Lithium battery safety monitoring method, device, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence, and provides a lithium battery safety monitoring method, device and equipment and a medium, which can collect the working state data of each battery cell in a lithium battery in real time for safety monitoring, avoid the situation that the temperature distribution of the whole lithium battery cannot be comprehensively reflected due to the temperature difference between the battery cells, and improve the monitoring accuracy. The dimension information of the features is improved through homologous dimension expansion processing, and weighting processing is performed on the normalized temperature feature set of each battery cell according to the dynamic weight, so that monitoring parameters can be dynamically adjusted according to the actual working state and environmental conditions of the lithium battery, and the monitoring accuracy is further improved; the abnormal risk of the lithium battery is predicted in combination with the temperature safety analysis model, and the reasonable abnormity processing strategy is matched according to the mapping table, so that deep and accurate abnormity analysis can be performed in combination with an artificial intelligence means, and an effective coping strategy can be provided to assist in timely and effective coping when the safety risk of the lithium battery is monitored.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a lithium battery safety monitoring method, device, equipment and medium. Background Art

[0002] With the rapid development of science and technology, lithium batteries have been widely used in electric vehicles, smart phones, laptops and other portable devices as an efficient and portable energy storage solution. However, the safety of lithium batteries has always been one of the main obstacles restricting their wider application. In particular, when the battery is overcharged, over-discharged or short-circuited, it may overheat, even cause fire or explosion, and cause serious safety accidents.

[0003] In view of the above, battery temperature management is the key to ensure the safe use of lithium batteries. The internal temperature of the battery will change during normal operation or abnormal conditions. Therefore, real-time monitoring and effective control of battery temperature are essential to prevent battery thermal runaway and extend battery life.

[0004] In the prior art, the field of lithium battery safety monitoring mainly relies on traditional temperature monitoring systems, which usually include temperature sensors, simple threshold alarms, basic data processing, etc. For example: embedding temperature sensors (such as thermistors or thermocouples) in lithium batteries or battery packs to measure the temperature on the surface or inside of the battery in real time, and setting temperature thresholds. When the monitored temperature exceeds these thresholds, the system will trigger an alarm to remind the user or automatically start the cooling system. The collected temperature data is used to monitor the current state of the battery through simple data recording and processing, but lacks in-depth analysis. In addition, traditional monitoring methods usually install sensors at specific locations of the battery, relying on simple temperature sensors and limited data processing capabilities, and it is difficult to accurately grasp the complex thermal characteristics inside the battery, especially in battery packs. Since the temperature of each battery cell may be different, a single temperature monitoring point cannot fully reflect the temperature distribution of the entire battery or battery pack. Simple threshold alarms and basic data processing are also unable to effectively identify the complex patterns behind battery temperature changes, and it is difficult to predict and identify potential temperature anomalies. In addition, existing systems often use static thresholds and rules, and lack the ability to dynamically adjust monitoring and response strategies according to the actual working state and environmental conditions of the battery. In the early stages of battery temperature abnormalities, traditional methods may not be able to identify subtle temperature changes in time, resulting in missing the best time to take preventive measures.

[0005] In view of this, how to conduct more effective lithium battery safety monitoring has become an urgent problem to be solved. Summary of the invention

[0006] In view of the above, it is necessary to provide a lithium battery safety monitoring method, device, equipment and medium to solve the problems of inaccurate lithium battery safety monitoring and untimely response.

[0007] A lithium battery safety monitoring method, the lithium battery safety monitoring method comprising: In response to a safety monitoring instruction for a target lithium battery, real-time collection of operating status data of each battery cell in the target lithium battery, and building a first temperature feature set for each battery cell according to the operating status data of each battery cell; Performing homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain a second temperature feature set of each battery cell; Normalizing the second temperature feature set of each battery cell to obtain a normalized temperature feature set of each battery cell; Acquire a dynamic weight of the real-time analysis, and perform weighted processing on the normalized temperature feature set of each battery cell according to the dynamic weight to obtain a target temperature feature of each battery cell; Vectorize the target temperature characteristics of each battery cell to obtain the initial temperature characteristic vector of each battery cell; The initial temperature feature vector of each battery cell is analyzed and processed by using a pre-trained temperature safety analysis model to obtain a predicted probability of temperature anomaly of each battery cell at each time step as a target safety monitoring result for the target lithium battery; When the target safety monitoring result shows that the predicted probability of temperature anomaly of any battery cell at any time step is greater than or equal to the configured threshold, it is determined that the target lithium battery has an abnormal risk; Obtaining a pre-maintained mapping table for storing corresponding relationships between security monitoring results and exception handling strategies, and matching the security monitoring results in the mapping table to obtain a target exception handling strategy; Generate abnormal warning information according to the target security monitoring result and the target abnormality handling strategy, and send the abnormal warning information to the designated terminal device.

