A battery management method and energy storage system based on cloud-edge integration

Abstract

The present application discloses a battery management method and an energy storage system based on cloud-edge cooperation, belonging to the field of computer technology. Through the technical solution provided by the embodiments of the present application, a first edge computing device and a second edge computing device are determined from multiple edge computing devices, and the first edge computing device and the second edge computing device are used to obtain multiple reference temperature management strategies and multiple reference charge-discharge management strategies. Combining the battery pack parameter set of the target energy storage device, the multiple reference temperature management strategies, and the multiple reference charge-discharge management strategies, the target temperature management strategy and the target charge-discharge management strategy of the target energy storage device are determined. The target temperature management strategy and the target charge-discharge management strategy are used to manage the target energy storage device, thereby improving the operation stability of the target energy storage device and realizing the effective management of the target energy storage device.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a battery management method and an energy storage system based on cloud-edge integration. Background Art

[0002] With the continuous growth of energy demand and the wide application of renewable energy sources (such as solar energy, wind energy, etc.), energy storage devices are becoming increasingly important in the energy system. The battery pack in the energy storage device is a key component, and its performance, lifespan, and safety directly affect the efficiency of the entire energy storage system. Effective battery management is of great significance for ensuring the stable operation of the energy storage device, improving energy utilization efficiency, and ensuring equipment safety.

[0003] Therefore, how to effectively manage the energy storage system is a research hotspot. Summary of the Invention

[0004] Embodiments of this application provide a battery management method and an energy storage system based on cloud-edge integration, which can effectively manage the energy storage system. The technical solutions are as follows:

[0005] On the one hand, a battery management method based on cloud-edge integration is provided, which is executed by a server. The method includes:

[0006] Determine a first edge computing device and a second edge computing device from multiple edge computing devices. The battery management credibility of both the first edge computing device and the second edge computing device is higher than the credibility threshold. The first edge computing device is used to manage the temperature of the battery pack of the energy storage device, and the second edge computing device is used to manage the charge and discharge power of the battery pack;

[0007] Obtain a battery pack parameter set of multiple battery packs of a target energy storage device and send the battery pack parameter set to the first edge computing device and the second edge computing device, so that the first edge computing device determines multiple reference temperature management strategies for the target energy storage device based on the battery pack parameter set, and the second edge computing device determines multiple reference charge and discharge management strategies for the target energy storage device based on the battery pack parameter set. The battery pack parameter set includes the battery pack parameters of each battery pack, and the battery pack parameters include operating parameters and attribute parameters;

[0008] Obtain multiple reference temperature management strategies returned by the first edge computing device and multiple reference charge and discharge management strategies returned by the second edge computing device;

[0009] Based on the battery pack parameter set, the multiple reference temperature management strategies, and the multiple reference charge and discharge management strategies, determine the target temperature management strategy and the target charge and discharge management strategy of the target energy storage device, and manage the target energy storage device based on the target temperature management strategy and the target charge and discharge management strategy.

[0010] On the one hand, an energy storage system is provided, and the system includes:

[0011] A device determination module, configured to determine a first edge computing device and a second edge computing device from multiple edge computing devices, where the battery management credibility of the first edge computing device and the second edge computing device is higher than a credibility threshold, the first edge computing device is configured to manage the temperature of the battery pack of the energy storage device, and the second edge computing device is configured to manage the charge and discharge power of the battery pack;

[0012] A parameter acquisition module, configured to acquire a battery pack parameter set of multiple battery packs of a target energy storage device and send the battery pack parameter set to the first edge computing device and the second edge computing device, so that the first edge computing device determines multiple reference temperature management strategies of the target energy storage device based on the battery pack parameter set, and the second edge computing device determines multiple reference charge and discharge management strategies of the target energy storage device based on the battery pack parameter set, the battery pack parameter set includes battery pack parameters of each battery pack, and the battery pack parameters include working parameters and attribute parameters;

[0013] A strategy acquisition module, configured to acquire the multiple reference temperature management strategies returned by the first edge computing device and the multiple reference charge and discharge management strategies returned by the second edge computing device;

[0014] A management module, configured to determine the target temperature management strategy and the target charge and discharge management strategy of the target energy storage device based on the battery pack parameter set, the multiple reference temperature management strategies, and the multiple reference charge and discharge management strategies, and manage the target energy storage device based on the target temperature management strategy and the target charge and discharge management strategy.

[0015] In a possible implementation manner, the device determination module is configured to obtain a set of device screening parameters of the multiple edge computing devices, where the set of device screening parameters includes the historical device management policies of each of the edge computing devices, the set of historical battery pack parameters corresponding to the historical device management policies, and historical management evaluation data; based on the set of device screening parameters, determine multiple candidate edge computing devices from the multiple edge computing devices, where the battery management credibility of the multiple candidate edge computing devices is higher than the credibility threshold; and determine the first edge computing device and the second edge computing device from the multiple candidate edge computing devices.

[0016] In a possible implementation manner, the device determination module is configured to determine a first management credibility and a second management credibility of each of the edge computing devices based on the set of device screening parameters, where the first management credibility is determined based on the historical management evaluation data, and the second management credibility is determined based on the historical device management policy and the corresponding set of historical battery pack parameters; determine the battery management credibility of each of the edge computing devices based on the first management credibility and the second management credibility of each of the edge computing devices; and determine the edge computing devices with a battery management credibility greater than or equal to the credibility threshold among the multiple edge computing devices as candidate edge computing devices, so as to obtain the multiple candidate edge computing devices.

[0017] In a possible implementation manner, the device determination module is configured to determine the historical temperature management evaluation data and the historical charge and discharge management evaluation data of each of the candidate edge computing devices from the historical management evaluation data of the multiple candidate edge computing devices; divide the multiple candidate edge computing devices into a first device group and a second device group based on the historical temperature management evaluation data and the historical charge and discharge management evaluation data of each of the candidate edge computing devices, where the edge computing devices in the first device group are the edge computing devices with good temperature management evaluation effects indicated by the temperature management evaluation data, and the edge computing devices in the second device group are the edge computing devices with good charge and discharge management evaluation effects indicated by the charge and discharge management evaluation data; and determine the first edge computing device from the first device group and determine the second edge computing device from the second device group based on the historical device management policy of each of the edge computing devices and the set of historical battery pack parameters corresponding to the historical device management policy.

[0018] In a possible implementation manner, the method by which the first edge computing device determines multiple reference temperature management policies of the target energy storage device based on the set of battery pack parameters includes:

[0019] The first edge computing device obtains the battery pack parameters of each of the battery packs from the battery pack parameter set; the first edge computing device determines a plurality of first initial management policies based on the operating parameters in the battery pack parameters of each of the battery packs; the first edge computing device determines a plurality of first reference management policies from the plurality of first initial management policies based on the attribute parameters in the battery pack parameters of each of the battery packs, where the first reference management policy is the first initial management policy that matches the attribute parameter; the first edge computing device determines the temperature management policy among the plurality of first reference management policies as the plurality of reference temperature management policies;

[0020] The method for the second edge computing device to determine a plurality of reference charge-discharge management policies of the target energy storage device based on the battery pack parameter set includes:

[0021] The second edge computing device obtains the battery pack parameters of each of the battery packs from the battery pack parameter set; the second edge computing device determines a plurality of second initial management policies based on the operating parameters in the battery pack parameters of each of the battery packs; the second edge computing device determines a plurality of second reference management policies from the plurality of second initial management policies based on the attribute parameters in the battery pack parameters of each of the battery packs, where the second reference management policy is the second initial management policy that matches the attribute parameter; the second edge computing device determines the charge-discharge management policy among the plurality of second reference management policies as the plurality of reference charge-discharge management policies.

[0022] In a possible implementation manner, the first edge computing device determines a plurality of first initial management policies based on the operating parameters in the battery pack parameters of each of the battery packs, including:

[0023] The first edge computing device determines the first battery pack management policy of each of the battery packs and the first energy storage device management policy of the target energy storage device based on the operating parameters of each of the battery packs; the first edge computing device determines the plurality of first initial management policies based on the first battery pack management policy of each of the battery packs and the first energy storage device management policy of the target energy storage device; the second edge computing device determines a plurality of second initial management policies based on the operating parameters in the battery pack parameters of each of the battery packs, including: the second edge computing device determines the second battery pack management policy of each of the battery packs and the second energy storage device management policy of the target energy storage device based on the operating parameters of each of the battery packs; the second edge computing device determines the plurality of second initial management policies based on the second battery pack management policy of each of the battery packs and the second energy storage device management policy of the target energy storage device.

[0024] In a possible implementation, the management module is configured to determine a first estimated effect parameter, a second estimated effect parameter, and a first risk parameter for each of the reference temperature management policies based on the battery pack parameter set and the multiple reference temperature management policies. The first estimated effect parameter is used to represent the temperature management effect of the target energy storage device after executing the corresponding reference temperature management policy, and the second estimated effect parameter is used to represent the temperature management effect of each battery pack after executing the corresponding reference temperature management policy. Based on the battery pack parameter set and the multiple reference charge and discharge management policies, determine a third estimated effect parameter, a fourth estimated effect parameter, and a second risk parameter for each of the reference charge and discharge management policies. The third estimated effect parameter is used to represent the charge and discharge management effect of the target energy storage device after executing the corresponding reference charge and discharge management policy, and the fourth estimated effect parameter is used to represent the charge and discharge management effect of each battery pack after executing the corresponding reference charge and discharge management policy. Based on the first estimated effect parameter, the second estimated effect parameter, and the first risk parameter of each of the reference temperature management policies, determine the target temperature management policy from the multiple reference temperature management policies. Based on the third estimated effect parameter, the fourth estimated effect parameter, and the second risk parameter of each of the reference charge and discharge management policies, determine the target charge and discharge management policy from the multiple reference charge and discharge management policies.

