A home energy storage system device remote upgrading method and system

Abstract

The present application relates to the technical field of remote upgrading, and particularly relates to a household energy storage system device remote upgrading method and system. The method comprises the following steps: collecting temperature distribution data of a battery pack surface through sensors arranged in a core area, a middle area and a peripheral area of the battery pack, and recording temperatures of each area as initial temperature reference values according to the temperature distribution data; performing function component identification and thermal characteristic evaluation processing on a pre-acquired upgrade package to obtain heat production hierarchical component data; performing serial execution sequencing processing on the heat production hierarchical component data according to the initial temperature reference values to obtain batch execution strategy data; performing isolated upgrading processing on each function component in a double-buffer A / B partition structure of the household energy storage system according to the batch execution strategy data to obtain component upgrading results. The present application dynamically adjusts an execution sequence according to an initial temperature state and component thermal characteristics, and ensures that a system temperature is always kept within a safe range during the upgrading process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote upgrading, and in particular to a remote upgrading method and system for a home energy storage system device. BACKGROUND

[0002] A home energy storage system is a device that can store electrical energy and release it when needed, typically during periods of abundant or low-cost electricity (such as night-time off-peak rates or solar power peak periods). Remote upgrading is a critical aspect of home energy storage system maintenance and optimization, allowing users to obtain the latest features and security fixes without the need for professional on-site assistance. Remote upgrading of a home energy storage system essentially involves transferring a new software package to the device and applying the update: at the communication level, the energy storage system typically establishes a connection with a cloud server via a home WiFi network, 4G / 5G mobile network, or dedicated communication module. The system regularly checks for updates or receives push notifications, then downloads and verifies the update package. At the execution level, the system uses dual partition (A / B partition) or differential update techniques to ensure the safety of the upgrade process. The dual partition mechanism installs updates on an inactive partition and only switches to the new system after successful verification; differential updates only transfer the changed parts, reducing data transfer volume.

[0003] However, traditional remote upgrading methods for home energy storage system devices often have the following problems: existing upgrading methods generally do not consider the thermal sensitivity of the battery pack in the home energy storage system; the performance and safety of lithium-ion batteries are closely related to temperature, and performing resource-intensive upgrade tasks in a high-temperature environment can further increase the temperature, accelerating battery aging and even causing safety risks. Compared with consumer electronics products, the processor performance and storage resources of a home energy storage system are generally limited, and standard OTA upgrade processes can lead to depletion of system resources, affecting the normal operation of control functions. SUMMARY

[0004] Therefore, it is necessary to provide a remote upgrading method and system for a home energy storage system device to solve at least one of the above technical problems.

[0005] To achieve the above-mentioned purpose, a remote upgrading method for a home energy storage system device includes the following steps:

[0006] Step S1: Collecting temperature distribution data of the battery pack surface by sensors arranged in the core area, intermediate area, and peripheral area of the battery pack, and recording the temperature of each area as an initial temperature reference value according to the temperature distribution data;

[0007] Step S2: Perform function component identification and thermal characteristic evaluation processing on the pre-acquired upgrade package to obtain heat-producing hierarchical component data; perform serial execution sequencing processing on the heat-producing hierarchical component data according to the initial temperature reference value to obtain batch execution strategy data;

[0008] Step S3: Perform isolated upgrade processing on each function component in the dual-buffer A / B partition structure of the home energy storage system according to the batch execution strategy data to obtain component upgrade results; record real-time temperature change data during the upgrade process according to the component upgrade results; perform threshold comparison processing on the real-time temperature change data to obtain execution strategy adjustment data;

[0009] Step S4: Generate an upgrade verification report containing component thermal characteristic analysis according to the execution strategy adjustment data and the real-time temperature change data, and transmit the upgrade verification report to the cloud server.

[0010] In the intelligent upgrading process of the battery pack, the partition monitoring and thermal management optimization strategy can ensure the stability and safety of the system. First, by arranging sensors in the core area, intermediate area and peripheral area of the battery pack, the temperature distribution data of each area can be accurately obtained, and the initial temperature reference value is established, which provides a reference for temperature monitoring and strategy adjustment in the subsequent upgrading process. This can predict the heat dissipation state of the battery pack before upgrading, avoiding potential thermal runaway risks. Then, the function component identification and thermal characteristic evaluation of the upgrading package to be executed are performed, so that the system can intelligently classify according to the heat generation characteristics of each component, and sort according to the initial temperature reference value, so that the high-heat generation components and low-heat generation components form an optimal execution order, and then a reasonable batch execution strategy is developed. This way not only optimizes the execution order of the upgrade, avoids local overheating caused by simultaneous operation of high-heat generation components, but also reduces the impact of thermal stress on the performance of the battery pack, improving the overall stability of the upgrading process. During the upgrading process, the double-buffer A / B partition structure of the home energy storage system is used to isolate the function components for upgrading, realizing the orderly updating of different components in an independent environment, reducing the impact of upgrading on the normal operation of the system, and dynamically adjusting the load distribution between different partitions to improve the response speed and thermal management capability of the system. At the same time, the temperature changes of each component are recorded in real time during the upgrading process, and compared with the preset threshold value, so that the temperature abnormality can be detected in time, and the execution strategy is adjusted based on the actual temperature feedback, so that the upgrading efficiency is guaranteed while avoiding safety problems caused by temperature accumulation. Finally, using the temperature change data collected during the upgrading process, combined with the execution strategy adjustment, the system can automatically generate an upgrading verification report containing component thermal characteristic analysis, and transmit it to the cloud server for remote monitoring and analysis, providing data support for future system optimization. This whole set of strategies makes the upgrading process more intelligent, stable and safe, ensuring the reliability of the energy storage system, and also improving the long-term service life and overall operating efficiency of the battery pack.

[0011] The application also provides a home energy storage system device remote upgrading system for executing the home energy storage system device remote upgrading method described above, which comprises:

[0012] A temperature acquisition module is arranged to acquire temperature distribution data of the surface of the battery pack through sensors arranged in the core area, intermediate area and peripheral area of the battery pack, and record the temperature of each area as an initial temperature reference value according to the temperature distribution data;

[0013] An upgrading sorting module is arranged to perform function component identification and thermal characteristic evaluation on the pre-acquired upgrading package to obtain heat generation classification component data, and perform serial execution sorting processing on the heat generation classification component data according to the initial temperature reference value to obtain batch execution strategy data;

[0014] The batch upgrading module is configured to perform isolated upgrading of each functional component in the dual-buffer A / B partition structure of the home energy storage system according to the batch execution strategy data, to obtain a component upgrading result; record real-time temperature change data in the upgrading process according to the component upgrading result; and perform threshold comparison processing on the real-time temperature change data to obtain execution strategy adjustment data.

[0015] The verification reporting module is configured to generate an upgrading verification report containing component thermal characteristic analysis according to the execution strategy adjustment data and the real-time temperature change data, and transmit the upgrading verification report to the cloud server.

[0016] The temperature acquisition module in the application can accurately collect the temperature distribution data of the battery pack surface by setting sensors in the core area, intermediate area and peripheral area of the battery pack. This data provides a preliminary temperature reference value for subsequent thermal management and performance optimization, which helps to determine the working temperature range of the system in different areas, thereby providing reliable basic data for subsequent upgrade and adjustment operations. By recording the temperature of each area, the temperature acquisition module ensures that the state of the system before upgrading is accurately grasped, reducing unnecessary risks caused by temperature fluctuations. The role of the upgrade sequencing module is to identify and evaluate the thermal characteristics of the pre-acquired upgrade package. Through this process, the thermal generation characteristics of each functional component can be evaluated, and the initial temperature reference value of the battery pack is used to perform serial execution sequencing for each component. This sequencing ensures that the upgrade of different functional components is performed in the order of optimal temperature adaptability, thereby avoiding the impact of heat accumulation on system stability. Based on this data, the upgrade sequence of the components is reasonably planned, optimizing resource allocation during the upgrade process and ensuring efficient and safe execution. The batch upgrade module plays a crucial role in this process. According to the batch execution strategy data, the component performs isolated upgrade processing of each functional component in the dual-buffer A / B partition structure of the home energy storage system. In this way, not only is interference caused by component interaction during the upgrade process avoided, but also each component has sufficient isolation during the upgrade, reducing system risk. During the upgrade process, real-time temperature change data is continuously recorded, and through threshold comparison processing of these data, temperature abnormal fluctuations can be quickly responded to, so that the upgrade strategy can be adjusted in time to ensure that the temperature of the entire system is always within a safe range. Finally, the verification and reporting module generates a detailed upgrade verification report based on the execution strategy adjustment data and real-time temperature change data, and transmits the encrypted report to the cloud server. This component not only provides traceable data support for the upgrade process, but also provides detailed temperature management and performance evaluation information for operation and maintenance personnel, so that the upgrade effect of the system can be comprehensively audited and analyzed. By uploading to the cloud server remotely, the report can provide data basis for subsequent monitoring and optimization, enhancing the continuous optimization capability and remote management function of the system, and providing a solid guarantee for the safety, stability and long-term performance of the home energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0017] Other features, objects, and advantages of the application will become more apparent from the following detailed description of non-limiting implementations, made with reference to the drawings:

[0018] Figure 1 A schematic diagram of the steps of a remote upgrade method for a home energy storage system device of the application;

[0019] Figure 2 For Figure 1The detailed step flow diagram of step S1 is shown in the following figure;

[0020] Figure 3 For Figure 1 The detailed step flow diagram of step S2 is shown in the following figure. DETAILED DESCRIPTION

[0021] The technical method of the present application will be described clearly and completely in combination with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0022] In addition, the accompanying drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated description thereof will be omitted. Some block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] To achieve the above-mentioned purpose, please refer to Figures 1 to 3 The present application provides a remote upgrading method for a household energy storage system device, which comprises the following steps:

[0025] Step S1: Collecting temperature distribution data of the surface of the battery pack by sensors arranged in the core area, the middle area and the peripheral area of the battery pack, and recording the temperature of each area as an initial temperature reference value according to the temperature distribution data;

[0026] In this embodiment, the battery pack in the home energy storage system is selected, which is composed of multiple series and parallel cells and is in a closed or semi-closed heat dissipation management structure. In order to obtain accurate temperature distribution data, high-precision temperature sensors (model PT1000, measurement accuracy ±0.1℃) are arranged in the core area, middle area and peripheral area of the battery pack. The sensors are connected to the data acquisition unit through the CAN bus. The system collects the initial temperature data of each area under the static condition of the ambient temperature T0 (assuming T0 = 25℃) and no additional heat source interference, the sampling frequency is set to 1Hz, and the continuous recording time is 300 seconds to ensure data stability. The initial temperature reference value T i,j (where i represents the area number, such as the core area, middle area and peripheral area, and j represents the jth sensor) is processed according to the formula:

[0027]

[0028] wherein, is the average temperature of area i, N i is the number of sensors in area i. The temperature data of all areas is stored in the local database and used as the reference value for subsequent thermal characteristic evaluation.