[0008] According to a preferred embodiment of the present invention, the real-time acquisition of the working status data of each battery cell in the target lithium battery and the construction of the first temperature feature set of each battery cell according to the working status data of each battery cell include: Using the temperature sensor preset in the target lithium battery to collect the working status data of each battery cell in real time; Acquire first battery cell temperature data of each battery cell under input current and second battery cell temperature data of each battery cell under output current from the working status data of each battery cell; Calculate the difference between the first battery cell temperature data and the corresponding second battery cell temperature data of each battery cell to obtain the battery cell temperature difference corresponding to each battery cell; Perform data dimension reduction on the cell temperature difference corresponding to each cell to extract the temperature characteristics of each cell; A first temperature feature set for each battery cell is constructed according to the temperature feature of each battery cell.

[0009] According to a preferred embodiment of the present invention, performing homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain the second temperature feature set of each battery cell includes: For each temperature feature in the first temperature feature set of each battery cell, calculate the square value of each temperature feature, and / or perform polynomial feature expansion processing on each temperature feature to obtain an expanded dimension feature of each temperature feature; The temperature characteristics and expanded dimension characteristics corresponding to each battery cell are integrated to obtain a second temperature characteristic set for each battery cell.

[0010] According to a preferred embodiment of the present invention, the method further comprises: Obtain real-time analysis of each security monitoring result and the accuracy of each security monitoring result; The dynamic weight is adjusted in real time according to the accuracy of each safety monitoring result.

[0011] According to a preferred embodiment of the present invention, after obtaining the normalized temperature feature set of each battery cell, the method further includes: Extracting a preset number of features from a normalized temperature feature set of each battery cell according to the dynamic weight; Calculate the mean and standard deviation of the extracted features; The feature dispersion in the normalized temperature feature set of each battery cell is determined based on the calculated mean and standard deviation.

[0012] According to a preferred embodiment of the present invention, the use of a pre-trained temperature safety analysis model to analyze and process the initial temperature feature vector of each battery cell to obtain the temperature anomaly prediction probability of each battery cell at each time step as the target safety monitoring result of the target lithium battery includes: Extracting hidden state features in the initial temperature feature vector of each battery cell using the gated cycle unit in the temperature safety analysis model; Inputting each hidden state feature into a fully connected layer of the temperature safety analysis model to obtain each output feature; Each output feature is processed using an activation function to obtain the temperature anomaly prediction probability of each battery cell at each time step as the target safety monitoring result.

[0013] According to a preferred embodiment of the present invention, the method further comprises: Acquire new lithium battery working status data at preset time intervals; The temperature safety analysis model is optimized and trained according to the newly added lithium battery working status data.

[0014] A lithium battery safety monitoring device, the lithium battery safety monitoring device comprising: A construction unit, configured to, in response to a safety monitoring instruction for a target lithium battery, collect working state data of each battery cell in the target lithium battery in real time, and construct a first temperature feature set for each battery cell according to the working state data of each battery cell; A dimension expansion processing unit, used for performing homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain a second temperature feature set of each battery cell; A normalization processing unit, used to perform normalization processing on the second temperature feature set of each battery cell to obtain a normalized temperature feature set of each battery cell; A weighted processing unit, used to obtain a dynamic weight for real-time analysis, and perform weighted processing on a normalized temperature feature set of each battery cell according to the dynamic weight to obtain a target temperature feature of each battery cell; A vector processing unit, used for performing vector processing on the target temperature characteristics of each battery cell to obtain an initial temperature characteristic vector of each battery cell; An analysis and processing unit, used to analyze and process the initial temperature feature vector of each battery cell using a pre-trained temperature safety analysis model, and obtain a predicted probability of temperature anomaly of each battery cell at each time step as a target safety monitoring result for the target lithium battery; A determination unit, configured to determine that the target lithium battery has an abnormal risk when the target safety monitoring result shows that the predicted probability of temperature abnormality of any battery cell at any time step is greater than or equal to a configured threshold; A matching unit, used to obtain a pre-maintained mapping table for storing the corresponding relationship between security monitoring results and exception handling strategies, and use the security monitoring results to match the mapping table to obtain a target exception handling strategy; A generating unit is used to generate abnormal warning information according to the target security monitoring result and the target abnormality handling strategy, and send the abnormal warning information to a designated terminal device.