[0025] In a possible implementation, the operating parameters include operating temperature, operating current, operating voltage, remaining power, number of cycles, and charge and discharge power. The attribute parameters include electrolyte type, electrode type, and battery pack connection relationship. The management module is configured to determine the first estimated effect parameter for each of the reference temperature management policies based on the operating temperature, electrolyte type, electrode type, battery pack connection relationship of each battery pack in the battery pack parameter set and the multiple reference temperature management policies. Based on the operating temperature, operating current, operating voltage, remaining power, number of cycles, charge and discharge power of each battery pack in the battery pack parameter set and the multiple reference temperature management policies, determine the second estimated effect parameter for each of the reference temperature management policies. Based on the operating temperature, operating current, operating voltage, charge and discharge power, electrolyte type, electrode type of each battery pack in the battery pack parameter set and the multiple reference temperature management policies, determine the first risk parameter for each of the reference temperature management policies.

[0026] The management module is configured to determine a third estimated effect parameter of each of the reference charge-discharge management strategies based on the charge-discharge power, electrolyte type, electrode type, battery pack connection relationship of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies; determine a fourth estimated effect parameter of each of the reference charge-discharge management strategies based on the operating temperature, operating current, operating voltage, charge-discharge power of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies; and determine a second risk parameter of each of the reference charge-discharge management strategies based on the operating temperature, operating current, operating voltage, electrolyte type, electrode type of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies.

[0027] In a possible implementation manner, the management module is configured to fuse the first estimated effect parameter and the second estimated effect parameter of each of the reference temperature management strategies to obtain a first target estimated effect parameter of each of the reference temperature management strategies; fuse the first target estimated effect parameter of each of the reference temperature management strategies with the first risk parameter of each of the reference temperature management strategies to obtain a first target strategy evaluation score of each of the reference temperature management strategies; and determine the reference temperature management strategy with the highest first target strategy evaluation score among the multiple reference temperature management strategies as the target temperature management strategy.

[0028] The management module is configured to fuse the third estimated effect parameter and the fourth estimated effect parameter of each of the reference charge-discharge management strategies to obtain a second target estimated effect parameter of each of the reference charge-discharge management strategies; fuse the second target estimated effect parameter of each of the reference charge-discharge management strategies with the second risk parameter of each of the reference charge-discharge management strategies to obtain a second target strategy evaluation score of each of the reference charge-discharge management strategies; and determine the reference charge-discharge management strategy with the highest second target strategy evaluation score among the multiple reference charge-discharge management strategies as the target charge-discharge management strategy.

[0029] On the one hand, a server is provided. The server includes one or more processors and one or more memories. At least one computer program is stored in the one or more memories, and the computer program is loaded and executed by the one or more processors to implement the cloud-edge combined battery management method.

[0030] On the one hand, a computer-readable storage medium is provided. At least one computer program is stored in the computer-readable storage medium, and the computer program is loaded and executed by a processor to implement the cloud-edge combined battery management method.

[0031] On the one hand, a computer program product or a computer program is provided. The computer program product or the computer program includes program code, which is stored in a computer-readable storage medium. The processor of the server reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the server executes the above battery management method based on cloud-edge combination.

[0032] Through the technical solution provided by the embodiments of the present application, the first edge computing device and the second edge computing device are determined from multiple edge computing devices, and the first edge computing device and the second edge computing device are used to obtain multiple reference temperature management strategies and multiple reference charge-discharge management strategies. Combining the battery pack parameter set of the target energy storage device, the multiple reference temperature management strategies, and the multiple reference charge-discharge management strategies, the target temperature management strategy and the target charge-discharge management strategy of the target energy storage device are determined. The target energy storage device is managed by using the target temperature management strategy and the target charge-discharge management strategy, so as to improve the operation stability of the target energy storage device and realize the effective management of the target energy storage device. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] Figure 1 It is a schematic diagram of the implementation environment of a battery management method based on cloud-edge combination provided by the embodiments of the present application;

[0035] Figure 2 It is a flowchart of a battery management method based on cloud-edge combination provided by the embodiments of the present application;

[0036] Figure 3 It is a flowchart of another battery management method based on cloud-edge combination provided by the embodiments of the present application;

[0037] Figure 4 It is a schematic diagram of the structure of an energy storage system provided by the embodiments of the present application;

[0038] Figure 5 It is a schematic diagram of the structure of a server provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the drawings.

[0040] In this application, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and effects. It should be understood that there is no logical or temporal dependency among "first", "second", and "nth", nor are the quantity and execution order limited.

[0041] Energy storage device: An energy storage device is a device that can store energy and release it when needed. It plays a crucial role in energy management and utilization. Especially in the application of renewable energy, energy storage devices can address the temporal or local differences between energy supply and demand.

[0042] Cloud-edge collaboration: Cloud-edge collaboration refers to the combination of cloud computing and edge computing. By performing data processing and analysis between the cloud and edge devices, more efficient and intelligent computing services can be achieved. This combination can give full play to the powerful computing capabilities of cloud computing and the low-latency and high-bandwidth advantages of edge computing, providing users with a better service experience.

[0043] Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain better results.

[0044] Machine Learning (ML) is an interdisciplinary field that involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge sub-models to continuously improve their own performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications cover all fields of artificial intelligence. Machine learning and deep learning usually include technologies such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and learning from demonstration.

[0045] Normalization: Mapping a sequence of numbers with different value ranges to the interval (0, 1) to facilitate data processing. In some cases, the normalized values can be directly implemented as probabilities.

[0046] Embedded Coding: Embedded coding mathematically represents a correspondence relationship, that is, data in the X space is mapped to the Y space through a function F, where the function F is an injective function, and the result of the mapping is structure preservation. The injective function means that the data after mapping corresponds uniquely to the data before mapping, and structure preservation means that the size relationship of the data before mapping is the same as that of the data after mapping. For example, there are data X1 and X2 before mapping, and Y1 corresponding to X1 and Y2 corresponding to X2 are obtained after mapping. If the data X1 > X2 before mapping, then correspondingly, the data Y1 after mapping is greater than Y2. For words, it is to map the words to another space to facilitate subsequent machine learning and processing.

[0047] Attention weight: It can represent the importance of a certain data during the training or prediction process. Importance represents the magnitude of the influence of the input data on the output data. Data with high importance has a higher corresponding attention weight value, and data with low importance has a lower corresponding attention weight value. In different scenarios, the importance of data is not the same, and the process of training the attention weight of the model is also the process of determining the importance of data.

[0048] After introducing some terms related to the embodiments of the present application, the application scenarios of the embodiments of the present application will be introduced below. Figure 1 It is a schematic diagram of the implementation environment of a thermal management strategy optimization method for an energy storage device provided by the embodiments of the present application. Refer to Figure 1 The technical solution provided by the embodiments of the present application can be applied in the energy storage device 100, and the energy storage device 100 includes:

[0049] Battery 110: As a key technical route for new energy storage, the battery plays a crucial role in improving the utilization rate of renewable energy and ensuring the safe and stable operation of the power system. Lithium batteries are the most commonly used energy storage batteries in the market at present and are widely recognized for their high efficiency and long life.

[0050] Thermal management system 120: By adjusting the temperature of the energy storage system, it ensures that it operates in the best temperature environment, extends the service life of the battery, and improves the overall performance of the system. Bidirectional energy storage converter (PCS, Power Conversion System) 130: It can convert the alternating current of the power grid into the direct current required by the battery, and at the same time, it can also convert the direct current stored in the battery into alternating current for the power grid to use.

[0051] Energy management system (EMS, Energy Management System) 140: It is responsible for collecting, processing, and analyzing the data of each part of the energy storage system to ensure the safe and efficient operation of the energy storage system.

[0052] Battery Management System (BMS) 150: Its main function is to improve the utilization efficiency of the battery, prevent overcharging and discharging of the battery, and comprehensively ensure the safe operation of the energy storage system.

[0053] After introducing the implementation environment of the embodiments of this application, the battery management method based on cloud-edge collaboration provided by the embodiments of this application will be described below. Figure 2 It is a flowchart of a battery management method based on cloud-edge collaboration provided by an embodiment of this application. Refer to Figure 2 Taking the server of the energy storage system as the execution subject as an example, the method includes the following steps.

[0054] 201. The server determines a first edge computing device and a second edge computing device from multiple edge computing devices.

[0055] Among them, the battery management credibility of the first edge computing device and the second edge computing device is higher than the credibility threshold. The first edge computing device is used to manage the temperature of the battery pack of the energy storage device, and the second edge computing device is used to manage the charge and discharge power of the battery pack. The credibility threshold is set by technicians according to the actual situation, and this application does not limit it. The first edge computing device and the second edge computing device both belong to the edge computing device cluster. Different edge computing devices in the edge computing device cluster are deployed in different regions and are used to directly control different energy storage devices. The above-mentioned obtaining of the first edge computing device refers to screening out the first edge computing device for temperature management of the battery pack from multiple edge computing devices. Correspondingly, the above-mentioned obtaining of the second edge computing device refers to screening out the second edge computing device for charge and discharge power management of the battery pack from multiple edge computing devices. In the embodiments of this application, "cloud" in cloud-edge collaboration refers to the server, and "edge" refers to edge computing devices.

[0056] 202. The server obtains the battery pack parameter sets of multiple battery packs of the target energy storage device and sends the battery pack parameter sets to the first edge computing device and the second edge computing device, so that the first edge computing device determines multiple reference temperature management strategies for the target energy storage device based on the battery pack parameter sets, and the second edge computing device determines multiple reference charge and discharge management strategies for the target energy storage device based on the battery pack parameter sets.

