Battery data processing method and device, computer equipment, storage medium and system

Abstract

The invention relates to a battery data processing method and device, computer equipment, a storage medium and a system. The method comprises the following steps: in response to a data analysis operation triggered by power equipment for at least one charging and discharging management function, acquiring equipment data reported by the power equipment through a data reporting channel; calling a decision analysis model matched with the charging and discharging management function, performing management decision analysis on the power equipment according to the equipment data, and determining equipment management information of the power equipment for the charging and discharging management function; and sending the equipment management information to the power equipment by using an information issuing channel matched with the charging and discharging management function. By adopting the method, high-efficiency and high-accuracy charging and discharging decision analysis management service can be provided for the power equipment.

Description

Technical Field

[0001] This application relates to the technical field of battery management, and particularly to a battery data processing method, apparatus, computer device, storage medium, and computer program product. Background Art

[0002] Power equipment is equipment that uses a battery as a power source. By converting the chemical energy stored in the battery into electrical energy, and then converting the electrical energy into mechanical energy through devices such as motors, the energy can be used. Therefore, battery technology has become one of the core factors determining the performance and service life of power equipment, such as new energy vehicles. Conducting charge and discharge decision analysis and management of power equipment is an effective means to improve the operation reliability of power equipment, optimize the charge and discharge states of battery cells in power equipment, and extend the battery life.

[0003] Currently, when conducting charge and discharge decision analysis and management of power equipment, the commonly used method is to configure fixed battery management strategies or decision analysis programs in the battery management unit of the battery itself to conduct charge and discharge decision analysis and management of power equipment. However, limited by its own fewer operating resources, the battery management unit can only handle simple analysis and management tasks and cannot provide high-efficiency and high-accuracy charge and discharge decision analysis and management services for power equipment. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a charge and discharge data processing method, apparatus, computer device, computer-readable storage medium, and computer program product that can provide high-efficiency and high-accuracy charge and discharge decision analysis and management services for power equipment.

[0005] In a first aspect, this application provides a battery data processing method, and the method includes:

[0006] In response to a data analysis operation triggered by a power equipment for at least one charge and discharge management function, obtain the device data reported by the power equipment through a data reporting channel;

[0007] Invoke a decision analysis model matching the charge and discharge management function, conduct management decision analysis on the power equipment according to the device data, and determine the device management information of the power equipment for the charge and discharge management function;

[0008] Use an information distribution channel matching the charge and discharge management function to send the device management information to the power equipment.

[0009] In the above embodiments, on the one hand, by setting up multiple decision analysis models, which are respectively matched with each charge and discharge management function, when the power equipment triggers data analysis operations for the charge and discharge management function, the decision analysis platform can quickly match and call the corresponding decision analysis model to conduct management decision analysis on the power equipment, effectively improving the accuracy and management efficiency of equipment management for the power equipment. On the other hand, by separately setting up a data reporting channel and an information distribution channel, the data transmission processes of data reporting and information distribution can be separated, effectively reducing the risk of a decline in equipment management efficiency caused by transmission interference, channel congestion, etc.

[0010] In some of the embodiments, the step of calling the decision analysis model that matches the charge and discharge management function to conduct management decision analysis on the power equipment according to the equipment data, and determining the equipment management information of the power equipment for the charge and discharge management function includes:

[0011] Call the data transmission interface preset by the decision analysis model that matches the charge and discharge management function, and input the equipment data into the decision analysis model;

[0012] Receive the analysis result returned by the decision analysis model after conducting management decision analysis on the power equipment based on the equipment data;

[0013] Based on the analysis result, determine the equipment management information of the power equipment for the charge and discharge management function.

[0014] In the above embodiments, by isolating the storage of the decision analysis model from the decision analysis platform and presetting data transmission interfaces for the decision analysis platform and each decision analysis model, while effectively improving the operating resources of the decision analysis platform, the real-time nature of the decision analysis model for conducting management decision analysis on the power equipment can be maintained, improving the accuracy and efficiency of equipment management for the power equipment.

[0015] In some of the embodiments, the number of the power equipment is multiple, and the step of inputting the equipment data into the decision analysis model includes:

[0016] Conduct data comparison on the equipment data of each of the power equipment, and determine the reference equipment data from the equipment data:

[0017] Based on the reference equipment data, determine the data difference information between each of the equipment data and the reference equipment data;

[0018] Encode and compress the reference equipment data and each of the data difference information to obtain the composite data packet of the decision analysis model;

[0019] Transmit the composite data packet to the decision analysis model.

[0020] In the above embodiments, when multiple power devices trigger the same charge-discharge management function, by reducing the amount of redundant data, the reference device data and data difference information in the device data of each power device are extracted and encoded and compressed, which can effectively reduce the amount of data of the device data of each power device during data transmission, save the transmission bandwidth, and achieve high-speed data transmission.

[0021] In some embodiments, the using the information distribution channel matching the charge-discharge management function to send the device management information to the power device includes:

[0022] Determine the timeliness requirement information matching the function identifier of the charge-discharge management function;

[0023] Invoke the mapping relationship between each timeliness requirement and each information distribution channel set in advance to determine the target information distribution channel matching the timeliness requirement information;

[0024] Use the target information distribution channel to send the device management information to the power device.

[0025] In the above embodiments, by dividing corresponding timeliness requirements for each charge-discharge management function and configuring corresponding information distribution channels for each timeliness requirement, it is possible to effectively save the information transmission cost while achieving efficient management of power devices.

[0026] In some embodiments, the using the target information distribution channel to send the device management information to the power device includes:

[0027] Based on the charge-discharge management function, determine at least one instruction type required to carry the device management information;

[0028] Extract the instruction information corresponding to the instruction type from the device management information according to the instruction type to obtain the instruction information corresponding to the instruction type;

[0029] Generate a to-be-issued instruction corresponding to the instruction type based on the instruction information;

[0030] Use the target information distribution channel to send the to-be-issued instruction to the power device.

[0031] In the above embodiments, for each charge-discharge management function, the instruction types carrying the device management information of the charge-discharge management function are determined in advance. After obtaining the charge-discharge management information, the instruction information of the device management information can be quickly extracted according to the instruction types, so as to generate corresponding instructions to be sent, so that the power device can quickly respond to the instructions to be sent and execute the device management tasks, effectively improving the device management efficiency of the power device.

[0032] In some of these embodiments, generating the instruction to be sent corresponding to the instruction type based on the instruction information includes:

[0033] Determining an encryption algorithm matching the power device and an encryption key matching the instruction type;

[0034] Based on the encryption algorithm and the encryption key, encrypting and compressing the instruction information to obtain the instruction to be sent corresponding to the instruction type.

[0035] In the above embodiments, by encrypting the instruction information using an encryption algorithm matching the power device and an encryption key matching the instruction type, the most suitable encryption method can be determined for the instruction information from two dimensions of the power device and the instruction type, which can improve the transmission security of the instruction information while effectively reducing the risk of information decryption failure caused by incompatible encryption algorithms at the device terminal.

[0036] In some of these embodiments, the number of instructions to be sent is multiple, and each of the instructions to be sent corresponds to one of the instruction types;

[0037] Sending the instruction to be sent to the power device using the target information sending channel includes:

[0038] Calculating the total transmission capacity of each of the instructions to be sent according to the respective instruction transmission capacities of each of the instructions to be sent;

[0039] In the case where the total transmission capacity is less than or equal to a preset capacity threshold, merging and compressing each of the instructions to be sent to obtain a composite instruction;

[0040] Using the target information sending channel to send the composite instruction to the power device.

[0041] In the above embodiments, by comparing the total transmission capacity of each of the instructions to be sent with the preset capacity threshold, and in the case where the total transmission capacity is less than or equal to the preset capacity threshold, merging and compressing each of the instructions to be sent into a composite instruction and sending it to the power device at one time can not only improve the real-time performance of instruction transmission, but also significantly reduce the network load and improve the transmission efficiency.

[0042] In some of these embodiments, the method further includes:

[0043] When the total transmission capacity is greater than the preset capacity threshold, determine the instruction relevance of each to-be-issued instruction according to the instruction type corresponding to each to-be-issued instruction;

[0044] Based on the instruction relevance of each instruction and the preset capacity threshold, perform instruction partitioning on each to-be-issued instruction to obtain at least two instruction sets;

[0045] For each instruction set, merge and compress the to-be-issued instructions included in the instruction set to obtain a sub-composite instruction for the power device;

[0046] Use the target information distribution channel to sequentially send each sub-composite instruction to the power device.