[0015] A computer device, comprising: a memory storing at least one instruction; and A processor executes the instructions stored in the memory to implement the lithium battery safety monitoring method.

[0016] A computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the lithium battery safety monitoring method.

[0017] It can be seen from the above technical solutions that the present invention can collect the working status data of each battery cell in the lithium battery in real time for safety monitoring, avoiding the inability to fully reflect the temperature distribution of the entire lithium battery due to temperature differences between battery cells, thereby improving the accuracy of monitoring; the dimensional information of the features is improved through homologous dimension expansion processing, and the normalized temperature feature set of each battery cell is weighted according to the dynamic weight, so that the monitoring parameters can be dynamically adjusted according to the actual working status and environmental conditions of the lithium battery, further improving the accuracy of monitoring; combined with the temperature safety analysis model to predict the abnormal risk of the lithium battery, and match the reasonable abnormal handling strategy according to the mapping table, it can not only combine artificial intelligence means to conduct in-depth and accurate abnormal analysis, but also provide effective response strategies to assist in timely and effective response when the lithium battery is monitored to have safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a preferred embodiment of the lithium battery safety monitoring method of the present invention; Figure 2 It is a functional module diagram of a preferred embodiment of the lithium battery safety monitoring device of the present invention; Figure 3 It is a structural schematic diagram of a computer device of a preferred embodiment of the lithium battery safety monitoring method of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 FIG. 1 is a flow chart of a preferred embodiment of the lithium battery safety monitoring method of the present invention. According to different requirements, the order of the steps in the flow chart can be changed, and some steps can be omitted.

[0021] The lithium battery safety monitoring method is applied to one or more computer devices, which are devices that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and their hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0022] The computer device may be any electronic product that can perform human-computer interaction with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an interactive network television (IPTV), a smart wearable device, etc.

[0023] The computer device may also include a network device and / or a user device, wherein the network device includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud consisting of a large number of hosts or network servers based on cloud computing.

[0024] The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), as well as big data and artificial intelligence platforms.

[0025] Among them, Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0026] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0027] The network where the computer device is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0028] S10, in response to a safety monitoring instruction for a target lithium battery, collecting working status data of each battery cell in the target lithium battery in real time, and constructing a first temperature feature set for each battery cell according to the working status data of each battery cell.

[0029] In this embodiment, the target lithium battery may include, but is not limited to, lithium batteries installed in electric vehicles, smart phones, laptops and other portable devices, and may also include lithium battery systems in renewable energy systems such as solar energy and wind energy.

[0030] In this embodiment, the safety monitoring instruction can be triggered by relevant personnel according to actual use requirements, or it can be automatically triggered when it is detected that the target lithium battery is in working state or the device to which the target lithium battery belongs is started. Different triggering methods can be used according to different monitoring scenarios.

[0031] In this embodiment, the real-time acquisition of the working status data of each battery cell in the target lithium battery and the construction of the first temperature feature set of each battery cell according to the working status data of each battery cell include: Using the temperature sensor preset in the target lithium battery to collect the working status data of each battery cell in real time; Acquire first battery cell temperature data of each battery cell under input current and second battery cell temperature data of each battery cell under output current from the working status data of each battery cell; Calculate the difference between the first battery cell temperature data and the corresponding second battery cell temperature data of each battery cell to obtain the battery cell temperature difference corresponding to each battery cell; Perform data dimension reduction on the cell temperature difference corresponding to each cell to extract the temperature characteristics of each cell; A first temperature feature set for each battery cell is constructed according to the temperature feature of each battery cell.

[0032] The temperature sensor may be installed at a location such as the physical center, central angle, central axis, etc. of each battery cell where the temperature of each battery cell can be accurately collected.

[0033] The working status data of each battery cell may include, but is not limited to: working voltage, working overcurrent, first battery cell temperature data under input current, second battery cell temperature data under output current, etc.

[0034] Among them, dimensionality reduction algorithms such as Isomap (Isometric Mapping) can be used to reduce the data dimension of the battery cell temperature difference corresponding to each battery cell, so as to extract the effective temperature characteristics of each battery cell.

[0035] S11, performing homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain a second temperature feature set of each battery cell.