[0057] Among them, the battery pack parameter set includes the battery pack parameters of each battery pack, and the battery pack parameters include operating parameters and attribute parameters. The multiple reference temperature management strategies are the strategies for the first edge computing device to manage the temperature of the target energy storage device. Managing the temperature of the target energy storage device means controlling the heat dissipation component of the target energy storage device according to the operating conditions of the target energy storage device, so that the temperature of the target energy storage device can be kept stable, thereby improving the stability of the target energy storage device. The temperature management strategy provides temperature control methods under different conditions. The multiple reference charge-discharge management strategies are the strategies for the second edge computing device to manage the charge-discharge power of the target energy storage device. Managing the charge-discharge power of the target energy storage device means controlling the energy storage component of the target energy storage device according to the operating conditions of the target energy storage device, so that the energy storage component of the target energy storage device can be kept stable, thereby improving the stability of the target energy storage device. The charge-discharge management strategy provides charge-discharge power control methods under different conditions.

[0058] 203. The server obtains the multiple reference temperature management strategies returned by the first edge computing device and the multiple reference charge-discharge management strategies returned by the second edge computing device.

[0059] 204. The server determines the target temperature management strategy and the target charge-discharge management strategy of the target energy storage device based on the battery pack parameter set, the multiple reference temperature management strategies, and the multiple reference charge-discharge management strategies.

[0060] Among them, the target temperature management strategy refers to the temperature management strategy finally used to manage the temperature of the target energy storage device, and the target charge-discharge management strategy refers to the charge-discharge management strategy finally used to manage the charge-discharge of the target energy storage device.

[0061] 205. The server manages the target energy storage device based on the target temperature management strategy and the target charge-discharge management strategy.

[0062] Through the technical solution provided by the embodiments of the present application, the first edge computing device and the second edge computing device are determined from multiple edge computing devices, and the multiple reference temperature management strategies and the multiple reference charge-discharge management strategies are obtained by using the first edge computing device and the second edge computing device. Combining the battery pack parameter set of the target energy storage device, the multiple reference temperature management strategies, and the multiple reference charge-discharge management strategies, the target temperature management strategy and the target charge-discharge management strategy of the target energy storage device are determined. The target energy storage device is managed by using the target temperature management strategy and the target charge-discharge management strategy, thereby improving the operating stability of the target energy storage device and realizing the effective management of the target energy storage device.

[0063] The above steps 201-205 are a brief introduction to the cloud-edge integrated battery management method provided by the embodiments of this application. Below, some examples will be combined to more clearly illustrate the cloud-edge integrated battery management method provided by the embodiments of this application. Refer to Figure 3 , taking the execution entity as the server as an example, the method includes the following steps.

[0064] 301. The server determines a plurality of edge computing devices.

[0065] Among them, the plurality of edge computing devices belong to the selected edge computing device cluster. Different edge computing devices in the edge computing device cluster are deployed in different regions and are used to directly control different energy storage devices. In the embodiments of this application, "cloud" in cloud-edge integration refers to the server, and "edge" refers to the edge computing device.

[0066] In a possible implementation manner, the server determines a plurality of initial edge computing devices, and the plurality of initial edge computing devices are currently online edge computing devices. The server determines the plurality of edge computing devices from the plurality of initial edge computing devices, and the plurality of edge computing devices are initial edge computing devices whose activity level is greater than or equal to the activity level threshold.

[0067] Among them, the activity level of the edge computing device is used to represent at least one of the online duration, the number of invocations, and the energy storage device management duration of the edge computing device. The activity level threshold is set by those skilled in the art according to the actual situation, and the embodiments of this application do not limit this.

[0068] For example, the server determines a plurality of initial edge computing devices that are currently online. The server obtains at least one of the online duration, the number of invocations, and the energy storage device management duration of each initial edge computing device. The server determines the activity level of each initial edge computing device based on at least one of the online duration, the number of invocations, and the energy storage device management duration of each initial edge computing device. The server determines the initial edge computing devices whose activity level is greater than or equal to the activity level threshold among the plurality of initial edge computing devices as the edge computing devices to obtain the plurality of edge computing devices.

[0069] 302. The server determines a first edge computing device and a second edge computing device from the plurality of edge computing devices.

[0070] Among them, the battery management credibility of the first edge computing device and the second edge computing device is higher than the credibility threshold. The first edge computing device is used to manage the temperature of the battery pack of the energy storage device, and the second edge computing device is used to manage the charging and discharging power of the battery pack. The credibility threshold is set by technicians according to the actual situation, and this application embodiment does not limit it. The first edge computing device and the second edge computing device are different edge computing devices, and both the first edge computing device and the second edge computing device belong to the edge computing device cluster. The above-mentioned obtaining of the first edge computing device refers to screening out the first edge computing device for temperature management of the battery pack from multiple edge computing devices. Correspondingly, the above-mentioned obtaining of the second edge computing device refers to screening out the second edge computing device for charging and discharging power management of the battery pack from multiple edge computing devices.

[0071] In a possible implementation manner, the server obtains a device screening parameter set of the multiple edge computing devices. The device screening parameter set includes the historical device management strategies of each edge computing device, the historical battery pack parameter set corresponding to the historical device management strategies, and the historical management evaluation data. The server determines multiple candidate edge computing devices from the multiple edge computing devices based on the device screening parameter set. The battery management credibility of the multiple candidate edge computing devices is higher than the credibility threshold. The server determines the first edge computing device and the second edge computing device from the multiple candidate edge computing devices.

[0072] Among them, the historical device management strategy includes the strategy when the edge computing device historically manages the energy storage device. The historical battery pack parameter set refers to the battery pack parameter set of the energy storage device when the corresponding historical device management strategy is used to manage the energy storage device. The management evaluation data is used to represent the evaluation of the historical device management strategy by the management personnel or downstream users after using the historical device management strategy.

[0073] To illustrate the above implementation manner more clearly, the above implementation manner will be described in several parts below.

[0074] The first part: The server obtains a device screening parameter set of the multiple edge computing devices. The device screening parameter set includes the historical device management strategies of each edge computing device, the historical battery pack parameter set corresponding to the historical device management strategies, and the historical management evaluation data.

[0075] In a possible implementation manner, the server sends a parameter acquisition request to the multiple edge computing devices. The parameter acquisition request is used to request the acquisition of the device screening parameter set of the multiple edge computing devices. The server obtains the device screening parameter set returned by the multiple edge computing devices.

[0076] Second part: The server determines a plurality of candidate edge computing devices from the plurality of edge computing devices based on the device screening parameter set.

[0077] In a possible implementation manner, the server determines the first management credibility and the second management credibility of each edge computing device based on the device screening parameter set. The first management credibility is determined based on historical management evaluation data, and the second management credibility is determined based on historical device management policies and the corresponding historical battery pack parameter set. The server determines the battery management credibility of each edge computing device based on the first management credibility and the second management credibility of each edge computing device. The server determines the edge computing devices with battery management credibility greater than or equal to the credibility threshold among the plurality of edge computing devices as candidate edge computing devices, and obtains the plurality of candidate edge computing devices.

[0078] For example, the server inputs the historical management evaluation data of each edge computing device into the first credibility determination model, extracts features from the historical management evaluation data of each edge computing device through the first credibility determination model, and obtains the management evaluation features of each edge computing device. The server maps the management evaluation features of each edge computing device through the first credibility determination model to obtain the first management credibility of each edge computing device. The server inputs the historical device management policies and the corresponding historical battery pack parameter set of each edge computing device into the second credibility determination model, extracts features from the historical device management policies and the corresponding historical battery pack parameter set of each edge computing device through the second credibility determination model, and obtains the historical device management policy features and the historical battery pack parameter set features of each edge computing device. The server determines the matching degree between the historical device management policies and the corresponding historical battery pack parameter set of each edge computing device based on the historical device management policy features and the historical battery pack parameter set features of each edge computing device through the second credibility determination model, and determines the matching degree as the second management credibility of each edge computing device. The server performs weighted fusion on the first management credibility and the second management credibility of each edge computing device to obtain the battery management credibility of each edge computing device. The server determines the edge computing devices with battery management credibility greater than or equal to the credibility threshold among the plurality of edge computing devices as candidate edge computing devices, and obtains the plurality of candidate edge computing devices.

[0079] Among them, the weights for performing weighted fusion on the first management credibility and the second management credibility are provided by each edge computing device, and the embodiments of the present application do not limit this. The first credibility determination model is a regression model, and the second credibility determination model is a matching model. The embodiments of the present application do not limit the types and results of the first credibility determination model and the second credibility determination model.

[0080] Part III. The server determines the first edge computing device and the second edge computing device from the multiple candidate edge computing devices.

[0081] In a possible implementation, the server determines the historical temperature management evaluation data and the historical charge and discharge management evaluation data of each candidate edge computing device from the historical management evaluation data of the multiple candidate edge computing devices. Based on the historical temperature management evaluation data and the historical charge and discharge management evaluation data of each candidate edge computing device, the server divides the multiple candidate edge computing devices into a first device group and a second device group. The edge computing devices in the first device group are those with good temperature management evaluation effects indicated by the temperature management evaluation data, and the edge computing devices in the second device group are those with good charge and discharge management evaluation effects indicated by the charge and discharge management evaluation data. The server determines the first edge computing device from the first device group and the second edge computing device from the second device group based on the historical device management strategies of each edge computing device and the historical battery pack parameter sets corresponding to the historical device management strategies.

[0082] Among them, the historical management evaluation data includes data for evaluating the management of multiple dimensions of the edge computing device. For example, in the embodiments of the present application, the historical management evaluation data at least includes the historical temperature management evaluation data and the historical charge and discharge management evaluation data. The historical temperature management evaluation data is used to represent the evaluation of the management temperature of the edge computing device, and the historical charge and discharge management evaluation parameter is used to represent the evaluation of the management charge and discharge power of the edge computing device.