[0047] In the above embodiments, when the total transmission capacity is greater than the preset capacity threshold, by calculating the instruction relevance between each to-be-issued instruction, the to-be-issued instructions are partitioned to obtain each instruction set, so that high-relevance instructions can be preferentially merged and transmitted to reduce communication transmission costs, and low-relevance instructions can be processed independently to reduce the resource competition rate. On the premise of meeting the capacity constraint, the internal relevance of each to-be-issued instruction in the instruction set is maximized, thereby optimizing the transmission efficiency and the equipment execution reliability of the power device.

[0048] In some of these embodiments, the method further includes:

[0049] Statistically analyze the model usage frequency of each pre-configured candidate decision analysis model, and determine the theoretical operating resources matching each model usage frequency;

[0050] Obtain the actual operating resources of each candidate decision analysis model;

[0051] For each candidate decision analysis model, determine the resource adjustment value of the candidate decision analysis model according to the theoretical operating resources and the actual operating resources of the candidate decision analysis model;

[0052] Perform resource partitioning and adjustment on the candidate decision analysis model according to the resource adjustment value.

[0053] In the above embodiments, through the model usage frequency of each candidate decision analysis model, dynamic resource partitioning is performed on each candidate decision analysis model, so that the frequently used models can be automatically expanded, and the infrequently used models can release resources in a timely manner, thereby realizing the operation stability and efficiency of the candidate decision analysis models corresponding to the core business.

[0054] In a second aspect, the present application further provides a battery data processing device, the device comprising:

[0055] A response module, configured to, in response to a data analysis operation triggered by a power device for at least one charge and discharge management function, obtain device data reported by the power device through a data reporting channel;

[0056] A data analysis module, configured to call a decision analysis model matching the charge and discharge management function, perform a management decision analysis on the power device according to the device data, and determine device management information of the power device for the charge and discharge management function;

[0057] An information distribution module, configured to use an information distribution channel matching the charge and discharge management function to send the device management information to the power device.

[0058] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0059] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0060] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0061] In a sixth aspect, the present application further provides a battery data processing system, the system comprising a decision analysis platform and at least one power device; a data reporting channel and an information distribution channel are pre-constructed between the decision analysis platform and the power device;

[0062] The power device reports device data to the decision analysis platform through the data reporting channel;

[0063] The decision analysis platform distributes device management information to the power device through the information distribution channel;

[0064] The decision analysis platform is configured to implement the battery data processing method as described above.

[0065] For the above battery data processing method, device, computer equipment, storage medium, and computer program product, the decision analysis platform can, in response to a data analysis operation triggered by a power device for at least one charge and discharge management function, obtain device data reported by the power device through a data reporting channel, call a decision analysis model matching the charge and discharge management function, perform management decision analysis on the power device based on the device data, determine device management information of the power device for the charge and discharge management function, and use an information distribution channel matching the charge and discharge management function to send the device management information to the power device. For the above battery data processing method, on the one hand, by setting multiple decision analysis models, each matching a respective charge and discharge management function, when the power device triggers a data analysis operation for the charge and discharge management function, the decision analysis platform can quickly match and call the corresponding decision analysis model to perform management decision analysis on the power device, effectively improving the accuracy and management efficiency of device management for the power device. On the other hand, by separately setting a data reporting channel and an information distribution channel, the data transmission processes of data reporting and information distribution can be separated, effectively reducing the risk of a decline in device management efficiency caused by transmission interference, channel congestion, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic structural diagram of a battery data processing system in some embodiments;

[0067] Figure 2 It is a schematic structural diagram of a battery data processing system in some other embodiments;

[0068] Figure 3 It is a schematic flowchart of a battery data processing method in some embodiments;

[0069] Figure 4 It is a schematic flowchart of calling a decision analysis model matching the charge and discharge management function, performing management decision analysis on the power device based on the device data, and determining device management information of the power device for the charge and discharge management function in some embodiments;

[0070] Figure 5 It is a schematic flowchart of inputting device data into the decision analysis model in some embodiments;

[0071] Figure 6 It is a schematic flowchart of using an information distribution channel matching the charge and discharge management function to send the device management information to the power device in some embodiments;

[0072] Figure 7 It is a schematic flowchart of using a target information distribution channel to send the device management information to the power device in some embodiments;

[0073] Figure 8Schematic diagram of the process of using the target information distribution channel to send the instruction to be sent to the power device in some embodiments;

[0074] Figure 9 Schematic diagram of the process of the battery data processing method in some other embodiments;

[0075] Figure 10 Schematic diagram of the process of the battery data processing method in some other embodiments;

[0076] Figure 11 System architecture diagram of the battery data processing system in some embodiments;

[0077] Figure 12 Schematic diagram of the process of the battery data processing method in some other embodiments;

[0078] Figure 13 Structural block diagram of the battery data processing device in some embodiments;

[0079] Figure 14 Internal structure diagram of the computer device in some embodiments. Detailed implementation manners

[0080] Hereinafter, embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solution of the present application more clearly, and thus are only examples and should not be used to limit the protection scope of the present application.

[0081] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above accompanying drawings are intended to cover non-exclusive inclusion.

[0082] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment may be included in at least some embodiments of this application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0083] In the description of the embodiments of this application, the term "plurality" means two or more (including two), and similarly, "multiple groups" means two or more groups (including two groups), and "multiple pieces" means two or more pieces (including two pieces).

[0084] In the description of the embodiments of the present application, the orientation or positional relationship indicated by technical terms such as "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the embodiments of the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the embodiments of the present application.

[0085] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "attachment", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to specific circumstances.

[0086] A power device is a device that uses a battery as a power source and operates by converting the chemical energy stored in the battery into electrical energy and then converting the electrical energy into mechanical energy. For example, a new energy vehicle. In a power device, the battery can be considered the core of the device. The operating state of the battery will directly affect the operating state of the power device. And performing charge and discharge management on the power battery is an effective means to optimize the charge and discharge state of the battery cells in the power device, extend the battery life, and improve the device performance.

[0087] Currently, when making charge and discharge decision analysis and management for a power device, the commonly used method is to configure a fixed battery management strategy or decision analysis program in the battery management unit of the battery itself to perform charge and discharge decision analysis and management on the power device. However, limited by the relatively small operating resources of itself, the battery management unit can only handle simple analysis and management tasks and cannot provide an efficient and highly accurate charge and discharge decision analysis and management service for the power device.

[0088] In order to improve the accuracy of charge and discharge decision-making analysis and the efficiency of equipment management, the decision-making analysis platform can, in response to a data analysis operation triggered by a power device for at least one charge and discharge management function, obtain the device data reported by the power device through a data reporting channel, call a decision-making analysis model matching the charge and discharge management function, perform a management decision-making analysis on the power device based on the device data, determine the device management information of the power device for the charge and discharge management function, and use an information distribution channel matching the charge and discharge management function to send the device management information to the power device. In the above battery data processing method, on the one hand, by setting multiple decision-making analysis models, each matching a respective charge and discharge management function, when the power device triggers a data analysis operation for the charge and discharge management function, the decision-making analysis platform can quickly match and call the corresponding decision-making analysis model to perform a management decision-making analysis on the power device, effectively improving the accuracy and management efficiency of device management for the power device. On the other hand, by separately setting a data reporting channel and an information distribution channel, the data transmission processes of data reporting and information distribution can be separated, effectively reducing the risk of a decline in device management efficiency caused by transmission interference, channel congestion, etc.

[0089] The battery data processing method provided by the embodiments of this application can be applied to a battery data processing system 100 as Figure 1 shown. Among them, the battery data processing system 100 includes a decision-making analysis platform 101 and at least one power device 102. A data reporting channel 103 and an information distribution channel 104 are pre-established between the decision-making analysis platform 101 and the power device 102.

[0090] The power device 102 reports device data to the decision-making analysis platform 101 through the data reporting channel 103, and the decision-making analysis platform 101 distributes device management information to the power device 102 through the information distribution channel 104.

[0091] Among them, the power device 102 can be any device powered by a battery, such as a new energy vehicle, an electric aircraft, an industrial transport vehicle, etc. The power device 102 is provided with its own battery management unit. The battery management unit (Battery Management System, BMS) can implement functions such as battery data acquisition, status monitoring, battery protection, balance management, communication and control, and thermal management. At the same time, corresponding information acquisition devices will also be set in the power device 102. Taking a new energy vehicle as an example, on-board sensors such as environmental sensors will be set in the new energy vehicle to obtain the device operation information during the vehicle operation in real time.

[0092] Among them, the data reporting channel 103 is a dedicated data transmission path for reporting the data generated by the power equipment to the decision analysis platform 101. The information distribution channel 104 is a dedicated data transmission path for distributing the instruction information generated by the decision analysis platform 101 to the power equipment 102. It can be understood that the data reporting channel 103 and the information distribution channel 104 have opposite data transmission directions. The specific number of channels of the data reporting channel 103 and the information distribution channel 104 can be determined according to the actual communication situation.