[0036] In this embodiment, performing homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain the second temperature feature set of each battery cell includes: For each temperature feature in the first temperature feature set of each battery cell, calculate the square value of each temperature feature, and / or perform polynomial feature expansion processing on each temperature feature to obtain an expanded dimension feature of each temperature feature; The temperature characteristics and expanded dimension characteristics corresponding to each battery cell are integrated to obtain a second temperature characteristic set for each battery cell.

[0037] In the above embodiment, the effective temperature characteristics of each battery cell obtained after the dimensionality reduction processing are further expanded, which can expand the dimension of the effective characteristics, help improve the abstract expression ability of the characteristics, increase the sensitivity to temperature changes, and thus improve the accuracy of lithium battery safety monitoring.

[0038] S12, normalizing the second temperature feature set of each battery cell to obtain a normalized temperature feature set of each battery cell.

[0039] For example, the mean and standard deviation of the features in each second temperature feature set may be calculated, and based on the calculated mean and standard deviation, the second temperature feature set of each battery cell may be normalized using a zero mean unit variance normalization algorithm to obtain a normalized temperature feature set of each battery cell.

[0040] Through the above embodiments, the features in each second temperature feature set can be normalized to facilitate subsequent processing.

[0041] S13, obtaining a dynamic weight of real-time analysis, and performing weighted processing on the normalized temperature feature set of each battery cell according to the dynamic weight to obtain a target temperature feature of each battery cell.

[0042] In this embodiment, the method further includes: Obtain real-time analysis of each security monitoring result and the accuracy of each security monitoring result; The dynamic weight is adjusted in real time according to the accuracy of each safety monitoring result.

[0043] In the above embodiment, the weights can be dynamically adjusted based on the accuracy of the real-time safety monitoring results, and dynamic analysis of the weights can be achieved, so that the monitoring parameters can be dynamically adjusted according to the actual working status and environmental conditions of the lithium battery, making the analysis of temperature characteristics more flexible and intelligent, and further improving the accuracy of monitoring.

[0044] In this embodiment, for the normalized temperature feature set of each battery cell, a weighted calculation can be performed according to the weight corresponding to each feature in the set, so as to obtain the target temperature feature of each battery cell, and the comprehensive performance of the feature is further improved through feature fusion. By weighting the temperature feature according to the real-time situation, the adaptability to temperature changes is also improved.

[0045] In this embodiment, after obtaining the normalized temperature feature set of each battery cell, the method further includes: Extracting a preset number of features from a normalized temperature feature set of each battery cell according to the dynamic weight; Calculate the mean and standard deviation of the extracted features; The feature dispersion in the normalized temperature feature set of each battery cell is determined based on the calculated mean and standard deviation.

[0046] The preset number can be configured according to actual needs.

[0047] For example: If the normalized temperature feature set of battery cell A includes M features, N typical features can be extracted in descending order of feature weight to calculate the mean and standard deviation, and the feature dispersion in the normalized temperature feature set of battery cell A can be determined based on the calculated mean and standard deviation for subsequent analysis.

[0048] S14, performing vectorization processing on the target temperature characteristics of each battery cell to obtain an initial temperature characteristic vector of each battery cell.

[0049] In this embodiment, a vectorization algorithm such as a vector mapping technology may be used to vectorize the target temperature characteristics of each battery cell, thereby obtaining an initial temperature characteristic vector of each battery cell that can be identified by the temperature safety analysis model.

[0050] The initial temperature feature vector of each battery cell can be used for subsequent safety monitoring analysis and capacity-based temperature anomaly detection.

[0051] S15, using a pre-trained temperature safety analysis model to analyze and process the initial temperature feature vector of each battery cell, and obtain the temperature anomaly prediction probability of each battery cell at each time step as the target safety monitoring result of the target lithium battery.

[0052] In this embodiment, before analyzing and processing the initial temperature feature vector of each battery cell using the pre-trained temperature safety analysis model, the method further includes: Obtain historical safety monitoring data; The historical safety monitoring data is used as a training sample, and the initial model is trained based on back propagation and gradient descent algorithms to obtain the temperature safety analysis model.

[0053] The initial model may include a deep learning model with a prediction function. The initial model may include multiple temperature anomaly detection networks and decision networks, the temperature anomaly detection network includes a gated recurrent unit (GRU), and the decision network may include a fully connected layer and a Sigmoid activation function.

[0054] Specifically, the number of the temperature anomaly monitoring network and the decision network can be obtained by continuous optimization and adjustment according to the prediction accuracy of the model.

