Multi-source data acquisition method and device applied to energy storage battery PACK packet

Abstract

The invention provides a multi-source data acquisition method and device applied to an energy storage battery PACK package, and relates to the field of battery management systems. The method comprises the following steps: acquiring physical signal parameter data of an energy storage battery PACK packet, and classifying the physical signal parameter data according to heterogeneous data interfaces corresponding to different physical signal parameter data; obtaining first data and second data in the classified physical signal parameter data; through the first data, constructing inter-system interaction characteristics between internal structure units of the energy storage battery PACK packet; external cooperation features between the energy storage battery PACK package and the external connection device are constructed through the second data; and inputting the inter-system interaction features and the external cooperation features into a multi-source feature analysis model, and outputting multi-source data corresponding to the energy storage battery PACK packet based on the multi-source feature analysis model. According to the method and the device, the problem that the acquisition precision of the related parameter data of the energy storage battery PACK packet is relatively low due to the fact that the actual requirements of multi-source information fusion and control closed-loop coordination in a complex application scene are difficult to meet is solved.

Description

Technical Field

[0001] The present application relates to the field of battery management systems, and in particular to a multi-source data acquisition method and device for energy storage battery PACK packages. Background Art

[0002] In recent years, with the increasing proportion of renewable energy and the growing demand for flexible regulation of power systems, energy storage battery PACKs, as core energy units in industrial and commercial energy storage, electric transportation, and distributed energy systems, have gradually become the focus of research and engineering implementation for their intelligent perception and dynamic response capabilities.

[0003] However, in traditional technical solutions, data collection and status analysis of energy storage battery PACK packages are mostly independently modeled based on a single data stream, lacking the ability to jointly model the coupling relationship between internal structural units and the collaborative relationship between external control systems. In other words, traditional solutions cannot fit the dynamic evolution relationship between the multi-physical domain parameters within the energy storage battery PACK package and the control logic consistency mapping relationship between it and the external energy control system. Therefore, it is difficult to meet the actual needs of multi-source information fusion and control closed-loop coordination in complex application scenarios, resulting in low accuracy in the acquisition of relevant parameter data of the energy storage battery PACK package.

[0004] Therefore, there is an urgent need for a multi-source data acquisition method and device for energy storage battery PACK packages. Summary of the Invention

[0005] The present application provides a multi-source data acquisition method and device for energy storage battery PACK packages, which solves the problem that traditional data acquisition methods are difficult to meet the actual needs of multi-source information fusion and control closed-loop collaboration in complex application scenarios, resulting in low accuracy in obtaining parameter data related to energy storage battery PACK packages.

[0006] In a first aspect of the present application, a multi-source data acquisition method for an energy storage battery PACK package is provided, the method comprising: acquiring physical signal parameter data of the energy storage battery PACK package, and classifying the physical signal parameter data according to heterogeneous data interfaces corresponding to different physical signal parameter data; acquiring first data and second data from the classified physical signal parameter data, wherein the first data is internal data in the energy storage battery PACK package, and the second data is external data of the energy storage battery PACK package; constructing inter-system interaction features between internal structural units of the energy storage battery PACK package based on the first data; constructing external collaboration features between the energy storage battery PACK package and externally connected devices based on the second data; inputting the inter-system interaction features and the external collaboration features into a multi-source feature parsing model, and outputting multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model.

[0007] Optionally, physical signal parameter data of the energy storage battery PACK package is obtained, and the physical signal parameter data is classified according to the heterogeneous data interfaces corresponding to different physical signal parameter data, specifically including: obtaining the physical signal parameter data of the energy storage battery PACK package, the physical signal parameter data including voltage data, temperature data and current data; classifying the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data, the heterogeneous data interfaces including analog signal interfaces, digital quantity acquisition interfaces and pulse signal acquisition interfaces.

[0008] Optionally, the first data is used to construct inter-system interaction characteristics between the internal structural units of the energy storage battery PACK package, specifically including: establishing an internal electrical state coupling relationship of the battery cell module based on the battery cell voltage data, battery cell temperature data and battery cell module current data in the first data, and constructing voltage consistency characteristics, temperature balance characteristics and current diversion characteristics based on the electrical state coupling relationship; dynamically adjusting the virtual inductance parameters based on the output power change rate and power difference of the battery cell module to construct an adaptive adjustment feature between the virtual inductance and the power dynamic response; constructing an insulation integrity association feature between modules based on the module insulation state data and the electrical state coupling relationship in the first data; constructing a heat exchange coupling feature based on the coolant flow rate data, coolant pressure data and the electrical state coupling relationship in the first data; and using the voltage consistency feature, adaptive adjustment feature, temperature balance feature, current diversion feature, insulation integrity association feature and heat exchange coupling feature as inter-system interaction characteristics.

[0009] Optionally, based on the output power change rate and power difference of the battery module, the virtual inductor parameters are dynamically adjusted to establish an adaptive adjustment feature between the virtual inductor and the power dynamic response, specifically including:

[0010]

[0011] Among them, ΔL is the virtual inductance coarse adjustment increment, k1 is the power change rate adjustment coefficient, k2 is the power difference adjustment coefficient, is the output power change rate, (P ref -P) is the power difference between the power command and the actual output power;

[0012] L fine (t) = L fine (t-1)+α·ΔL;

[0013] Among them, L fine (t) is the virtual inductance fine-tuning value of the current sampling period, L fine (t-1) is the virtual inductance fine-tuning value of the previous sampling period, and α is the fine-tuning step coefficient;

[0014] Lreturn =L init +β·(L fine (t)-L init );

[0015] Among them, L return is the virtual inductance after returning to the initial value, L init is the initial setting value of virtual inductance, β is the regression adjustment coefficient;

[0016] L final =L base +ΔL+L fine ;

[0017] Among them, L base is the basic virtual inductance value, L final is the virtual inductance parameter.