[0093] Among them, the decision analysis platform 101 is an intelligent analysis platform that can implement various charge and discharge management functions. It can provide decision analysis services for the charge and discharge management problems in various power equipment usage scenarios by integrating multi-source data and pre-trained algorithm models. It can be understood that the decision analysis platform 101 can be implemented by an independent server or a server cluster composed of multiple servers, or can be implemented by a cloud server.

[0094] In some of the embodiments, in order not to be restricted by operating resources, the decision analysis platform 101 can be a cloud service platform, set in a cloud server, and the model repository storing each pre-trained decision analysis model can be stored in other cloud servers. The decision analysis platform 101 can be communicatively connected to the model repository in other cloud servers through a pre-set communication interface. When the power equipment 102 triggers a data analysis operation for a certain or multiple charge and discharge management functions, the corresponding decision analysis model can be quickly matched from the model repository for the charge and discharge management function, and the decision analysis model is called to perform management decision analysis on the power equipment 102 according to the device data.

[0095] In some embodiments, the decision analysis platform 101 can respond to the data analysis operation triggered by the power equipment 102 for at least one charge and discharge management function, obtain the device data reported by the power equipment through the data reporting channel, call the decision analysis model matching the charge and discharge management function, perform management decision analysis on the power equipment according to the device data, determine the device management information of the power equipment for the charge and discharge management function, and use the information distribution channel matching the charge and discharge management function to send the device management information to the power equipment.

[0096] In the above embodiments, when the decision analysis platform in the battery data processing system triggers a data analysis operation for the charge and discharge management function of the power equipment, it can quickly match and call the corresponding decision analysis model to perform management decision analysis on the power equipment, effectively improving the accuracy and management efficiency of device management for the power equipment. At the same time, by separately setting the data reporting channel and the information distribution channel, the data transmission processes of data reporting and information distribution can be separated, effectively reducing the risk of decline in device management efficiency caused by transmission interference, channel congestion, etc.

[0097] In some embodiments, such as Figure 2 As shown, a battery management unit 1021, an information collection device 1022 are provided in the power device 102, and a real-time database module 1024 communicatively connected to the battery management unit 1021 and the information collection device 1022 via a communication bus 1023, such as a CAN bus. The decision analysis platform 101 is communicatively connected to the real-time database module 1024 via a data reporting channel 103 and an information distribution channel 104 to achieve a communication connection with the power device 102.

[0098] Among them, the real-time database module 1024 can collect battery pack parameters from the battery management unit 1021, such as single-cell voltage / temperature, total voltage, insulation resistance, historical charge and discharge curves, remaining cruising range, etc. The environmental temperature, environmental humidity, altitude, positioning information, etc. of the power device 102 can also be obtained through the information collection device 1022.

[0099] In some of these embodiments, the data flow of the data reporting channel 103 is as follows: the device data is transparently transmitted from the battery management unit 1021 to the real-time database module 1024, and then the real-time database module 1024 transmits the device data to the decision analysis platform 101 through a distributed stream processing platform, such as kafka. By performing distributed management of data transmission in the data reporting channel 103 through the distributed stream processing platform, the transmission efficiency and transmission stability of device data can be further improved in high-throughput scenarios.

[0100] In some of these embodiments, the data flow of the information distribution channel 104 is as follows: the decision analysis platform 101 writes device management information into the real-time database module 1024 through a preset communication protocol, such as the HTTP protocol, and the real-time database module 1024 then transparently transmits the received device management information into the battery management unit 1021.

[0101] In some embodiments, such as Figure 3 As shown, a battery data processing method is provided. Taking the decision analysis platform 101 in Figure 1 as an example for illustration, the method includes the following steps:

[0102] S302, in response to a data analysis operation triggered by the power device for at least one charge and discharge management function, obtain the device data reported by the power device through the data reporting channel.

[0103] Among them, the charge and discharge management function is a platform function for managing the charge and discharge process of power equipment. For example, the charge and discharge management function may include functions such as charge and discharge strategy recommendation, charge and discharge curve optimization, charge and discharge fault prediction, and charging pile recommendation. It can be understood that multiple charge and discharge management functions are pre-configured in the decision analysis platform and can be matched with various usage scenarios of the power equipment.

[0104] The data analysis operation is an operation triggered when the power equipment has a data analysis requirement for the charge and discharge management function. It can be understood that the data analysis operation can be an operation directly triggered by the equipment user of the power equipment for the charge and discharge management function. For example, when there is a charge and discharge requirement, the equipment user can directly log in to the decision analysis platform through the equipment terminal and select the charging pile recommendation function on the function selection page of the decision analysis platform to trigger the data analysis operation for the charging pile recommendation function. The data analysis operation can also be indirectly triggered by the equipment usage operation during the use of the power equipment. Taking the power equipment as a new energy vehicle as an example, when the new energy vehicle is plugged in for charging, the data analysis operation can be triggered for charge and discharge curve optimization, charge and discharge strategy recommendation and other charge and discharge management functions. Another example is that during the driving of a new energy vehicle, if the battery temperature rises too fast, the data analysis operation can also be triggered for the charge and discharge fault prediction function.

[0105] Among them, the equipment data is the data information required for decision analysis of the charge and discharge management function. These equipment data can be real-time data generated during the operation of the power equipment, such as battery usage data, charge and discharge curves, charge and discharge strategies, battery temperature, environmental information, etc. during the operation of the power equipment. It can also be historical data recorded during the historical operation of the power equipment, such as historical charge and discharge curves, fault repair records, fault alarm records, fault maintenance records, operation logs, user usage portraits, etc.

[0106] It can be understood that different charge and discharge functions correspond to different equipment data required. For example, the equipment data required for the charge and discharge strategy recommendation function may include the state of charge information, battery health information, charge and discharge curves, historical charge and discharge curves, user portrait data, etc. during the operation of the power equipment. And the equipment data required for the charging pile recommendation function may include the location of the power equipment, the historical charge and discharge habits of the power equipment, the power information of the power equipment at the current moment, etc.

[0107] In some embodiments, the decision analysis platform can obtain the equipment data reported by the power equipment through the data reporting channel in response to the data analysis operation triggered by the power equipment for at least one charge and discharge management function.

[0108] S304. Call the decision analysis model that matches the charge and discharge management function, perform management decision analysis on the power equipment based on the equipment data, and determine the equipment management information of the power equipment for the charge and discharge management function.

[0109] Among them, the decision analysis model is a preset model tool for dynamically performing decision analysis on the charge and discharge process managed by the matching charge and discharge management function. The decision analysis model can include a rule model formed by encapsulating predefined decision analysis logic, a prediction model capable of predicting the charge and discharge process, an optimization model that can solve the optimal solution, a deep learning model with complex decision-making capabilities trained through machine learning, etc. By calling the decision analysis model that matches the charge and discharge management function, accurate and rapid management decision analysis of the power equipment can be performed, improving the management efficiency and management accuracy of the power equipment.

[0110] In some embodiments, the decision analysis models that match each charge and discharge management function can be directly installed in the decision analysis platform, and the decision analysis platform can directly call and perform real-time management decision analysis with each decision analysis model.

[0111] Among them, management decision analysis refers to the operation in which the decision analysis model analyzes the power equipment according to the preset model analysis decision logic based on the equipment data and outputs the analysis result. The equipment management information is the management information obtained by converting the analysis result into an executable form. By converting the analysis result into equipment management information, the power equipment can quickly execute when receiving the equipment management information, improving the equipment management efficiency of the power equipment.

[0112] In some embodiments, the decision analysis model can determine the charge and discharge management function to be used according to the trigger operation of the power equipment, then call the decision analysis model that matches the charge and discharge management function, input the equipment data reported by the power equipment into the decision analysis model, perform decision analysis on the power equipment based on the decision analysis model, obtain the analysis result output by the decision analysis model, and perform feasibility conversion on the analysis result to obtain the equipment management information of the power equipment for the charge and discharge function.

[0113] In some of these embodiments, a mapping relationship between each decision analysis model and each charge and discharge management function is preset in the decision analysis model. The decision analysis model can search for the mapping relationship according to the determined charge and discharge management function and determine at least one decision analysis model that matches the charge and discharge management function.

[0114] S306. Use the information distribution channel that matches the charge and discharge management function to send the equipment management information to the power equipment.

[0115] Among them, multiple information distribution channels are set between the decision analysis platform and the power equipment. Different charge and discharge management functions may correspond to different information distribution channels, and the decision analysis platform needs to determine the matching information distribution channel according to the charge and discharge management function.

[0116] In some embodiments, the decision analysis platform determines the matching information distribution channel according to the charge and discharge management function, and then uses this information distribution channel to send device management information to the power equipment.