[0055] In the above embodiment, the model can learn the abnormal patterns of lithium batteries in historical safety monitoring data through training, so that it can be used for subsequent real-time safety monitoring of lithium batteries.

[0056] This embodiment applies deep learning technology to temperature monitoring of lithium batteries, which can more accurately analyze the working status of lithium batteries, predict and identify potential temperature anomalies, thereby achieving more effective battery safety management, improving the use efficiency and life of lithium batteries, and promoting the application of lithium battery technology in a wider range of fields.

[0057] In this embodiment, the use of the pre-trained temperature safety analysis model to analyze and process the initial temperature feature vector of each battery cell to obtain the temperature anomaly prediction probability of each battery cell at each time step as the target safety monitoring result of the target lithium battery includes: Extracting hidden state features in the initial temperature feature vector of each battery cell using the gated cycle unit in the temperature safety analysis model; Inputting each hidden state feature into a fully connected layer of the temperature safety analysis model to obtain each output feature; Each output feature is processed using an activation function to obtain the temperature anomaly prediction probability of each battery cell at each time step as the target safety monitoring result.

[0058] The activation function may be a Sigmoid activation function.

[0059] The gated recurrent unit can capture long-term dependencies in time series data.

[0060] Through the above embodiments, it is possible to combine artificial intelligence means to conduct in-depth and accurate abnormal analysis of lithium batteries. Through the predictive ability of the deep learning model, it is also possible to timely identify subtle temperature changes in the early stages of lithium battery temperature abnormalities, achieve early warning, and take preventive measures in time to avoid the occurrence of safety accidents.

[0061] S16, when the target safety monitoring result shows that the predicted probability of temperature anomaly of any battery cell at any time step is greater than or equal to the configured threshold, it is determined that the target lithium battery has an abnormality risk.

[0062] The configuration threshold can be configured according to actual needs to meet the demand for temperature monitoring in the current use scenario of the lithium battery.

[0063] S17, obtaining a pre-maintained mapping table for storing the corresponding relationship between security monitoring results and exception handling strategies, and using the security monitoring results to match in the mapping table to obtain a target exception handling strategy.

[0064] In this embodiment, the mapping table may be maintained according to historical security monitoring data, and is used to store the mapping relationship between typical security monitoring results and effective exception handling strategies.

[0065] In this embodiment, after matching the safety monitoring results in the mapping table, an effective response strategy can be obtained to assist in timely and effective response when safety risks of the lithium battery are detected, so as to ensure the safe operation of the lithium battery.

[0066] S18, generating abnormal warning information according to the target security monitoring result and the target abnormality handling strategy, and sending the abnormal warning information to a designated terminal device.

[0067] Among them, the designated terminal device can be a terminal device of the operation and maintenance personnel, or a terminal device of the user of the equipment to which the lithium battery belongs, so that relevant personnel can be warned of equipment battery abnormalities in a timely manner, so that necessary safety measures can be taken in a timely manner.

[0068] In this embodiment, the method further includes: Acquire new lithium battery working status data at preset time intervals; The temperature safety analysis model is optimized and trained according to the newly added lithium battery working status data.

[0069] The preset time interval may be configured as 1 month or the like.

[0070] Through the above-mentioned embodiment, the temperature safety analysis model can be updated regularly using newly collected data, so that the temperature safety analysis model can adapt to changes in lithium battery performance, thereby maintaining the accuracy and effectiveness of safety monitoring results; the monitoring parameters and thresholds are dynamically adjusted according to the actual working status and historical working data of the battery, and can also adapt to different working conditions and environmental changes.

[0071] It can be seen from the above technical solutions that the present invention can collect the working status data of each battery cell in the lithium battery in real time for safety monitoring, avoiding the inability to fully reflect the temperature distribution of the entire lithium battery due to temperature differences between battery cells, thereby improving the accuracy of monitoring; the dimensional information of the features is improved through homologous dimension expansion processing, and the normalized temperature feature set of each battery cell is weighted according to the dynamic weight, so that the monitoring parameters can be dynamically adjusted according to the actual working status and environmental conditions of the lithium battery, further improving the accuracy of monitoring; combined with the temperature safety analysis model to predict the abnormal risk of the lithium battery, and match the reasonable abnormal handling strategy according to the mapping table, it can not only combine artificial intelligence means to conduct in-depth and accurate abnormal analysis, but also provide effective response strategies to assist in timely and effective response when the lithium battery is monitored to have safety risks.

