy storage related information by the timestamps carried by each piece of information.

[0070] The current status data is the real-time battery operating data collected by each device, such as the current charging voltage and the real-time remaining power.

[0071] Historical status data refers to the past operating data stored by each device corresponding to the battery management identifier, such as past charging records and historical battery swap counts.

[0072] The current and historical status data can be easily determined from the charging-related information, the battery swapping-related information, and the energy storage-related information using timestamps.

[0073] S3034: Determine the battery latent data based on the historical state data.

[0074] In this embodiment of the application, the battery implicit data is battery status data that cannot be directly detected and obtained in real time, but needs to be obtained by statistical analysis, calculation and deduction based on historical status data, including but not limited to cumulative number of charge and swap cycles, average charge and discharge parameters, capacity decay rate, frequency of fault occurrence, energy storage efficiency trend, etc.

[0075] Specifically, the server, with battery management identifiers as the core, filters out historical data related to charging, battery swapping, and energy storage from the received historical status data. It then performs spatiotemporal alignment of the three types of historical data according to the timeline and removes invalid and redundant data.

[0076] Then, we can count the number of all charging history records corresponding to the battery management identifier to get the cumulative number of charging times, count the number of all battery swapping history records corresponding to the identifier to get the cumulative number of battery swapping times, and sum the two to get the cumulative number of charging and swapping times.

[0077] Based on historical charging data, the peak charging voltage, peak charging current, and charging duration of each charging process are extracted. The average voltage, average current, and average duration of all charging cycles are calculated using the arithmetic mean method to obtain the average charging parameters.

[0078] The rated capacity is retrieved from the basic information bound to the battery management identifier. Combined with the full-capacity records after each charging in the historical charging data, the attenuation difference between each full-capacity and the rated capacity is calculated, and then the capacity attenuation rate is calculated.

[0079] Based on historical data from three categories—charging, battery swapping, and energy storage—all historical records containing "abnormal markers" are filtered out, such as overcharge markers, abnormal battery swapping connection markers, and abnormal energy storage voltage drift markers. The total number of such abnormal records is then counted to determine the frequency of fault occurrence.

[0080] Based on historical energy storage data, the energy storage input power and energy storage output power for each energy storage cycle are extracted, the single-cycle energy storage efficiency is calculated, and then the energy storage efficiency trend is calculated.

[0081] S3035: Determine the battery explicit data based on the current state data.

[0082] In this embodiment of the application, the battery explicit data refers to real-time battery status data that can be directly detected and visualized by the device, including but not limited to current charging voltage, real-time temperature, remaining power, casing status, ambient temperature and humidity, and grid voltage fluctuation value.

[0083] S3036: Determine the full-cycle information based on the battery implicit data and the battery explicit data.

[0084] The server verifies and integrates the battery's implicit and explicit data, removing abnormal data and filling in missing data to ensure data integrity and accuracy. The integrated implicit and explicit data together constitute the full-cycle information of the target battery throughout its charging, battery swapping, and energy storage lifecycle. This full-cycle information is bound to the battery management identifier and stored to provide a data source for subsequent data fusion and feature extraction.

[0085] S304: Perform data fusion and feature extraction on the full-cycle information to obtain fused feature information.

[0086] In one optional embodiment, the step of performing data fusion and feature extraction on the full-cycle information to obtain fused feature information includes: S3041: Obtain the correlation parameters, timeliness parameters, and accuracy parameters of the battery's implicit data.

[0087] Since battery latent data is determined by historical state data, it is necessary to adjust the weights of the battery latent data to avoid the influence of battery latent data that is too old, has low accuracy, or low correlation on feature fusion.

[0088] In one optional embodiment, the correlation parameter is used to characterize the degree of correlation between implicit data and battery failure and health status; the timeliness parameter is used to characterize the time coverage of historical data corresponding to implicit data; and the accuracy parameter is used to characterize the calculation accuracy and error range in the process of implicit data derivation.

[0089] S3042: Determine the first fusion weight of the battery latent data based on the correlation parameter, the timeliness parameter and the accuracy parameter.

[0090] In this embodiment, a weighted summation method is adopted, and weights are assigned according to the importance of each parameter. For example, the correlation parameter has a weight of 50%, the accuracy parameter has a weight of 30%, and the timeliness parameter has a weight of 20%. The first fusion weight of the battery implicit data is calculated. The stronger the correlation, the higher the accuracy, and the more complete the time coverage, the higher the first fusion weight.