[0018] Optionally, through the second data, an external collaborative feature is constructed between the energy storage battery PACK package and the external connection device, specifically including: establishing an electric energy interaction relationship based on the charging and discharging power instruction data in the second data, and constructing a power adaptation feature based on the electric energy interaction relationship; establishing a grid synchronization relationship based on the grid connection status data and the high-voltage connection system busbar voltage data in the second data, and constructing a grid consistency feature based on the grid synchronization relationship; establishing a thermal management collaborative relationship based on the ambient temperature data, coolant flow rate data and coolant pressure data in the second data, and constructing a thermal load response feature based on the thermal management collaborative relationship; establishing a communication synchronization relationship based on the communication link status and the energy management platform control instruction data in the second data, and constructing an instruction interaction consistency feature based on the communication synchronization relationship; using the power adaptation feature, grid consistency feature, thermal load response feature and instruction interaction consistency feature as external collaborative features.

[0019] Optionally, the system interaction features and external collaborative features are input into a multi-source feature parsing model, and the multi-source data corresponding to the energy storage battery PACK package is output based on the multi-source feature parsing model, specifically including: performing feature normalization processing on the system interaction features and external collaborative features through the multi-source feature parsing model; based on the result of the feature normalization processing, performing multi-source feature fusion processing, and mapping the multi-dimensional features representing the physical state, control logic and collaborative relationship to a unified feature space; in the unified feature space, performing time series modeling on the feature change trend through the multi-source feature parsing model; according to the multi-source feature fusion processing results and the time series modeling results, performing state classification and dynamic prediction reasoning, and outputting multi-source data through the multi-source feature parsing model.

[0020] Optionally, after inputting the inter-system interaction features and external collaborative features into the multi-source feature parsing model and outputting the multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model, the method also includes: performing a multi-source data comprehensive analysis on the energy storage battery PACK package through a preset method based on the multi-source data, and the multi-source data comprehensive analysis includes health status analysis, remaining service life prediction analysis, and dynamic adjustment analysis of charging and discharging strategies.

[0021] In a second aspect of the present application, a multi-source data acquisition device for an energy storage battery PACK package is provided, the device including an acquisition module and a processing module, wherein:

[0022] An acquisition module is used to acquire physical signal parameter data of the energy storage battery PACK package and classify the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data; and acquire first data and second data from the classified physical signal parameter data, where the first data is internal data in the energy storage battery PACK package and the second data is external data of the energy storage battery PACK package.

[0023] The processing module is used to construct the inter-system interaction characteristics between the internal structural units of the energy storage battery PACK package through the first data; construct the external collaboration characteristics between the energy storage battery PACK package and the external connection device through the second data; input the inter-system interaction characteristics and the external collaboration characteristics into the multi-source feature parsing model, and output the multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model.

[0024] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0025] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform any of the above methods.

[0026] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0027] 1. Acquire the physical signal parameter data of the energy storage battery PACK package, and classify the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data; acquire the first data and the second data in the classified physical signal parameter data; construct the inter-system interaction characteristics between the internal structural units of the energy storage battery PACK package through the first data; construct the external collaboration characteristics between the energy storage battery PACK package and the external connection equipment through the second data; input the inter-system interaction characteristics and the external collaboration characteristics into the multi-source feature parsing model, and output the multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model, thereby realizing the structured acquisition, feature modeling and time series analysis of the multi-source heterogeneous data of the energy storage battery PACK package, and systematically solving the problem that traditional solutions are difficult to cope with the actual needs of multi-source information fusion and control closed-loop collaboration in complex application scenarios, resulting in low parameter data acquisition accuracy.

[0028] 2. By classifying the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data, it is possible to implement structured management of physical parameters of multiple types, multiple sources, and multiple sampling characteristics in the energy storage battery PACK package, so that different physical quantities can complete interface adaptation, channel identification, and function differentiation at the acquisition layer, thereby effectively improving the parsability and acquisition consistency of subsequent multi-source data fusion.

[0029] 3. Perform feature normalization on the interaction features between systems and external collaborative features through a multi-source feature analysis model; based on the results of feature normalization, perform multi-source feature fusion processing, and map the multi-dimensional features representing the physical state, control logic, and collaborative relationship to a unified feature space; in the unified feature space, perform time series modeling on the feature change trend through a multi-source feature analysis model; perform state classification and dynamic prediction reasoning based on the multi-source feature fusion processing results and time series modeling results, and output multi-source data through a multi-source feature analysis model, thereby achieving deep perception and structural reconstruction of the operating status of the energy storage battery PACK package, so that the correlation between various physical quantities, the state evolution path, and the response mechanism can be uniformly expressed and dynamically tracked, significantly improving the system's prediction accuracy under high-frequency disturbances, control changes, and complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a multi-source data acquisition method for an energy storage battery PACK provided in an embodiment of the present application;

[0031] Figure 2 This is a module diagram of a multi-source data acquisition device for an energy storage battery PACK provided in an embodiment of the present application;

[0032] Figure 3This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0033] Explanation of the reference numerals: 21, acquisition module; 22, processing module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0035] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "said", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more of the listed items.

[0036] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0037] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0038] Please refer to Figure 1 , which shows a flow chart of a multi-source data acquisition method for an energy storage battery PACK package provided by an embodiment of the present application, the flow chart mainly includes the following steps: S101 to S105.

[0039] Step S101: obtaining physical signal parameter data of the energy storage battery PACK package, and classifying the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data.