[0117] In some of these embodiments, an information distribution channel can be set for each charge and discharge management function, which can effectively improve the information distribution efficiency, and further improve the device management efficiency of the power equipment. Each information distribution channel is set with a function identifier corresponding to the charge and discharge management function, and the decision analysis platform can directly determine the information distribution channel matching the charge and discharge management function from each information distribution channel according to the function identifier of the charge and discharge management function.

[0118] In the above battery data processing method, the decision analysis platform can respond to the data analysis operation triggered by the power equipment for at least one charge and discharge management function, obtain the device data reported by the power equipment through the data reporting channel, call the decision analysis model matching the charge and discharge management function, perform management decision analysis on the power equipment according to the device data, determine the device management information of the power equipment for the charge and discharge management function, and use the information distribution channel matching the charge and discharge management function to send the device management information to the power equipment. The above battery data processing method, on the one hand, by setting multiple decision analysis models, respectively matching with each charge and discharge management function, when the power equipment triggers a data analysis operation for the charge and discharge management function, the decision analysis platform can quickly match and call the corresponding decision analysis model to perform management decision analysis on the power equipment, effectively improving the accuracy and management efficiency of device management for the power equipment. On the other hand, by separately setting the data reporting channel and the information distribution channel, the data transmission process of data reporting and information distribution can be separated, effectively reducing the risk of device management efficiency decline caused by transmission interference, channel congestion, etc.

[0119] When performing management decision analysis, the operating resources of the decision analysis platform will affect the real-time performance of the management decision analysis. In order to further improve the operating resources of the decision analysis platform, in some embodiments, such as Figure 4 shown in S304, call the decision analysis model matching the charge and discharge management function, perform management decision analysis on the power equipment according to the device data, and determine the device management information of the power equipment for the charge and discharge management function, including:

[0120] S402, call the data transmission interface preset in the decision analysis model matching the charge and discharge management function, and input the device data into the decision analysis model.

[0121] Among them, the data transmission interface is a pre-configured communication channel interface, which is used to provide a data transmission path for the decision analysis platform and the corresponding decision analysis model. It can be understood that the data transmission interface can be an Application Programming Interface (API).

[0122] In order to improve the operating resources of the decision analysis platform, each decision analysis model can be stored in other remote servers. The decision analysis platform can pre-build a data transmission interface with each decision analysis model. When it is necessary to call the decision analysis model to perform management decision analysis on the power equipment, there is no need to call the decision analysis model to the decision analysis platform for configuration. Instead, the device data required for management decision analysis is directly input into the decision analysis model through the data transmission interface for processing.

[0123] In some embodiments, after the decision analysis platform determines the decision analysis model matching the charge and discharge management function, it can call the data transmission interface preset with the decision analysis model and directly input the device data into the decision analysis model through the data transmission interface.

[0124] S404, receive the analysis result returned by the decision analysis model after performing management decision analysis on the power equipment based on the device data.

[0125] Among them, the analysis result is the decision analysis conclusion information output by the decision analysis model after performing management decision analysis on the power equipment based on the device data. It can be understood that the decision analysis conclusion information output by the decision analysis models corresponding to different charge and discharge management functions is different. For example, the analysis result output by the decision analysis model corresponding to the charge and discharge strategy recommendation function is the target charge and discharge strategy, and the analysis result output by the decision analysis model corresponding to the charge and discharge curve optimization function is the optimized charge and discharge curve.

[0126] In some embodiments, the decision analysis model can perform management decision analysis on the power equipment based on the device data, output the analysis result, and return the analysis result to the decision analysis platform through the data transmission interface. The decision analysis platform receives the analysis result returned by the decision analysis model through the data transmission interface.

[0127] S406, based on the analysis result, determine the device management information of the power equipment for the charge and discharge management function.

[0128] In some embodiments, the decision analysis platform may determine device management information for the power device regarding the charge-discharge management function based on the received analysis results. For example, in the case where the analysis result is an optimized charge-discharge curve, the decision analysis platform may, based on the optimized charge-discharge curve, determine time points and charge-discharge durations for each charge-discharge stage, and generate corresponding charge-discharge control instructions according to the time points and charge-discharge durations of each charge-discharge stage. Each charge-discharge instruction is the device management information for the power device regarding the charge-discharge management function. After receiving each charge-discharge control instruction, the power device can perform actual charge-discharge control according to each charge-discharge control instruction.

[0129] In the above embodiments, by isolating the storage of the decision analysis model from the decision analysis platform and pre-setting data transmission interfaces for the decision analysis platform and each decision analysis model, it is possible to effectively improve the operating resources of the decision analysis platform while maintaining the real-time nature of the decision analysis model for managing and making decisions about the power device, and improving the accuracy and efficiency of device management of the power device.

[0130] To further improve the analysis efficiency of the decision analysis model, in some embodiments, the number of power devices is multiple. As Figure 5 shown, inputting the device data into the decision analysis model in S402 includes:

[0131] S502, performing data comparison on the device data of each power device respectively, and determining reference device data from the device data of each device.

[0132] Among them, the reference device data refers to the common data, that is, the same data information, in the device data of each power device. When multiple power devices trigger data analysis operations for the same charge-discharge management function, the device data reported by each of them, since they are all data required by the same decision analysis model, there will be some common data. For example, if the reported device data includes temperature data, and the temperature data reported by each power device is 25°C, 25°C, 26°C, 28°C, 24°C, then the reference device data can be determined as 25°C.

[0133] In some embodiments, when the decision analysis platform receives data analysis operations triggered by multiple power devices for the same charge-discharge management function, it obtains the device data of each power device respectively, performs data comparison on the device data of each device, and determines reference device data from the device data of each device.

[0134] S504, based on the reference device data, determining the data difference information between each device data and the reference device data respectively.

[0135] In some embodiments, the decision analysis platform may compare each device data with the reference device data respectively to determine the data difference information between each device data and the reference device data.

[0136] Taking the temperature data reported by each power device as 25°C, 25°C, 26°C, 28°C, and 24°C as an example, and the reference device data as 25°C, the data difference information between each device data and the reference device data is 0, 0, +1, +3, and -4 respectively.

[0137] S506. Encode and compress the reference device data and each data difference information to obtain a composite data packet of the decision analysis model.

[0138] Among them, encoding and compression is a data processing method that compresses data through a compression algorithm to reduce the data size. The composite data packet is a data packet obtained by compressing and merging multiple data. It can be understood that in addition to the reference device data and each data difference information, the composite data packet can also be attached with common metadata, such as timestamps, device group identifiers, etc.

[0139] In some embodiments, the decision analysis platform may use a preset encoding algorithm, such as the lossless data compression (Huffman) algorithm, to encode and compress the reference device data and each data difference information to obtain a composite data packet of the decision analysis model.

[0140] S508. Transmit the composite data packet to the decision analysis model.

[0141] In some embodiments, the decision analysis platform may transmit the composite data packet to the decision analysis model so that the decision analysis model can perform management decision analysis on each power device according to the device data of each power device carried in the composite data packet.

[0142] In the above embodiments, in the case where multiple power devices trigger the same charge and discharge management function, by reducing the amount of redundant data, the reference device data and the data difference information in the device data of each power device are extracted and encoded and compressed, which can effectively reduce the amount of data of the device data of each power device during data transmission, save the transmission bandwidth, and achieve high-speed data transmission.

[0143] In some of these embodiments, in the case where there are multiple data dimensions for device data, the decision analysis platform determines, for each data dimension, the reference device data and data difference information in each device data under that data dimension, binds each data dimension to its corresponding reference device data to obtain a set of reference device data, binds the device identifier of each power device to the data difference information of the power device in each data dimension respectively to obtain a set of data difference information for the power device, and finally encodes and compresses the set of reference device data and the sets of data difference information of each power device respectively to obtain a composite data packet.

[0144] Taking the power devices including Device 1, 2, and 3 and the data dimensions including temperature, voltage, and current as an example for illustration. For example, the temperatures of Device 1, 2, and 3 are 25°C, 25°C, and 26°C respectively, the voltages are 300V, 320V, and 300V respectively, and the currents are 100A, 150A, and 140A respectively. Then the temperature reference device data can be determined as 25°C, the voltage reference device data as 300V, and the current reference device data as 140A. Taking them as a set, the set of reference device data obtained is (temperature reference device data: 25°C, voltage reference device data: 300V, current reference device data: 140A), while the set of data difference information of Device 1 is (Device 1, 0°C, 0V, -40A), the set of data difference information of Device 2 is (Device 2, 0°C, 20V, 10A), and the set of data difference information of Device 3 is (Device 3, 1°C, 0V, 0A). Encoding and compressing (temperature reference device data: 25°C, voltage reference device data: 300V, current reference device data: 140A), (Device 1, 0°C, 0V, -40A), (Device 2, 0°C, 20V, 10A), and (Device 3, 1°C, 0V, 0A) obtains a composite data packet.