[0029] Step S2: Function component identification and thermal characteristic evaluation processing are performed on the pre-acquired upgrade package to obtain heat generating component data; the heat generating component data is sequentially executed and sorted according to the initial temperature reference value to obtain batch execution strategy data;

[0030] In this embodiment, the pre-acquired upgrade package contains multiple function components, which specifically refer to software function units that can be independently upgraded. The system first classifies and identifies each function component. The identification process uses a code semantic analysis method based on TF-IDF (Term Frequency-Inverse Document Frequency) and LSTM (Long Short-Term Memory Network) to extract component names and function characteristics, and establishes a feature vector matrix M feature . Then, based on the Fourier heat conduction equation, the heat generation per unit time Q m of each component is calculated:

[0031] Q m = P m *η m ;

[0032] wherein, P m is the power consumption of component m, unit W, and η m is its thermal conversion coefficient. According to the initial temperature reference value , a scheduling method based on the greedy algorithm is adopted to sort all function components according to the heat generation Q m , ensuring that high-heat components are executed first, while limiting the total heat of single batch execution components to not exceed ΔTmax (maximum allowed temperature rise, e.g. set ΔT max = 5℃), thereby generating the batch execution strategy data.

[0033] Step S3: Isolating upgrading of each functional component in the dual-buffer A / B partition structure of the home energy storage system according to the batch execution strategy data, obtaining a component upgrading result; recording real-time temperature change data in the upgrading process according to the component upgrading result; performing threshold comparison processing on the real-time temperature change data, obtaining execution strategy adjustment data;

[0034] In this embodiment, the dual-buffer A / B partition structure (i.e. dual firmware storage structure) of the home energy storage system is used to upgrade each functional component according to the batch execution strategy data. Specifically, in the case of running the current firmware in the A partition, the B partition loads the new firmware and performs functional component upgrading, and each functional component is subjected to integrity check (e.g. SHA-256 hash check) after upgrading. During the component upgrading process, the system records the temperature change data of different areas of the battery pack in real time, with a recording period of 2 seconds / time and a data storage format of T ij (t), i.e. the temperature value of sensor j at time t. When the temperature change at a certain time point is detected to exceed the safety threshold (e.g. set ΔT crit = 3℃), the system triggers the adjustment strategy, adjusts the order or interval time, and updates the batch execution strategy data.

[0035] Step S4: Generating an upgrading verification report containing component thermal characteristic analysis according to the execution strategy adjustment data and real-time temperature change data, and transmitting the upgrading verification report to the cloud server.

[0036] In this embodiment, the system calculates the temperature rise characteristics of each functional component according to the temperature data T ij (t) recorded during the upgrading process and the execution strategy adjustment data, and generates an upgrading verification report. The specific calculation method uses the least squares fitting method to fit the temperature change curve, and the expression is as follows:

[0037]

[0038] where a j , b j , c j are the fitting coefficients, representing the trend of temperature rise and stable temperature. Then, the thermal influence factor β m of each component is calculated:

[0039]

[0040] The component execution influence on the system thermal environment is evaluated. The generated upgrade verification report contains component upgrade sequence, temperature change trend, policy adjustment record and thermal characteristic analysis, and is uploaded to the cloud server through the MQTT protocol to realize remote monitoring and analysis.

[0041] In the intelligent upgrade process of the battery pack, the partition monitoring and thermal management optimization strategy can ensure the stability and safety of the system. First, by arranging sensors in the core area, intermediate area and peripheral area of the battery pack, the temperature distribution data of each area can be accurately obtained, and the initial temperature reference value is established, which provides a reference for temperature monitoring and policy adjustment in the subsequent upgrade process. This can predict the heat dissipation state of the battery pack before upgrading, avoiding potential thermal runaway risks. Then, the function component identification and thermal characteristic evaluation of the upgrade package to be executed are performed, so that the system can intelligently classify according to the heat generation characteristics of each component, and sort according to the initial temperature reference value, so that high-heat components and low-heat components form an optimal execution sequence, and then a reasonable batch execution strategy is formulated. This way not only optimizes the execution sequence of the upgrade, avoids local overheating caused by simultaneous operation of high-heat components, but also reduces the impact of thermal stress on the performance of the battery pack, improving the overall stability of the upgrade process. During the upgrade implementation process, the double-buffer A / B partition structure of the home energy storage system is used for isolated upgrade of each functional component, realizing orderly update of different components in independent environment, reducing the impact of upgrade on normal operation of the system, and improving the response speed and thermal management ability of the system. At the same time, the temperature change of each component is recorded in real time during the upgrade process, and compared with the preset threshold value, so that temperature abnormalities can be detected in time, and the execution strategy is adjusted based on the actual temperature feedback, so as to ensure the efficiency of the upgrade while avoiding safety problems caused by temperature accumulation. Finally, using the temperature change data collected during the upgrade process, combined with the execution strategy adjustment, the system can automatically generate an upgrade verification report containing component thermal characteristic analysis, and transmit it to the cloud server for remote monitoring and analysis, providing data support for future system optimization. This whole set of strategies makes the upgrade process more intelligent, stable and safe, ensuring the reliability of the energy storage system, and also improving the long-term service life and overall operation efficiency of the battery pack.

[0042] Preferably, step S1 comprises the following steps:

[0043] Step S11: installing 4 NTC thermistor type temperature sensors on the core area of the battery pack surface, 4 NTC thermistor type temperature sensors on the intermediate area, and 4 NTC thermistor type temperature sensors on the peripheral area, wherein the NTC thermistor type temperature sensors are connected to the main control board through I 2 The C bus is connected to the main control board;

[0044] In this embodiment, in order to accurately obtain the temperature distribution of the battery pack surface, four NTC (negative temperature coefficient) thermistor type temperature sensors (model NTC 10kΩ B value is 3950K, accuracy ±0.1℃) are installed in the core area, the middle area and the peripheral area respectively. The sensor number of the core area is set to S c,1 to S c,4 , the sensor number of the middle area is S m,1 to S m,4 , and the sensor number of the peripheral area is S o,1 to S o,4 . The sensors are connected to the main control board (for example, using STM32F407 as the main control MCU) through I 2 C bus, and the I 2 C communication addresses are set to 0x48 to 0x4F respectively, to ensure the independence of data acquisition. The sensor installation method uses heat conducting glue fixation and cooperates with an aluminum sheet heat dissipation layer to reduce the influence of local hot spots, and uses three layers of shielding wires to reduce electromagnetic interference and improve data acquisition stability.

[0045] Step S12: set the temperature sampling accuracy to 0.1℃, and collect the temperature data of each area at a frequency of once per minute under the normal operating state of the home energy storage system;

[0046] In this embodiment, the temperature sampling accuracy is set to 0.1℃, and the main control board polls all sensors once every 60 seconds through I 2 C protocol to obtain the temperature data of each area. The specific sampling formula is as follows:

[0047] T ij (i,j)(t)=A·V i,j (i,j)(t)+B;

[0048] Where T ij (i,j)(t) represents the temperature value of the jth sensor of area i at time t, V i,j (i,j)(t) is the output voltage of the sensor at time t, and A and B are calibration coefficients (determined by the characteristics of the sensor, for example, A=100, B=-50). After data acquisition, the data is stored in the internal Flash storage of the MCU and transmitted to the data management unit of the home energy storage system through the UART interface to ensure complete data recording.

[0049] Step S13: calculate the average temperature value, the maximum temperature value and the temperature difference value between areas for each area temperature data, to obtain the temperature state description data;

[0050] In this embodiment, the system processes the temperature data collected for each area to obtain the temperature state description data. First, the average temperature value of each area is calculated

[0051]

[0052] wherein, N i = 4 (number of sensors in each zone). Then, the highest temperature value of each zone is calculated:

[0053]

[0054] Then, the temperature difference value between zones is calculated, such as the temperature difference between the core zone and the intermediate zone:

[0055]

[0056] All the calculation results constitute the temperature state description data and are stored in the system log for subsequent analysis and adjustment strategy.

[0057] Step S14: The temperature state description data of the previous 30 minutes before the home energy storage system receives the upgrade instruction is taken as historical reference, combined with the current temperature state description data, to determine the initial temperature reference value of each zone.

[0058] In this embodiment, in order to more accurately determine the initial temperature reference value of each zone, the system continuously records the temperature state description data once every minute within the previous 30 minutes before receiving the upgrade instruction to form a historical reference data matrix H:

[0059]

[0060] Combined with the current temperature state description data, the initial temperature reference value is calculated by the exponential weighted moving average (EWMA) method

[0061]

[0062] wherein, a is the smoothing factor (for example, take 0.2). The final reference value is used for temperature evaluation in the upgrade process to ensure that the system performs the upgrade operation in a stable temperature environment.

[0063] In the process of battery pack temperature monitoring and management, the present application can effectively improve the thermal management capability of the system through precise sensor arrangement and high-precision data acquisition method. First, four NTC thermistor type temperature sensors are installed in the core zone, the intermediate zone and the peripheral zone of the battery pack respectively, and the temperature data of each zone is collected through the I 2The C bus is connected to the main control board to realize real-time collection and unified management of the temperature of different areas. Such a distributed sensor layout can ensure comprehensive monitoring of temperature changes inside, on the surface and at the edges of the battery pack, avoiding the situation where temperature abnormalities are not discovered in time due to insufficient local data. Secondly, the temperature sampling accuracy is set to 0.1°C, and data is collected at a frequency of once per minute under the normal operating state of the home energy storage system, which can ensure accurate tracking of temperature changes and make the system have higher thermal management response capability. By calculating the average temperature value, the maximum temperature value and the temperature difference between areas, the current temperature state can be accurately described, providing more detailed data support for subsequent analysis. In addition, the temperature state data of the home energy storage system 30 minutes before receiving the upgrade instruction is used as historical reference, and combined with the current temperature state data to determine the initial temperature reference value of each area, which can more accurately evaluate the thermal balance state of the battery pack, and ensure that the upgrade process can be optimized and controlled according to the actual temperature. This combination of long-term monitoring and real-time analysis enables the system to more intelligently identify potential thermal runaway risks, improving the stability and safety of the energy storage system, while also providing accurate temperature references for subsequent upgrade processes, ensuring the efficiency and reliability of component updates.

[0064] Preferably, step S2 comprises the following steps:

[0065] Step S21: performing file structure analysis on the pre-acquired upgrade package to identify functional component type data including user interface resource files, system parameter configuration files and core firmware code files;

[0066] In this embodiment, the system first performs file structure analysis on the pre-acquired upgrade package to identify the functional component type data contained therein. The specific analysis method is to use a file analysis algorithm based on ELF (Executable and Linkable Format) and JSON configuration structure to first analyze the directory structure in the upgrade package, and then classify according to file type and content mode. During the analysis process, the system extracts the file index of the upgrade package and calculates the hash value H(f) = SHA256(f) for each file to ensure integrity. User interface resource files (such as UI images, CSS style sheets, etc.) are identified by extension matching (such as.png,.css,.json), system parameter configuration files (such as system initialization parameters, device identification information, etc.) are identified by XML or INI format matching, and core firmware code files (such as binary firmware or executable files) are identified by ELF format analysis tools (such as objdump) and the code segment (.text), data segment (.data) and read-only segment (.rodata) are extracted. Finally, the functional component type data parsed is stored in the database for subsequent thermal characteristic evaluation.

