Battery state control methods, devices, equipment and media

Abstract

The present invention relates to the field of artificial intelligence technology, and provides a battery state control method, device, equipment and medium, which retrieves a model corresponding to a target environment from a pre-constructed model library as a target model, so that the corresponding model can be specifically retrieved according to the battery working environment for data processing, and the operating state data of each battery in the target battery group and the load demand data of the target battery group are collected in real time. The health state data of each battery and the target operating mode under the load demand data are predicted in combination with the retrieved model, and the state of the target battery group is controlled according to the health state data of each battery and the target operating mode. Therefore, the battery group can be reasonably controlled based on the real-time load demand and the real-time operating state of the battery group in combination with artificial intelligence means, so as to improve the operating performance of the battery group and extend the battery life.

Description

Technical Field

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

[0002] With the continuous development of new energy technologies, the application of battery packs is becoming more and more extensive, not only involving various new energy storage systems, but also various fields such as new energy vehicles.

[0003] Therefore, effective state control of the battery pack to ensure the optimal performance of the battery has become an urgent problem to be solved. Summary of the invention

[0004] In view of the above, it is necessary to provide a battery status control method, device, equipment and medium, aiming to solve the problem of being unable to effectively control the battery status.

[0005] A battery status control method, the battery status control method comprising:

[0006] In response to a state control instruction for a target battery group, real-time collection of operating state data of each battery in the target battery group and load demand data of the target battery group;

[0007] Acquire the operating environment of the target battery pack as the target environment, and retrieve a model corresponding to the target environment from a pre-built model library as the target model;

[0008] Inputting the operating status data of each battery and the load demand data into the target model to obtain predicted health status data of each battery and a target operating mode under the load demand data;

[0009] The state of the target battery group is controlled according to the health status data of each battery and the target operation mode.

[0010] According to a preferred embodiment of the present invention, the real-time collection of the operating status data of each battery in the target battery group and the load demand data of the target battery group includes:

[0011] Determine battery operating indicators that correlate with battery failure and battery life;

[0012] Collecting in real time the index value of each battery under the battery operation index as the operation status data of each battery;

[0013] The load demand change data of the target battery group under the operating environment is obtained as the load demand data.

[0014] According to a preferred embodiment of the present invention, before retrieving a model corresponding to the target environment from a pre-built model library as a target model, the method further includes:

[0015] Get historical data for each operating environment;

[0016] Retrieve a network model with prediction function;

[0017] Using historical data from each operating environment to train the network model, respectively, to obtain a model corresponding to each operating environment;

[0018] Configure labels for each operating environment;

[0019] The model corresponding to each operating environment is identified based on the label of each operating environment, and the identified model is stored in the model library.

[0020] According to a preferred embodiment of the present invention, the state control of the target battery group according to the health status data of each battery and the target operation mode includes:

[0021] When it is determined that a target battery has a risk of failure according to the health status data of each battery, a pre-built fault response strategy table is obtained;

[0022] Using the health status data of the target battery to match the fault response strategy table, and determining the matched fault response strategy as the target strategy;

[0023] Generate a fault warning prompt based on the health status data of the target battery and the target strategy.

[0024] According to a preferred embodiment of the present invention, before obtaining the pre-built fault response strategy table, the method further includes:

[0025] Determine the fault response strategy corresponding to each health status data;

[0026] Constructing the fault response strategy table according to the fault response strategy corresponding to each health status data;

[0027] Among them, in the fault response strategy table, when the health status data shows that the battery service life is less than or equal to the configured time, the corresponding fault response strategy is determined to execute battery switching to replace the battery; when the health status data shows that the battery has a potential fault, the corresponding fault response strategy is determined to adjust the battery maintenance cycle and adjust the abnormal indicator value.

[0028] According to a preferred embodiment of the present invention, the state control of the target battery group according to the health status data of each battery and the target operation mode further includes:

[0029] The current operation mode of the target battery pack is adjusted to the target operation mode.