[0083] For example, the server determines the historical temperature management evaluation data and the historical charge and discharge management evaluation data of each candidate edge computing device from the historical management evaluation data of the multiple candidate edge computing devices. Based on the historical temperature management evaluation data and the historical charge and discharge management evaluation data of each candidate edge computing device, the server determines the historical temperature management score and the historical charge and discharge management score of each candidate edge computing device. The historical temperature management score is positively correlated with the temperature management evaluation effect, and the historical charge and discharge management score is positively correlated with the charge and discharge management evaluation effect. The server divides the top N candidate edge computing devices with the highest historical temperature management scores among the multiple candidate edge computing devices into the first device group, and divides the top M candidate edge computing devices with the highest historical charge and discharge management scores among the multiple candidate edge computing devices into the second device group, where N and M are both positive integers. The server determines the comprehensive management evaluation score of each edge computing device based on the historical device management strategy of each edge computing device and the historical battery pack parameter set corresponding to the historical device management strategy. The comprehensive management score is positively correlated with the comprehensive management evaluation effect. The server fuses the historical temperature management score and the comprehensive management evaluation score of the N candidate edge computing devices in the first device group to obtain the first device screening score of the N candidate edge computing devices in the first device group. The server determines the candidate edge computing device with the highest first device screening score among the N candidate edge computing devices as the first edge computing device. The server fuses the historical charge and discharge management score and the comprehensive management evaluation score of the M candidate edge computing devices in the second device group to obtain the second device screening score of the M candidate edge computing devices in the second device group. The server determines the candidate edge computing device with the highest second device screening score among the M candidate edge computing devices as the second edge computing device.

[0084] Among them, the method by which the above server determines the comprehensive management evaluation score of each edge computing device based on the historical device management strategy of each edge computing device and the historical battery pack parameter set corresponding to the historical device management strategy is actually to determine the matching degree between the historical device management strategy of each edge computing device and the historical battery pack parameter set corresponding to the historical device management strategy, map the matching degree to the comprehensive management evaluation score, and the method of determining the matching degree belongs to the same inventive concept as the description in the second part above. For the implementation process, refer to the relevant description in the second part above and will not be elaborated here.

[0085] 303. The server obtains the battery pack parameter sets of multiple battery packs of the target energy storage device and sends the battery pack parameter sets to the first edge computing device and the second edge computing device, so that the first edge computing device determines multiple reference temperature management strategies for the target energy storage device based on the battery pack parameter sets, and the second edge computing device determines multiple reference charge and discharge management strategies for the target energy storage device based on the battery pack parameter sets.

[0086] Among them, the target energy storage device is the energy storage device to be managed, and the target energy storage device includes multiple battery packs. The battery pack parameter set includes the battery pack parameters of each battery pack, and the battery pack parameters include operating parameters and attribute parameters. The multiple reference temperature management strategies are the strategies for the first edge computing device to manage the temperature of the target energy storage device. Managing the temperature of the target energy storage device means controlling the heat dissipation components of the target energy storage device according to the operating conditions of the target energy storage device, so that the temperature of the target energy storage device can be kept stable, thereby improving the stability of the target energy storage device. The temperature management strategy provides temperature control methods in different situations. The multiple reference charge and discharge management strategies are the strategies for the second edge computing device to manage the charge and discharge power of the target energy storage device. Managing the charge and discharge power of the target energy storage device means controlling the energy storage components of the target energy storage device according to the operating conditions of the target energy storage device, so that the energy storage components of the target energy storage device can be kept stable, thereby improving the stability of the target energy storage device. The charge and discharge management strategy provides charge and discharge power control methods in different situations.

[0087] To illustrate step 303 more clearly, the following describes the manner in which the first edge computing device determines multiple reference temperature management strategies for the target energy storage device in step 303.

[0088] In a possible implementation manner, the first edge computing device obtains the battery pack parameters of each battery pack from the battery pack parameter set. The first edge computing device determines multiple first initial management strategies based on the operating parameters in the battery pack parameters of each battery pack. The first edge computing device determines multiple first reference management strategies from the multiple first initial management strategies based on the attribute parameters in the battery pack parameters of each battery pack. The first reference management strategy is the first initial management strategy that matches the attribute parameters. The first edge computing device determines the temperature management strategies among the multiple first reference management strategies as the multiple reference temperature management strategies.

[0089] To illustrate the above implementation manner more clearly, the following will illustrate the above implementation manner in several parts.

[0090] Part 1: The first edge computing device obtains the battery pack parameters of each battery pack from the battery pack parameter set.

[0091] In a possible implementation, the first edge computing device unpacks the battery pack parameter set to obtain the battery pack parameters of each battery pack.

[0092] Part 2: The first edge computing device determines a plurality of first initial management strategies based on the operating parameters in the battery pack parameters of each battery pack.

[0093] In a possible implementation, the first edge computing device determines the first battery pack management strategy for each battery pack and the first energy storage device management strategy for the target energy storage device based on the operating parameters of each battery pack. The first edge computing device determines the plurality of first initial management strategies based on the first battery pack management strategies of each battery pack and the first energy storage device management strategy of the target energy storage device.

[0094] Among them, the first battery pack management strategy refers to the strategy for managing the battery pack, and the first energy storage device management strategy refers to the overall management strategy for the target energy storage device. In the embodiments of the present application, the first initial management strategy is a combined management strategy, that is, it includes both the strategy for managing the battery pack and the overall management strategy for the target energy storage device. In some embodiments, the operating parameters include operating temperature, operating current, operating voltage, remaining power, number of cycles, and charge and discharge power.

[0095] For example, for any one of the multiple battery packs of the target energy storage device, the first edge computing device inputs the operating parameters of the battery pack into the first local management strategy determination model, extracts features from the operating parameters of the battery pack through the first local management strategy determination model to obtain the first battery pack operating characteristics of the battery pack. The first edge computing device performs multiple rounds of iterative decoding based on the first battery pack operating characteristics of the battery pack through the first local management strategy determination model to obtain the first battery pack management strategy of the battery pack. The first edge computing device inputs the operating parameters of the battery pack into the first global management strategy determination model, extracts features from the operating parameters of each battery pack through the first global management strategy determination model to obtain the first energy storage device operating characteristics of the target energy storage device. The first edge computing device performs multiple rounds of iterative decoding based on the first energy storage device operating characteristics of the target energy storage device through the first global management strategy determination model to obtain the first energy storage device management strategy of the target energy storage device. The first edge computing device performs strategy regeneration based on the first battery pack management strategies of the multiple battery packs and the first energy storage device management strategy of the target energy storage device to obtain the plurality of first initial management strategies.

[0096] Among them, both the first local management policy determination model and the first global management policy determination model are models with policy generation capabilities. From another perspective, the first local management policy determination model and the first global management policy determination model are sequence (feature) to sequence (policy) generative models. In the embodiments of the present application, both the first local management policy determination model and the first global management policy determination model are models trained based on the BERT model. The encoder of the BERT model is used in the feature extraction process, and the decoder of the BERT model is used in the process of decoding the features. In addition, the above policy regeneration can be achieved through a first large language model that has been pre-trained and fine-tuned. That is, the first edge computing device inputs the first battery pack management policy of the multiple battery packs, the first energy storage device management policy of the target energy storage device, and the first prompt text template into the first large language model, and performs policy regeneration through the first large language model to obtain the multiple first initial management policies. The first prompt text template is used to prompt policy regeneration, and the form of the first prompt text template is set by those skilled in the art according to the actual situation, and the embodiments of the present application do not limit this.

[0097] Part Three: The first edge computing device determines multiple first reference management policies from the multiple first initial management policies based on the attribute parameters in the battery pack parameters of each battery pack.

[0098] Among them, the attribute parameters include electrolyte type, electrode type, and battery pack connection relationship.

[0099] In a possible implementation manner, the first edge computing device determines the matching degree between the target energy storage device and the multiple first initial management policies based on the attribute parameters in the battery pack parameters of each battery pack. The first edge computing device determines the first initial management policies in the multiple first initial management policies whose matching degree with the target energy storage device is greater than or equal to the matching degree threshold as the first reference management policies.

[0100] For example, the first edge computing device extracts features from the attribute parameters in the battery pack parameters of each battery pack to obtain the attribute features of multiple battery packs in the target energy storage device. The first edge computing device extracts features from the multiple first initial management policies to obtain the first management policy features of each initial first initial management policy. The first edge computing device determines the matching degree between the target energy storage device and the multiple first initial management policies based on the attribute features of multiple battery packs in the target energy storage device and the first management policy features of each initial first initial management policy. The first edge computing device determines the first initial management policies in the multiple first initial management policies whose matching degree with the target energy storage device is greater than or equal to the matching degree threshold as the first reference management policies.

[0101] Part 4: The first edge computing device determines the temperature management policy among the multiple first reference management policies as the multiple reference temperature management policies.

[0102] In a possible implementation, the first edge computing device disassembles the multiple first reference policies to obtain the multiple reference temperature management policies.

[0103] To illustrate step 303 more clearly, the following describes the manner in which the second edge computing device determines multiple reference charge-discharge management policies for the target energy storage device in step 303 above.

[0104] In a possible implementation, the second edge computing device obtains the battery pack parameters of each battery pack from the battery pack parameter set. The second edge computing device determines multiple second initial management policies based on the operating parameters in the battery pack parameters of each battery pack. The second edge computing device determines multiple second reference management policies from the multiple second initial management policies based on the attribute parameters in the battery pack parameters of each battery pack. The second reference management policy is the second initial management policy that matches the attribute parameters. The second edge computing device determines the charge-discharge management policy among the multiple second reference management policies as the multiple reference charge-discharge management policies.

[0105] To illustrate the above implementation more clearly, the following will describe the above implementation in several parts.

[0106] Part 1: The second edge computing device obtains the battery pack parameters of each battery pack from the battery pack parameter set.

[0107] In a possible implementation, the second edge computing device unpacks the battery pack parameter set to obtain the battery pack parameters of each battery pack.

[0108] Part 2: The second edge computing device determines multiple second initial management policies based on the operating parameters in the battery pack parameters of each battery pack.

[0109] In a possible implementation, the second edge computing device determines the second battery pack management policy for each battery pack and the second energy storage device management policy for the target energy storage device based on the operating parameters of each battery pack. The second edge computing device determines the multiple second initial management policies based on the second battery pack management policy for each battery pack and the second energy storage device management policy for the target energy storage device.