[0091] S3043: Determine the second fusion weight of the battery explicit data based on the first fusion weight.

[0092] In this embodiment of the application, the second fusion weight = 1 - the first fusion weight, to ensure that the weight allocation of the two types of data is reasonable and takes into account the impact of trend data and real-time data.

[0093] S3044: Based on the first fusion weight, the second fusion weight, the battery implicit data and the battery explicit data, a weighted fusion is performed to obtain full-cycle fusion information.

[0094] Implicit and explicit data are weighted according to their respective fusion weights and merged into a unified multi-dimensional dataset, eliminating data redundancy and data silos, and achieving efficient integration of information throughout the entire lifecycle.

[0095] S3045: Extract features from the full-cycle fusion information to obtain the fusion feature information.

[0096] Feature extraction algorithms are used to extract core features related to battery failure risk and health status from the full-cycle fusion information. These features include trend features (voltage / temperature / capacity change trends derived from implicit data), anomaly features (real-time parameter anomalies extracted from explicit data and historical anomalies derived from implicit data), health features (implicit data such as capacity decay rate and energy storage efficiency), and adaptation features (matching degree of charging, swapping, and storage conditions). After extraction, the features are normalized to eliminate dimensional differences and obtain standardized fusion feature information.

[0097] S305: Input the basic information, the environmental information, and the fused feature information into the trained prediction model to obtain the warning level information of the target battery.

[0098] In an optional embodiment, the step of inputting the basic information, the environmental information, and the fused feature information into a trained prediction model to obtain the warning level information of the target battery includes: S3051: Convert the format of the basic information, the environmental information and the fusion feature information to obtain the format-converted input information.

[0099] Because the data formats of basic information, environmental information, and fused feature information differ, a format conversion module is needed to convert these three types of information into a unified format that the trained prediction model can recognize, such as vector format, to ensure that the information can be input into the model for inference.

[0100] S3052: Input the converted input information into the trained prediction model for hierarchical weighted inference, and output the fault risk score, potential fault type and fault inducing factors of the target battery.

[0101] In this embodiment of the application, the trained prediction model is a time-series prediction model built based on LSTM, Transformer or CNN-LSTM hybrid algorithms. This model has been trained and optimized using a large amount of battery historical data, fault data and environmental data, and has high-precision fault prediction capabilities.

[0102] The model adopts a hierarchical weighted reasoning mechanism, which assigns reasoning weights to basic information, environmental information, and fused feature information respectively. It focuses on combining manufacturer parameters in basic information, abnormal environmental factors in environmental information, and fault trend features in fused feature information to finally output the fault risk score, potential fault type, and fault inducing factors of the target battery.

[0103] In this embodiment of the application, the fault risk score is 0-100 points, with higher scores indicating higher risk. Potential fault types may include thermal runaway, abnormal capacity decay, voltage drift, etc. Fault inducing factors may include environmental overheating, power grid fluctuations, and unreasonable operating conditions.

[0104] S3053: Determine the warning level information based on the fault risk score, the potential fault type, the fault inducing factors, and the preset warning threshold.

[0105] Potential fault types and fault-inducing factors each have corresponding adjustment coefficients. Multiplying different adjustment coefficients by the fault risk score yields the actual fault risk score.

[0106] The preset warning thresholds in this application are divided into three levels, corresponding to three warning levels: Level 1 warning (attention level, fault risk score 30-50 points), Level 2 warning (abnormal level, fault risk score 50-80 points), and Level 3 warning (danger level, fault risk score above 80 points).

[0107] The server compares the actual fault risk score with the preset warning threshold, and determines the warning level information of the target battery by combining the potential fault type and fault inducing factors. At the same time, it marks the fault inducing factors in the warning level information to provide a basis for matching subsequent handling strategies.

[0108] S306: Execute the corresponding processing strategy based on the warning level information.

[0109] After the server determines the warning level information of the target battery, it matches the corresponding handling strategy and coordinates the charging equipment, battery swapping equipment, and energy storage equipment to perform handling operations, realizing closed-loop management from warning to handling, timely avoiding battery failure risks, and ensuring the safe and stable operation of the battery.

[0110] Figure 4This is a flowchart illustrating a method for executing a processing strategy according to an embodiment of this application. Executing the corresponding processing strategy based on the warning level information may include: S401: If the warning level information is Level 1, execute the first processing strategy.