[0030] According to a preferred embodiment of the present invention, the method further comprises:

[0031] When a new operating environment is added, the operating status data of each battery of the target battery group under the new operating environment and the load demand data of the target battery group are collected within a preset time period;

[0032] The models in the model library are supplemented with training according to the collected data.

[0033] A battery status control device, the battery status control device comprising:

[0034] A collection unit, configured to collect, in response to a state control instruction for a target battery group, operating state data of each battery in the target battery group and load demand data of the target battery group in real time;

[0035] A calling unit, used for acquiring the operating environment of the target battery pack as a target environment, and calling a model corresponding to the target environment from a pre-built model library as a target model;

[0036] An input unit, used to input the operating status data of each battery and the load demand data into the target model to obtain predicted health status data of each battery and a target operating mode under the load demand data;

[0037] A control unit is used to control the state of the target battery group according to the health status data of each battery and the target operation mode.

[0038] A computer device, comprising:

[0039] a memory storing at least one instruction; and

[0040] A processor executes the instructions stored in the memory to implement the battery state control method.

[0041] 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 battery state control method.

[0042] It can be seen from the above technical scheme that the present invention retrieves the model corresponding to the target environment from the pre-constructed model library as the target model, so that the corresponding model can be specifically retrieved according to the battery working environment for data processing, and the operating status data of each battery in the target battery pack and the load demand data of the target battery pack are collected in real time. The health status data of each battery and the target operating mode under the load demand data are predicted in combination with the retrieved model, and the state of the target battery pack is controlled according to the health status data of each battery and the target operating mode. Therefore, the battery pack can be reasonably controlled based on the real-time load demand and the real-time operating status of the battery pack, combined with artificial intelligence means, so as to improve the operating performance of the battery pack and extend the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a preferred embodiment of the battery status control method of the present invention.

[0044] Figure 2 It is a functional module diagram of a preferred embodiment of the battery status control device of the present invention.

[0045] Figure 3 It is a structural schematic diagram of a computer device of a preferred embodiment of the battery status control method of the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] like Figure 1 FIG. 1 is a flow chart of a preferred embodiment of the battery state control 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.

[0048] The battery status control 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 whose hardware includes but is not limited to microprocessors, application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), digital signal processors (DSP), embedded devices, etc.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] S10, in response to a state control instruction for a target battery group, collecting operating state data of each battery in the target battery group and load demand data of the target battery group in real time.

[0056] In this embodiment, the target battery pack may be a battery pack working in a large energy storage system, or a battery pack working in a machine such as a new energy vehicle that requires batteries to continuously provide power.

[0057] In this embodiment, the state control instruction can be automatically triggered when the target battery pack is in a working state, so as to achieve all-round monitoring of the working process of the target battery pack.

[0058] Of course, in other embodiments, the state control instruction can also be triggered by relevant staff after the target battery pack has been in operation for a period of time. For example, the state control instruction can be started after the target battery pack has been in use for 3 months, which not only saves the cost of the first three months (new batteries generally do not have operating problems), but also does not affect the subsequent effective monitoring of the battery.

[0059] In this embodiment, the real-time collection of the operating status data of each battery in the target battery group and the load demand data of the target battery group includes:

[0060] Determine battery operating indicators that correlate with battery failure and battery life;

[0061] Collecting in real time the index value of each battery under the battery operation index as the operation status data of each battery;

[0062] The load demand change data of the target battery group under the operating environment is obtained as the load demand data.

[0063] For example, the battery operation indicators associated with battery failure and battery life may include, but are not limited to, charging current rate, temperature, and state of charge.

[0064] For example, the load demand change data of the target battery pack in the operating environment can be determined according to the usage of the machine equipment supported by the target battery pack.

[0065] S11, acquiring the operating environment of the target battery pack as a target environment, and retrieving a model corresponding to the target environment from a pre-built model library as a target model.