[0110] Among them, the second battery pack management strategy refers to the strategy for managing the battery pack, and the second energy storage device management strategy refers to the overall management strategy for the target energy storage device. In the embodiment of the present application, the second initial management strategy is a combined management strategy, that is, it includes both the strategy for managing the battery pack and the overall management strategy for the target energy storage device.

[0111] For example, for any one of the multiple battery packs of the target energy storage device, the second edge computing device inputs the working parameters of the battery pack into the second local management strategy determination model, and the second local management strategy determination model extracts features from the working parameters of the battery pack to obtain the second battery pack working characteristics of the battery pack. The second edge computing device performs multiple rounds of iterative decoding based on the second battery pack working characteristics of the battery pack through the second local management strategy determination model to obtain the second battery pack management strategy of the battery pack. The second edge computing device inputs the working parameters of the battery pack into the second global management strategy determination model, and the second global management strategy determination model extracts features from the working parameters of each battery pack to obtain the second energy storage device working characteristics of the target energy storage device. The second edge computing device performs multiple rounds of iterative decoding based on the second energy storage device working characteristics of the target energy storage device through the second global management strategy determination model to obtain the second energy storage device management strategy of the target energy storage device. The second edge computing device performs multiple rounds of iterative decoding based on the second energy storage device working characteristics of the target energy storage device through the second global management strategy determination model to obtain the second energy storage device management strategy of the target energy storage device. The second edge computing device performs strategy regeneration based on the second battery pack management strategies of the multiple battery packs and the second energy storage device management strategy of the target energy storage device to obtain the multiple second initial management strategies.

[0112] Among them, both the second local management policy determination model and the second global management policy determination model are models with policy generation capabilities. From another perspective, the second local management policy determination model and the second global management policy determination model are sequence (feature) - to - sequence (policy) generative models. In the embodiments of the present application, both the second local management policy determination model and the second global management policy determination model are models trained based on the BERT model. The encoder of the BERT model is used in the feature extraction process, and the decoder of the BERT model is used in the process of decoding the features. Moreover, the second local management policy determination model and the first local management policy determination model are independently trained by the second edge computing device and the first edge computing device respectively using different data sets; the second global management policy determination model and the first global management policy determination model are also independently trained by the second edge computing device and the first edge computing device respectively using different data sets. In addition, the above - mentioned policy regeneration can be achieved through a fine - tuned second large - language model that has been pre - trained. That is, the second edge computing device inputs the second battery pack management policies of the multiple battery packs, the second energy storage device management policy of the target energy storage device, and the second prompt text template into the second large - language model, and performs policy regeneration through the second large - language model to obtain the multiple second initial management policies. The second prompt text template is used to prompt policy regeneration, and the form of the second prompt text template is set by those skilled in the art according to the actual situation, and the embodiments of the present application do not limit this.

[0113] Part Three: The second edge computing device determines multiple second reference management policies from the multiple second initial management policies based on the attribute parameters in the battery pack parameters of each battery pack.

[0114] In a possible implementation manner, the second edge computing device determines the matching degree between the target energy storage device and the multiple second initial management policies based on the attribute parameters in the battery pack parameters of each battery pack. The second edge computing device determines the second initial management policies with a matching degree greater than or equal to the matching degree threshold between the multiple second initial management policies and the target energy storage device as the second reference management policies.

[0115] For example, the second edge computing device extracts features from the attribute parameters in the battery pack parameters of each battery pack to obtain the attribute features of multiple battery packs in the target energy storage device. The second edge computing device extracts features from the multiple second initial management strategies to obtain the first management strategy features of each initial second initial management strategy. The second edge computing device determines the matching degree between the target energy storage device and the multiple second initial management strategies based on the attribute features of multiple battery packs in the target energy storage device and the first management strategy features of each initial second initial management strategy. The second edge computing device determines the second initial management strategy whose matching degree with the target energy storage device among the multiple second initial management strategies is greater than or equal to the matching degree threshold as the second reference management strategy.

[0116] Part Four: The second edge computing device determines the charge and discharge management strategies among the multiple second reference management strategies as the multiple reference charge and discharge management strategies.

[0117] In a possible implementation manner, the second edge computing device disassembles the multiple second reference strategies to obtain the multiple reference charge and discharge management strategies.

[0118] 304. The server obtains the multiple reference temperature management strategies returned by the first edge computing device and the multiple reference charge and discharge management strategies returned by the second edge computing device.

[0119] 305. The server determines the target temperature management strategy and the target charge and discharge management strategy of the target energy storage device based on the battery pack parameter set, the multiple reference temperature management strategies, and the multiple reference charge and discharge management strategies.

[0120] Among them, the target temperature management strategy refers to the temperature management strategy finally used to manage the target energy storage device, and the target charge and discharge management strategy refers to the charge and discharge management strategy finally used to manage the target energy storage device.

[0121] In a possible implementation, the server determines a first estimated effect parameter, a second estimated effect parameter, and a first risk parameter for each reference temperature management strategy based on the battery pack parameter set and the multiple reference temperature management strategies. The first estimated effect parameter is used to represent the temperature management effect of the target energy storage device after executing the corresponding reference temperature management strategy, and the second estimated effect parameter is used to represent the temperature management effect of each battery pack after executing the corresponding reference temperature management strategy. The server determines a third estimated effect parameter, a fourth estimated effect parameter, and a second risk parameter for each reference charge-discharge management strategy based on the battery pack parameter set and the multiple reference charge-discharge management strategies. The third estimated effect parameter is used to represent the charge-discharge management effect of the target energy storage device after executing the corresponding reference charge-discharge management strategy, and the fourth estimated effect parameter is used to represent the charge-discharge management effect of each battery pack after executing the corresponding reference charge-discharge management strategy. The server determines the target temperature management strategy from the multiple reference temperature management strategies based on the first estimated effect parameter, the second estimated effect parameter, and the first risk parameter of each reference temperature management strategy. The server determines the target charge-discharge management strategy from the multiple reference charge-discharge management strategies based on the third estimated effect parameter, the fourth estimated effect parameter, and the second risk parameter of each reference charge-discharge management strategy.

[0122] For a clearer description of the above implementation, the above implementation will be described in several parts below.

[0123] Part 1: The server determines a first estimated effect parameter, a second estimated effect parameter, and a first risk parameter for each reference temperature management strategy based on the battery pack parameter set and the multiple reference temperature management strategies.

[0124] In a possible implementation, the operating parameters include operating temperature, operating current, operating voltage, remaining power, number of cycles, and charge-discharge power, and the attribute parameters include electrolyte type, electrode type, and battery pack connection relationship. The server determines the first estimated effect parameter of each reference temperature management strategy based on the operating temperature, electrolyte type, electrode type, and battery pack connection relationship of each battery pack in the battery pack parameter set and the multiple reference temperature management strategies. The server determines the second estimated effect parameter of each reference temperature management strategy based on the operating temperature, operating current, operating voltage, remaining power, number of cycles, and charge-discharge power of each battery pack in the battery pack parameter set and the multiple reference temperature management strategies. The server determines the first risk parameter of each reference temperature management strategy based on the operating temperature, operating current, operating voltage, charge-discharge power, electrolyte type, and electrode type of each battery pack in the battery pack parameter set and the multiple reference temperature management strategies.

[0125] For example, the server inputs the working temperature, electrolyte type, electrode type, battery pack connection relationship of each battery pack in the battery pack parameter set and the multiple reference temperature management strategies into the first evaluation model. The first evaluation model extracts features from the working temperature, electrolyte type, electrode type, battery pack connection relationship of each battery pack and the multiple reference temperature management strategies, and obtains the first evaluation features of each reference temperature management strategy. The server performs full connection and normalization on the first evaluation features of each reference temperature management strategy through the first evaluation model, and obtains the first estimated effect parameters of each reference temperature management strategy. The server inputs the working temperature, working current, working voltage, remaining power, number of cycles, charge-discharge power of each battery pack in the battery pack parameter set and the multiple reference temperature management strategies into the second evaluation model. The second evaluation model extracts features from the working temperature, working current, working voltage, remaining power, number of cycles, charge-discharge power of each battery pack and the multiple reference temperature management strategies, and obtains the second evaluation features of each reference temperature management strategy. The server performs full connection and normalization on the second evaluation features of each reference temperature management strategy through the second evaluation model, and obtains the second estimated effect parameters of each reference temperature management strategy. The server inputs the working temperature, working current, working voltage, charge-discharge power, electrolyte type, electrode type of each battery pack in the battery pack parameter set and the multiple reference temperature management strategies into the first risk assessment model. The first risk assessment model extracts features from the working temperature, working current, working voltage, charge-discharge power, electrolyte type, electrode type of each battery pack and the multiple reference temperature management strategies, and obtains the first risk features of each reference temperature management strategy. The server performs full connection and normalization on the first risk features of each reference temperature management strategy through the first risk assessment model, and obtains the first risk parameters of each reference temperature management strategy.

[0126] Part Two: The server determines the third estimated effect parameter, the fourth estimated effect parameter and the second risk parameter of each reference charge-discharge management strategy based on the battery pack parameter set and the multiple reference charge-discharge management strategies.

[0127] In a possible implementation, the server determines the third estimated effect parameter of each reference charge-discharge management strategy based on the charge-discharge power, electrolyte type, electrode type, battery pack connection relationship of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies. The server determines the fourth estimated effect parameter of each reference charge-discharge management strategy based on the operating temperature, operating current, operating voltage, charge-discharge power of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies. The server determines the second risk parameter of each reference charge-discharge management strategy based on the operating temperature, operating current, operating voltage, electrolyte type, electrode type of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies.