[0040] Specifically, in a variety of practical application scenarios, the acquisition requirements and classification methods of physical signal parameter data are significantly different, and adaptive design is required for the operating goals, perception depth and acquisition response speed under different working conditions. For example, in conventional industrial and commercial energy storage operation scenarios, energy storage battery PACK packages are usually in a long-term steady-state operation state in applications such as day and night load regulation, peak shaving and valley filling, and photovoltaic storage coordination. The goal of data acquisition focuses on power balance, battery cell consistency and thermal management stability. In such scenarios, priority is given to collecting low-frequency changes such as battery cell voltage, battery cell module temperature, current, pressure difference between batteries, and coolant temperature difference. The classification method is divided according to physical type, such as analog signal type (voltage, current, temperature), switch type (relay status, contactor position) and system instruction type (EMS sends control parameters). Heterogeneous interfaces mainly include analog ADC interface, digital GPIO interface and CAN communication protocol interface; in the high-rate charging and discharging scenario of electric vehicles, the PACK package needs to support short-term high-power fast response. At this time, the system focuses on the rapid identification of the change trend of high-frequency dynamic signals and the state close to the extreme working condition boundary. In such scenarios, key parameters such as dynamic current, voltage fluctuation rate, voltage balance deviation rate, conductor heating rate, and thermal management response delay time need to be collected at high sampling frequencies. Classification is based on response characteristics, including high-speed real-time signal types (such as voltage spikes and current surges), thermal delay response types (such as temperature rise rate and cooling rate), and dynamic electrical load types. Heterogeneous interfaces are mainly high-speed sampling ADCs, FPGA bus interfaces, and SPI / I2C-type on-chip high-speed acquisition interfaces. In the remote operation and maintenance and fault diagnosis scenarios of energy storage systems, the data collection goal focuses on fault precursor feature extraction and historical trajectory reconstruction, focusing on data structuring, temporal integrity, and remote synchronization capabilities. In such scenarios, the collected content covers timestamp sequences of various operating data (such as temperature curves, pressure difference evolution trajectories, current offset trends), alarm trigger event streams, communication status codes, etc. The data is classified according to data usage and interaction mode, including historical status class (for trajectory playback), abnormal event class (for fault location), and remote transmission class (for cloud analysis). The interfaces used mainly include Ethernet interface, 4G / 5G IoT module, and Modbus-TCP or MQTT protocol interface transferred by edge gateway.

[0041] In a possible implementation, step S101 further includes: obtaining physical signal parameter data of the energy storage battery PACK package, the physical signal parameter data including voltage data, temperature data, and current data; classifying the physical signal parameter data according to heterogeneous data interfaces corresponding to different physical signal parameter data, the heterogeneous data interfaces including analog signal interfaces, digital quantity acquisition interfaces, and pulse signal acquisition interfaces.

[0042] Specifically, physical signal parameter data include voltage data, temperature data and current data. Among them, voltage data includes battery cell voltage, module end voltage and busbar total voltage, which is used to reflect the potential state of electrical units at all levels in the energy storage battery PACK package. Its variation range is large and the accuracy requirement is high. It is usually sampled by connecting to a high-resolution ADC module through an analog signal interface; temperature data includes battery cell temperature, module housing temperature, coolant temperature and ambient temperature, which are used to monitor thermal imbalance, overheating risk and thermal management response effect. Analog sampling is also performed through the analog signal interface. Some high-reliability systems can be configured with thermocouples or thermistor networks for parallel acquisition; current data includes charging and discharging main loop current, module current and balancing current. Among them, the main loop current and module current are usually formed into continuous analog signals using Hall sensors, shunts, etc., and input through the analog interface; and balancing current control or relay status detection often obtains switch status signals through the digital acquisition interface to determine whether the balancing circuit or protection circuit is closed.

[0043] In addition, the pulse signal acquisition interface is mainly used to obtain event data or periodic counting information triggered by external devices or battery management systems, such as the frequency signal output by the coolant flow rate sensor, the pulse pressure sensor counting signal, or the module wake-up signal. This type of signal has a clear rising edge or falling edge trigger feature and needs to be identified and counted through the pulse capture module or FPGA interface.

[0044] Through the mapping relationship between physical quantities and interface types described above, physical signal parameter data can be categorized into: continuous analog input data, suitable for acquiring continuous state quantities such as voltage, temperature, and high-current signals; discrete digital signals, suitable for system state detection and action confirmation; and high-precision pulse signals, suitable for timing and capturing periodic dynamic data or edge events. Each signal type is bound to a corresponding data interface type and synchronized with data collection through a unified data acquisition framework, thereby building an acquisition architecture that adapts to the multi-dimensional perception of the operating status of energy storage battery packs.

[0045] Step S102: Acquire first data and second data from the classified physical signal parameter data.

[0046] Specifically, the first data is the internal data in the energy storage battery PACK package, which refers to the original operating parameters generated by or directly related to the internal structural units of the energy storage battery PACK package itself, reflecting the state evolution process of the battery cell module, battery management system, thermal management system and high and low voltage connection system; the second data is the external data of the energy storage battery PACK package, which refers to the control instructions, environmental feedback signals or interface status information transmitted by the external device or upper control system to which it is connected during the operation of the PACK package, reflecting the collaborative relationship and response mechanism between the PACK package and the external operating environment. For example, in a typical integrated photovoltaic storage and charging scenario, the first data includes but is not limited to single cell voltage, cell temperature, cell module current, module insulation status, balancing circuit operating status, coolant flow rate, pressure and main loop current, etc. These data are collected by the voltage acquisition module, thermistor, Hall current sensor, pressure transmitter, insulation detection unit and other sensor devices deployed inside the PACK package, reflecting the health status, thermal stability and load response capability of the energy storage battery PACK package; and the second data includes the charging and discharging power instructions, remote switching control commands, grid connection status, power quality monitoring parameters, ambient temperature and humidity, external cooling system working status and communication link feedback status issued by the external EMS system. These data are injected into the PACK package control system through CAN communication, 485 protocol, Modbus TCP / IP interface or IoT remote channel, etc., to guide or regulate internal operating behavior.