[0066] In this embodiment, before retrieving the model corresponding to the target environment from the pre-built model library as the target model, the method further includes:

[0067] Get historical data for each operating environment;

[0068] Retrieve a network model with prediction function;

[0069] Using historical data from each operating environment to train the network model, respectively, to obtain a model corresponding to each operating environment;

[0070] Configure labels for each operating environment;

[0071] The model corresponding to each operating environment is identified based on the label of each operating environment, and the identified model is stored in the model library.

[0072] Through the above embodiments, multiple models matching different operating environments can be pre-built to specifically predict the health status data of each battery and the target operating mode under the load demand data. At the same time, multiple models corresponding to different operating environments are stored in a unified model library, which can be easily and quickly retrieved when used.

[0073] S12, inputting the operating status data of each battery and the load demand data into the target model to obtain predicted health status data of each battery and a target operating mode under the load demand data.

[0074] In this embodiment, by predicting the health status data of each battery, it is possible to determine whether any battery is about to reach the end of its service life and needs to be replaced, and it is also possible to determine whether any battery has a potential failure risk due to improper use, so as to respond in a timely manner.

[0075] In this embodiment, by predicting the target operation mode under the load demand data, the operation mode of the battery pack can be adjusted in time according to the latest system load demand.

[0076] S13, controlling the state of the target battery group according to the health status data of each battery and the target operation mode.

[0077] In this embodiment, controlling the state of the target battery group according to the health status data of each battery and the target operation mode includes:

[0078] When it is determined that a target battery has a risk of failure according to the health status data of each battery, a pre-built fault response strategy table is obtained;

[0079] Using the health status data of the target battery to match the fault response strategy table, and determining the matched fault response strategy as the target strategy;

[0080] Generate a fault warning prompt based on the health status data of the target battery and the target strategy.

[0081] Before obtaining the pre-built fault response strategy table, the method further includes:

[0082] Determine the fault response strategy corresponding to each health status data;

[0083] Constructing the fault response strategy table according to the fault response strategy corresponding to each health status data;

[0084] Among them, in the fault response strategy table, when the health status data shows that the battery service life is less than or equal to the configured time, the corresponding fault response strategy is determined to execute battery switching to replace the battery; when the health status data shows that the battery has a potential fault, the corresponding fault response strategy is determined to adjust the battery maintenance cycle and adjust the abnormal indicator value.

[0085] Among them, the configured duration can be a smaller value. When the health status data shows that the battery life is less than or equal to the configured duration, it means that the battery is about to reach its service life. At this time, no other measures are needed. The battery can be directly replaced through intelligent switching to avoid adverse effects on the normal power supply of the system.

[0086] When health status data indicates that a battery has a potential fault, it can automatically match the appropriate fault response strategy and promptly warn of the potential fault, so as to respond to possible abnormalities in a timely manner through reasonable means, thereby extending the battery life. At the same time, since battery problems can be discovered earlier and battery life can be extended by optimizing usage strategies, the frequency of battery replacement can also be reduced.

[0087] In this embodiment, the state control of the target battery group according to the health status data of each battery and the target operation mode further includes:

[0088] The current operation mode of the target battery pack is adjusted to the target operation mode.

[0089] Through the above-mentioned embodiments, the battery pack operating mode can be dynamically adjusted when the system load demand changes, so as to adjust the energy regulation configuration of the battery pack in real time, ensure the optimal performance under various working conditions, and ensure that the system always operates in the most appropriate mode under different loads, thereby improving the overall energy utilization rate. It is particularly suitable for scenarios such as renewable energy systems, smart grids, industrial backup and electric transportation.

[0090] This embodiment can ensure that the system can operate smoothly under different working conditions through intelligent switching and load response, avoid risks such as battery overload or overcurrent, and ensure the safety of the system.

[0091] In this embodiment, the method further includes:

[0092] When a new operating environment is added, the operating status data of each battery of the target battery group under the new operating environment and the load demand data of the target battery group are collected within a preset time period;

[0093] The models in the model library are supplemented with training according to the collected data.