[0067] Step S22: thermal characteristic evaluation according to the functional component type data, and analysis of the processor occupancy, memory access frequency and execution duration of each functional component in the execution process, so as to obtain component thermal characteristic data;

[0068] In this embodiment, in order to accurately evaluate the thermal characteristics of the functional components, the system analyzes the processor occupancy, memory access frequency and execution duration of each functional component in the execution process. The processor occupancy is measured by instrumentation profiling, that is, a timestamp t end is inserted before and after each function call start , the execution period T m of the component is calculated as t end -t start , and the CPU occupancy is calculated in combination with the processor clock frequency f cpu The memory access frequency is obtained by monitoring the cache hit rate and DRAM access times, and the calculation formula is as follows:

[0069]

[0070] wherein Misses cache represents the number of cache misses, and Accesses DRAM represents the number of DRAM accesses. The execution duration is recorded by task scheduling logs, and the start and completion times of each component are analyzed. Finally, these data constitute a component thermal characteristic data table, which is used for thermal generation level division.

[0071] Step S23: thermal generation level division of each functional component according to the component thermal characteristic data, so as to obtain thermal generation classification component data; wherein the thermal generation level division is specifically that the user interface resource file is divided into a low thermal generation component, the core area temperature is expected to rise by no more than 2℃ during the execution process; the system parameter configuration file is divided into a medium thermal generation component, the core area temperature is expected to rise between 2℃ and 5℃ during the execution process; and the core firmware code file is divided into a high thermal generation component, the core area temperature is expected to rise by more than 5℃ during the execution process;

[0072] In this embodiment, based on the component thermal characteristic data, the thermal generation level division of each functional component is performed. The division standard is as follows: if the core area temperature is expected to rise by no more than 2℃ during the execution process of a component, the component is divided into a low thermal generation component; if the temperature rises between 2℃ and 5℃, the component is divided into a medium thermal generation component; and if the temperature rises by more than 5℃, the component is divided into a high thermal generation component. The expected rise value ΔT c of the core area temperature is calculated by the following heat conduction model:

[0073] ​ΔT c = P m × R th ;

[0074] wherein, P m is the average power consumption of the component (unit: W), which is obtained by looking up the processor load and power characteristics table; R th is the thermal resistance of the core area (unit: ℃ / W), which is measured by experiment. The calculated ΔT c is further used for component grading, and generates heat generating component data, including component name, heat generating level and estimated temperature rise value, which is stored in the scheduling system.

[0075] Step S24: According to the initial temperature reference value, the heat generating component data is processed in series to obtain the batch execution strategy data.

[0076] In this embodiment, in order to optimize the heat management in the upgrading process, the system processes the heat generating component data in series according to the initial temperature reference value to form the batch execution strategy data. The specific method is as follows: first, sort the components according to the heat generating level from low to high to ensure that the low heat generating components are executed first; second, consider the initial temperature reference value to make dynamic adjustment to ensure that the medium or high heat generating components are executed on the premise that the current temperature does not exceed the set threshold. Set the temperature safety threshold T safe = 50℃, then the execution scheduling constraints are as follows:

[0077]

[0078] wherein, n is the current batch of components, ΔT k is the temperature rise contribution value of the components in the batch. The system adopts a greedy scheduling strategy to select the highest level component allowed by the current temperature each time to form the final execution batch. The final batch execution strategy data is stored in JSON format, including the list of components in each batch, the estimated execution time and the temperature rise estimate value, and is transmitted to the upgrading management unit for execution.

[0079] In the upgrading management process, the present application can effectively reduce the influence of the upgrading process on the thermal stability of the battery pack through accurate file analysis and thermal characteristic evaluation. First, the file structure of the pre-acquired upgrade package is analyzed to accurately identify the functional component types of the user interface resource file, the system parameter configuration file and the core firmware code file, ensuring that the system can develop targeted upgrade strategies. Subsequently, thermal characteristic analysis is performed in combination with the functional component type data to evaluate the processor occupancy rate, memory access frequency and execution duration of each component during execution to quantify its influence on the system temperature. Based on this analysis result, the functional components are classified according to their heat generation characteristics, wherein the user interface resource file is classified as a low heat generation component, the core area temperature change during the upgrading process is not more than 2℃, the system parameter configuration file is classified as a medium heat generation component, the core area temperature change is between 2℃ and 5℃, and the core firmware code file is classified as a high heat generation component due to its high execution load, and the core area temperature change exceeds 5℃. This classification method enables the system to adopt appropriate execution sequences for different types of components, reduces the concentrated execution of high heat load components, and reduces the risk of temperature sudden increase. Finally, the heat generation classified component data is serially executed according to the initial temperature reference value of the battery pack to ensure batch execution under the allowable temperature environment, thereby optimizing the thermal management of the upgrading process. Through this strategy, the rational allocation of thermal load during the upgrading process can be achieved, the system instability problem caused by temperature overrun can be avoided, and the long-term operation reliability and safety of the home energy storage system can be improved.

[0080] Preferably, step S22 comprises the following steps:

[0081] Step S221: performing static code analysis on the functional component type data in the isolated test environment to detect code complexity, loop structure quantity and resource call frequency, thereby generating a static thermal characteristic evaluation matrix;

[0082] In this embodiment, the system performs static code analysis on the functional component type data in the isolated test environment to detect code complexity, loop structure quantity and resource call frequency to generate a static thermal characteristic evaluation matrix. The specific implementation method is to calculate the code complexity using McCabe cycle complexity metric C m = E-N+2P (wherein E is the number of edges in the control flow graph, N is the number of nodes, and P is the number of connected components), and to extract the loop structure using AST (Abstract Syntax Tree) analysis to count the loop depth and iteration times. In addition, the external resource access frequency of the component is analyzed through a system call tracking tool (such as strace or DTrace), including the number of file I / O, network communication and hardware calls. Finally, a static thermal characteristic evaluation matrix is constructed:

[0083] H static = [C m Ldepth F syscall ];

[0084] wherein C m is the cycle complexity, L depth is the maximum loop depth, and F syscall is the number of system calls per unit time. The matrix data is used for subsequent thermodynamic behavior prediction.

[0085] Step S222: According to the energy consumption characteristics of the processor instruction set and the memory access mode, the energy consumption coefficient of the unit operation is calculated, and a thermal energy conversion function is established.

[0086] In this embodiment, the system calculates the energy consumption coefficient of the unit operation according to the energy consumption characteristics of the processor instruction set and the memory access mode, and establishes a thermal energy conversion function. First, the basic instruction power consumption of the processor is tested through SPEC CPU benchmark, including integer operation (ADD, SUB), floating point operation (FADD, FMUL), branch prediction overhead (JMP, CALL), etc., and the average power consumption P i of each instruction is recorded. Second, the storage access power P m is analyzed in combination with the memory hierarchy (L1 / L2 cache, DDR access), and finally the thermal energy conversion function is constructed:

[0087]

[0088] wherein N i is the execution frequency of instruction i, P i is the unit power consumption of the instruction; A m is the access frequency of the memory level m, and P m is the average power consumption of the level. The function is used to calculate the thermal power consumption in the execution process of the component, and provides a theoretical estimate of the heat generation rate.

[0089] Step S223: Extract the execution records of the same type of component from the pre-acquired historical upgrade database to construct a thermodynamic feature library of component execution, wherein the execution records include CPU utilization time series, memory access mode and IO operation intensity.

[0090] In this embodiment, the system extracts the execution records of the same type of component from the historical upgrade database to construct a thermodynamic feature library of component execution. The database contains multiple historical versions of upgrade execution data, including measurement results under different hardware platforms and different execution environments. The system classifies according to component categories (UI resources, system parameters, firmware code), and extracts the execution records of each component, including CPU utilization time series U(t), memory access mode M(t) and IO operation intensity I(t). The construction method of the thermodynamic feature library is:

[0091]

[0092] wherein, U k (t) record CPU occupancy at different time points, M k (t) record storage access behavior at different time points, I k (t) record I / O access times at different time points. Finally, the database is used for subsequent thermal prediction calculation.

[0093] Step S224: Perform thermodynamic behavior feature prediction according to the static thermal characteristic evaluation matrix and the thermodynamic feature library, so as to obtain thermal prediction data including peak heat generation rate, heat accumulation curve and heat decay characteristic;

[0094] In this embodiment, the system performs thermodynamic behavior feature prediction according to the static thermal characteristic evaluation matrix and the thermodynamic feature library, so as to obtain thermal prediction data such as peak heat generation rate, heat accumulation curve and heat decay characteristic. The specific implementation method is to use a time series prediction model (such as LSTM) to fit historical execution data, and combine code complexity and loop structure data in the static thermal characteristic matrix to perform thermodynamic simulation. The thermal prediction data includes the following indexes: peak heat generation rate P peak = max(Q(t)), wherein Q(t) is a time series calculated by a thermal energy conversion function; heat accumulation curve wherein, T env is the ambient temperature, C th is the heat capacity; heat decay characteristic T d (t) = T0e -t / τ , wherein T0 is the initial temperature and τ is the heat decay time constant. The thermal prediction data is used to guide component execution order optimization.

[0095] Step S225: Perform sampling execution test on the functional component type data in an isolated test environment, record the real-time power curve in the execution process by using a high-precision power consumption monitoring circuit, and calculate the corresponding heat generation rate by using a thermoelectric conversion function, so as to obtain heat generation data;

[0096] In this embodiment, the system performs sampling execution test on the functional component type data in an isolated test environment, and records the real-time power curve in the execution process by using a high-precision power consumption monitoring circuit. The specific method is to use a NI-USB-6343 data acquisition card (sampling rate 1 MS / s, resolution 16 bit) to monitor the power supply current I(t) and voltage V(t) of the component during operation, and calculate the real-time power: P(t) = V(t) · I(t); in addition, the system uses a thermocouple sensor (such as a K-type thermocouple with an accuracy of 0.1℃) to monitor the temperature change T(t) of the component during execution. Based on the previously established thermal energy conversion function, the corresponding heat generation rate is calculated: The heat generation data is compared with the predicted value.

[0097] Step S226: The thermal prediction data is compared with the heat generation data, and parameter calibration is performed to generate the component thermal characteristic data.

[0098] In this embodiment, the system compares the thermal prediction data with the heat generation data, and performs parameter calibration to generate the final component thermal characteristic data. The comparison method is to calculate the mean square error (MSE) between the predicted heat generation rate Q pred (t i ) and the measured heat generation rate Q meas (t i ):

[0099]

[0100] If the error exceeds a set threshold (e.g. 1%), the system adjusts the power consumption coefficients P i and P m in the heat energy conversion function using least square regression. Finally, the calibrated thermal characteristic data is stored in the database, including the heat generation rate, temperature rise change during execution, and power consumption characteristics of each functional component, to provide support for subsequent scheduling optimization.