[0128] For example, the server inputs the charge-discharge power, electrolyte type, electrode type, battery pack connection relationship of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies into a third evaluation model. The third evaluation model extracts features from the charge-discharge power, electrolyte type, electrode type, battery pack connection relationship of each battery pack, and the multiple reference charge-discharge management strategies to obtain the third evaluation features of each reference charge-discharge management strategy. The server performs full connection and normalization on the third evaluation features of each reference charge-discharge management strategy through the third evaluation model to obtain the third estimated effect parameter of each reference charge-discharge management strategy. The server inputs the operating temperature, operating current, operating voltage, charge-discharge power of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies into a fourth evaluation model. The fourth evaluation model extracts features from the operating temperature, operating current, operating voltage, charge-discharge power of each battery pack, and the multiple reference charge-discharge management strategies to obtain the fourth evaluation features of each reference charge-discharge management strategy. The server performs full connection and normalization on the fourth evaluation features of each reference charge-discharge management strategy through the fourth evaluation model to obtain the fourth estimated effect parameter of each reference charge-discharge management strategy. The server inputs the operating temperature, operating current, operating voltage, electrolyte type, electrode type of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies into a second risk evaluation model. The second risk evaluation model extracts features from the operating temperature, operating current, operating voltage, electrolyte type, electrode type of each battery pack, and the multiple reference charge-discharge management strategies to obtain the second risk features of each reference charge-discharge management strategy. The server performs full connection and normalization on the second risk features of each reference charge-discharge management strategy through the second risk evaluation model to obtain the second risk parameter of each reference charge-discharge management strategy.

[0129] Part III. The server determines the target temperature management strategy from the multiple reference temperature management strategies based on the first estimated effect parameter, the second estimated effect parameter, and the first risk parameter of each reference temperature management strategy.

[0130] In a possible implementation, the server fuses the first estimated effect parameter and the second estimated effect parameter of each reference temperature management strategy to obtain the first target estimated effect parameter of each reference temperature management strategy. The server fuses the first target estimated effect parameter of each reference temperature management strategy with the first risk parameter of each reference temperature management strategy to obtain the first target strategy evaluation score of each reference temperature management strategy. The server determines the reference temperature management strategy with the highest first target strategy evaluation score among the multiple reference temperature management strategies as the target temperature management strategy.

[0131] Part IV. The server determines the target charge-discharge management strategy from the multiple reference charge-discharge management strategies based on the third estimated effect parameter, the fourth estimated effect parameter, and the second risk parameter of each reference charge-discharge management strategy.

[0132] In a possible implementation, the server fuses the third estimated effect parameter and the fourth estimated effect parameter of each reference charge-discharge management strategy to obtain the second target estimated effect parameter of each reference charge-discharge management strategy. The server fuses the second target estimated effect parameter of each reference charge-discharge management strategy with the second risk parameter of each reference charge-discharge management strategy to obtain the second target strategy evaluation score of each reference charge-discharge management strategy. The server determines the reference charge-discharge management strategy with the highest second target strategy evaluation score among the multiple reference charge-discharge management strategies as the target charge-discharge management strategy.

[0133] 306. The server manages the target energy storage device based on the target temperature management strategy and the target charge-discharge management strategy.

[0134] In a possible implementation, the server sends the target temperature management strategy and the target charge-discharge management strategy to the target energy storage device, so that the target energy storage device operates based on the target temperature management strategy and the target charge-discharge management strategy, thereby realizing the management of the target energy storage device.

[0135] All of the above optional technical solutions can be combined arbitrarily to form optional embodiments of the present application, which will not be elaborated here one by one.

[0136] Through the technical solution provided by the embodiments of the present application, the first edge computing device and the second edge computing device are determined from multiple edge computing devices, and the first edge computing device and the second edge computing device are used to obtain multiple reference temperature management strategies and multiple reference charge and discharge management strategies. Combining the battery pack parameter set of the target energy storage device, the multiple reference temperature management strategies, and the multiple reference charge and discharge management strategies, the target temperature management strategy and the target charge and discharge management strategy of the target energy storage device are determined. The target energy storage device is managed by using the target temperature management strategy and the target charge and discharge management strategy, thereby improving the operation stability of the target energy storage device and realizing the effective management of the target energy storage device.

[0137] Figure 4 is a schematic structural diagram of an energy storage system provided by an embodiment of the present application. Refer to Figure 4 , the system includes: a device determination module 401, a parameter acquisition module 402, a policy acquisition module 403, and a management module 404.

[0138] The device determination module 401 is configured to determine a first edge computing device and a second edge computing device from multiple edge computing devices. The battery management credibility of the first edge computing device and the second edge computing device is higher than the credibility threshold. The first edge computing device is used to manage the temperature of the battery pack of the energy storage device, and the second edge computing device is used to manage the charge and discharge power of the battery pack.

[0139] The parameter acquisition module 402 is configured to acquire the battery pack parameter sets of multiple battery packs of the target energy storage device and send the battery pack parameter sets to the first edge computing device and the second edge computing device, so that the first edge computing device determines multiple reference temperature management strategies of the target energy storage device based on the battery pack parameter sets, and the second edge computing device determines multiple reference charge and discharge management strategies of the target energy storage device based on the battery pack parameter sets. The battery pack parameter set includes the battery pack parameters of each battery pack, and the battery pack parameters include working parameters and attribute parameters.

[0140] The policy acquisition module 403 is configured to acquire the multiple reference temperature management strategies returned by the first edge computing device and the multiple reference charge and discharge management strategies returned by the second edge computing device.

[0141] The management module 404 is configured to determine the target temperature management strategy and the target charge and discharge management strategy of the target energy storage device based on the battery pack parameter set, the multiple reference temperature management strategies, and the multiple reference charge and discharge management strategies, and manage the target energy storage device based on the target temperature management strategy and the target charge and discharge management strategy.

[0142] In a possible implementation, the device determination module 401 is configured to obtain a set of device screening parameters for the multiple edge computing devices. The set of device screening parameters includes the historical device management policies of each of the edge computing devices, the set of historical battery pack parameters corresponding to the historical device management policies, and the historical management evaluation data. Based on the set of device screening parameters, multiple candidate edge computing devices are determined from the multiple edge computing devices. The battery management credibility of the multiple candidate edge computing devices is higher than the credibility threshold. The first edge computing device and the second edge computing device are determined from the multiple candidate edge computing devices.

[0143] In a possible implementation, the device determination module 401 is configured to determine the first management credibility and the second management credibility of each of the edge computing devices based on the set of device screening parameters. The first management credibility is determined based on the historical management evaluation data, and the second management credibility is determined based on the historical device management policies and the corresponding set of historical battery pack parameters. Based on the first management credibility and the second management credibility of each of the edge computing devices, the battery management credibility of each of the edge computing devices is determined. The edge computing devices among the multiple edge computing devices whose battery management credibility is greater than or equal to the credibility threshold are determined as candidate edge computing devices, and the multiple candidate edge computing devices are obtained.

[0144] In a possible implementation, the device determination module 401 is configured to determine the historical temperature management evaluation data and the historical charge and discharge management evaluation data of each of the candidate edge computing devices from the historical management evaluation data of the multiple candidate edge computing devices. Based on the historical temperature management evaluation data and the historical charge and discharge management evaluation data of each of the candidate edge computing devices, the multiple candidate edge computing devices are divided into a first device group and a second device group. The edge computing devices in the first device group are those with good temperature management evaluation effects indicated by the temperature management evaluation data, and the edge computing devices in the second device group are those with good charge and discharge management evaluation effects indicated by the charge and discharge management evaluation data. Based on the historical device management policies of each of the edge computing devices and the set of historical battery pack parameters corresponding to the historical device management policies, the first edge computing device is determined from the first device group, and the second edge computing device is determined from the second device group.

[0145] In a possible implementation, the method by which the first edge computing device determines multiple reference temperature management policies for the target energy storage device based on the set of battery pack parameters includes:

[0146] The first edge computing device obtains the battery pack parameters of each battery pack from the set of battery pack parameters. The first edge computing device determines a plurality of first initial management policies based on the operating parameters in the battery pack parameters of each battery pack. The first edge computing device determines a plurality of first reference management policies from the plurality of first initial management policies based on the attribute parameters in the battery pack parameters of each battery pack, where the first reference management policies are the first initial management policies that match the attribute parameters. The first edge computing device determines the temperature management policies among the plurality of first reference management policies as the plurality of reference temperature management policies.

[0147] The method for the second edge computing device to determine a plurality of reference charge-discharge management policies for the target energy storage device based on the set of battery pack parameters includes:

[0148] The second edge computing device obtains the battery pack parameters of each battery pack from the set of battery pack parameters. The second edge computing device determines a plurality of second initial management policies based on the operating parameters in the battery pack parameters of each battery pack. The second edge computing device determines a plurality of second reference management policies from the plurality of second initial management policies based on the attribute parameters in the battery pack parameters of each battery pack, where the second reference management policies are the second initial management policies that match the attribute parameters. The second edge computing device determines the charge-discharge management policies among the plurality of second reference management policies as the plurality of reference charge-discharge management policies.

[0149] In a possible implementation manner, the first edge computing device determines a plurality of first initial management policies based on the operating parameters in the battery pack parameters of each battery pack, including:

[0150] The first edge computing device determines the first battery pack management policy for each battery pack and the first energy storage device management policy for the target energy storage device based on the operating parameters of each battery pack. The first edge computing device determines the plurality of first initial management policies based on the first battery pack management policy for each battery pack and the first energy storage device management policy for the target energy storage device. The second edge computing device determines a plurality of second initial management policies based on the operating parameters in the battery pack parameters of each battery pack, including: The second edge computing device determines the second battery pack management policy for each battery pack and the second energy storage device management policy for the target energy storage device based on the operating parameters of each battery pack. The second edge computing device determines the plurality of second initial management policies based on the second battery pack management policy for each battery pack and the second energy storage device management policy for the target energy storage device.