[0145] In some of these embodiments, the decision analysis model includes a data preprocessing sub-model and a data analysis sub-model. Among them, a decoding rule is configured in the data preprocessing sub-model for decoding the composite data packet to obtain a set of reference device data and sets of data difference information. A data recovery rule is also configured in the data preprocessing sub-model for restoring each set of data difference information into the device data of each power device respectively according to the set of reference device data. After the data preprocessing sub-model restores each set of data difference information into the device data of each power device respectively, the device data can be input into the data analysis sub-model in sequence to perform management decision analysis on each power device to obtain the analysis results of each power device respectively.

[0146] The setting of the information distribution channel between the decision analysis platform and the power device has an important impact on the transmission timeliness of device management information. In some embodiments, such as Figure 6As shown in S306, use the information distribution channel that matches the charge and discharge management function to send device management information to the power device, including:

[0147] S602, determine the timeliness requirement information that matches the function identifier of the charge and discharge management function.

[0148] Among them, the function identifier is the identifier information that uniquely identifies the charge and discharge management function, and the charge and discharge management function corresponds one-to-one with the function identifier.

[0149] The timeliness requirement information is the information data used to characterize the management timeliness requirement of the charge and discharge management function, and can reflect the specific requirements of the charge and discharge management function for time sensitivity. It can be understood that the timeliness requirements of the charge and discharge management function are sorted from high to low according to time sensitivity, and can include quasi-real-time, real-time, and batch. Among them, the quasi-real-time timeliness requirement has the highest time sensitivity, the real-time timeliness requirement has the second highest time sensitivity, and the batch timeliness requirement has the lowest time sensitivity.

[0150] In some embodiments, the corresponding relationship between each function identifier and each timeliness requirement information is pre-set in the decision analysis platform, and the timeliness requirement information that matches the charge and discharge management function can be determined by looking up the corresponding relationship according to the function identifier of the charge and discharge management function.

[0151] S604, call the mapping relationship between each pre-set timeliness requirement and each information distribution channel to determine the target information distribution channel that matches the timeliness requirement information.

[0152] Among them, the mapping relationship between each timeliness requirement and each information distribution channel is pre-set by the designer according to the actual situation. It can be understood that different timeliness requirements result in different communication bandwidths and different information transmission speeds for the corresponding information distribution channels. The time sensitivity of the timeliness requirement is proportional to the channel bandwidth of the information distribution channel, that is, the higher the time sensitivity of the timeliness requirement, the wider the communication bandwidth of the corresponding information distribution channel and the faster the information transmission speed. Therefore, the communication bandwidth of the information distribution channel corresponding to quasi-real-time is the widest, the communication bandwidth of the information distribution channel corresponding to real-time is the second widest, and the communication bandwidth of the information distribution channel corresponding to batch is the narrowest. The designer divides the bandwidth of each information distribution channel according to the actual situation and matches the corresponding information distribution channel for each timeliness requirement according to the time sensitivity of each timeliness requirement.

[0153] In some embodiments, the decision analysis platform can determine the timeliness requirement of the charge and discharge management function according to the timeliness requirement information, and then call the mapping relationship between each pre-set timeliness requirement and each information distribution channel to find the target information distribution channel that matches the timeliness requirement of the charge and discharge management function.

[0154] S606. Use the target information distribution channel to send the device management information to the power device.

[0155] In some embodiments, after determining the target information distribution channel, the decision analysis platform may use the target information distribution channel to send the device management information to the power device.

[0156] In the above embodiments, by dividing corresponding timeliness requirements for each charge and discharge management function and configuring corresponding information distribution channels for each timeliness requirement, it is possible to effectively save information transmission costs while achieving efficient management of power devices.

[0157] For the device management of power devices, especially the device management of new energy vehicles, the security of device management information distribution is one of the key factors affecting the operation reliability of power devices. To reduce the risk of information tampering during transmission, in some embodiments, as Figure 7 shown, S606. Use the target information distribution channel to send the device management information to the power device, including:

[0158] S702. Based on the charge and discharge management function, determine at least one instruction type required to carry the device management information.

[0159] Among them, the instruction type is a type parameter formed by dividing instructions according to the control objects corresponding to the instructions. For example, for the charge and discharge curve optimization function, when optimizing the charge and discharge curve of a power device, it is necessary to control and adjust the state of charge of the power device battery, battery health, and charge and discharge voltage. Then, the instruction types required to carry its device management information may include state of charge adjustment instructions, battery health adjustment instructions, and voltage adjustment instructions. It can be understood that for different charge and discharge management functions, the instruction types required to carry their device management information will also be different.

[0160] In some embodiments, the decision analysis platform may determine at least one instruction type required to carry the device management information based on the charge and discharge management function.

[0161] In some of these embodiments, the corresponding relationship between each charge and discharge management function and each instruction type is pre-set in the decision analysis platform. The corresponding relationship can be searched according to the charge and discharge management function to determine at least one instruction type corresponding to the charge and discharge management function, and this instruction type is determined as the instruction type required to carry the device management information of the charge and discharge management function.

[0162] S704. Extract the instruction information from the device management information according to the instruction type to obtain the instruction information corresponding to the instruction type.

[0163] In some embodiments, the decision analysis platform may extract instruction information from the device management information according to the instruction type, and extract the instruction information corresponding to the instruction type from the device management information. For example, if the instruction type is a charge state adjustment instruction, the decision analysis platform may extract the charge state adjustment parameter from the device management information and determine the charge state adjustment parameter as the instruction information of the charge state adjustment instruction.

[0164] S706. Generate a to-be-issued instruction corresponding to the instruction type based on the instruction information.

[0165] In some embodiments, the decision analysis platform may perform compression processing on the instruction information to obtain a to-be-issued instruction corresponding to the instruction type.

[0166] S708. Use the target information distribution channel to send the to-be-issued instruction to the power device.

[0167] In some embodiments, the decision analysis platform may use the target information distribution channel to send the to-be-issued instruction to the power device.

[0168] In the above embodiments, for each charge and discharge management function, the instruction type of the device management information carrying the charge and discharge management function is determined in advance. After obtaining the charge and discharge management information, the instruction information can be quickly extracted from the device management information according to the instruction type, so as to generate the corresponding to-be-issued instruction, so that the power device can quickly respond to the to-be-issued instruction to execute the device management task, effectively improving the device management efficiency of the power device.

[0169] In some embodiments, S706. Generating a to-be-issued instruction corresponding to the instruction type based on the instruction information includes: determining an encryption algorithm matching the power device and an encryption key matching the instruction type. Based on the encryption algorithm and the encryption key, perform encryption and compression on the instruction information to obtain a to-be-issued instruction corresponding to the instruction type.

[0170] Among them, the encryption algorithm is an encryption algorithm determined by the decision analysis platform through handshake communication with the power device in advance, and is used to encrypt the transmission data between the decision analysis platform and the power device. It can be understood that since different power devices may have different security requirements or hardware capabilities, the encryption algorithms that can be supported may be different. For example, some power devices support national encryption algorithms, while some power devices can only support international algorithms such as AES and RSA. Therefore, when it is necessary to encrypt and issue the instruction information, it is necessary to first determine the encryption algorithm that the power device can use. The encryption algorithm matching the power device can be determined by prior negotiation between the decision analysis platform and the power device.

[0171] In some embodiments, the decision analysis platform may, according to the device identifier of the power device and the mapping relationship preset between each device identifier and each encryption algorithm, search for the encryption algorithm that matches the power device. At the same time, according to the type identifier of the instruction type and the mapping relationship preset between each type identifier and each encryption key, search for the encryption key that matches the instruction type. Subsequently, based on the encryption algorithm and the encryption key, encrypt the instruction information to obtain encrypted information, and then perform compression processing on the encrypted information, and encapsulate it into a complete instruction packet according to the communication protocol to obtain the instruction to be issued corresponding to the instruction type.

[0172] In the above embodiments, by using the encryption algorithm that matches the power device and the encryption key that matches the instruction type to encrypt the instruction information, the most suitable encryption method can be determined for the instruction information from two dimensions of the power device and the instruction type, which can improve the transmission security of the instruction information while effectively reducing the risk of information decryption failure caused by incompatible encryption algorithms at the device terminal.

[0173] Further, in some embodiments, the number of instructions to be issued is multiple, and each instruction to be issued corresponds to each instruction type one by one. For example, Figure 8 as shown, S708, using the target information distribution channel, send the instruction to be issued to the power device, including:

[0174] S802, calculate the total transmission capacity of each instruction to be issued according to the instruction transmission capacity of each instruction to be issued.