[0101] In the upgrading management process, the accurate thermal characteristic evaluation method can ensure that the thermal management of the system during upgrading is more scientific and reasonable. First, in the isolated test environment, static code analysis is performed on the functional component type data to identify the code complexity, the number of loop structures and the resource call frequency, thereby constructing a static thermal characteristic evaluation matrix to provide a basis for subsequent thermal analysis. Subsequently, based on the energy consumption characteristics of the processor instruction set and the memory access mode, the energy consumption coefficient of the unit operation is calculated, and a thermal energy conversion function is established to make the energy consumption calculation more accurate. At the same time, the execution records of the same type of components are extracted from the historical upgrading database to construct a thermodynamic characteristic library of the components, which contains key indicators such as CPU utilization time series, memory access mode and IO operation intensity, so as to provide accurate historical data support. Based on these data, combined with the static thermal characteristic evaluation matrix, the thermodynamic behavior is predicted to generate thermal prediction data such as peak heat generation rate, heat accumulation curve and thermal decay characteristics, so as to predict the temperature change trend of the component in advance. Subsequently, the sampling test of the functional component is performed in the isolated test environment, the real-time power curve during the execution process is recorded by using the high-precision power consumption monitoring circuit, and the heat generation rate of the component is calculated by using the thermal energy conversion function to ensure the accuracy of the thermal evaluation data. Finally, by comparing and analyzing the thermal prediction data and the actual heat generation data, and calibrating the parameters, the component thermal characteristic data is more consistent with the actual operation, so as to improve the accurate control of the temperature change in the upgrading process and ensure the safety and stability of the system operation.

[0102] Preferably, step S24 comprises the following steps:

[0103] Step S241: calculating the core area temperature risk value, the inter-regional temperature difference value and the temperature change trend value according to the initial temperature reference value, so as to obtain the temperature state evaluation data;

[0104] In the operation process of the household energy storage system, the real-time temperature data of the core area and the temperature data of different areas are first obtained, and the core area temperature risk value, the inter-regional temperature difference value and the temperature change trend value are calculated. The core area temperature risk value is used to evaluate the deviation degree of the current core area temperature relative to the initial temperature reference value, and the calculation method is to subtract the initial temperature reference value from the current core area temperature and then divide by the initial temperature reference value, and finally convert it into percentage form. The inter-regional temperature difference value is used to measure the temperature balance between regions, and the calculation method is to take the difference between the current maximum temperature and the minimum temperature. The temperature change trend value reflects the rate of change of the core area temperature with time, which can be obtained by calculating the average value of the core area temperature change in a short time. Finally, these three indicators are combined to form the temperature state evaluation data for subsequent upgrading suitability determination.

[0105] Step S242: upgrade suitability determination of the household energy storage system according to the temperature state evaluation data, so as to obtain suitability determination data, wherein the upgrade suitability determination is specifically determining that it is suitable for upgrading when the core area temperature is lower than 30 DEG C and the maximum temperature difference between regions is less than 8 DEG C, determining that it is attention to upgrade when the core area temperature is between 30 DEG C and 40 DEG C or the maximum temperature difference between regions is between 8 DEG C and 12 DEG C, and determining that it is not suitable for upgrading when the core area temperature is higher than 40 DEG C or the maximum temperature difference between regions is higher than 12 DEG C;

[0106] The embodiment of the application determines the upgrade suitability of the household energy storage system according to the temperature state evaluation data. First, three determination standards are set: when the core area temperature is lower than 30 DEG C and the maximum temperature difference between regions is less than 8 DEG C, the system determines that it is suitable for upgrading; when the core area temperature is between 30 DEG C and 40 DEG C or the maximum temperature difference between regions is between 8 DEG C and 12 DEG C, the system determines that it is attention to upgrade; and when the core area temperature is higher than 40 DEG C or the maximum temperature difference between regions is higher than 12 DEG C, the system determines that it is not suitable for upgrading. In specific application, the system continuously monitors the temperature state evaluation data, and performs real-time calculation based on the preset determination rule to obtain suitability determination data, which is used to guide the subsequent upgrade strategy.

[0107] Step S243: execution sequence determination processing of the heat production classification component data is performed according to the suitability determination data, so as to obtain upgrade sequence data, wherein the execution sequence determination processing is specifically arranging the low heat generation component, the medium heat generation component and the high heat generation component in sequence when it is determined that it is suitable for upgrading, arranging the low heat generation component and the medium heat generation component to be executed immediately and postponing the execution of the high heat generation component when it is determined that it is attention to upgrade, and delaying the upgrade process when it is determined that it is not suitable for upgrading.

[0108] The embodiment of the application determines the execution sequence of components of different heat generation levels according to the suitability determination data. When the system determines that it is suitable for upgrading, the low heat generation component, the medium heat generation component and the high heat generation component are executed in sequence to reduce the overall temperature rise. When the system determines that it is attention to upgrade, the low heat generation component and the medium heat generation component are arranged to be executed immediately, and the execution of the high heat generation component is postponed to a period of time when the system load is low, such as night or a period of time when the cooling equipment runs intensively. When the system determines that it is not suitable for upgrading, the system will postpone all upgrade tasks and continuously monitor the temperature state, and wait for the temperature to fall to a reasonable range before re-arranging the execution sequence. In specific implementation, the system dynamically adjusts the upgrade time of each component through a task scheduling algorithm to ensure that the heat accumulation does not exceed the safety threshold.

[0109] Step S244: Calculate the minimum cooling waiting time between each functional component based on the upgrade sequence data and the component thermal characteristic data, and obtain cooling time configuration data;

[0110] After determining the execution sequence of the components, the embodiment of the present application further calculates the minimum cooling waiting time between different components. The cooling time depends on the thermal characteristic data of the components, including the heat generation rate, the specific heat capacity of the component material, and the thermal mass of the component. First, the total heat generated by the component is calculated according to the peak heat generation rate and the heat accumulation time of the component, and then the shortest time required for the temperature of the component to drop to the safe range is calculated in combination with the heat capacity parameter of the component material. In actual application, the system can use thermodynamic simulation to analyze the heat generated by different components during operation, and adjust the execution time interval between the components according to these data to ensure the overall temperature stability of the system.

[0111] Step S245: Integrate the upgrade sequence data and the cooling time configuration data into batch execution strategy data.

[0112] The embodiment of the present application generates the final batch execution strategy data by combining the upgrade sequence data and the cooling time configuration data. First, the upgrade tasks of the components are arranged according to the execution sequence determined in step S243, and the cooling waiting time calculated in step S244 is inserted between different components. For example, if a low-heat-generation component needs 2 minutes to complete the upgrade, a medium-heat-generation component needs 5 minutes, and the minimum cooling waiting time calculated between them is 3 minutes, then the final execution strategy is to execute the low-heat-generation component first, wait for 3 minutes to cool after completion, and then execute the medium-heat-generation component. For the execution of high-heat-generation components, the specific execution time needs to be further determined in combination with the current system load. In actual application, the system will dynamically adjust the execution strategy according to the real-time monitored temperature data to ensure that local overheating or temperature mutation will not occur during the upgrade process, and to ensure the stability and safety of the entire energy storage system.

[0113] In the upgrading process, the rationality of the upgrading sequence can be ensured and the system risk caused by temperature abnormalities can be reduced by comprehensively analyzing the temperature state of the battery pack. First, the core area temperature risk value, the inter-regional temperature difference value and the temperature change trend value are calculated based on the initial temperature reference value to quantify the current thermal state of the system and form temperature state evaluation data. Then, the upgrading suitability of the home energy storage system is determined according to the evaluation data, and the upgrading strategy under different temperature conditions is determined. When the core area temperature is low and the inter-regional temperature difference is small, the system can normally upgrade; when the temperature reaches a certain threshold or the temperature difference is large, a more cautious strategy needs to be taken; if the temperature is too high or the temperature difference is too large, the upgrade is delayed to avoid system instability caused by heat accumulation. Next, based on the suitability determination result, the execution sequence of each functional component is optimized to ensure that the components with low heat are executed first, and the high-heat generation components are delayed if necessary to reduce the impact on the core area temperature. At the same time, the minimum cooling waiting time between components is calculated to ensure that the system operates after the heat is fully diffused, avoiding excessive temperature accumulation. Finally, the upgrading sequence data and cooling time configuration data are integrated into batch execution strategy data, making the upgrading process more safe and efficient, reducing system abnormalities caused by temperature overload, and improving the stability and reliability of the home energy storage system.

[0114] Preferably, the isolated upgrading of each functional component in step S3 comprises:

[0115] Initialize the double-buffer A / B partition structure of the home energy storage system, identify the current active partition, determine the inactive partition as the upgrading target partition, perform data erasure processing on the inactive partition, and obtain the ready-to-upgrade partition;

[0116] According to the batch execution strategy data, the component content and the corresponding digital signature of each functional component are extracted, signature verification and integrity check processing are performed, and the security-verified to-be-upgraded component data is obtained;

[0117] In the upgrading partition, independent storage space and resource access permission are allocated for the to-be-upgraded component data, thereby constructing a secure execution area that prevents cross-interference as a component isolation area;

[0118] The to-be-upgraded component data is written into the component isolation area, and a function verification test is performed to obtain the component verification state, wherein the function verification test specifically verifies the interface resource integrity when the component is a user interface component, verifies the parameter validity when the component is a system parameter configuration component, and performs a basic function test when the component is a core firmware component;

[0119] According to the component verification state, a component verification success rate is calculated, when the success rate is higher than 95%, the next component upgrade is executed, when the success rate is lower than 80%, a rollback operation is executed on the failed component, thereby obtaining a component upgrade result.