[0072] like Figure 2 As shown, it is a functional module diagram of a preferred embodiment of the lithium battery safety monitoring device of the present invention. The lithium battery safety monitoring device 11 includes a construction unit 110, a dimension expansion processing unit 111, a normalization processing unit 112, a weighted processing unit 113, a vectorization processing unit 114, an analysis processing unit 115, a determination unit 116, a matching unit 117, and a generation unit 118. The module / unit referred to in the present invention refers to a series of computer program segments that can be executed by a processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0073] The construction unit 110 is used to collect the working state data of each battery cell in the target lithium battery in real time in response to the safety monitoring instruction of the target lithium battery, and construct a first temperature feature set of each battery cell according to the working state data of each battery cell; The dimension expansion processing unit 111 is used to perform homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain a second temperature feature set of each battery cell; The normalization processing unit 112 is used to perform normalization processing on the second temperature feature set of each battery cell to obtain a normalized temperature feature set of each battery cell; The weighted processing unit 113 is used to obtain the dynamic weight of the real-time analysis, and perform weighted processing on the normalized temperature feature set of each battery cell according to the dynamic weight to obtain the target temperature feature of each battery cell; The vectorization processing unit 114 is used to perform vectorization processing on the target temperature characteristics of each battery cell to obtain an initial temperature characteristic vector of each battery cell; The analysis and processing unit 115 is used to analyze and process the initial temperature feature vector of each battery cell using a pre-trained temperature safety analysis model to obtain a predicted probability of temperature anomaly of each battery cell at each time step as a target safety monitoring result for the target lithium battery; The determination unit 116 is configured to determine that the target lithium battery has an abnormal risk when the target safety monitoring result shows that the predicted probability of temperature abnormality of any battery cell at any time step is greater than or equal to a configured threshold; The matching unit 117 is used to obtain a pre-maintained mapping table for storing the corresponding relationship between the security monitoring results and the exception handling strategies, and use the security monitoring results to match the mapping table to obtain the target exception handling strategy; The generating unit 118 is used to generate abnormal warning information according to the target security monitoring result and the target abnormality handling strategy, and send the abnormal warning information to a designated terminal device.

[0074] It can be seen from the above technical solutions that the present invention can collect the working status data of each battery cell in the lithium battery in real time for safety monitoring, avoiding the inability to fully reflect the temperature distribution of the entire lithium battery due to temperature differences between battery cells, thereby improving the accuracy of monitoring; the dimensional information of the features is improved through homologous dimension expansion processing, and the normalized temperature feature set of each battery cell is weighted according to the dynamic weight, so that the monitoring parameters can be dynamically adjusted according to the actual working status and environmental conditions of the lithium battery, further improving the accuracy of monitoring; combined with the temperature safety analysis model to predict the abnormal risk of the lithium battery, and match the reasonable abnormal handling strategy according to the mapping table, it can not only combine artificial intelligence means to conduct in-depth and accurate abnormal analysis, but also provide effective response strategies to assist in timely and effective response when the lithium battery is monitored to have safety risks.

[0075] like Figure 3 FIG. 1 is a schematic diagram of the structure of a computer device according to a preferred embodiment of the lithium battery safety monitoring method of the present invention.

[0076] The computer device 1 may include a memory 12 , a processor 13 , and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13 , such as a lithium battery safety monitoring program.

[0077] Those skilled in the art will appreciate that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation on the computer device 1. The computer device 1 may be a bus-type structure or a star-type structure. The computer device 1 may also include more or less other hardware or software than shown in the diagram, or a different arrangement of components. For example, the computer device 1 may also include input and output devices, network access devices, etc.

[0078] It should be noted that the computer device 1 is only an example, and other existing or future electronic products that are suitable for the present invention should also be included in the protection scope of the present invention and included here by reference.

[0079] The memory 12 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 12 may be an internal storage unit of the computer device 1, such as a mobile hard disk of the computer device 1. In other embodiments, the memory 12 may also be an external storage device of the computer device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 1. Further, the memory 12 may also include both an internal storage unit of the computer device 1 and an external storage device. The memory 12 may not only be used to store application software and various types of data installed in the computer device 1, such as the code of the lithium battery safety monitoring program, etc., but may also be used to temporarily store data that has been output or is to be output.

[0080] In some embodiments, the processor 13 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control core (Control Unit) of the computer device 1, and uses various interfaces and lines to connect various components of the entire computer device 1, and executes various functions and processes data of the computer device 1 by running or executing programs or modules stored in the memory 12 (for example, executing a lithium battery safety monitoring program, etc.), and calling data stored in the memory 12.