[0111] In one optional embodiment, the first processing strategy includes pushing a prompt message labeled with the battery management identifier to the terminal interaction layer, acquiring the full-cycle information and environmental information corresponding to the battery management identifier at a first frequency (e.g., once every 10 seconds), tracking changes in battery status in real time, ensuring timely detection of abnormal fluctuations in battery status, without increasing the device's data acquisition burden or affecting the normal charging, swapping, and energy storage operations of the target battery.

[0112] When the server determines that the warning level of the target battery is Level 1 (attention level), it triggers the execution of the first processing strategy. This strategy focuses on "key monitoring without affecting normal operation" and takes into account both the timeliness of monitoring and the normal use needs of the battery. Specifically, it achieves precise correlation control through battery management identifiers.

[0113] S402: Or, if the warning level information is level two, execute the second processing strategy.

[0114] In one optional embodiment, the second processing strategy includes pushing warning information labeled with the battery management identifier and the fault-inducing factors to the terminal interaction layer, facilitating staff to quickly locate the target battery, understand the cause of the fault, and conduct timely targeted investigations. It also involves sending first restriction information to charging equipment, battery swapping equipment, and energy storage equipment, and acquiring full-cycle information and environmental information corresponding to the battery management identifier at a second frequency (e.g., once every 2 seconds). This enables high-frequency monitoring of the target battery, accurately tracking the development trend of potential faults, and providing data support for subsequent handling and adjustments. The second frequency is greater than the first frequency.

[0115] When the server determines that the warning level of the target battery is level two (abnormal level), it triggers the execution of the second processing strategy. This strategy is based on "restricting operation, initial protection, and enhanced monitoring". It takes targeted control measures against potential fault risks to prevent the fault from developing further. The battery management identifier is the core of the entire process to ensure that the control instructions are accurately implemented.

[0116] The first restriction information is used to instruct the charging device, the battery swapping device, and the energy storage device to limit the charging and discharging power and activate preliminary protection measures, while simultaneously increasing the data acquisition frequency. Specifically, after receiving the first restriction information, the charging device, the battery swapping device, and the energy storage device locate the target battery based on the battery management identifier, limit its charging and discharging power to a safe range, activate preliminary protection measures (such as activating the heat dissipation device when the environment is too hot, and adjusting the adaptation parameters when the power grid fluctuates), and simultaneously increase their own data acquisition frequency to ensure accurate capture of changes in battery status.

[0117] S403: Or, if the warning level information is level three, execute the third processing strategy.

[0118] In one optional embodiment, the third processing strategy includes pushing emergency warning information marked with the battery management identifier to the terminal interaction layer, and simultaneously triggering an audible and visual alarm to remind staff to take emergency action. This allows staff to quickly locate the specific location, device, and historical operating data of the target battery through the identifier, and send second restriction information to the charging device, the battery swapping device, and the energy storage device.

[0119] When the server determines that the warning level of the target battery is Level 3 (dangerous level), the execution of the third handling strategy is triggered. This strategy is based on "emergency loss prevention, isolation and protection, and risk elimination" to minimize the expansion of serious faults (such as thermal runaway) and ensure the safety of the entire battery warning management system and surrounding equipment and personnel. The entire process is based on the battery management identifier to achieve precise control and rapid handling.

[0120] The second restriction information is used to instruct the charging equipment, the battery swapping equipment, and the energy storage equipment to disconnect the charging and discharging links, isolate the faulty module, and activate fire prevention actions, prohibiting the target battery from participating in the charging, swapping, and energy storage processes. Specifically, after receiving the second restriction information, each device accurately locates the target battery based on the battery management identifier, immediately disconnects the battery's charging and discharging links to prevent further spread of the fault; isolates the faulty module containing the target battery from other normal modules to prevent the fault from spreading; if there are serious safety hazards such as thermal runaway, simultaneously activates fire prevention actions and prepares for emergency response; at the same time, prohibits the target battery corresponding to the battery management identifier from participating in any charging, swapping, or energy storage processes until the fault is thoroughly investigated and resolved, ensuring the overall safety of the system.

[0121] This application also provides a battery warning management device. Figure 5 This is a schematic diagram of the structure of a battery early warning management device provided in an embodiment of this application, as shown below. Figure 5 As shown, the device 500 includes: The identifier generation module 510 is used to generate a battery management identifier for the target battery when an access signal for the target battery is received. The identifier binding module 520 is used to bind the battery management identifier to the basic information of the target battery; The acquisition module 530 is used to acquire the full-cycle information and environmental information of the target battery based on the battery management identifier; the full-cycle information represents the relevant information of the target battery throughout the entire cycle of charging, battery swapping and energy storage. The fusion module 540 is used to perform data fusion and feature extraction on the full-cycle information to obtain fused feature information; The prediction module 550 is used to input the basic information, the environmental information and the fused feature information into the trained prediction model to obtain the warning level information of the target battery; The execution module 560 is used to execute the corresponding processing strategy based on the warning level information.