[0120] The home energy storage system in the embodiment of the application adopts a double-buffering A / B partition structure, wherein the A area and the B area store a current running version and a version to be upgraded respectively. First, the system determines a current active partition by reading identification data of a startup management component, for example, if the startup identification points to the A area, the A area is the active partition, and the B area is the non-active partition. Next, the B area is taken as a target partition for upgrading, and a data erasing process is performed to clear old version data and release storage space. The erasing operation adopts a block-level fast erasing technology, and the specific process is to traverse all storage blocks of the B area and write a full "0" or full "1" mode to each storage block to ensure that the data is completely cleared. For example, if the B area size is 128 MB and the size of each erasing block is 4 KB, the erasing process needs to traverse 128 MB ÷ 4 KB = 32768 storage blocks and perform erasing block by block. After the erasing is completed, the B area enters an "idle writable" state, that is, a ready state for the upgrading partition. According to batch execution strategy data, the system loads data of components to be upgraded in a predetermined order. First, the system extracts binary data of each functional component and a corresponding digital signature from the upgrade package and stores them to a buffer area. The digital signature is encrypted by using an SHA-256 hash algorithm and an RSA-2048 private key, and the verification process is as follows: ①calculating an SHA-256 hash value H = SHA256(M) of the extracted component data; ②decrypting a signature S attached to the component by using an RSA-2048 public key to obtain a hash value H' calculated at the time of the signature; and ③comparing H and H', if they are the same, the verification is passed, otherwise, the verification fails. The integrity verification further adopts a CRC32 check algorithm to calculate a check code of the extracted component data and compare it with a stored check code to ensure that the data is not damaged. For example, if the component data is 1 MB, the system calculates a CRC32 check code thereof and compares it with an expected value, if the check values are consistent, it is considered that the component is complete and correct, and enters a subsequent upgrading process. In the upgrading partition B area, the system allocates independent storage space for each component according to the storage requirement of the component. For example, assuming that a user interface component needs 4 MB of storage space, a system parameter component needs 2 MB of storage space, and a core firmware component needs 8 MB of storage space, the system will divide 4 MB, 2 MB and 8 MB of storage blocks in the B area in sequence to ensure that the components do not interfere with each other at the physical storage level. In addition, in order to prevent access conflicts between different components, the system sets resource access permissions for each component through an access control list (ACL), for example: the user interface component can only access UI resource files, the system parameter component can only access configuration storage areas, and the core firmware component can only access hardware control interfaces. This process ensures the safe isolation between components during the upgrading process to prevent data leakage or damage. After writing the data of the components to be upgraded, which have passed the security verification, to the allocated isolated area storage space, the system performs function verification tests on the components in sequence.For user interface components, the system verifies the interface integrity by comparing the hash values of the UI resource files, such as performing SHA-256 hash calculation on interface images and font files. If the calculated hash value matches the expected value in the upgrade package, the integrity check is considered passed. For system parameter configuration components, the system reads the parameter storage area content and checks if the parameter value is within the expected range, such as voltage parameters must be between 100V-240V. If it exceeds the range, the parameter is considered invalid. For core firmware components, the system performs basic function tests, such as sending a simple control command to the processor (such as adjusting the output power to 50%) and monitoring the system response to see if it meets the expectations. After all the components pass the verification, the component verification status is updated to "passed", otherwise it is updated to "failed". After all components perform the function verification test, the system calculates the component verification success rate, the calculation method is the number of successful components N. pass Divide by the total number of components N total That is, For example, if the system contains 10 components to be upgraded, 9 of which pass the verification, the success rate is If the success rate is higher than 95%, the system continues to upgrade the next batch of components; if the success rate is lower than 80%, a rollback operation is performed, the rollback method is to erase the data of the upgrade partition and restore to the old version. For example, if the B zone upgrade fails, the system modifies the identification of the startup management component, reconfigures the A zone as the active partition, and forces the system to restart to roll back to the original version, ensuring stable operation of the system.

[0121] In the upgrading process of the home energy storage system, the double-buffer A / B partition structure is adopted to ensure the stability of the system during the upgrading. First, by identifying the current active partition, the inactive partition is determined as the upgrading target, and the data erasure process is performed to make it available, thereby avoiding the interference of old data to the upgrading process. Subsequently, according to the batch execution strategy, the contents of each functional component are extracted and the digital signature is verified to ensure the integrity and security of the upgrading data, preventing tampered or damaged components from affecting the system operation. In the upgrading partition, independent storage space is allocated for each component to be upgraded, and access permission is set to build a secure execution area, preventing cross interference between different components and improving the reliability of execution. Then, the data of the components to be upgraded are written into the component isolation area, and corresponding functional verification tests are performed for different types of components, such as user interface components that need to verify the integrity of interface resources, system parameter configuration components that need to verify the validity of parameters, and core firmware components that need to perform basic function tests to ensure that the components can operate normally after upgrading. Finally, according to the component verification state, the success rate is calculated, and when the success rate exceeds 95%, the subsequent component upgrading is continued to ensure the continuity of the upgrading process; if the success rate is less than 80%, the rollback mechanism is triggered to restore to the stable version, avoiding the impact of faults on the normal operation of the system. This mechanism not only improves the reliability of upgrading, but also reduces the risk of upgrading failure, ensuring the high availability and data security of the home energy storage system during the upgrading process.

[0122] Preferably, the step S3 of recording the real-time temperature change data during the upgrading process according to the component upgrading result comprises:

[0123] Collecting the real-time temperature sampling values of the core area, the middle area and the peripheral area of the battery pack on the main control board of the home energy storage system through the NTC thermistor type temperature sensor;

[0124] Based on the real-time temperature sampling values, the average temperature, the maximum temperature and the temperature difference between the regions are calculated to obtain multi-dimensional temperature data;

[0125] When the functional component starts to execute the upgrading operation, the timestamp and the component information are associated with the multi-dimensional temperature data to form a time series temperature record;

[0126] According to the time series temperature record, the temperature change rate, the temperature peak value and the temperature recovery time during the execution of each functional component are calculated;

[0127] Marking the execution stage with a temperature change rate exceeding 0.3℃ / min or a temperature peak value exceeding 8℃ of the initial temperature as a high-risk temperature interval;

[0128] Based on the high-risk temperature interval and the temperature recovery time, the heat dissipation characteristics of the functional component are evaluated;

[0129] Real-time temperature change data is generated according to heat dissipation characteristics of each component, temperature peaks, and temperature change rates.

[0130] In the home energy storage system, the battery pack is usually divided into core area, middle area and peripheral area according to the heat conduction characteristics, which correspond to the internal battery cell, the middle layer battery cell close to the internal battery cell and the outermost battery cell of the battery pack respectively. A plurality of NTC thermistor sensors are installed in each area, for example, 4 in the core area, 6 in the middle area and 8 in the peripheral area, to improve the spatial resolution of temperature collection. These sensors are connected to the analog-to-digital converter (ADC) on the main control board through the SPI or I2C bus, and the main control board collects temperature data at a sampling frequency of 1 Hz. In order to improve the measurement accuracy, the temperature-resistance conversion of the NTC thermistor is linearly compensated by the Steinhart-Hart formula, and the parameters of the temperature calculation formula are corrected according to the calibration data provided by the sensor manufacturer, and the high-precision temperature value is calculated in real time during temperature collection. After the main control board receives the data collected by the NTC thermistor sensor, it first cleans the data of the temperature values of multiple sensors in each area, and eliminates abnormal values, for example, using the three standard deviation method to eliminate temperature data outside the normal range. Then, the average temperature of each area is calculated, for example, the average temperature of the core area is the arithmetic mean of all valid sensor temperature values in the core area; the highest temperature is the highest temperature sampling value in the area; the temperature difference between areas is the difference between the highest temperatures of the core area, the middle area and the peripheral area. For example, if the highest temperature of the core area is 45.2℃, the highest temperature of the middle area is 43.1℃, and the highest temperature of the peripheral area is 41.5℃, the temperature difference between the core area and the middle area is 2.1℃, and the temperature difference between the core area and the peripheral area is 3.7℃. Finally, these calculation results are stored as multi-dimensional temperature data and stored in the storage unit of the main control board in time sequence for subsequent analysis and use. When the system detects that a functional component enters the upgrade mode, the main control board records the current timestamp (for example, Unix timestamp accurate to milliseconds) and the name of the component being upgraded (such as BMS control component, DC-DC conversion component, etc.) through system log. At this time, the control board binds the multi-dimensional temperature data at the current time with the timestamp and component information, and stores it in the database. For example, at a certain time T1, the BMS component starts to upgrade, and the system stores the record including: timestamp T1=1700000000ms, component name=BMS control component, core area average temperature=42.3℃, highest temperature=44.1℃, middle area average temperature=41.7℃, peripheral area average temperature=40.5℃, core area-peripheral area temperature difference=3.6℃. This process will continue until the upgrade is completed, forming a complete time series temperature record data. The main control board regularly (for example, every 30 seconds) extracts the temperature data during the upgrade from the time series temperature record, and calculates the temperature change rate, which is the change amplitude of the temperature within a certain time interval. For example, if the core area temperature rises from 42.3℃ to 43.5℃ within 30 seconds, the temperature change rate is calculated as (43.5-42.3) / (30 / 60)=2.4℃ / min.In addition, the temperature peak value is the highest temperature value during the upgrade process, for example, if the highest temperature of the core area during the entire upgrade process is 45.8°C, then this value is recorded as the temperature peak value. The temperature recovery time is defined as the time required for the temperature of the core area to recover from the highest value to within 2°C of the initial temperature after the end of the upgrade. For example, if the temperature of the core area at the end of the upgrade is 45.8°C and the initial temperature is 42.3°C, then the time required for the temperature to drop to 44.3°C is monitored, and if it is 150 seconds, then the recovery time is recorded as 150 seconds. The main control board performs a traversal analysis of all the time-sequential temperature records to determine whether the temperature change rate in a certain time period exceeds 0.3°C / min or whether the temperature peak value exceeds the initial temperature at the start of the upgrade by 8°C. If either condition is met, then the time period is marked as a temperature high-risk interval. For example, if the core temperature of the BMS control component rapidly rises from 42.3°C to 51.0°C during the upgrade process, exceeding the initial temperature of 42.3°C by 8°C, then this upgrade phase is marked as a temperature high-risk interval, and the risk occurrence time is recorded as the 80th to 200th second after the upgrade, in order to facilitate subsequent heat dissipation optimization. The main control board analyzes the temperature high-risk intervals and temperature recovery times of each functional component, and evaluates the heat dissipation capacity of the components in combination with the temperature evolution of different components under the same environmental conditions. First, the duration of the high-risk interval is calculated, for example, if the high-risk interval of the BMS control component lasts for 120 seconds, while the high-risk interval of the DC-DC conversion component lasts for only 50 seconds, then it indicates that the BMS component has a large heat accumulation and poor heat dissipation performance. Second, the heat dissipation efficiency is analyzed in combination with the temperature recovery time, for example, if the temperature recovery time of the BMS component is 300 seconds, while the DC-DC component only needs 180 seconds, then it can be determined that the heat dissipation capacity of the BMS component is weaker than that of the DC-DC component. Finally, the heat dissipation characteristics of each functional component are classified based on these indicators, for example, the BMS component is marked as "poor heat dissipation", and the DC-DC component is marked as "good heat dissipation". After the analysis is completed, the main control board stores the heat dissipation characteristics, maximum temperature peak value, and temperature change rate of all functional components in the database, and generates real-time temperature change data through data fusion technology. Data fusion includes weighted calculation of the temperature changes of different components, for example, if the maximum temperature peak value of the BMS component is 51.0°C and the temperature change rate is 0.45°C / min, the heat dissipation capacity is weak, while the maximum temperature peak value of the DC-DC component is only 46.5°C and the temperature change rate is 0.22°C / min, the heat dissipation capacity is good, then when calculating the overall temperature change trend, the control board gives the BMS component a higher weight, for example, 80%, and the DC-DC component a weight of 20%, to calculate the overall temperature rise trend of the system. These real-time temperature change data can be used to dynamically adjust the heat dissipation strategy of the air cooling or liquid cooling system, improving the safety of system operation.