[0175] Among them, the instruction transmission capacity refers to the size of the transmission data volume occupied by the instruction to be issued during the data transmission process, and the total transmission capacity refers to the sum of the instruction transmission capacities of all instructions to be issued.

[0176] In some embodiments, the decision analysis platform may obtain the instruction transmission capacity of each instruction to be issued and perform a summation calculation on each instruction transmission capacity to obtain the total transmission capacity of each instruction to be issued.

[0177] S804, in the case where the total transmission capacity is less than or equal to the preset capacity threshold, merge and compress each instruction to be issued to obtain a composite instruction.

[0178] Among them, the preset capacity threshold is the maximum value of the single transmission capacity of the target information distribution channel, which can represent the single transmission ability of the target information distribution channel.

[0179] Among them, merge and compress refers to an instruction processing operation that merges multiple instructions to be issued into a single instruction packet and performs compression processing on it, which can be completed through instruction splicing and a preset compression algorithm.

[0180] In some embodiments, the decision analysis platform may compare the total transmission capacity of each instruction to be issued with a preset capacity threshold. When the total transmission capacity is less than or equal to the preset capacity threshold, it indicates that the target information distribution channel can issue each instruction to be issued to the power equipment at one time. At this time, the decision analysis platform may merge and compress each instruction to be issued to obtain a composite instruction.

[0181] In some of these embodiments, the decision analysis platform may merge and encapsulate each instruction to be issued to obtain a merged instruction packet, then convert the merged instruction packet into a binary stream, and subsequently call a preset compression algorithm to compress it to obtain a composite instruction.

[0182] S806, Use the target information distribution channel to send the composite instruction to the power equipment.

[0183] In some embodiments, after obtaining the composite instruction, the decision analysis platform may use the target information distribution channel to send the composite instruction to the power equipment.

[0184] In the above embodiments, by comparing the total transmission capacity of each instruction to be issued with a preset capacity threshold, when the total transmission capacity is less than or equal to the preset capacity threshold, merging and compressing each instruction to be issued into a composite instruction and issuing it to the power equipment at one time can not only improve the real-time performance of instruction transmission, but also significantly reduce the network load and improve the transmission efficiency.

[0185] In other embodiments, such as Figure 9 shown, the battery data processing method further includes:

[0186] S902, When the total transmission capacity is greater than the preset capacity threshold, determine the instruction relevance of each instruction to be issued according to the instruction type corresponding to each instruction to be issued.

[0187] Among them, the instruction relevance can represent the degree of association of each instruction to be issued in terms of execution target, operation object, or timing dependence, and can provide a guiding basis for the combined issuance of each instruction to be issued. It can be understood that the higher the instruction relevance, the closer the connection between the control actions corresponding to each instruction to be issued, for example, it may be necessary to control simultaneously or continuously.

[0188] In some embodiments, when the decision analysis platform determines that the total transmission capacity is greater than the preset capacity threshold, it determines the instruction relevance of each instruction to be issued according to the instruction type corresponding to each instruction to be issued.

[0189] In some of these embodiments, a relevance analysis logic or a relevance analysis algorithm is preset in the decision analysis platform, and the instruction relevance of each instruction to be issued can be determined according to the instruction type of each instruction to be issued.

[0190] In some of these embodiments, instruction relevance information between each instruction type is preset in the decision analysis platform. By separately looking up the instruction relevance information based on the instruction types of each instruction to be issued, the instruction relevance of each instruction to be issued can be determined.

[0191] S904. Based on each instruction relevance and a preset capacity threshold, perform instruction partitioning on each instruction to be issued to obtain at least two instruction sets.

[0192] In some embodiments, the decision analysis platform can perform instruction partitioning on each instruction to be issued based on each instruction relevance and a preset capacity threshold to obtain at least two instruction sets.

[0193] In some of these embodiments, the decision analysis platform can perform a descending order sorting on the instruction relevance between each instruction to be issued and other instructions to be issued, and set a to-be-filled instruction set with a capacity threshold as the preset capacity threshold. Subsequently, the decision analysis platform can start from the instruction with the highest relevance and gradually add each instruction to be issued to the to-be-filled instruction set. When the capacity of each instruction to be issued filled in the to-be-filled instruction set reaches the capacity threshold, or when adding any unfilled instruction to be issued will cause the capacity to exceed the capacity threshold, the decision analysis platform will return to the step of setting a to-be-filled instruction set with a capacity threshold as the preset capacity threshold until all instructions to be issued are added to the corresponding instruction sets.

[0194] In some of these embodiments, the decision analysis platform can transform the instruction grouping problem into a knapsack problem, determine the number of instruction sets according to the difference between the total transmission capacity of each instruction to be issued and the preset capacity threshold, set corresponding to-be-filled instruction sets according to the number, and the capacity threshold of each to-be-filled instruction set is the preset capacity threshold. Subsequently, for each to-be-filled instruction set, with the goal of maximizing the sum of instruction relevance within the capacity threshold, solve the instruction grouping problem to obtain the final grouping result, that is, the filled instruction sets.

[0195] S906. For each instruction set, merge and compress the instructions to be issued included in the instruction set to obtain a sub-compound instruction for the power device.

[0196] In some embodiments, for each instruction set, the decision analysis platform can merge and compress the instructions to be issued included in the instruction set to obtain a sub-compound instruction for the power device. It can be understood that the specific method of merging and compressing each instruction to be issued is basically the same as that described above and will not be elaborated here.

[0197] S908. Use the target information distribution channel to sequentially send each sub-compound instruction to the power device.

[0198] In some embodiments, the decision analysis platform may use the target information distribution channel to sequentially send each sub-composite instruction to the power device.

[0199] In the above embodiments, when the transmission total capacity is greater than the preset capacity threshold, by calculating the instruction correlation degree between each instruction to be sent, the instructions to be sent are divided to obtain each instruction set, which can make the instructions with high correlation degree be preferentially merged for transmission to reduce the communication transmission cost, and the instructions with low correlation degree can be independently processed to reduce the resource competition rate. On the premise of meeting the capacity constraint, the internal correlation of each instruction to be sent in the instruction set is maximized, so as to optimize the transmission efficiency and the device execution reliability of the power device.

[0200] In order to further optimize the utilization efficiency of computing resources, in some embodiments, as Figure 10 shown, the battery data processing method further includes the following steps:

[0201] S1002, count the model usage frequency of each pre-configured candidate decision analysis model, and determine the theoretical operating resources matching each model usage frequency.

[0202] Among them, each candidate decision analysis model is the decision analysis model respectively matched with each charge and discharge management function in the decision analysis platform. The model usage frequency of the candidate decision analysis model is the ratio of the number of times the candidate decision analysis model is used within the preset detection period to the total number of times all models are used.

[0203] Among them, the theoretical operating resources are the theoretical values of the computing resources required to maintain the efficient operation of the candidate decision analysis model at the corresponding model usage frequency.

[0204] In some embodiments, the decision analysis platform may count the total number of times each candidate decision analysis model is used within the preset detection period and the number of times each candidate decision analysis model is used respectively according to the call logs of each candidate decision analysis model, calculate the ratio of each usage number to the total number of times, obtain the model usage frequency of each candidate decision analysis model, and then determine the theoretical operating resources matching each model usage frequency according to each model usage frequency.

[0205] In some of these embodiments, designers can, in advance, based on experimental data or empirical data, for each candidate decision analysis model, determine the theoretical value of the computing resources required to maintain efficient operation at different model usage frequencies, and bind the candidate decision analysis model, each model usage frequency, and the corresponding theoretical values of the computing resources for each model usage frequency to obtain a preset mapping relationship between each model usage frequency and the corresponding theoretical values of the computing resources. When it is necessary to determine the theoretical operating resources of a candidate decision analysis model, the preset mapping relationship between each model usage frequency and the corresponding theoretical values of the computing resources corresponding to the candidate decision analysis model can be called, and the preset mapping relationship can be found based on the model usage frequency of the candidate decision analysis model within a preset detection period, and then the theoretical operating resources corresponding to the candidate decision analysis model can be determined.

[0206] In some of these embodiments, a theoretical operating resource calculation function is preconfigured in the decision analysis platform. For each candidate decision analysis model, the decision analysis platform can call the theoretical operating resource calculation function to calculate the theoretical operating resources that match the model usage frequency according to the model usage frequency of the candidate decision analysis model.

[0207] S1004. Obtain the actual operating resources of each candidate decision analysis model.

[0208] Among them, the actual operating resources are the total computing resources that the candidate decision analysis model can use at the current moment.

[0209] In some embodiments, the decision analysis platform can obtain the model configuration information of each candidate decision analysis model, and determine the actual operating resources of each candidate decision analysis model according to the model configuration information of each model.