[0122] In an optional embodiment, it further includes: The first receiving module is used to receive the access signal of the target battery; the access signal carries the battery physical identifier and access scenario identifier of the target battery; The first generation module is used to generate the battery management identifier based on a preset encoding rule and the access scenario identifier if the battery physical identifier indicates that the target battery is accessing the database for the first time; the encoding rule includes a manufacturer encoding segment, an access time encoding segment, and a random check code segment.

[0123] In an optional embodiment, it further includes: The first acquisition module is used to acquire basic information about the target battery; The cleaning module is used to perform standardized cleaning processing on the basic information to obtain cleaned basic information; the standardized cleaning processing includes at least one of the following: abnormal data removal, missing parameter completion, and conversion of unstructured information into structured code; A module is established to create a mapping relationship between the battery management identifier and the cleaned basic information, and to store the mapping relationship and the cleaned basic information in the database. The update module is used to update the corresponding cleaned basic information in the database based on the battery management identifier and the mapping relationship if the update information of the target battery is received.

[0124] In an optional embodiment, it further includes: A sending module is used to send the battery management identifier to charging equipment, battery swapping equipment, and energy storage equipment; The second receiving module is used to receive the charging-related information, the battery swapping-related information, the energy storage-related information, and the environmental information returned by the charging device, the battery swapping device, and the energy storage device based on the battery management identifier; The first determining module is used to determine current state data and historical state data from the charging-related information, the battery swapping-related information, and the energy storage-related information; The second determining module is used to determine the battery's latent data based on the historical state data; The third determining module is used to determine the battery explicit data based on the current state data; The fourth determining module is used to determine the full-cycle information based on the battery latent data and the battery explicit data.

[0125] In an optional embodiment, it further includes: The second acquisition module is used to acquire the correlation parameters, timeliness parameters, and accuracy parameters of the battery's implicit data; The fifth determining module is used to determine the first fusion weight of the battery implicit data based on the correlation parameter, the timeliness parameter, and the accuracy parameter; The sixth determining module is used to determine the second fusion weight of the battery explicit data based on the first fusion weight; The weighted fusion module is used to perform weighted fusion based on the first fusion weight, the second fusion weight, the battery implicit data, and the battery explicit data to obtain full-cycle fusion information; The feature extraction module is used to extract features from the full-cycle fusion information to obtain the fusion feature information.

[0126] In an optional embodiment, it further includes: The format conversion module is used to convert the basic information, the environmental information, and the fused feature information into formats to obtain the format-converted input information. The first prediction module is used to input the format-converted input information into the trained prediction model for hierarchical weighted inference, and output the fault risk score, potential fault type and fault inducing factors of the target battery; the trained prediction model is a time-series prediction model built based on LSTM, Transformer or CNN-LSTM hybrid algorithm; The seventh determining module is used to determine the warning level information based on the fault risk score, the potential fault type, the fault inducing factors, and the preset warning threshold.

[0127] In an optional embodiment, it further includes: The first execution module is configured to execute a first processing strategy if the warning level information is level one; the first processing strategy includes pushing a prompt message labeled with the battery management identifier to the terminal interaction layer, and obtaining the full-cycle information and environmental information corresponding to the battery management identifier at a first frequency; The second execution module is used to execute a second processing strategy, or if the warning level information is level two; the second processing strategy includes pushing warning information labeled with the battery management identifier and the fault inducing factors to the terminal interaction layer, sending first restriction information to the charging device, battery swapping device and energy storage device, and acquiring full-cycle information and environmental information corresponding to the battery management identifier at a second frequency; the first restriction information is used to instruct the charging device, battery swapping device and energy storage device to limit charging and discharging power and activate preliminary protection measures, and simultaneously increase the data acquisition frequency; the second frequency is greater than the first frequency. The third execution module is used to execute a third processing strategy if the warning level information is level three. The third processing strategy includes pushing the emergency warning information marked with the battery management identifier to the terminal interaction layer and sending second restriction information to the charging device, the battery swapping device, and the energy storage device. The second restriction information is used to instruct the charging device, the battery swapping device, and the energy storage device to disconnect the charging and discharging link, isolate the fault module, activate fire prevention actions, and prohibit the target battery from participating in the charging, swapping, and storage process.