[0047] Furthermore, to ensure that the functional role of the data in the system can be accurately identified and support modeling processing, the first data and the second data will be bound to corresponding structural source tags and control attribution tags in the system respectively. For example, data such as voltage, current, and temperature will be labeled as "from module A / B / C" and "associated with electrical branch X", while external control instructions will be labeled as "EMS source instructions" and "environmental interface feedback", etc. In this way, the data source attributes, action links and logical directions are clarified, providing basic support for subsequent system internal and external coupling modeling and multi-source analysis.

[0048] Step S103: constructing inter-system interaction features between internal structural units of the energy storage battery PACK package through the first data.

[0049] Specifically, based on the battery cell voltage, temperature, current, insulation status and thermal management parameters in the first data, the operational correlation between the various structural units inside the energy storage battery PACK (such as battery cell modules, battery management system, high-voltage connection unit, thermal management subsystem) is identified, and by analyzing the change trends, response timing and state dependencies between different physical quantities, characteristic information describing the physical coupling, control linkage and functional feedback between the various structural units is extracted.

[0050] In a possible embodiment, step S103 also includes: establishing an internal electrical state coupling relationship of the battery cell module based on the battery cell voltage data, battery cell temperature data and battery cell module current data in the first data, and constructing a voltage consistency feature, a temperature balance feature and a current diversion feature according to the electrical state coupling relationship; dynamically adjusting the virtual inductance parameters based on the output power change rate and power difference of the battery cell module to construct an adaptive adjustment feature between the virtual inductance and the power dynamic response; constructing an insulation integrity association feature between modules based on the module insulation state data and the electrical state coupling relationship in the first data; constructing a heat exchange coupling feature based on the coolant flow rate data, the coolant pressure data and the electrical state coupling relationship in the first data; and using the voltage consistency feature, the adaptive adjustment feature, the temperature balance feature, the current diversion feature, the insulation integrity association feature and the heat exchange coupling feature as the interaction feature between systems.

[0051] Specifically, inside the energy storage battery PACK, the cell voltage, temperature and module current form a typical thermal-electric coupling relationship at the physical level. By performing statistical analysis on the voltage data of all cells in each module at the same time, the voltage range, standard deviation or maximum offset can be calculated to quantify the voltage consistency of the cells in the module, thereby forming a voltage consistency feature, which can be used to identify problems such as single cell performance degradation, uneven charging and discharging, or failure of balancing control. At the same time, by comparing the uniformity of the spatial distribution of the cell temperature in the module and its dynamic change gradient, the temperature balance feature can be extracted to reflect the temperature control effect and thermal field stability of thermal management in the single module dimension. In addition, combined with the estimated results of the total module current and the current shared by each cell, the load distribution ratio of each branch is derived through the series-parallel topology structure, and the current shunt feature is extracted to reflect the degree of load distribution balance between cells, thereby identifying potential problems such as connection impedance differences and cell internal resistance offset.

[0052] During load disturbances, control strategy switching, or external system responses, battery modules often experience sudden changes in output power or discontinuous changes. By calculating the rate of change of module output power per unit time and the difference between the current actual output power and the target reference power, the control amount of the corresponding virtual inductance can be dynamically adjusted. The specific calculation method is as follows:

[0053]

[0054] Among them, ΔL is the virtual inductance coarse adjustment increment, k1 is the power change rate adjustment coefficient, k2 is the power difference adjustment coefficient, is the output power change rate, (P ref -P) is the power difference between the power command and the actual output power;

[0055] L fine(t) = L fine (t-1)+α·ΔL;

[0056] Among them, L fine (t) is the virtual inductance fine-tuning value of the current sampling period, L fine (t-1) is the virtual inductance fine-tuning value of the previous sampling period, and α is the fine-tuning step coefficient;

[0057] L return =L init +β·(L fine (t)-L init );

[0058] Among them, L return is the virtual inductance after returning to the initial value, L init is the initial setting value of virtual inductance, β is the regression adjustment coefficient;

[0059] L final =L base +ΔL+L fine ;

[0060] Among them, L base is the basic virtual inductance value, L final is the virtual inductor parameter. The change of virtual inductance affects the voltage and current regulation rate and amplitude stability of the module. Therefore, a power feedback regulation formula is introduced to perform weighted superposition of the power difference and the rate of change through the proportional coefficient to generate an inductance correction value, and combine the fine-tuning and regression mechanism to control its continuity and long-term stability. Ultimately, the constructed adaptive regulation feature not only reflects the module's ability to respond to dynamic changes in the load, but also reflects the flexible regulation performance of its control strategy, and has a significant impact on the steady-state recovery speed and control accuracy after power disturbances.

[0061] In a multi-module high-voltage system, the insulation resistance and leakage current status between modules directly affect the safety of system operation. By collecting the insulation status value of each module relative to the shell or negative busbar (such as insulation resistance value, insulation alarm status bit), and combining the module voltage, current and thermal status, the evolution law of insulation stability during the change of its electrical load is analyzed. For example, if the insulation resistance of a module drops significantly at high temperature or high current output, it means that the module has a trend of electric field stress concentration or material degradation. By horizontally comparing the insulation status curves of multiple modules and their electrical status responses, the insulation integrity similarity, synergy and relative offset between modules are constructed, and finally the insulation integrity correlation characteristics are extracted to reflect the insulation balance and local insulation risk points of the system at different operation stages.