[0131] The application utilizes NTC thermistor type temperature sensors to sample the temperatures of the core area, the intermediate area and the peripheral area of the battery pack in real time during the operation of the home energy storage system, so that the system can accurately perceive the temperature state of different areas. Based on the collected temperature data, the average temperature, the maximum temperature and the temperature difference between areas of each area are calculated to form complete temperature monitoring data, which provides a basis for subsequent temperature analysis. When the functional components perform upgrade operations, the timestamp and component information are associated with the temperature data to construct a time series temperature record, ensuring that the temperature change trend during the upgrade process can be tracked. Through analysis of the time series temperature record, the temperature change rate, the temperature peak value and the temperature recovery time during the execution of each functional component are counted, so that the system can identify the overheating phenomenon that may occur during the operation of the component. For the execution stage where the temperature change rate exceeds 0.3℃ / min or the temperature peak value increases by more than 8℃ compared with the initial temperature, it is marked as a temperature high-risk interval, so that appropriate optimization strategies can be taken in the subsequent upgrade process. Combined with the temperature high-risk interval and the temperature recovery time, the heat dissipation characteristics of each functional component are evaluated to understand the influence of different components on the system temperature. Finally, according to the heat dissipation characteristics, the temperature peak value and the temperature change rate of each component, real-time temperature change data is generated to support the dynamic adjustment of the upgrade sequence and cooling strategy of the system. This series of processing methods ensures the fine management of temperature during the upgrade process, thereby reducing the risk of performance degradation or system abnormalities caused by excessive temperature and improving the operation stability of the energy storage system under complex working conditions.

[0132] Preferably, the threshold comparison process in step S3 includes:

[0133] The core area temperature change rate in the real-time temperature change data is compared with the preset temperature change rate threshold 0.5℃ / min to generate a temperature change rate risk assessment result;

[0134] The processor frequency reduction instruction data is determined based on the temperature change rate risk assessment result;

[0135] The core area current temperature value in the real-time temperature change data is compared with the temperature upper threshold 45℃ to obtain temperature safety margin data through difference calculation;

[0136] Based on the temperature safety margin data, a cooling strategy is developed, and when the temperature safety margin is less than 5℃, the cooling time extension coefficient is calculated as 150%, and cooling time adjustment data is generated;

[0137] The temperature values of each area in the real-time temperature change data are extracted, the maximum inter-regional temperature difference is calculated, and the temperature difference threshold 12℃ is compared to form temperature distribution uniformity evaluation data;

[0138] The fan speed and airflow direction parameters are determined according to the temperature distribution uniformity evaluation data to generate fan control instruction data;

[0139] The processor downclocking instruction data, cooling time adjustment data, and fan control instruction data are each subjected to influence factor calculation on the upgrade flow, thereby forming execution strategy adjustment data.

[0140] The main control board in the embodiment of the present application extracts the core area temperature value change from the real-time temperature change data, and calculates the temperature change rate per unit time. Assuming that the temperature data is stored in time sequence, the temperature difference of the core area between two consecutive temperature samplings is divided by the time interval (for example, 30 seconds), and the temperature change rate is obtained. For example, in a certain period of time, the core area temperature rises from 43.5°C to 44.5°C, and the time interval is 30 seconds, then the temperature change rate is (44.5°C-43.5°C) / 30 seconds=0.0333°C / s, and the temperature change rate per minute is 0.0333*60=2°C / min. The calculated temperature change rate is compared with the preset threshold 0.5°C / min, if the temperature change rate is greater than the threshold, the temperature change rate risk assessment result is generated, the time interval is marked as a high-risk interval, the alarm system is started, and related processing measures are provided. Once the main control board identifies the high-risk interval of the temperature change rate exceeding the preset threshold 0.5°C / min, the system will immediately issue a processor frequency reduction instruction according to the temperature change rate evaluation result. For example, if the temperature change rate reaches or exceeds 1°C / min, the main control board will instruct the processor to reduce the running frequency, for example, the CPU frequency is reduced from 2.5GHz to 1.8GHz, to reduce the processor power consumption and heat generation. At this time, the main control board generates the frequency reduction instruction data according to different temperature change rate levels, and the instruction will gradually reduce the frequency of the processor or take more stringent frequency reduction strategies according to the different temperature change rates. The real-time collected core area temperature value is compared with the preset temperature upper limit threshold (for example, 45°C), and the temperature margin data is calculated. Assuming that the current core area temperature is 43°C, the temperature safety margin is 45°C-43°C=2°C. If the temperature safety margin is less than a certain value (such as 5°C), it means that the core area temperature is close to the safety upper limit, and measures need to be taken for temperature control or heat dissipation optimization. This temperature safety margin data is used to develop a cooling strategy later and decide whether to start a more efficient cooling scheme. If the core area temperature is close to or below the safety margin threshold of 5°C through the temperature safety margin data, the main control board will start more stringent cooling measures. For example, assuming that the core area temperature is 44.5°C, and the temperature upper limit is 45°C, the safety margin is only 0.5°C. At this time, according to the set cooling strategy, the cooling time extension coefficient is set to 150%, that is, if the normal cooling time is 20 minutes, the cooling time will be adjusted to 30 minutes (20 minutes*1.5). This cooling time adjustment data will be transmitted to the cooling system to adjust the cooling time and start the high-efficiency cooling scheme, such as increasing the fan speed or starting the liquid cooling system. The main control board calculates the temperature difference between each region according to the temperature data of each region. For example, if the core area temperature is 44.2°C, the intermediate area is 42.3°C, and the peripheral area is 41.0°C, the temperature difference between the core area and the peripheral area is calculated to be 3.2°C, the temperature difference between the core area and the intermediate area is 1.9°C, and the temperature difference between the intermediate area and the peripheral area is 1.3°C.Further, by comparing whether the maximum temperature difference exceeds the temperature difference threshold of 12°C, if the maximum temperature difference exceeds the threshold, temperature distribution uniformity evaluation data is generated, and the alarm system issues a temperature non-uniformity warning. The system can further adjust the cooling strategy or increase additional cooling measures according to this data. The main control board determines whether there is a large temperature difference according to the temperature distribution uniformity evaluation data of each region, thereby determining the fan speed and air flow direction parameters that need to be adjusted. For example, if the core area temperature is much higher than other areas, the fan speed may need to be increased to speed up the cooling process in that area. If the system detects that the temperature of a specific area (such as the core area) is too high, the air flow direction of the fan may need to be adjusted to focus air flow on the high-temperature area for targeted cooling. The fan speed can be adjusted linearly or non-linearly according to the temperature difference, for example, when the core area temperature exceeds 44°C, the fan speed can be increased to 1200 rpm. If the temperature difference exceeds the set threshold of 12°C, the air flow direction may need to be changed to optimize the cooling effect. This control data will be transmitted to the fan drive system for execution. The main control board will calculate the impact of these instructions on the entire system upgrade process in real time according to the execution of each instruction (frequency reduction, cooling time adjustment, fan control). For example, if the processor frequency reduction may cause the processing speed to drop, the cooling time extension may delay the upgrade completion time, and the fan control may increase power consumption. The system will weight these factors to obtain an adjusted execution strategy. For example, if the temperature risk assessment instruction decides to reduce the processor frequency to 1.8 GHz, which may cause the upgrade time to be extended, the main control board will calculate the impact of this change on the upgrade completion time and adjust the cooling time and fan speed to ensure that the final upgrade time does not exceed the predetermined window. Finally, all data and adjustment parameters will be combined to generate final execution strategy adjustment data to ensure the best balance between temperature control and upgrade efficiency.

[0141] In the upgrading process of the home energy storage system, by extracting the core area temperature change rate in the real-time temperature change data and comparing it with the preset temperature change rate threshold 0.5℃ / min, the risk of too fast temperature change can be accurately identified, and a temperature change rate risk assessment result is generated, which provides a basis for subsequent temperature regulation strategies. Based on the assessment result, the processor frequency reduction instruction data is dynamically determined to reduce the computing load, so as to slow down the performance decline or hardware damage risk caused by high temperature. At the same time, the difference between the current core area temperature value and the temperature upper limit threshold 45℃ is calculated to obtain temperature safety margin data, so that the system can evaluate the remaining heat dissipation margin in real time. When the temperature safety margin is less than 5℃, the system calculates the cooling time extension coefficient as 150%, and adjusts the cooling time accordingly to avoid abnormal situations caused by rapid temperature rise in a short time. In addition, the temperature values of each region in the real-time temperature change data are extracted, and the maximum inter-regional temperature difference is calculated and compared with the temperature difference threshold 12℃ to form temperature distribution uniformity evaluation data, so as to ensure that the heat is reasonably diffused in the entire system. According to the temperature distribution uniformity evaluation result, the fan speed and air flow direction are adjusted to make the heat dissipation effect more balanced, and fan control instruction data is generated. Finally, the influence of the processor frequency reduction instruction data, the cooling time adjustment data and the fan control instruction data on the upgrading process is calculated to form execution strategy adjustment data, so as to optimize the overall upgrading process and improve the accuracy of temperature control management, thereby ensuring the stable operation of the system during the upgrading process and avoiding unnecessary risks caused by overheating.

[0142] Preferably, step S4 comprises the following steps:

[0143] Step S41: Extracting the verification state of each functional component in the component upgrade result, calculating the overall upgrade success rate and the component level success rate, and generating upgrade completion degree evaluation data;

[0144] The specific implementation method of the embodiment of the present application for extracting the verification state of each functional component in the component upgrade result, calculating the overall upgrade success rate and the component level success rate, and generating upgrade completion degree evaluation data is as follows: After each upgrade operation, the system will record the upgrade verification state of each functional component, and the state data includes information such as success, failure or partial success. By collecting the verification state of each component, the system first calculates the overall upgrade success rate, i.e. the proportion of the number of successfully upgraded components to the total number of components. For example, if there are 10 components and 8 components are verified to be successful, the overall upgrade success rate is 8 / 10=80%. Secondly, for each functional component, the system calculates the component level success rate to analyze whether each component passes the verification and calculates the proportion of the number of successful attempts to the total number of attempts. Based on these data, the system generates upgrade completion degree evaluation data to show the overall upgrade situation and the success rate of each component, and evaluates the upgrade progress and quality.

[0145] Step S42: Failure link and failure cause analysis during the upgrade process based on the upgrade completion degree evaluation data, and extraction of key features of the failed components to form upgrade risk point identification data;

[0146] The specific implementation method of the embodiment of the application for forming upgrade risk point identification data based on upgrade completion degree evaluation data to analyze failure links and failure causes during the upgrade process and extract key features of failed components is as follows: the system identifies which components or links have failed in the upgrade process according to the upgrade completion degree evaluation data in S41. By analyzing the failed components, the system collects and extracts their key features, such as component type, temperature change, execution duration, system resource consumption, etc., and conducts in-depth analysis on each failed component to find out the main causes of failure, such as temperature too high leading to performance degradation or hardware failure, etc. According to these analysis results, the system forms upgrade risk point identification data, points out the links or causes that may cause system failure during the upgrade process, and provides basis for subsequent improvement.