[0128] The apparatus and method embodiments in this application are based on the same application concept.

[0129] The methods and embodiments provided in this application can be executed on a computer terminal, server, or similar computing device. Taking running on a server as an example, Figure 6 This is a hardware structure block diagram of a server for a battery early warning management method provided in an embodiment of this application. For example... Figure 6As shown, the server 600 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 610 (CPUs 610 may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory 630 for storing data, and one or more storage media 620 (e.g., one or more mass storage devices) for storing application programs 623 or data 622. The memory 630 and storage media 620 may be temporary or persistent storage. The program stored in the storage media 620 may include one or more modules, each module may include a series of instruction operations on the server. Furthermore, the CPU 610 may be configured to communicate with the storage media 620 and execute the series of instruction operations stored in the storage media 620 on the server 600. Server 600 may also include one or more power supplies 660, one or more wired or wireless network interfaces 650, one or more input / output interfaces 640, and / or one or more operating systems 621, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0130] The input / output interface 640 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of server 600. In one example, input / output interface 640 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, input / output interface 640 may be a radio frequency (RF) module used for wireless communication with the Internet.

[0131] Those skilled in the art will understand that Figure 6 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, server 600 may also include... Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown.

[0132] This application provides an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The processor loads and executes the at least one instruction, at least one program, code set, or instruction set to implement the above-described data processing method.

[0133] Embodiments of this application also provide a computer-readable storage medium, which can be disposed in a server to store at least one instruction, at least one program, code set, or instruction set related to implementing a battery warning management method in the method embodiment. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the battery warning management method described above.

[0134] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0135] As can be seen from the embodiments of the battery early warning management method, device, electronic device, or storage medium provided in this application, when an access signal of a target battery is received, a battery management identifier for the target battery is generated; the battery management identifier is bound to the basic information of the target battery; the full-cycle information and environmental information of the target battery are obtained based on the battery management identifier; the full-cycle information represents the relevant information of the target battery throughout the charging, battery swapping, and energy storage cycles; data fusion and feature extraction are performed on the full-cycle information to obtain fused feature information; the basic information, the environmental information, and the fused feature information are input into a trained prediction model to obtain the early warning level information of the target battery; and the corresponding processing strategy is executed based on the early warning level information. In the embodiments of this application, by establishing a unique battery identification mechanism and achieving accurate binding of full-cycle data with the target battery through the battery management identifier, the problems of data confusion and unclear early warning direction in the prior art during multi-battery collaborative operation are solved, and full-cycle traceability of a single battery is achieved. Furthermore, by integrating information from the entire lifecycle of charging, battery swapping, and energy storage, as well as environmental information, and combining multi-source data fusion and feature extraction, the limitations of fragmented data from single scenarios are overcome. This provides comprehensive and accurate data support for early warning judgment, significantly reducing the probability of false alarms and missed alarms. Moreover, through predictive models, accurate output of early warning levels is achieved, coupled with corresponding processing strategies, realizing closed-loop management of the entire process from battery access, status monitoring, early warning prediction to disposal execution. This transforms passive early warning into proactive prediction, improving the timeliness and relevance of battery early warnings.

[0136] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0137] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0138] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0139] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A battery early warning management method, characterized in that, include: When an access signal for the target battery is received, a battery management identifier for the target battery is generated; Bind the battery management identifier to the basic information of the target battery; Based on the battery management identifier, obtain the full life cycle information and environmental information of the target battery; The full-cycle information characterizes the relevant information of the target battery throughout its charging, battery swapping, and energy storage cycles. Data fusion and feature extraction are performed on the full-cycle information to obtain fused feature information; The basic information, the environmental information, and the fused feature information are input into the trained prediction model to obtain the warning level information of the target battery; The corresponding processing strategy is executed based on the warning level information.

2. The battery early warning management method according to claim 1, characterized in that, When an access signal for the target battery is received, generating a battery management identifier for the target battery includes: Receive the access signal of the target battery; the access signal carries the battery physical identifier and access scenario identifier of the target battery; If the battery physical identifier indicates that the target battery is accessing the database for the first time, the battery management identifier is generated based on the preset encoding rules and the access scenario identifier; the encoding rules include the manufacturer encoding segment, the access time encoding segment, and the random check code segment.