[0151] In a possible implementation, the management module 404 is configured to determine a first estimated effect parameter, a second estimated effect parameter, and a first risk parameter for each of the reference temperature management policies based on the battery pack parameter set and the multiple reference temperature management policies. The first estimated effect parameter is used to represent the temperature management effect of the target energy storage device after executing the corresponding reference temperature management policy. The second estimated effect parameter is used to represent the temperature management effect of each battery pack after executing the corresponding reference temperature management policy. Based on the battery pack parameter set and the multiple reference charge-discharge management policies, determine a third estimated effect parameter, a fourth estimated effect parameter, and a second risk parameter for each of the reference charge-discharge management policies. The third estimated effect parameter is used to represent the charge-discharge management effect of the target energy storage device after executing the corresponding reference charge-discharge management policy. The fourth estimated effect parameter is used to represent the charge-discharge management effect of each battery pack after executing the corresponding reference charge-discharge management policy. Based on the first estimated effect parameter, the second estimated effect parameter, and the first risk parameter of each of the reference temperature management policies, determine the target temperature management policy from the multiple reference temperature management policies. Based on the third estimated effect parameter, the fourth estimated effect parameter, and the second risk parameter of each of the reference charge-discharge management policies, determine the target charge-discharge management policy from the multiple reference charge-discharge management policies.

[0152] In a possible implementation, the operating parameters include operating temperature, operating current, operating voltage, remaining power, number of cycles, and charge-discharge power. The attribute parameters include electrolyte type, electrode type, and battery pack connection relationship. The management module 404 is configured to determine the first estimated effect parameter of each of the reference temperature management policies based on the operating temperature, electrolyte type, electrode type, battery pack connection relationship of each battery pack in the battery pack parameter set and the multiple reference temperature management policies. Based on the operating temperature, operating current, operating voltage, remaining power, number of cycles, charge-discharge power of each battery pack in the battery pack parameter set and the multiple reference temperature management policies, determine the second estimated effect parameter of each of the reference temperature management policies. Based on the operating temperature, operating current, operating voltage, charge-discharge power, electrolyte type, electrode type of each battery pack in the battery pack parameter set and the multiple reference temperature management policies, determine the first risk parameter of each of the reference temperature management policies.

[0153] The management module 404 is configured to determine third estimated effect parameters of each of the reference charge-discharge management strategies based on the charge-discharge power, electrolyte type, electrode type, battery pack connection relationship of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies. Determine fourth estimated effect parameters of each of the reference charge-discharge management strategies based on the operating temperature, operating current, operating voltage, charge-discharge power of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies. Determine second risk parameters of each of the reference charge-discharge management strategies based on the operating temperature, operating current, operating voltage, electrolyte type, electrode type of each battery pack in the battery pack parameter set, and the multiple reference charge-discharge management strategies.

[0154] In a possible implementation manner, the management module 404 is configured to fuse the first estimated effect parameter and the second estimated effect parameter of each of the reference temperature management strategies to obtain a first target estimated effect parameter of each of the reference temperature management strategies. Fuse the first target estimated effect parameter of each of the reference temperature management strategies with the first risk parameter of each of the reference temperature management strategies to obtain a first target strategy evaluation score of each of the reference temperature management strategies. Determine the reference temperature management strategy with the highest first target strategy evaluation score among the multiple reference temperature management strategies as the target temperature management strategy.

[0155] The management module 404 is configured to fuse the third estimated effect parameter and the fourth estimated effect parameter of each of the reference charge-discharge management strategies to obtain a second target estimated effect parameter of each of the reference charge-discharge management strategies. Fuse the second target estimated effect parameter of each of the reference charge-discharge management strategies with the second risk parameter of each of the reference charge-discharge management strategies to obtain a second target strategy evaluation score of each of the reference charge-discharge management strategies. Determine the reference charge-discharge management strategy with the highest second target strategy evaluation score among the multiple reference charge-discharge management strategies as the target charge-discharge management strategy.

[0156] It should be noted that: when the energy storage system provided in the above embodiment performs model warning, only the above division of each functional module is used for illustration. In practical applications, the above functions can be assigned to different functional modules according to needs, that is, the internal structure of the energy storage system is divided into different functional modules to complete all or part of the functions described above. In addition, the energy storage system provided in the above embodiment and the embodiment of the battery management method based on cloud-edge combination belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0157] Through the technical solution provided by the embodiments of the present application, the first edge computing device and the second edge computing device are determined from multiple edge computing devices, and the multiple reference temperature management policies and the multiple reference charge and discharge management policies are obtained by using the first edge computing device and the second edge computing device. Combining the battery pack parameter set of the target energy storage device, the multiple reference temperature management policies, and the multiple reference charge and discharge management policies, the target temperature management policy and the target charge and discharge management policy of the target energy storage device are determined. The target energy storage device is managed by using the target temperature management policy and the target charge and discharge management policy, so as to improve the operation stability of the target energy storage device and realize the effective management of the target energy storage device.

[0158] Figure 5 FIG. 4 is a schematic structural diagram of a server provided by an embodiment of the present application. The server 500 may vary greatly due to configuration or performance differences, and may include one or more processors (Central Processing Units, CPUs) 501 and one or more memories 502. Among them, at least one computer program is stored in the one or more memories 502, and the at least one computer program is loaded and executed by the one or more processors 501 to implement the methods provided by the above-mentioned method embodiments. Of course, the server 500 may also have components such as wired or wireless network interfaces, keyboards, and input / output interfaces for input / output. The server 500 may also include other components for implementing device functions, which will not be elaborated here.

[0159] In an exemplary embodiment, a computer-readable storage medium is further provided, such as a memory including a computer program. The above computer program can be executed by a processor to complete the cloud-edge combined battery management method in the above embodiment. For example, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0160] In an exemplary embodiment, a computer program product or a computer program is further provided. The computer program product or the computer program includes program code, and the program code is stored in a computer-readable storage medium. The processor of the server reads the program code from the computer-readable storage medium, and the processor executes the program code, so that the server executes the above cloud-edge combined battery management method.

[0161] In some embodiments, the computer program involved in the embodiments of the present application may be deployed to execute on a single server, or on multiple servers located at one location, or on multiple servers distributed at multiple locations and interconnected through a communication network. The multiple servers distributed at multiple locations and interconnected through a communication network may form a blockchain system.

[0162] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above embodiments can be completed by hardware, or can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, or the like.

[0163] The above are only alternative embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A battery management method based on cloud-edge integration, characterized in that: Executed by a server, the method includes: Determine a first edge computing device and a second edge computing device from a plurality of edge computing devices, wherein the battery management credibility of the first edge computing device and the second edge computing device are both higher than a credibility threshold, the first edge computing device is used to manage the temperature of a battery pack of an energy storage device, and the second edge computing device is used to manage the charging and discharging power of the battery pack; Acquire a battery pack parameter set of multiple battery packs of a target energy storage device and send the battery pack parameter set to the first edge computing device and the second edge computing device, so that the first edge computing device determines multiple reference temperature management strategies for the target energy storage device based on the battery pack parameter set, and the second edge computing device determines multiple reference charge and discharge management strategies for the target energy storage device based on the battery pack parameter set, wherein the battery pack parameter set includes battery pack parameters of each of the battery packs, and the battery pack parameters include operating parameters and attribute parameters, wherein the operating parameters include operating temperature, operating current, operating voltage, remaining power, number of cycles, and charge and discharge power, and the attribute parameters include electrolyte type, electrode type, and battery pack connection relationship; The method for the first edge computing device to determine multiple reference temperature management strategies for the target energy storage device based on the battery pack parameter set includes: The first edge computing device obtains battery pack parameters of each of the battery packs from the battery pack parameter set; the first edge computing device determines a plurality of first initial management strategies based on operating parameters in the battery pack parameters of each of the battery packs; the first edge computing device determines a plurality of first reference management strategies from the plurality of first initial management strategies based on attribute parameters in the battery pack parameters of each of the battery packs, the first reference management strategy being a first initial management strategy that matches the attribute parameters; the first edge computing device determines a temperature management strategy in the plurality of first reference management strategies as the plurality of reference temperature management strategies; The method for the second edge computing device to determine multiple reference charge and discharge management strategies for the target energy storage device based on the battery pack parameter set includes: The second edge computing device obtains the battery pack parameters of each of the battery packs from the battery pack parameter set; the second edge computing device determines a plurality of second initial management strategies based on the operating parameters in the battery pack parameters of each of the battery packs; the second edge computing device determines a plurality of second reference management strategies from the plurality of second initial management strategies based on the attribute parameters in the battery pack parameters of each of the battery packs, the second reference management strategy being a second initial management strategy matching the attribute parameters; the second edge computing device determines the charge and discharge management strategy in the plurality of second reference management strategies as the plurality of reference charge and discharge management strategies; Obtain multiple reference temperature management strategies returned by the first edge computing device and multiple reference charge and discharge management strategies returned by the second edge computing device; Based on the battery pack parameter set, the multiple reference temperature management strategies and the multiple reference charge and discharge management strategies, a target temperature management strategy and a target charge and discharge management strategy of the target energy storage device are determined, and the target energy storage device is managed based on the target temperature management strategy and the target charge and discharge management strategy.

2. The method according to claim 1, characterized in that The determining of a first edge computing device and a second edge computing device from a plurality of edge computing devices includes: Acquire a device screening parameter set of the plurality of edge computing devices, the device screening parameter set comprising a historical device management strategy of each of the edge computing devices, a historical battery pack parameter set corresponding to the historical device management strategy, and historical management evaluation data; Based on the device screening parameter set, determine a plurality of candidate edge computing devices from the plurality of edge computing devices, wherein the battery management credibility of the plurality of candidate edge computing devices is higher than the credibility threshold; The first edge computing device and the second edge computing device are determined from the plurality of candidate edge computing devices.