[0081] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-mentioned lithium battery safety monitoring method embodiments, for example Figure 1 Steps shown.

[0082] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into a construction unit 110, a dimension expansion processing unit 111, a normalization processing unit 112, a weighted processing unit 113, a vectorization processing unit 114, an analysis processing unit 115, a determination unit 116, a matching unit 117, and a generation unit 118.

[0083] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, and includes a number of instructions for enabling a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to execute a part of the lithium battery safety monitoring method described in each embodiment of the present invention.

[0084] If the module / unit integrated in the computer device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware devices through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0085] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, etc.

[0086] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0087] The blockchain referred to in this invention is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the blockchain underlying platform, platform product service layer, and application service layer.

[0088] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by only one straight line, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 12 and at least one processor 13, etc.

[0089] Although not shown, the computer device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 13 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The computer device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0090] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used to establish a communication connection between the computer device 1 and other computer devices.

[0091] Optionally, the computer device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the computer device 1 and to display a visual user interface.

[0092] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0093] It can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the computer device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0094] Combination Figure 1 The memory 12 in the computer device 1 stores a plurality of instructions to implement a lithium battery safety monitoring method, and the processor 13 can execute the plurality of instructions to implement: In response to a safety monitoring instruction for a target lithium battery, real-time collection of operating status data of each battery cell in the target lithium battery, and building a first temperature feature set for each battery cell according to the operating status data of each battery cell; Performing homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain a second temperature feature set of each battery cell; Normalizing the second temperature feature set of each battery cell to obtain a normalized temperature feature set of each battery cell; Acquire a dynamic weight of the real-time analysis, and perform weighted processing on the normalized temperature feature set of each battery cell according to the dynamic weight to obtain a target temperature feature of each battery cell; Vectorize the target temperature characteristics of each battery cell to obtain the initial temperature characteristic vector of each battery cell; The initial temperature feature vector of each battery cell is analyzed and processed by using a pre-trained temperature safety analysis model to obtain a predicted probability of temperature anomaly of each battery cell at each time step as a target safety monitoring result for the target lithium battery; When the target safety monitoring result shows that the predicted probability of temperature anomaly of any battery cell at any time step is greater than or equal to the configured threshold, it is determined that the target lithium battery has an abnormal risk; Obtaining a pre-maintained mapping table for storing corresponding relationships between security monitoring results and exception handling strategies, and matching the security monitoring results in the mapping table to obtain a target exception handling strategy; Generate abnormal warning information according to the target security monitoring result and the target abnormality handling strategy, and send the abnormal warning information to the designated terminal device.

[0095] Specifically, the specific implementation method of the processor 13 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0096] It should be noted that the data involved in this case were all obtained legally.

[0097] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0098] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0099] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0101] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0102] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0103] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the present invention can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A lithium battery safety monitoring method, characterized in that: The lithium battery safety monitoring method comprises: In response to a safety monitoring instruction for a target lithium battery, real-time collection of operating status data of each battery cell in the target lithium battery, and building a first temperature feature set for each battery cell according to the operating status data of each battery cell; Performing homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain a second temperature feature set of each battery cell; Normalizing the second temperature feature set of each battery cell to obtain a normalized temperature feature set of each battery cell; Acquire a dynamic weight of the real-time analysis, and perform weighted processing on the normalized temperature feature set of each battery cell according to the dynamic weight to obtain a target temperature feature of each battery cell; Vectorize the target temperature characteristics of each battery cell to obtain the initial temperature characteristic vector of each battery cell; The initial temperature feature vector of each battery cell is analyzed and processed by using a pre-trained temperature safety analysis model to obtain a predicted probability of temperature anomaly of each battery cell at each time step as a target safety monitoring result for the target lithium battery; When the target safety monitoring result shows that the predicted probability of temperature anomaly of any battery cell at any time step is greater than or equal to the configured threshold, it is determined that the target lithium battery has an abnormal risk; Obtaining a pre-maintained mapping table for storing corresponding relationships between security monitoring results and exception handling strategies, and matching the security monitoring results in the mapping table to obtain a target exception handling strategy; Generate abnormal warning information according to the target security monitoring result and the target abnormality handling strategy, and send the abnormal warning information to the designated terminal device.