3. The battery early warning management method according to claim 2, characterized in that, The step of binding the battery management identifier to the basic information of the target battery includes: Obtain the basic information of the target battery; The basic information is subjected to standardized cleaning to obtain cleaned basic information; the standardized cleaning process includes at least one of the following: abnormal data removal, missing parameter completion, and conversion of unstructured information into structured code; Establish a mapping relationship between the battery management identifier and the cleaned basic information, and store the mapping relationship and the cleaned basic information in the database; If the target battery update information is received, the corresponding cleaned basic information in the database is updated based on the battery management identifier and the mapping relationship.

4. The battery early warning management method according to claim 1, characterized in that, The step of obtaining the full-cycle information and environmental information of the target battery based on the battery management identifier includes: The battery management identifier is sent to the charging device, battery swapping device, and energy storage device; Receive charging-related information, battery swapping-related information, energy storage-related information, and environmental information returned by the charging device, the battery swapping device, and the energy storage device based on the battery management identifier; Determine the current status data and historical status data from the charging-related information, the battery swapping-related information, and the energy storage-related information; Battery latent data is determined based on the aforementioned historical state data; The battery explicit data is determined based on the current state data; The full-cycle information is determined based on the battery's implicit data and explicit data.

5. The battery early warning management method according to claim 4, characterized in that, The process of data fusion and feature extraction of the full-cycle information to obtain fused feature information includes: Obtain the correlation parameters, timeliness parameters, and accuracy parameters of the battery's implicit data; The first fusion weight of the battery latent data is determined based on the correlation parameter, the timeliness parameter, and the accuracy parameter. The second fusion weight of the battery explicit data is determined based on the first fusion weight; Based on the first fusion weight, the second fusion weight, the battery implicit data, and the battery explicit data, a weighted fusion is performed to obtain full-cycle fusion information; Feature extraction is performed on the full-cycle fusion information to obtain the fusion feature information.

6. The battery early warning management method according to claim 1, characterized in that, The step of inputting the basic information, the environmental information, and the fused feature information into a trained prediction model to obtain the warning level information of the target battery includes: The basic information, the environmental information, and the fused feature information are converted into formats to obtain the converted input information. The converted input information is input into the trained prediction model for hierarchical weighted inference, and the failure risk score, potential failure type and failure inducing factors of the target battery are output; the trained prediction model is a time series prediction model built based on LSTM, Transformer or CNN-LSTM hybrid algorithm; The warning level information is determined based on the fault risk score, the potential fault type, the fault inducing factors, and the preset warning threshold.

7. The battery early warning management method according to claim 6, characterized in that, The execution of the corresponding processing strategy based on the warning level information includes: If the warning level information is Level 1, execute the first processing strategy; the first processing strategy includes pushing the prompt information marked with the battery management identifier to the terminal interaction layer, and obtaining the full life cycle information and environmental information corresponding to the battery management identifier at a first frequency; Alternatively, if the warning level information is level two, a second processing strategy is executed; the second processing strategy includes pushing warning information labeled with the battery management identifier and the fault-inducing factors to the terminal interaction layer, sending first restriction information to the charging device, battery swapping device, and energy storage device, and acquiring full-cycle information and environmental information corresponding to the battery management identifier at a second frequency; the first restriction information is used to instruct the charging device, battery swapping device, and energy storage device to limit charging and discharging power and activate preliminary protection measures, and simultaneously increase the data acquisition frequency; the second frequency is greater than the first frequency; Alternatively, if the warning level information is level three, a third processing strategy is executed; the third processing strategy includes pushing the emergency warning information marked with the battery management identifier to the terminal interaction layer, and sending second restriction information to the charging device, the battery swapping device, and the energy storage device; the second restriction information is used to instruct the charging device, the battery swapping device, and the energy storage device to disconnect the charging and discharging link, isolate the fault module, activate fire prevention actions, and prohibit the target battery from participating in the charging, swapping, and storage process.

8. A battery early warning management device, characterized in that, The device includes: The identifier generation module is used to generate a battery management identifier for the target battery when an access signal for the target battery is received. The identifier binding module is used to bind the battery management identifier to the basic information of the target battery; The acquisition module is used to acquire the full-cycle information and environmental information of the target battery based on the battery management identifier; the full-cycle information represents the relevant information of the target battery throughout the entire cycle of charging, battery swapping and energy storage. The fusion module is used to perform data fusion and feature extraction on the full-cycle information to obtain fused feature information; The prediction module is used to input the basic information, the environmental information and the fused feature information into the trained prediction model to obtain the warning level information of the target battery; The execution module is used to execute the corresponding processing strategy based on the warning level information.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the battery warning management method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the battery warning management method as described in any one of claims 1-7.

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