[0147] Step S43: Extract the temperature change curve of each functional component during execution from the real-time temperature change data, and conduct correlation analysis with the component verification state to obtain temperature-performance relationship mapping data;

[0148] The specific implementation method of the embodiment of the application for extracting the temperature change curve of each functional component during execution from the real-time temperature change data and conducting correlation analysis with the component verification state to obtain temperature-performance relationship mapping data is as follows: the system continuously monitors the temperature change of each functional component during the upgrade process, and records the temperature change curve of each component during execution. By correlating the temperature change curve with the verification state of each component, the system can identify the influence of temperature change on component performance. Specifically, the system pairs the temperature data with the verification state (success or failure) for analysis to determine whether the temperature has an impact on the component verification result. For example, if a component fails verification when the temperature reaches 45℃, but succeeds verification at other temperatures, it indicates that the component may be very sensitive to temperature. Through this process, the system obtains the relationship mapping data between temperature and component performance, helping to identify which temperature change has the greatest impact on component performance.

[0149] Step S44: According to the temperature-performance relationship mapping data, identify temperature-sensitive components, and when the core area temperature rises more than 6℃ during component execution and the verification failure probability is higher than 15%, mark the component as a high-temperature sensitive component, and generate component temperature sensitivity classification data;

[0150] The embodiment of the application identifies temperature-sensitive components according to temperature-performance relationship mapping data, and marks the components as high-temperature-sensitive components when the core area temperature of the components rises by more than 6℃ and the verification failure probability is higher than 15% during the execution of the components. The specific implementation method of generating component temperature sensitivity classification data is as follows: based on the temperature-performance relationship mapping data generated in S43, the system can further analyze which components exhibit a high verification failure probability when the temperature changes greatly. The system will identify components whose core area temperature rises by more than 6℃ and whose verification failure probability is higher than 15%, and mark these components as high-temperature-sensitive components. At this time, if a component has a large temperature rise during temperature change and causes the verification failure probability to be higher than 15%, the component will be marked as a high-temperature-sensitive component. In addition, the system will also generate component temperature sensitivity classification data according to the temperature change characteristics of each component, and classify the components according to temperature sensitivity, such as high-sensitive, medium-sensitive and low-sensitive components, so as to take different temperature control strategies for different types of components in subsequent steps.

[0151] Step S45: analyzing the effectiveness of temperature control of the execution strategy adjustment data based on the component temperature sensitivity classification data, calculating the temperature control effect of each adjustment measure, and forming strategy effectiveness evaluation data;

[0152] The embodiment of the application analyzes the effectiveness of temperature control of the execution strategy adjustment data based on the component temperature sensitivity classification data, calculates the temperature control effect of each adjustment measure, and forms the specific implementation method of strategy effectiveness evaluation data as follows: after identifying components with different temperature sensitivities, the system adjusts the execution strategy according to these classification data. For example, for high-temperature-sensitive components, the system may use more stringent temperature control measures, such as increasing the heat dissipation capacity or adjusting the working frequency; for low-temperature-sensitive components, the system may use conventional temperature control strategies. The system will monitor and calculate the effect of each adjustment measure in actual operation, such as by analyzing the temperature change rate, component performance and verification results, to evaluate the temperature control effect of each adjustment measure. Finally, the system will generate strategy effectiveness evaluation data to show the influence of each adjustment strategy on the temperature control effect, helping to optimize the subsequent strategy adjustment and upgrade process.

[0153] Step S46: integrating the upgrade completion degree evaluation data, the upgrade risk point identification data, the temperature-performance relationship mapping data and the strategy effectiveness evaluation data to generate an upgrade verification report;

[0154] The specific implementation method of integrating the upgrade completion degree evaluation data, the upgrade risk point identification data, the temperature-performance relationship mapping data, and the strategy effectiveness evaluation data to generate the upgrade verification report is as follows: after completing each step of S41 to S45, the system integrates the generated various types of data to form the final upgrade verification report. The report content includes the overall upgrade success rate, the component level success rate, the failure link and failure reason in the upgrade process, the influence of temperature on component performance, the temperature sensitivity classification of components, the effectiveness analysis of each adjustment strategy, and the like. The system summarizes these data into a structured report for further review and reference, ensuring the transparency and traceability of the entire upgrade process.

[0155] Step S47: The network communication component of the home energy storage system encrypts and transmits the upgrade verification report to the cloud server.

[0156] The specific implementation method of the home energy storage system's network communication component encrypting and transmitting the upgrade verification report to the cloud server is as follows: after generating the upgrade verification report, the system uploads the encrypted report to the cloud server through the built-in network communication component. First, the system encrypts the report data to ensure that the data cannot be stolen or tampered with by unauthorized third parties during transmission. Then, the system transmits the encrypted report data to the cloud server through the network communication interface of the home energy storage system (such as Wi-Fi, LTE, etc.) for storage, analysis, and further processing. This process ensures the security and integrity of the data, and also makes it convenient to view and manage the report on the cloud.

[0157] In the upgrading process of the home energy storage system, the verification state of each functional component is first extracted, the overall upgrading success rate and the component level success rate are calculated to generate upgrading completion evaluation data, so as to comprehensively reflect the overall completion of the upgrading and the independent performance of each component. Based on the data, the failure link and reason that may occur in the upgrading process are analyzed in depth, the key features of the failed components are extracted, and the upgrading risk point identification data is formed, so as to accurately locate the factors affecting the stability of the upgrading. At the same time, the temperature change curve of each functional component during execution is extracted from the real-time temperature change data, and is analyzed in association with the component verification state, so as to establish temperature-performance relationship mapping data, so that the system can identify the specific influence of temperature change on the execution success rate of each component. According to the mapping data, temperature-sensitive components are further identified, and when the core area temperature of a component rises by more than 6℃ during execution and the verification failure probability is higher than 15%, the component is marked as a high-temperature sensitive component, and component temperature sensitivity grading data is generated to provide accurate guidance for subsequent temperature optimization strategy. Subsequently, based on the component temperature sensitivity grading data, the effectiveness of temperature control of the execution strategy adjustment data is analyzed, the temperature regulation effect of each adjustment measure is calculated to quantify the effect of different control strategies on the stability of the system, and strategy effectiveness evaluation data is formed. Finally, the upgrading completion evaluation data, the upgrading risk point identification data, the temperature-performance relationship mapping data and the strategy effectiveness evaluation data are integrated to generate a comprehensive upgrading verification report, ensuring the traceability and clear optimization direction of the upgrading process. In order to ensure data security, the report is encrypted through the network communication component of the home energy storage system, and is safely transmitted to the cloud server for remote storage and subsequent analysis, so that the operation and maintenance personnel can remotely monitor and optimize the adjustment of potential risks in the upgrading process, thereby improving the overall safety and reliability of the system.

[0158] The application also provides a home energy storage system device remote upgrading system for executing the home energy storage system device remote upgrading method described above, which comprises:

[0159] A temperature collection module is configured to collect temperature distribution data of the battery pack surface through sensors arranged in the core area, the intermediate area and the peripheral area of the battery pack, and record the temperature of each area as an initial temperature reference value according to the temperature distribution data;

[0160] An upgrading sequencing module is configured to perform functional component identification and thermal characteristic evaluation processing on the pre-acquired upgrading package to obtain heat-producing graded component data, and perform serial execution sequencing processing on the heat-producing graded component data according to the initial temperature reference value to obtain batch execution strategy data;

[0161] The batch upgrading module is configured to perform isolated upgrading of each functional component in the dual-buffer A / B partition structure of the home energy storage system according to the batch execution strategy data, to obtain a component upgrading result; record real-time temperature change data in the upgrading process according to the component upgrading result; and perform threshold comparison processing on the real-time temperature change data to obtain execution strategy adjustment data.

[0162] The verification reporting module is configured to generate an upgrading verification report containing component thermal characteristic analysis according to the execution strategy adjustment data and the real-time temperature change data, and transmit the upgrading verification report to the cloud server.

[0163] The temperature acquisition module in the application can accurately collect the temperature distribution data of the surface of the battery pack by setting sensors in the core area, the middle area and the peripheral area of the battery pack. This data provides a preliminary temperature reference value for subsequent thermal management and performance optimization, which helps to determine the working temperature range of the system in different areas, thereby providing reliable basic data for subsequent upgrading and adjustment operations. By recording the temperature of each area, the temperature acquisition module ensures that the state of the system before upgrading is accurately grasped, reducing unnecessary risks caused by temperature fluctuations. The role of the upgrade sequencing module is to identify and evaluate the thermal characteristics of the pre-acquired upgrade package. Through this process, the thermal generation characteristics of each functional component can be evaluated, and the initial temperature reference value of the battery pack is used to perform serial execution sequencing for each component. This sequencing ensures that the upgrade of different functional components is performed in the order of optimal temperature adaptability, thereby avoiding the impact of heat accumulation on system stability. Based on this data, the upgrade sequence of the components is reasonably planned, optimizing resource allocation during the upgrade process and ensuring efficient and safe execution. The batch upgrade module plays a crucial role in this process. According to the batch execution strategy data, the module performs isolated upgrade processing of each functional component in the dual-buffer A / B partition structure of the home energy storage system. In this way, not only is interference caused by component interaction during the upgrade process avoided, but also each component has sufficient isolation during the upgrade, reducing system risk. During the upgrade process, real-time temperature change data is continuously recorded, and by comparing these data with threshold values, temperature abnormal fluctuations can be quickly responded to, so that the upgrade strategy can be adjusted in time to ensure that the temperature of the entire system is always within a safe range. Finally, the verification and reporting module generates a detailed upgrade verification report by combining the execution strategy adjustment data and real-time temperature change data, and transmits the encrypted report to the cloud server. This module not only provides traceable data support for the upgrade process, but also provides detailed temperature management and performance evaluation information for operation and maintenance personnel, so that the upgrade effect of the system can be comprehensively audited and analyzed. By uploading to the cloud server remotely, the report can provide data basis for subsequent monitoring and optimization, enhancing the continuous optimization capability and remote management function of the system, and providing a solid guarantee for the safety, stability and long-term performance of the home energy storage system.

[0164] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the application being defined by the appended claims rather than the above description, and it is intended to encompass all variations falling within the meaning and scope of the equivalent elements of the application file.