[0210] S1006. For each candidate decision analysis model, determine the resource adjustment value of the candidate decision analysis model according to the theoretical operating resources and the actual operating resources of the candidate decision analysis model.

[0211] In some embodiments, for each candidate decision analysis model, the decision analysis platform can compare the theoretical operating resources of the candidate decision analysis model with the actual operating resources of the candidate decision analysis model, and determine the difference between the two as the resource adjustment value of the candidate decision analysis model.

[0212] S1008. Adjust the resource allocation of the candidate decision analysis model according to the resource adjustment value.

[0213] In some embodiments, after the decision analysis platform determines the resource adjustment value of the candidate decision analysis model, it can adjust the resource allocation of the candidate decision analysis model according to the resource adjustment value.

[0214] In some of these embodiments, if each candidate decision analysis model is configured on the decision analysis platform, the decision analysis platform can directly adjust the resource allocation of the candidate decision analysis models according to the resource adjustment value.

[0215] In other embodiments, if each candidate decision analysis model is stored separately from the decision analysis platform, that is, each candidate decision analysis model is stored on other remote servers and only transmits information to the decision analysis platform through a data transmission interface, the decision analysis platform can generate a model resource adjustment instruction for the candidate decision analysis model according to the resource adjustment value, send the model resource adjustment instruction to the storage server of the candidate decision analysis model, and only instruct the storage server to adjust the resource allocation of the candidate decision analysis model according to the resource adjustment value.

[0216] In the above embodiments, through the model usage frequency of each candidate decision analysis model, dynamic resource allocation is performed on each candidate decision analysis model, enabling high-frequency usage models to automatically expand their capacity and low-frequency usage models to release resources in a timely manner, thereby achieving the operational stability and efficiency of the candidate decision analysis models corresponding to the core business.

[0217] In some embodiments, a battery data processing method is provided. Taking the example where this method is applied to a battery data processing system as shown in Figure 2 For illustration, the power device in this case is a new energy vehicle. To more intuitively reflect the data transmission process of the battery data processing system, the battery data processing system can be understood as a three-level architecture as shown in Figure 11 including an intelligent decision-making layer, a data acquisition layer, and a dynamic execution layer.

[0218] Among them, the intelligent decision-making layer is a cloud service layer, composed of a decision analysis platform and a model repository, and is used to implement management decision analysis tasks. The intelligent decision-making layer can be connected to the terminal program to facilitate users to manually trigger the charge and discharge management function and can intuitively view the management decision analysis results through the terminal program.

[0219] The data acquisition layer can be composed of RDB services. The RDB services can communicate with the BMU through the CAN bus to collect battery pack parameters, such as single-cell voltage / temperature, total voltage, insulation resistance, etc. It can also collect environmental parameters, such as ambient temperature and humidity, altitude, GPS positioning, and vehicle status information, such as remaining cruising range, historical charge and discharge curves.

[0220] The dynamic execution layer consists of a BMU and an RDB docking module. The RDB docking module can send the received instruction signals to the BMU, and the BMU executes control tasks according to the preset algorithm according to the instruction signals. For example, when receiving a charging curve optimization instruction, the BMU can dynamically adjust the charging parameters by combining internal and external parameters to achieve safe and efficient charging regulation of the vehicle battery.

[0221] The data transmission in the above three-layer architecture is designed with a dual-channel separation mechanism, a timing guarantee mechanism, and a data secure transmission mechanism.

[0222] Among them, the dual-channel separation mechanism includes a downlink instruction channel and an uplink data channel. The downlink instruction channel can be understood as: cloud service layer → HTTP protocol → data acquisition layer → transparent transmission → dynamic execution layer. The uplink data channel can be understood as: dynamic execution layer → transparent transmission → data acquisition layer → Kafka → cloud service layer (cloud parsing service).

[0223] The timing guarantee mechanism includes using a time synchronization server (NTP) to achieve a time error of <1ms for each node, and a data packet carrying a four-segment timestamp, namely the BMU generation time, the RDB reception time, the Kafka write time, and the cloud processing time.

[0224] The data secure transmission medium includes key exchange security and end-to-end encryption (E2EE). Among them, key exchange security means using the ECDHE or DH algorithm to implement key exchange, so that even if the long-term key is leaked, historical communications are still confidential. End-to-end encryption refers to application layer encryption, such as using the AES-GCM algorithm for encryption, and the key is generated by both communication parties and cannot be decrypted by a third party.

[0225] At the same time, the data acquisition layer can also be constructed as a sharded RDB service cluster, using the device identifier hash value to allocate computing nodes, and setting a dual anti-duplication mechanism. Among them, the dual anti-duplication mechanism includes implementing a distributed transaction lock based on Redisson to lock the combination key of "device ID + information type + number". And, a preprocessing buffer is established to quickly filter duplicate requests through a Bloom filter.

[0226] As Figure 12 shown, the method specifically includes the following steps:

[0227] S1201, in response to the data analysis operations triggered by each vehicle for the same charge and discharge management function, obtain the device data reported by each vehicle through the data reporting channel.

[0228] S1202, perform data comparison on the device data of each vehicle, and determine the reference device data from the device data of each vehicle.

[0229] S1203. Determine the data difference information between each device data and the reference device data based on the reference device data.

[0230] S1204. Encode and compress the reference device data and each data difference information to obtain a composite data packet for the decision analysis model.

[0231] S1205. Invoke the pre-set data transmission interface of the decision analysis model that matches the charge and discharge management function, and input the composite data packet into the decision analysis model.

[0232] S1206. For each vehicle, receive the analysis result returned by the decision analysis model after performing management decision analysis on the vehicle.

[0233] S1207. Determine the device management information of the vehicle for the charge and discharge management function based on the analysis result.

[0234] S1208. Determine the timeliness requirement information that matches the function identifier of the charge and discharge management function.

[0235] S1209. Invoke the pre-set mapping relationship between each timeliness requirement and each information distribution channel, and determine the target information distribution channel that matches the timeliness requirement information.

[0236] Among them, the timeliness requirements include quasi-real-time, real-time, and batch.

[0237] S1210. Based on the charge and discharge management function, determine at least one instruction type required to carry the device management information.

[0238] S1211. Extract the instruction information according to the instruction type from the device management information to obtain the instruction information corresponding to the instruction type.

[0239] S1212. Use the encryption algorithm pre-set with the vehicle to encrypt and compress the instruction information to obtain the instruction to be issued corresponding to the instruction type.

[0240] S1213. When the number of instructions to be issued is multiple, calculate the total transmission capacity of each instruction to be issued according to the instruction transmission capacity of each instruction to be issued.

[0241] S1214. Determine whether the total transmission capacity is less than or equal to the preset capacity threshold. If so, execute S1215 - S1216; if not, execute S1217.

[0242] Among them, the preset capacity threshold can be 512KB.

[0243] S1215. Merge and compress each instruction to be issued to obtain a composite instruction.

[0244] S1216, use the target information distribution channel to send the composite instruction to the vehicle.

[0245] S1217, determine the instruction relevance of each instruction to be distributed according to the instruction type corresponding to each instruction to be distributed.

[0246] S1218, based on each instruction relevance and a preset capacity threshold, perform instruction partitioning on each instruction to be distributed to obtain at least two instruction sets.

[0247] S1219, for each instruction set, merge and compress the instructions to be distributed included in the instruction set to obtain a sub-composite instruction of the vehicle.

[0248] S1220, use the target information distribution channel to sequentially send each sub-composite instruction to the vehicle.

[0249] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same moment, but can be executed at different moments. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0250] Based on the same inventive concept, an embodiment of the present application also provides a battery data processing device for implementing the battery data processing method involved above. The implementation solution provided by this device to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the battery data processing device provided below can refer to the limitations on the battery data processing method in the above text, and will not be repeated here.

[0251] In some embodiments, as Figure 13 shown, a battery data processing device 1300 is provided, including: a response module 1301, a data analysis module 1302, and an information distribution module 1303, where:

[0252] The response module 1301 is configured to, in response to a data analysis operation triggered by a power device for at least one charge and discharge management function, obtain device data reported by the power device through a data reporting channel.

[0253] The data analysis module 1302 is configured to call a decision analysis model that matches the charge and discharge management function, perform management decision analysis on the power equipment based on the equipment data, and determine the equipment management information of the power equipment for the charge and discharge management function.

[0254] The information distribution module 1303 is configured to use an information distribution channel that matches the charge and discharge management function to send the equipment management information to the power equipment.

[0255] In some embodiments, the data analysis module 1302 is configured to: call a pre-set data transmission interface of the decision analysis model that matches the charge and discharge management function, input the equipment data into the decision analysis model; receive the analysis result returned by the decision analysis model after performing management decision analysis on the power equipment based on the equipment data; and determine the equipment management information of the power equipment for the charge and discharge management function based on the analysis result.