[0062] The thermal management system achieves rapid transfer and balancing of the module's thermal load by adjusting the coolant flow rate and pressure. In this step, the coolant sensor is first used to obtain the time series data of the flow rate and pressure. At the same time, the module temperature gradient, cell power consumption and current data are combined to establish a dynamic model between thermal power generation and heat transfer system response. By analyzing the trend of coolant flow changes corresponding to unit temperature rise and the amplitude of changes in heat exchange efficiency under different pressure states, the degree of match between the dynamic response capability of the thermal management system and the actual temperature control capability is identified. By further comparing the hysteresis, linearity and saturation characteristics of the module thermal load change and the coolant flow adjustment, a heat exchange coupling feature can be constructed. This feature is used to characterize the accuracy of the thermal management response, the adjustment boundary and the mismatch risk, and provide an input basis for thermal runaway risk prediction and liquid cooling strategy optimization.

[0063] Step S104: constructing an external collaboration feature between the energy storage battery PACK and the external connection device through the second data.

[0064] Specifically, the interaction patterns between the energy storage battery PACK package and the external system during operation are identified, and by analyzing the control instructions, communication status, grid information and environmental conditions reflected in the second data, a linkage relationship is established between the external energy system, power interface, communication link and thermal management unit and the PACK package, and key characteristic parameters reflecting collaborative control, energy consistency and communication synchronization are extracted as a data expression form to characterize external collaborative capabilities.

[0065] In a possible embodiment, step S104 also includes: establishing an electric energy interaction relationship based on the charging and discharging power instruction data in the second data, and constructing a power adaptation feature based on the electric energy interaction relationship; establishing a grid synchronization relationship based on the grid connection status data and the high-voltage connection system busbar voltage data in the second data, and constructing a grid consistency feature based on the grid synchronization relationship; establishing a thermal management collaboration relationship based on the ambient temperature data, coolant flow rate data and coolant pressure data in the second data, and constructing a thermal load response feature based on the thermal management collaboration relationship; establishing a communication synchronization relationship based on the communication link status and the energy management platform control instruction data in the second data, and constructing an instruction interaction consistency feature based on the communication synchronization relationship; and using the power adaptation feature, the grid consistency feature, the thermal load response feature and the instruction interaction consistency feature as external collaborative features.

[0066] Specifically, by obtaining the charge and discharge power commands issued by the energy management system (EMS) or charging pile controller and comparing them with the actual output power of the PACK package or the module current data, the system is evaluated to see whether it can respond to the control target quickly, accurately, and stably. If the deviation between the command power and the output power is small and the adjustment rate matches, it indicates that the energy interaction mechanism is good. The extracted power adaptation features include response delay time, power tracking error, adjustment slope, etc., which are used to reflect the PACK package's compliance with external power scheduling and its precision control capability.

[0067] The grid connection status indicates whether the PACK is in grid-connected or islanded operation, while the high-voltage connection system busbar voltage reflects the actual voltage amplitude and frequency of the grid-connected interface. By performing time-series analysis on the state evolution during grid switching, disturbances, and recovery, the consistency between grid-connected voltage fluctuations and control commands can be identified. Based on this, a grid consistency feature is constructed to determine the energy storage system's synchronous control capability and voltage coordination performance under dynamic grid-side disturbances, thereby improving grid stability and compatibility.

[0068] By analyzing the correspondence between external ambient temperature changes and cooling system parameters, it is determined whether external thermal management units (such as cooling pumps, cooling plates, and heat exchangers) can adjust their operating status in a timely manner according to the external heat load. If the system can quickly increase the flow rate or pressure to maintain a stable module temperature under high ambient temperatures, it indicates that the thermal synergy mechanism is effective. Thermal load response characteristics include cooling response time, heat conduction hysteresis rate, adjustment range, etc., reflecting the dynamic coordination ability between the system and external temperature control resources.

[0069] Evaluate the delay, consistency and bit error rate between the control signal and the status feedback. By cross-analyzing the link status (such as signal strength, connection stability, and transmission cycle) and the command reception and transmission status (such as the control command issuance frequency and the response confirmation frame reception rate), extract the command interaction consistency characteristics. This is used to evaluate the stability of the control closed loop between the PACK package and the upper-level management system, the communication timeliness and anti-interference capability. It is a key supporting indicator for realizing remote scheduling and distributed control.

[0070] Step S105 : inputting the inter-system interaction features and the external collaborative features into a multi-source feature parsing model, and outputting multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model.

[0071] Specifically, by constructing a unified feature processing framework, the internal structural response characteristics of the energy storage battery PACK package and the external collaborative interaction characteristics extracted in the previous step are normalized, fused modeled and dynamically evolved. This allows multi-dimensional heterogeneous data to be structurally consistent and reconstructed in a unified model. Finally, structured, multi-dimensional, time-related multi-source data is output, which can be used for subsequent state estimation, predictive reasoning and energy optimization decision-making.

[0072] In a possible implementation, step S105 also includes: performing feature normalization processing on the interaction features between systems and the external collaborative features through a multi-source feature parsing model; based on the result of the feature normalization processing, performing multi-source feature fusion processing, and mapping the multi-dimensional features representing the physical state, control logic, and collaborative relationship to a unified feature space; in the unified feature space, performing time series modeling on the feature change trend through a multi-source feature parsing model; performing state classification and dynamic prediction reasoning based on the multi-source feature fusion processing results and the time series modeling results, and outputting multi-source data through the multi-source feature parsing model.