2. The lithium battery safety monitoring method according to claim 1, characterized in that: The real-time collection of the working status data of each battery cell in the target lithium battery and the construction of a first temperature feature set of each battery cell according to the working status data of each battery cell include: Using the temperature sensor preset in the target lithium battery to collect the working status data of each battery cell in real time; Acquire first battery cell temperature data of each battery cell under input current and second battery cell temperature data of each battery cell under output current from the working status data of each battery cell; Calculate the difference between the first battery cell temperature data and the corresponding second battery cell temperature data of each battery cell to obtain the battery cell temperature difference corresponding to each battery cell; Perform data dimension reduction on the cell temperature difference corresponding to each cell to extract the temperature characteristics of each cell; A first temperature feature set for each battery cell is constructed according to the temperature feature of each battery cell.

3. The lithium battery safety monitoring method according to claim 1, characterized in that: The performing homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain the second temperature feature set of each battery cell comprises: For each temperature feature in the first temperature feature set of each battery cell, calculate the square value of each temperature feature, and / or perform polynomial feature expansion processing on each temperature feature to obtain an expanded dimension feature of each temperature feature; The temperature characteristics and the expanded dimension characteristics corresponding to each battery cell are integrated to obtain a second temperature characteristic set for each battery cell.

4. The lithium battery safety monitoring method according to claim 1, characterized in that: The method further comprises: Obtain real-time analysis of each security monitoring result and the accuracy of each security monitoring result; The dynamic weight is adjusted in real time according to the accuracy of each safety monitoring result.

5. The lithium battery safety monitoring method according to claim 1, characterized in that: After obtaining the normalized temperature feature set of each battery cell, the method further includes: Extracting a preset number of features from a normalized temperature feature set of each battery cell according to the dynamic weight; Calculate the mean and standard deviation of the extracted features; The feature dispersion in the normalized temperature feature set of each battery cell is determined based on the calculated mean and standard deviation.

6. The lithium battery safety monitoring method according to claim 1, characterized in that: The method of analyzing and processing the initial temperature feature vector of each battery cell by using the pre-trained temperature safety analysis model to obtain the temperature anomaly prediction probability of each battery cell at each time step as the target safety monitoring result of the target lithium battery includes: Extracting hidden state features in the initial temperature feature vector of each battery cell using the gated cycle unit in the temperature safety analysis model; Inputting each hidden state feature into a fully connected layer of the temperature safety analysis model to obtain each output feature; Each output feature is processed using an activation function to obtain the temperature anomaly prediction probability of each battery cell at each time step as the target safety monitoring result.

7. The lithium battery safety monitoring method according to claim 1, characterized in that: The method further comprises: Acquire new lithium battery working status data at preset time intervals; The temperature safety analysis model is optimized and trained according to the newly added lithium battery working status data.

8. A lithium battery safety monitoring device, characterized in that: The lithium battery safety monitoring device comprises: A construction unit, configured to collect working state data of each battery cell in the target lithium battery in real time in response to a safety monitoring instruction for the target lithium battery, and construct a first temperature feature set for each battery cell according to the working state data of each battery cell; A dimension expansion processing unit, used for performing homologous dimension expansion processing on the first temperature feature set of each battery cell to obtain a second temperature feature set of each battery cell; A normalization processing unit, used to perform normalization processing on the second temperature feature set of each battery cell to obtain a normalized temperature feature set of each battery cell; A weighted processing unit, used to obtain a dynamic weight for real-time analysis, and perform weighted processing on a normalized temperature feature set of each battery cell according to the dynamic weight to obtain a target temperature feature of each battery cell; A vector processing unit, used for performing vector processing on the target temperature characteristics of each battery cell to obtain an initial temperature characteristic vector of each battery cell; An analysis and processing unit, used to analyze and process the initial temperature feature vector of each battery cell using a pre-trained temperature safety analysis model, and obtain a predicted probability of temperature anomaly of each battery cell at each time step as a target safety monitoring result for the target lithium battery; A determination unit, configured to determine that the target lithium battery has an abnormal risk when the target safety monitoring result shows that the predicted probability of temperature abnormality of any battery cell at any time step is greater than or equal to a configured threshold; A matching unit, used to obtain a pre-maintained mapping table for storing the corresponding relationship between security monitoring results and exception handling strategies, and use the security monitoring results to match the mapping table to obtain a target exception handling strategy; A generating unit is used to generate abnormal warning information according to the target security monitoring result and the target abnormality handling strategy, and send the abnormal warning information to a designated terminal device.

9. A computer device, characterized in that: The computer device comprises: a memory storing at least one instruction; and A processor executes instructions stored in the memory to implement the lithium battery safety monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the lithium battery safety monitoring method as described in any one of claims 1 to 7.

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