[0256] In some embodiments, the number of power equipment is multiple. The data analysis module 1302 is configured to: perform data comparison on the respective equipment data of each power equipment, and determine the reference equipment data from the respective equipment data; based on the reference equipment data, determine the respective data difference information between each equipment data and the reference equipment data; perform encoding and compression on the reference equipment data and each data difference information to obtain a composite data packet of the decision analysis model; and transmit the composite data packet to the decision analysis model.

[0257] In some embodiments, the information distribution module 1303 is configured to: determine the timeliness requirement information that matches the function identifier of the charge and discharge management function; call the mapping relationship between each timeliness requirement and each information distribution channel that is pre-set, and determine the target information distribution channel that matches the timeliness requirement information; and use the target information distribution channel to send the equipment management information to the power equipment.

[0258] In some embodiments, the information distribution module 1303 is configured to: based on the charge and discharge management function, determine at least one instruction type required to carry the equipment management information; perform instruction information extraction on the equipment management information according to the instruction type to obtain the instruction information corresponding to the instruction type; use an encryption algorithm pre-set with the power equipment to encrypt and compress the instruction information to obtain a to-be-transmitted instruction corresponding to the instruction type; and use the target information distribution channel to send the to-be-transmitted instruction to the power equipment.

[0259] In some embodiments, the number of to-be-transmitted instructions is multiple, and each to-be-transmitted instruction corresponds to each instruction type one by one. The information distribution module 1303 is configured to: calculate the total transmission capacity of each to-be-transmitted instruction according to the respective instruction transmission capacity of each to-be-transmitted instruction; in the case where the total transmission capacity is less than or equal to a preset capacity threshold, perform merging and compression on each to-be-transmitted instruction to obtain a composite instruction; and use the target information distribution channel to send the composite instruction to the power equipment.

[0260] In some embodiments, the information distribution module 1303 is further configured to: when the total transmission capacity is greater than a preset capacity threshold, determine the instruction relevance of each instruction to be distributed according to the instruction type corresponding to each instruction to be distributed; based on each instruction relevance and the preset capacity threshold, perform instruction division on each instruction to be distributed to obtain at least two instruction sets; for each instruction set, merge and compress the instructions to be distributed included in the instruction set to obtain a sub-compound instruction of the power device; and use the target information distribution channel to sequentially send each sub-compound instruction to the power device.

[0261] In some embodiments, the battery data processing device 1300 further includes:

[0262] A theoretical operation resource determination module, configured to count the model usage frequency of each pre-configured candidate decision analysis model and determine the theoretical operation resources matching each model usage frequency.

[0263] An actual operation resource acquisition module, configured to acquire the actual operation resources of each candidate decision analysis model.

[0264] A resource adjustment value determination module, configured to, for each candidate decision analysis model, determine the resource adjustment value of the candidate decision analysis model according to the theoretical operation resources and the actual operation resources of the candidate decision analysis model.

[0265] A resource division and adjustment module, configured to perform resource division and adjustment on the candidate decision analysis models according to the resource adjustment values.

[0266] Each module in the above battery data processing device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0267] In some embodiments, a computer device is provided. The computer device can be a server integrated with a decision analysis platform, and its internal structure diagram can be as Figure 14As shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as device data and device management information. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a battery data processing method.

[0268] Those skilled in the art can understand that Figure 14 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0269] In some embodiments, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements the specific steps of the above battery data processing method embodiment.

[0270] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the specific steps of the above battery data processing method embodiment.

[0271] In some embodiments, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it implements the specific steps of the above battery data processing method embodiment.

[0272] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. And the acquisition, storage, processing, transmission, etc. of the data all comply with the relevant regulations of laws and regulations.

[0273] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0274] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0275] The above-described embodiments merely represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A battery data processing method, characterized in that, The method includes: In response to a data analysis operation triggered by a power device for at least one charge and discharge management function, obtaining device data reported by the power device through a data reporting channel; Invoking a decision analysis model matching the charge and discharge management function, performing management decision analysis on the power device according to the device data, and determining device management information of the power device for the charge and discharge management function; Using an information distribution channel matching the charge and discharge management function to send the device management information to the power device.

2. The method according to claim 1, wherein The invoking a decision analysis model matching the charge and discharge management function, performing management decision analysis on the power device according to the device data, and determining device management information of the power device for the charge and discharge management function includes: Invoking a data transmission interface preset in the decision analysis model matching the charge and discharge management function, and inputting the device data into the decision analysis model; Receiving an analysis result returned by the decision analysis model after performing management decision analysis on the power device based on the device data; Based on the analysis result, determining device management information of the power device for the charge and discharge management function.

3. The method according to claim 2, wherein The number of the power devices is multiple, and the inputting the device data into the decision analysis model includes: Performing data comparison on the device data of each power device, and determining reference device data from the device data; Based on the reference device data, determining data difference information between each device data and the reference device data; Encoding and compressing the reference device data and each data difference information to obtain a composite data packet of the decision analysis model; Transmitting the composite data packet to the decision analysis model.

4. The method according to any one of claims 1 to 3, characterized in that, The using an information distribution channel matching the charge and discharge management function to send the device management information to the power device includes: Determining timeliness requirement information matching the function identifier of the charge and discharge management function; Invoking a mapping relationship between each timeliness requirement and each information distribution channel preset, and determining a target information distribution channel matching the timeliness requirement information; the timeliness requirement is positively correlated with the channel bandwidth of the information distribution channel; Using the target information distribution channel to send the device management information to the power device.

5. The method according to claim 4, characterized in that The using the target information distribution channel to send the device management information to the power device includes: Based on the charge and discharge management function, determining at least one instruction type required to carry the device management information; Performing instruction information extraction on the device management information according to the instruction type to obtain instruction information corresponding to the instruction type; Generating a to-be-distributed instruction corresponding to the instruction type based on the instruction information; Using the target information distribution channel to send the to-be-distributed instruction to the power device.

6. The method according to claim 5, characterized in that, The generating a to-be-distributed instruction corresponding to the instruction type based on the instruction information includes: Determining an encryption algorithm matching the power device and an encryption key matching the instruction type; Based on the encryption algorithm and the encryption key, encrypt and compress the instruction information to obtain the instruction to be sent corresponding to the instruction type.

7. The method according to claim 5, wherein The number of the instructions to be sent is multiple, and each of the instructions to be sent corresponds to each of the instruction types one by one; Sending the instruction to be sent to the power device by using the target information sending channel includes: Calculating the total transmission capacity of each of the instructions to be sent according to the instruction transmission capacity of each of the instructions to be sent; When the total transmission capacity is less than or equal to the preset capacity threshold, merging and compressing each of the instructions to be sent to obtain a composite instruction; Using the target information sending channel to send the composite instruction to the power device.

8. The method according to claim 7, wherein The method further includes: When the total transmission capacity is greater than the preset capacity threshold, determining the instruction relevance of each of the instructions to be sent according to the instruction type corresponding to each of the instructions to be sent; Based on each of the instruction relevances and the preset capacity threshold, performing instruction division on each of the instructions to be sent to obtain at least two instruction sets; For each of the instruction sets, merging and compressing each of the instructions to be sent included in the instruction set to obtain a sub-composite instruction of the power device; Using the target information sending channel to sequentially send each of the sub-composite instructions to the power device.

9. The method according to any one of claims 1 to 3, characterized in that The method further includes: Statistical the model usage frequency of each pre-configured candidate decision analysis model, and determining the theoretical operation resources matching each of the model usage frequencies; Obtaining the actual operation resources of each of the candidate decision analysis models; For each of the candidate decision analysis models, determining the resource adjustment value of the candidate decision analysis model according to the theoretical operation resources and the actual operation resources of the candidate decision analysis model; Performing resource division and adjustment on the candidate decision analysis model according to the resource adjustment value.

10. A battery data processing device, characterized in that, The device includes: A response module, configured to, in response to a data analysis operation triggered by a power device for at least one charge and discharge management function, obtain the device data reported by the power device through a data reporting channel; A data analysis module, configured to call a decision analysis model matching the charge and discharge management function, perform management decision analysis on the power device according to the device data, and determine device management information of the power device for the charge and discharge management function; An information sending module, configured to use an information sending channel matching the charge and discharge management function to send the device management information to the power device.

11. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 9 are implemented.

13. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 9 are implemented.

14. A battery data processing system, characterized in that, The system includes a decision analysis platform and at least one power device; a data reporting channel and an information sending channel are pre-constructed between the decision analysis platform and the power device; The power equipment reports device data to the decision-making and analysis platform through the data reporting channel; The decision-making and analysis platform issues device management information to the power equipment through the information distribution channel; The decision-making and analysis platform is used to implement the battery data processing method described in any one of claims 1 to 9.

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