[0094] Among them, the preset duration can be configured according to the actual usage scenario, such as 3 months.

[0095] Through the above-mentioned embodiments, when a new operating environment is added, relevant data in the new operating environment can be collected to perform supplementary training on the model, so as to improve the generalization ability.

[0096] It can be seen from the above technical scheme that the present invention retrieves the model corresponding to the target environment from the pre-constructed model library as the target model, so that the corresponding model can be specifically retrieved according to the battery working environment for data processing, and the operating status data of each battery in the target battery pack and the load demand data of the target battery pack are collected in real time. The health status data of each battery and the target operating mode under the load demand data are predicted in combination with the retrieved model, and the state of the target battery pack is controlled according to the health status data of each battery and the target operating mode. Therefore, the battery pack can be reasonably controlled based on the real-time load demand and the real-time operating status of the battery pack, combined with artificial intelligence means, so as to improve the operating performance of the battery pack and extend the battery life.

[0097] like Figure 2 As shown, it is a functional module diagram of a preferred embodiment of the battery state control device of the present invention. The battery state control device 11 includes a collection unit 110, a retrieval unit 111, an input unit 112, and a control unit 113. 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.

[0098] The acquisition unit 110 is used to collect the operating status data of each battery in the target battery group and the load demand data of the target battery group in real time in response to the state control instruction of the target battery group;

[0099] The retrieval unit 111 is used to obtain the operating environment of the target battery pack as a target environment, and retrieve a model corresponding to the target environment from a pre-built model library as a target model;

[0100] The input unit 112 is used to input the operating status data of each battery and the load demand data into the target model to obtain the predicted health status data of each battery and the target operating mode under the load demand data;

[0101] The control unit 113 is used to control the state of the target battery group according to the health status data of each battery and the target operation mode.

[0102] It can be seen from the above technical scheme that the present invention retrieves the model corresponding to the target environment from the pre-constructed model library as the target model, so that the corresponding model can be specifically retrieved according to the battery working environment for data processing, and the operating status data of each battery in the target battery pack and the load demand data of the target battery pack are collected in real time. The health status data of each battery and the target operating mode under the load demand data are predicted in combination with the retrieved model, and the state of the target battery pack is controlled according to the health status data of each battery and the target operating mode. Therefore, the battery pack can be reasonably controlled based on the real-time load demand and the real-time operating status of the battery pack, combined with artificial intelligence means, so as to improve the operating performance of the battery pack and extend the battery life.

[0103] like Figure 3 FIG. 1 is a schematic diagram of the structure of a computer device according to a preferred embodiment of the battery status control method of the present invention.

[0104] 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 battery status control program.

[0105] 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.

[0106] 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.

[0107] 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 be used not only to store application software and various types of data installed in the computer device 1, such as the code of the battery status control program, etc., but also to temporarily store data that has been output or is to be output.

[0108] 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 or executes programs or modules stored in the memory 12 (for example, executes a battery status control program, etc.), and calls data stored in the memory 12 to execute various functions of the computer device 1 and process data.

[0109] 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 various battery status control method embodiments, for example Figure 1 Steps shown.

[0110] 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 collection unit 110, a retrieval unit 111, an input unit 112, and a control unit 113.

[0111] 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 portion of the battery state control method described in each embodiment of the present invention.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The blockchain referred to in this invention is a new application mode 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 underlying blockchain platform, platform product service layer, and application service layer.

[0116] 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.

[0117] Although not shown, the computer device 1 may also include a power source (such as a battery) for supplying power to each component. 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, power status indicators, etc. The computer device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0118] 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.

[0119] 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, or 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.

[0120] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0121] It can be understood by those skilled in the art that Figure 3The 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.