[0165] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A method for remote upgrading of a home energy storage system device, characterized in that, The method comprises the following steps: Step S1: Collecting temperature distribution data of the battery pack surface through sensors arranged in the core area, the middle area and the peripheral area of the battery pack, and recording the temperature of each area as an initial temperature reference value according to the temperature distribution data; Step S2: Performing function component identification and thermal characteristic evaluation processing on the pre-acquired upgrade package to obtain heat-producing hierarchical component data; According to the initial temperature reference value, the heat-producing hierarchical component data is subjected to serial execution sequencing processing to obtain batch execution strategy data; wherein, step S2 comprises the following steps: Step S21: Performing file structure analysis on the pre-acquired upgrade package to identify function component type data including user interface resource files, system parameter configuration files and core firmware code files; Step S22: Performing thermal characteristic evaluation according to the function component type data, and analyzing the processor occupancy rate, memory access frequency and execution duration of each function component in the execution process to obtain component thermal characteristic data; Step S23: Dividing each function component into a heat generation level according to the component thermal characteristic data to obtain heat-producing hierarchical component data; wherein the heat generation level division specifically divides the user interface resource files into low-heat generation components, the core area temperature is expected to rise by no more than 2℃ during the execution process; divides the system parameter configuration files into medium-heat generation components, the core area temperature is expected to rise between 2℃ and 5℃ during the execution process; and divides the core firmware code files into high-heat generation components, the core area temperature is expected to rise by more than 5℃ during the execution process; Step S24: According to the initial temperature reference value, the heat-producing hierarchical component data is subjected to serial execution sequencing processing to obtain batch execution strategy data; Step S3: According to the batch execution strategy data, each function component is subjected to isolated upgrade processing in the double-buffer A / B partition structure of the home energy storage system to obtain component upgrade results; real-time temperature change data during the upgrade process is recorded according to the component upgrade results; execution strategy adjustment data is obtained by threshold comparison processing of the real-time temperature change data; Step S4: Generating an upgrade verification report containing component thermal characteristic analysis according to the execution strategy adjustment data and the real-time temperature change data, and transmitting the upgrade verification report to the cloud server.

2. The method of claim 1, wherein, Step S1 comprises the following steps: Step S11: Installing 4 NTC thermistor type temperature sensors in the core area, 4 NTC thermistor type temperature sensors in the middle area and 4 NTC thermistor type temperature sensors in the peripheral area of the battery pack surface, wherein the NTC thermistor type temperature sensors are connected to the main control board through the I2C bus; Step S12: Setting the temperature sampling precision to 0.1℃, and collecting the temperature data of each area at a frequency of once per minute under the normal running state of the home energy storage system; Step S13: Calculating the average temperature value, the maximum temperature value and the inter-area temperature difference value of each area for each area temperature data to obtain temperature state description data; Step S14: Taking the temperature state description data of the home energy storage system 30 minutes before receiving the upgrade instruction as a historical reference, combining the current temperature state description data, and determining the initial temperature reference value of each area.

3. The method of claim 1, wherein, Step S22 includes the following steps: Step S221: performing static code analysis on the functional component type data in an isolated test environment, detecting code complexity, number of loop structures, and resource call frequency, to generate a static thermal characteristic evaluation matrix; Step S222: calculating the energy consumption coefficient of a unit operation according to the energy consumption characteristics of the processor instruction set and the memory access mode, to establish a thermal energy conversion function; Step S223: extracting execution records of the same type of components from the pre-acquired historical upgrade database, to construct a thermodynamic feature library of component execution, wherein the execution records include CPU utilization time series, memory access mode, and IO operation intensity; Step S224: performing thermodynamic behavior feature prediction according to the static thermal characteristic evaluation matrix and the thermodynamic feature library, to obtain thermal prediction data including peak heat generation rate, heat accumulation curve, and heat decay characteristics; Step S225: performing sampling tests on the functional component type data in an isolated test environment, recording real-time power curves during the execution process by using a high-precision power consumption monitoring circuit, and calculating the corresponding heat generation rate by using the thermoelectric conversion function, to obtain heat generation data; Step S226: comparing and analyzing the thermal prediction data and the heat generation data, and performing parameter calibration processing, to generate component thermal characteristic data.

4. The method of claim 1, wherein, Step S24 includes the following steps: Step S241: calculating core area temperature risk value, inter-regional temperature difference value, and temperature change trend value according to the initial temperature reference value, to obtain temperature state evaluation data; Step S242: performing upgrade suitability determination on the household energy storage system according to the temperature state evaluation data, to obtain suitability determination data, wherein the upgrade suitability determination specifically refers to determining as suitable for upgrading when the core area temperature is lower than 30℃ and the maximum inter-regional temperature difference is less than 8℃, determining as attention to upgrading when the core area temperature is between 30℃ and 40℃ or the maximum inter-regional temperature difference is between 8℃ and 12℃, and determining as unsuitable for upgrading when the core area temperature exceeds 40℃ or the maximum inter-regional temperature difference exceeds 12℃; Step S243: performing execution order determination processing on the heat production classification component data according to the suitability determination data, to obtain upgrade order data, wherein the execution order determination processing specifically refers to arranging the low heat generation component, the medium heat generation component, and the high heat generation component in sequence when determining as suitable for upgrading, arranging the low heat generation component and the medium heat generation component to be executed immediately and postponing the high heat generation component to be executed when the system load is low when determining as attention to upgrading, and delaying the upgrade process when determining as unsuitable for upgrading; Step S244: calculating the minimum cooling waiting time between each functional component based on the upgrade order data and the component thermal characteristic data, to obtain cooling time configuration data; Step S245: integrating the upgrade order data and the cooling time configuration data into batch execution strategy data.

5. The method of claim 1, wherein, The isolated upgrade processing of each functional component in step S3 includes: Initialize the double-buffering A / B partition structure of the home energy storage system, identify the current active partition, determine the inactive partition as the target partition for upgrading, perform data erasure processing on the inactive partition, and obtain a ready-to-upgrade partition; According to the batch execution strategy data, the component content and corresponding digital signature of each functional component are extracted, signature verification and integrity check processing are performed, and the security-verified to-be-upgraded component data is obtained; In the upgrade partition, independent storage space and resource access permission are allocated for the to-be-upgraded component data, thereby constructing a secure execution area that prevents cross-interference as a component isolation area; The to-be-upgraded component data is written into the component isolation area, and function verification testing is performed to obtain the component verification state, wherein the function verification testing specifically includes verifying the interface resource integrity when the component is a user interface component, verifying the parameter validity when the component is a system parameter configuration component, and performing basic function testing when the component is a core firmware component; According to the component verification state, the component verification success rate is calculated, when the success rate is higher than 95%, the next component upgrade is continued, and when the success rate is lower than 80%, the rollback operation is performed on the failed component, thereby obtaining the component upgrade result.

6. The method of claim 1, wherein, The real-time temperature change data recorded according to the component upgrade result in step S3 includes: Collecting real-time temperature sampling values of the core area, the middle area and the peripheral area of the battery pack on the main control board of the home energy storage system through an NTC thermistor type temperature sensor; Based on the real-time temperature sampling values, the average temperature, the maximum temperature and the temperature difference between regions of each region are calculated to obtain multi-dimensional temperature data; When the functional component starts to perform the upgrade operation, the timestamp and component information are associated with the multi-dimensional temperature data to form a time series temperature record; According to the time series temperature record, the temperature change rate, the temperature peak value and the temperature recovery time during the execution of each functional component are counted; Marking the execution stage with a temperature change rate exceeding 0.3℃ / min or a temperature peak value exceeding the initial temperature by 8℃ as a high-risk temperature interval; Based on the high-risk temperature interval and the temperature recovery time, the heat dissipation characteristics of the functional component are evaluated; According to the heat dissipation characteristics, the temperature peak value and the temperature change rate of each component, real-time temperature change data is generated.

7. The method of claim 1, wherein, The threshold comparison processing in step S3 includes: Comparing the core area temperature change rate in the real-time temperature change data with the preset temperature change rate threshold 0.5℃ / min to generate a temperature change rate risk evaluation result; Based on the temperature change rate risk evaluation result, processor frequency reduction instruction data is determined; According to the difference between the core area current temperature value in the real-time temperature change data and the temperature upper limit threshold 45℃, temperature safety margin data is obtained; Based on the temperature safety margin data, a cooling strategy is formulated, when the temperature safety margin is less than 5℃, the cooling time extension coefficient is calculated as 150%, and cooling time adjustment data is generated; Extracting the temperature values of each region in the real-time temperature change data, calculating the maximum inter-regional temperature difference, and comparing with the temperature difference threshold 12℃ to form temperature distribution uniformity evaluation data; According to the temperature distribution uniformity evaluation data, fan speed and air flow direction parameters are determined to generate fan control instruction data; The processor reduces the frequency of instruction data, cooling time adjustment data and fan control instruction data, and the influence factor calculation of each instruction on the upgrade flow, thereby forming the execution strategy adjustment data.

8. The method for remote upgrade of home energy storage system device of claim 1, wherein, Step S4 includes the following steps: Step S41: Extract the verification state of each functional component in the component upgrade result, calculate the overall upgrade success rate and component level success rate, and generate upgrade completion degree evaluation data; Step S42: Based on the upgrade completion degree evaluation data, analyze the failure link and failure reason in the upgrade process, and extract the key features of the failed components, thereby forming the upgrade risk point identification data; Step S43: Extract the temperature change curve of each functional component during execution from the real-time temperature change data, and perform correlation analysis with the component verification state, thereby obtaining temperature-performance relationship mapping data; Step S44: According to the temperature-performance relationship mapping data, identify temperature-sensitive components. When the core area temperature rises more than 6℃ during the execution of the component and the verification failure probability is higher than 15%, mark the component as a high-temperature sensitive component, and generate component temperature sensitivity classification data; Step S45: Based on the component temperature sensitivity classification data, analyze the effectiveness of temperature control of the execution strategy adjustment data, calculate the temperature control effect of each adjustment measure, and thereby form strategy effectiveness evaluation data; Step S46: Integrate the upgrade completion degree evaluation data, upgrade risk point identification data, temperature-performance relationship mapping data and strategy effectiveness evaluation data to generate an upgrade verification report; Step S47: The upgrade verification report is encrypted and transmitted to the cloud server through the network communication component of the home energy storage system.

9. A home energy storage system device remote upgrade system, comprising: The home energy storage system device remote upgrade method according to claim 1, the home energy storage system device remote upgrade system comprises: A temperature acquisition module is configured to acquire temperature distribution data of the battery pack surface through sensors arranged in the core area, the intermediate area and the peripheral area of the battery pack, and record the temperature of each area as an initial temperature reference value according to the temperature distribution data; An upgrade sorting module is configured to identify functional components and perform thermal characteristic evaluation processing on the pre-acquired upgrade package to obtain heat-producing component data; and perform serial execution sorting processing on the heat-producing component data according to the initial temperature reference value to obtain batch execution strategy data; A batch upgrade module is configured to perform isolated upgrade processing on each functional component in the double-buffer A / B partition structure of the home energy storage system according to the batch execution strategy data to obtain component upgrade results; record real-time temperature change data during the upgrade process according to the component upgrade results; and perform threshold comparison processing on the real-time temperature change data to obtain execution strategy adjustment data; A verification reporting module is configured to generate an upgrade verification report containing component thermal characteristic analysis according to the execution strategy adjustment data and the real-time temperature change data, and transmit the upgrade verification report to the cloud server.

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