[0073] Specifically, for the inter-system interaction features constructed in step S103 (such as voltage consistency features, temperature balance features, virtual inductance adjustment features, etc.) and the external collaborative features constructed in step S104 (such as power adaptation features, grid consistency features, thermal load response features and instruction interaction consistency features), due to their different data dimensions, numerical distributions and sampling frequencies, they need to be normalized first. Normalization processing includes using methods such as range scaling, Z-score standardization or wavelet decomposition boundary adjustment for each eigenvalue to uniformly map all features to a comparable numerical space and eliminate the inconsistency of physical quantities at the numerical level. After the normalization process is completed, a feature fusion algorithm (such as feature embedding mapping, collaborative feature tensor construction or multidimensional aggregation based on attention mechanism) is used to jointly map the multidimensional features representing electrical status, thermal management response, communication interaction and control synchronization to a unified feature space. This space serves as the basic structure for describing the system state, maintaining the functional independence of each feature in the spatial dimension, and having the ability to model dynamic interactions across features in the time dimension. In a unified feature space, a time series modeling mechanism is introduced to model the time evolution trend of features. This modeling can be implemented based on recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory networks (LSTMs), or attention-based time graph neural networks, extracting the response lag, prediction trajectory, and joint evolution path of each feature at different operating stages. For example, after a specific load disturbance, it is analyzed whether the degradation of temperature uniformity and communication lag constitute a potential safety risk linkage. Combining the aforementioned feature fusion results with the time series modeling output, the system state classification and prediction tasks are further performed. State classification is used to identify the current operating range of the energy storage battery PACK package (such as normal, sub-healthy, fault warning, abnormal offline, etc.), while dynamic prediction reasoning is based on target parameters such as output power, temperature distribution, and voltage uniformity, constructing prediction trajectories in the future time window, and providing quantitative inference of future operating trends.

[0074] In a possible implementation, step S105 further includes: performing a multi-source data comprehensive analysis on the energy storage battery PACK package according to the multi-source data by a preset method, wherein the multi-source data comprehensive analysis includes health status analysis, remaining service life prediction analysis, and charge and discharge strategy dynamic adjustment analysis.

[0075] Specifically, the health status analysis includes: identifying the performance stability of each module within the energy storage battery PACK package at different operating stages based on the voltage consistency characteristics, temperature balance characteristics and current diversion characteristics in multi-source data, combining historical operating trajectories with current state offsets, quantifying the health assessment score of each module, and constructing a module health level mapping matrix to identify local abnormal units, balance failure trends and early signs of thermal runaway.

[0076] The remaining service life prediction analysis includes: based on the dynamic characteristics of the battery cell temperature rise rate, internal resistance drift trend, balanced load change and cooling response lag in multi-source data, building a degradation path evolution model through the life degradation modeling algorithm, combining historical degradation curves with current parameter incremental changes, estimating the future capacity retention rate and performance degradation rate of the battery cell module, and outputting the corresponding remaining service life prediction value and failure confidence interval for each module, realizing individualized modeling and system-level aggregate evaluation of life prediction.

[0077] The dynamic adjustment analysis of the charging and discharging strategy includes: based on the power adaptation characteristics, grid consistency characteristics, thermal load response characteristics and control instruction interaction consistency characteristics, combined with the current load conditions, electricity price signals, grid status and operation history, through the control optimization algorithm to dynamically calculate the optimal charging and discharging power distribution, switching timing and load response path, and adjust the charge state management strategy, balancing strategy and power buffer strategy in real time to achieve adaptive charging and discharging scheduling control under the dual goals of economy and life.

[0078] The present application adopts the above method to obtain physical signal parameter data of the energy storage battery PACK package, and classifies the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data; obtains first data and second data in the classified physical signal parameter data; constructs the inter-system interaction characteristics between the internal structural units of the energy storage battery PACK package through the first data; constructs the external collaboration characteristics between the energy storage battery PACK package and the external connection device through the second data; inputs the inter-system interaction characteristics and the external collaboration characteristics into the multi-source feature parsing model, and outputs the multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model, thereby realizing structured acquisition, feature modeling and time series analysis of multi-source heterogeneous data of the energy storage battery PACK package, and systematically solves the problem that traditional solutions are difficult to cope with the actual needs of multi-source information fusion and control closed-loop collaboration in complex application scenarios, resulting in low parameter data acquisition accuracy.

[0079] Please refer to Figure 2 , which shows a module schematic diagram of a multi-source data acquisition device for an energy storage battery PACK package provided by an embodiment of the present application, the device includes an acquisition module 21 and a processing module 22, wherein,

[0080] The acquisition module 21 is used to obtain the physical signal parameter data of the energy storage battery PACK package and classify the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data; obtain the first data and the second data in the classified physical signal parameter data, where the first data is the internal data in the energy storage battery PACK package and the second data is the external data of the energy storage battery PACK package.

[0081] The processing module 22 is used to construct the inter-system interaction characteristics between the internal structural units of the energy storage battery PACK package through the first data; construct the external collaboration characteristics between the energy storage battery PACK package and the external connection device through the second data; input the inter-system interaction characteristics and the external collaboration characteristics into the multi-source feature parsing model, and output the multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model.

[0082] In a possible embodiment, the acquisition module 21 is used to obtain the physical signal parameter data of the energy storage battery PACK package, and classify the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data, specifically including: obtaining the physical signal parameter data of the energy storage battery PACK package, the physical signal parameter data including voltage data, temperature data and current data; classifying the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data, the heterogeneous data interfaces including analog signal interfaces, digital quantity acquisition interfaces and pulse signal acquisition interfaces.