3. The method according to claim 2, characterized in that The determining a plurality of candidate edge computing devices from the plurality of edge computing devices based on the device screening parameter set includes: Based on the device screening parameter set, determine a first management credibility and a second management credibility of each of the edge computing devices, wherein the first management credibility is determined based on historical management evaluation data, and the second management credibility is determined based on historical device management policies and corresponding historical battery pack parameter sets; Determining the battery management credibility of each edge computing device based on the first management credibility and the second management credibility of each edge computing device; An edge computing device whose battery management credibility is greater than or equal to the credibility threshold among the multiple edge computing devices is determined as a candidate edge computing device to obtain the multiple candidate edge computing devices.

4. The method according to claim 2, characterized in that: The determining the first edge computing device and the second edge computing device from the plurality of candidate edge computing devices includes: Determine historical temperature management evaluation data and historical charge and discharge management evaluation data of each of the candidate edge computing devices from the historical management evaluation data of the plurality of candidate edge computing devices; Based on the historical temperature management evaluation data and the historical charge and discharge management evaluation data of each of the candidate edge computing devices, the multiple candidate edge computing devices are divided into a first device group and a second device group, the edge computing devices in the first device group are edge computing devices with good temperature management evaluation effects as indicated by the temperature management evaluation data, and the edge computing devices in the second device group are edge computing devices with good charge and discharge management evaluation effects as indicated by the charge and discharge management evaluation data; Based on the historical device management policies of each of the edge computing devices and the historical battery pack parameter sets corresponding to the historical device management policies, the first edge computing device is determined from the first device group, and the second edge computing device is determined from the second device group.

5. The method according to claim 1, characterized in that The first edge computing device determines a plurality of first initial management strategies based on operating parameters in the battery pack parameters of each of the battery packs, including: The first edge computing device determines a first battery pack management strategy for each of the battery packs and a first energy storage device management strategy for the target energy storage device based on operating parameters of each of the battery packs; The first edge computing device determines the plurality of first initial management strategies based on the first battery pack management strategy of each of the battery packs and the first energy storage device management strategy of the target energy storage device; The second edge computing device determines a plurality of second initial management strategies based on the operating parameters in the battery pack parameters of each of the battery packs, including: The second edge computing device determines a second battery pack management strategy for each of the battery packs and a second energy storage device management strategy for the target energy storage device based on the operating parameters of each of the battery packs; The second edge computing device determines the multiple second initial management strategies based on the second battery pack management strategy of each of the battery packs and the second energy storage device management strategy of the target energy storage device.

6. The method according to claim 1, characterized in that The determining, based on the battery pack parameter set, the multiple reference temperature management strategies, and the multiple reference charge and discharge management strategies, a target temperature management strategy and a target charge and discharge management strategy of the target energy storage device includes: Based on the battery pack parameter set and the multiple reference temperature management strategies, determine a first estimated effect parameter, a second estimated effect parameter and a first risk parameter of each of the reference temperature management strategies, wherein the first estimated effect parameter is used to represent the temperature management effect of the target energy storage device after executing the corresponding reference temperature management strategy, and the second estimated effect parameter is used to represent the temperature management effect of each of the battery packs after executing the corresponding reference temperature management strategy; Based on the battery pack parameter set and the multiple reference charge and discharge management strategies, determine the third estimated effect parameter, the fourth estimated effect parameter and the second risk parameter of each of the reference charge and discharge management strategies, the third estimated effect parameter is used to represent the charge and discharge management effect of the target energy storage device after executing the corresponding reference charge and discharge management strategy, and the fourth estimated effect parameter is used to represent the charge and discharge management effect of each of the battery packs after executing the corresponding reference charge and discharge management strategy; Determining the target temperature management strategy from the plurality of reference temperature management strategies based on the first estimated effect parameter, the second estimated effect parameter, and the first risk parameter of each of the reference temperature management strategies; The target charge and discharge management strategy is determined from the plurality of reference charge and discharge management strategies based on the third estimated effect parameter, the fourth estimated effect parameter and the second risk parameter of each of the reference charge and discharge management strategies.

7. The method according to claim 6, characterized in that The determining, based on the battery pack parameter set and the plurality of reference temperature management strategies, a first estimated effect parameter, a second estimated effect parameter, and a first risk parameter of each of the reference temperature management strategies comprises: Determine a first estimated effect parameter of each of the reference temperature management strategies based on the operating temperature, electrolyte type, electrode type, battery pack connection relationship of each of the battery packs in the battery pack parameter set and the multiple reference temperature management strategies; Determine a second estimated effect parameter of each of the reference temperature management strategies based on the operating temperature, operating current, operating voltage, remaining power, number of cycles, charge and discharge power of each of the battery packs in the battery pack parameter set and the multiple reference temperature management strategies; Determine a first risk parameter of each of the reference temperature management strategies based on the operating temperature, operating current, operating voltage, charge and discharge power, electrolyte type, electrode type and the plurality of reference temperature management strategies of each of the battery packs in the battery pack parameter set; The determining, based on the battery pack parameter set, a third estimated effect parameter, a fourth estimated effect parameter, and a second risk parameter of each of the reference charge and discharge management strategies includes: Determine a third estimated effect parameter of each of the reference charge and discharge management strategies based on the charge and discharge power, electrolyte type, electrode type, battery pack connection relationship of each of the battery packs in the battery pack parameter set and the multiple reference charge and discharge management strategies; Determine a fourth estimated effect parameter of each of the reference charge and discharge management strategies based on the operating temperature, operating current, operating voltage, charge and discharge power of each of the battery packs in the battery pack parameter set and the multiple reference charge and discharge management strategies; Based on the operating temperature, operating current, operating voltage, electrolyte type, electrode type and the multiple reference charge and discharge management strategies of each of the battery packs in the battery pack parameter set, a second risk parameter of each of the reference charge and discharge management strategies is determined.

8. The method according to claim 6, characterized in that The determining the target temperature management strategy from the plurality of reference temperature management strategies based on the first estimated effect parameter, the second estimated effect parameter, and the first risk parameter of each of the reference temperature management strategies comprises: Merging the first estimated effect parameter and the second estimated effect parameter of each of the reference temperature management strategies to obtain the first target estimated effect parameter of each of the reference temperature management strategies; Merging the first target estimated effect parameter of each reference temperature management strategy with the first risk parameter of each reference temperature management strategy to obtain a first target strategy evaluation score of each reference temperature management strategy; Determine the reference temperature management strategy with the highest first target strategy evaluation score among the multiple reference temperature management strategies as the target temperature management strategy; The step of determining the target charge and discharge management strategy from the plurality of reference charge and discharge management strategies based on the third estimated effect parameter, the fourth estimated effect parameter and the second risk parameter of each of the reference charge and discharge management strategies comprises: The third estimated effect parameter and the fourth estimated effect parameter of each of the reference charge and discharge management strategies are merged to obtain the second target estimated effect parameter of each of the reference charge and discharge management strategies; The second target estimated effect parameter of each of the reference charge and discharge management strategies is integrated with the second risk parameter of each of the reference charge and discharge management strategies to obtain a second target strategy evaluation score of each of the reference charge and discharge management strategies; The reference charge and discharge management strategy with the highest second target strategy evaluation score among the multiple reference charge and discharge management strategies is determined as the target charge and discharge management strategy.

9. An energy storage system, characterized in that: include: a device determination module, configured to determine a first edge computing device and a second edge computing device from a plurality of edge computing devices, wherein the battery management credibility of the first edge computing device and the second edge computing device are both higher than a credibility threshold, the first edge computing device is configured to manage the temperature of a battery pack of an energy storage device, and the second edge computing device is configured to manage the charge and discharge power of the battery pack; a parameter acquisition module, configured to acquire a battery pack parameter set of multiple battery packs of a target energy storage device and send the battery pack parameter set to the first edge computing device and the second edge computing device, so that the first edge computing device determines multiple reference temperature management strategies for the target energy storage device based on the battery pack parameter set, and the second edge computing device determines multiple reference charge and discharge management strategies for the target energy storage device based on the battery pack parameter set, wherein the battery pack parameter set includes battery pack parameters of each of the battery packs, and the battery pack parameters include operating parameters and attribute parameters, wherein the operating parameters include operating temperature, operating current, operating voltage, remaining power, number of cycles, and charge and discharge power, and the attribute parameters include electrolyte type, electrode type, and battery pack connection relationship; The method for the first edge computing device to determine multiple reference temperature management strategies for the target energy storage device based on the battery pack parameter set includes: The first edge computing device obtains battery pack parameters of each of the battery packs from the battery pack parameter set; the first edge computing device determines a plurality of first initial management strategies based on operating parameters in the battery pack parameters of each of the battery packs; the first edge computing device determines a plurality of first reference management strategies from the plurality of first initial management strategies based on attribute parameters in the battery pack parameters of each of the battery packs, the first reference management strategy being a first initial management strategy that matches the attribute parameters; the first edge computing device determines a temperature management strategy in the plurality of first reference management strategies as the plurality of reference temperature management strategies; The method for the second edge computing device to determine multiple reference charge and discharge management strategies for the target energy storage device based on the battery pack parameter set includes: The second edge computing device obtains the battery pack parameters of each of the battery packs from the battery pack parameter set; the second edge computing device determines a plurality of second initial management strategies based on the operating parameters in the battery pack parameters of each of the battery packs; the second edge computing device determines a plurality of second reference management strategies from the plurality of second initial management strategies based on the attribute parameters in the battery pack parameters of each of the battery packs, the second reference management strategy being a second initial management strategy matching the attribute parameters; the second edge computing device determines the charge and discharge management strategy in the plurality of second reference management strategies as the plurality of reference charge and discharge management strategies; A strategy acquisition module, used to acquire multiple reference temperature management strategies returned by the first edge computing device and multiple reference charge and discharge management strategies returned by the second edge computing device; A management module is used to determine a target temperature management strategy and a target charge and discharge management strategy of the target energy storage device based on the battery pack parameter set, the multiple reference temperature management strategies and the multiple reference charge and discharge management strategies, and manage the target energy storage device based on the target temperature management strategy and the target charge and discharge management strategy.

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