[0122] Combination Figure 1 , the memory 12 in the computer device 1 stores a plurality of instructions to implement a battery status control method, and the processor 13 can execute the plurality of instructions to implement:

[0123] In response to a state control instruction for a target battery group, real-time collection of operating state data of each battery in the target battery group and load demand data of the target battery group;

[0124] Acquire the operating environment of the target battery pack as the target environment, and retrieve a model corresponding to the target environment from a pre-built model library as the target model;

[0125] Inputting the operating status data of each battery and the load demand data into the target model to obtain predicted health status data of each battery and a target operating mode under the load demand data;

[0126] The state of the target battery group is controlled according to the health status data of each battery and the target operation mode.

[0127] 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.

[0128] It should be noted that the data involved in this case were all obtained legally.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 battery status control method, characterized in that: The battery state control method comprises: In response to a state control instruction for a target battery group, real-time collection of operating state data of each battery in the target battery group and load demand data of the target battery group; Acquire the operating environment of the target battery pack as the target environment, and retrieve a model corresponding to the target environment from a pre-built model library as the target model; Inputting the operating status data of each battery and the load demand data into the target model to obtain predicted health status data of each battery and a target operating mode under the load demand data; The state of the target battery group is controlled according to the health status data of each battery and the target operation mode.

2. The battery status control method according to claim 1, characterized in that: The real-time collection of the operating status data of each battery in the target battery group and the load demand data of the target battery group includes: Determine battery operating indicators that correlate with battery failure and battery life; Collecting in real time the index value of each battery under the battery operation index as the operation status data of each battery; The load demand change data of the target battery group under the operating environment is obtained as the load demand data.

3. The battery status control method according to claim 1, characterized in that: Before retrieving the model corresponding to the target environment from the pre-built model library as the target model, the method further includes: Get historical data for each operating environment; Retrieve a network model with prediction function; Using historical data from each operating environment to train the network model, respectively, to obtain a model corresponding to each operating environment; Configure tags for each operating environment; The model corresponding to each operating environment is identified based on the label of each operating environment, and the identified model is stored in the model library.

4. The battery status control method according to claim 1, characterized in that: The controlling the state of the target battery group according to the health status data of each battery and the target operation mode includes: When it is determined that a target battery has a risk of failure according to the health status data of each battery, a pre-built fault response strategy table is obtained; Using the health status data of the target battery to match the fault response strategy table, and determining the matched fault response strategy as the target strategy; Generate a fault warning prompt based on the health status data of the target battery and the target strategy.

5. The battery status control method according to claim 4, characterized in that: Before obtaining the pre-built fault response strategy table, the method further includes: Determine the fault response strategy corresponding to each health status data; Constructing the fault response strategy table according to the fault response strategy corresponding to each health status data; Among them, in the fault response strategy table, when the health status data shows that the battery service life is less than or equal to the configured time, the corresponding fault response strategy is determined to execute battery switching to replace the battery; when the health status data shows that the battery has a potential fault, the corresponding fault response strategy is determined to adjust the battery maintenance cycle and adjust the abnormal indicator value.

6. The battery status control method according to claim 1, characterized in that: The controlling the state of the target battery group according to the health status data of each battery and the target operation mode further comprises: The current operation mode of the target battery pack is adjusted to the target operation mode.

7. The battery status control method according to claim 1, characterized in that: The method further comprises: When a new operating environment is added, the operating status data of each battery of the target battery group under the new operating environment and the load demand data of the target battery group are collected within a preset time period; The models in the model library are supplemented with training according to the collected data.

8. A battery status control device, characterized in that: The battery state control device comprises: A collection unit, configured to collect, in response to a state control instruction for a target battery group, operating state data of each battery in the target battery group and load demand data of the target battery group in real time; A calling unit, used for acquiring the operating environment of the target battery pack as a target environment, and calling a model corresponding to the target environment from a pre-built model library as a target model; An input unit, used to input the operating status data of each battery and the load demand data into the target model to obtain predicted health status data of each battery and a target operating mode under the load demand data; A control unit is used to control the state of the target battery group according to the health status data of each battery and the target operation mode.

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 battery status control 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 battery status control method according to any one of claims 1 to 7.

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