[0083] In one possible embodiment, the processing module 22 is used to construct inter-system interaction characteristics between internal structural units of the energy storage battery PACK package through the first data, specifically including: establishing an internal electrical state coupling relationship of the battery cell module based on the battery cell voltage data, battery cell temperature data and battery cell module current data in the first data, and constructing a voltage consistency feature, a temperature balance feature and a current diversion feature according to the electrical state coupling relationship; dynamically adjusting the virtual inductance parameters based on the output power change rate and power difference of the battery cell module to construct an adaptive adjustment feature between the virtual inductance and the power dynamic response; constructing an insulation integrity association feature between modules based on the module insulation state data and the electrical state coupling relationship in the first data; constructing a heat exchange coupling feature based on the coolant flow rate data, coolant pressure data and the electrical state coupling relationship in the first data; and using the voltage consistency feature, adaptive adjustment feature, temperature balance feature, current diversion feature, insulation integrity association feature and heat exchange coupling feature as inter-system interaction features.

[0084] In one possible implementation, the processing module 22 is configured to dynamically adjust the virtual inductance parameters based on the output power change rate and power difference of the battery cell module to establish an adaptive adjustment feature between the virtual inductance and the power dynamic response, specifically including:

[0085]

[0086] Among them, ΔL is the virtual inductance coarse adjustment increment, k1 is the power change rate adjustment coefficient, k2 is the power difference adjustment coefficient, is the output power change rate, (P ref -P) is the power difference between the power command and the actual output power;

[0087] L fine (t) = L fine (t-1)+α·ΔL;

[0088] Among them, L fine (t) is the virtual inductance fine-tuning value of the current sampling period, L fine (t-1) is the virtual inductance fine-tuning value of the previous sampling period, and α is the fine-tuning step coefficient;

[0089] L return =L init +β·(L fine (t)-L init );

[0090] Among them, L return is the virtual inductance after returning to the initial value, L init is the initial setting value of virtual inductance, β is the regression adjustment coefficient;

[0091] L final =L base +ΔL+L fine ;

[0092] Among them, L base is the basic virtual inductance value, L final is the virtual inductance parameter.

[0093] In one possible embodiment, the processing module 22 is used to construct an external collaborative feature between the energy storage battery PACK package and the external connection device through the second data, specifically including: establishing an electric energy interaction relationship based on the charging and discharging power instruction data in the second data, and constructing a power adaptation feature based on the electric energy interaction relationship; establishing a grid synchronization relationship based on the grid connection status data and the high-voltage connection system busbar voltage data in the second data, and constructing a grid consistency feature based on the grid synchronization relationship; establishing a thermal management collaborative relationship based on the ambient temperature data, coolant flow rate data and coolant pressure data in the second data, and constructing a thermal load response feature based on the thermal management collaborative relationship; establishing a communication synchronization relationship based on the communication link status and energy management platform control instruction data in the second data, and constructing an instruction interaction consistency feature based on the communication synchronization relationship; using the power adaptation feature, grid consistency feature, thermal load response feature and instruction interaction consistency feature as external collaborative features.

[0094] In one possible embodiment, the processing module 22 is used to input the inter-system interaction features and external collaborative features into the multi-source feature parsing model, and output the multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model, specifically including: performing feature normalization processing on the inter-system interaction features and external collaborative features through the multi-source feature parsing model; based on the result of the feature normalization processing, performing multi-source feature fusion processing, and mapping the multi-dimensional features representing the physical state, control logic and collaborative relationship to a unified feature space; in the unified feature space, performing time series modeling on the feature change trend through the multi-source feature parsing model; according to the multi-source feature fusion processing result and the time series modeling result, performing state classification and dynamic prediction reasoning, and outputting multi-source data through the multi-source feature parsing model.

[0095] In one possible embodiment, the processing module 22 is used to input the inter-system interaction features and external collaborative features into the multi-source feature parsing model, and output the multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model. The method also includes: based on the multi-source data, performing a multi-source data comprehensive analysis on the energy storage battery PACK package through a preset method. The multi-source data comprehensive analysis includes health status analysis, remaining service life prediction analysis, and dynamic adjustment analysis of charging and discharging strategies.

[0096] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0097] This application also provides an electronic device. Figure 3 , Figure 3 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0098] The communication bus 302 is used to implement the connection and communication between these components.

[0099] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0100] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0101] The processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and circuits to connect various components within the server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by accessing data stored in the memory 305, the processor 301 performs various server functions and processes data. Optionally, the processor 301 may be implemented using at least one hardware form selected from the group consisting of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is responsible for handling wireless communications. It is understood that the modem may not be integrated into the processor 301 and may be implemented separately on a single chip.

[0102] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , as a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface module, and a multi-source data acquisition application for an energy storage battery PACK package.

[0103] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the multi-source data acquisition application stored in the memory 305 for the energy storage battery PACK package. When executed by one or more processors 301, the electronic device executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the described order of actions, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0104] The present application also provides a computer-readable storage medium storing instructions, which, when executed by one or more processors, enable an electronic device to execute one or more of the methods described in the above embodiments.

[0105] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0107] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0108] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0110] The above descriptions are merely exemplary embodiments disclosed in this application and are not intended to limit the scope of this application. That is, any equivalent changes and modifications made based on the teachings disclosed in this application are still within the scope of this application.

[0111] This application is intended to cover any modifications, uses or adaptations disclosed in this application, which follow the general principles disclosed in this application and include common knowledge or customary technical means in the technical field not disclosed in this application.

Claims

1. A multi-source data acquisition method applied to energy storage battery PACK, characterized in that: The method comprises: Obtain physical signal parameter data of the energy storage battery PACK package, and classify the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data; Acquire first data and second data from the classified physical signal parameter data, where the first data is internal data in the energy storage battery PACK package, and the second data is external data of the energy storage battery PACK package; Constructing inter-system interaction features between internal structural units of the energy storage battery PACK package based on the first data; Building an external collaboration feature between the energy storage battery PACK and an external connection device using the second data; The inter-system interaction features and the external collaborative features are input into a multi-source feature parsing model, and multi-source data corresponding to the energy storage battery PACK package is output based on the multi-source feature parsing model.

2. The method according to claim 1, characterized in that The acquiring of the physical signal parameter data of the energy storage battery PACK package and classifying the physical signal parameter data according to the heterogeneous data interfaces corresponding to different physical signal parameter data specifically includes: Acquire physical signal parameter data of the energy storage battery PACK package, wherein the physical signal parameter data includes voltage data, temperature data, and current data; The physical signal parameter data are classified according to heterogeneous data interfaces corresponding to different physical signal parameter data, and the heterogeneous data interfaces include analog signal interfaces, digital quantity acquisition interfaces, and pulse signal acquisition interfaces.

3. The method according to claim 1, characterized in that The constructing of the inter-system interaction features between the internal structural units of the energy storage battery PACK package using the first data specifically includes: Based on the cell voltage data, cell temperature data, and cell module current data in the first data, establish an internal electrical state coupling relationship of the cell module, and construct a voltage consistency feature, a temperature balance feature, and a current diversion feature according to the electrical state coupling relationship; Dynamically adjust the virtual inductor parameters based on the output power change rate and power difference of the battery module to establish an adaptive adjustment feature between the virtual inductor and the power dynamic response; Constructing inter-module insulation integrity correlation features based on the module insulation status data in the first data and the electrical status coupling relationship; Constructing a heat exchange coupling feature based on the coolant flow rate data, the coolant pressure data and the electrical state coupling relationship in the first data; The voltage consistency feature, the adaptive adjustment feature, the temperature balance feature, the current shunting feature, the insulation integrity association feature, and the heat exchange coupling feature are used as the inter-system interaction features.

4. The method according to claim 3, characterized in that The method of dynamically adjusting the virtual inductance parameters based on the output power change rate and power difference of the battery module to establish an adaptive adjustment feature between the virtual inductance and the power dynamic response specifically includes: Among them, ΔL is the virtual inductance coarse adjustment increment, k1 is the power change rate adjustment coefficient, k2 is the power difference adjustment coefficient, is the output power change rate, (P ref -P) is the power difference between the power command and the actual output power; L fine (t)=L fine (t-1)+α·ΔL; Among them, L fine (t) is the virtual inductance fine-tuning value of the current sampling period, L fine (t-1) is the virtual inductance fine-tuning value of the previous sampling period, and α is the fine-tuning step coefficient; L return =L init +β·(L fine (t)-L init ); Among them, L return is the virtual inductance after returning to the initial value, L init is the initial setting value of virtual inductance, β is the regression adjustment coefficient; L final =L base +ΔL+L fine ; Among them, L base is the basic virtual inductance value, L final is the virtual inductance parameter.

5. The method according to claim 1, wherein The step of constructing an external collaboration feature between the energy storage battery PACK and an external connection device using the second data specifically includes: establishing an electric energy interaction relationship based on the charge and discharge power instruction data in the second data, and constructing a power adaptation feature according to the electric energy interaction relationship; Establishing a grid synchronization relationship based on the grid connection status data and the high-voltage connection system busbar voltage data in the second data, and constructing a grid consistency feature based on the grid synchronization relationship; establishing a thermal management collaboration relationship based on the ambient temperature data, the coolant flow rate data, and the coolant pressure data in the second data, and constructing a thermal load response characteristic according to the thermal management collaboration relationship; Establishing a communication synchronization relationship based on the communication link status in the second data and the energy management platform control instruction data, and constructing an instruction interaction consistency feature according to the communication synchronization relationship; The power adaptation feature, the grid connection consistency feature, the thermal load response feature, and the instruction interaction consistency feature are used as the external coordination feature.

6. The method according to claim 1, characterized in that The step of inputting the inter-system interaction feature and the external collaborative feature into a multi-source feature parsing model, and outputting multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model, specifically includes: Performing feature normalization processing on the inter-system interaction features and the external collaborative features through the multi-source feature parsing model; Based on the result of the feature normalization processing, a multi-source feature fusion processing is performed, and the multi-dimensional features representing the physical state, control logic and collaborative relationship are mapped into a unified feature space; In a unified feature space, time series modeling of feature change trends is performed using the multi-source feature parsing model; According to the multi-source feature fusion processing result and the time series modeling result, state classification and dynamic prediction reasoning are performed, and the multi-source data is output through the multi-source feature parsing model.

7. The method according to claim 1, characterized in that After inputting the inter-system interaction feature and the external collaborative feature into a multi-source feature parsing model and outputting multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model, the method further includes: Based on the multi-source data, a multi-source data comprehensive analysis is performed on the energy storage battery PACK package through a preset method. The multi-source data comprehensive analysis includes health status analysis, remaining service life prediction analysis, and charge and discharge strategy dynamic adjustment analysis.

8. A multi-source data acquisition device applied to energy storage battery PACK, characterized in that: The device includes an acquisition module and a processing module, wherein: The acquisition module is configured to acquire physical signal parameter data of the energy storage battery PACK package and classify the physical signal parameter data according to heterogeneous data interfaces corresponding to different physical signal parameter data; and acquire first data and second data from the classified physical signal parameter data, where the first data is internal data in the energy storage battery PACK package and the second data is external data of the energy storage battery PACK package; The processing module is used to construct the inter-system interaction characteristics between the internal structural units of the energy storage battery PACK package through the first data; construct the external collaboration characteristics between the energy storage battery PACK package and the external connection device through the second data; input the inter-system interaction characteristics and the external collaboration characteristics into a multi-source feature parsing model, and output the multi-source data corresponding to the energy storage battery PACK package based on the multi-source feature parsing model.

9. An electronic device, characterized in that: The electronic device comprises a processor, a communication bus, a user interface, a network interface and a memory, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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