UPS high-voltage lithium battery pack operation data acquisition method and system

Abstract

The application discloses a UPS high-voltage lithium battery pack operation data acquisition method and system, and relates to the field of data acquisition.The method comprises the following steps: acquiring cell operation parameter information of each cell; acquiring module first operation parameter information of a basic point and module second operation parameter information of an enhanced point; generating acquisition data of a target battery pack according to the cell operation parameter information, the module first operation parameter information and the module second operation parameter information; and storing the acquisition data of the target battery pack in a local storage end based on a fixed frequency and uploading the acquisition data to a cloud management platform based on a dynamic frequency.The application has the advantages of improving early perception ability of UPS lithium battery pack thermal safety and performance change, effectively reducing data processing pressure and deployment difficulty, and having significant advantages in system stability, maintainability and security guarantee.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of data collection, and more specifically, to a UPS high-voltage lithium battery pack operation data collection method and system. BACKGROUND

[0002] In data centers, medical systems and critical industrial loads, UPS (Uninterruptible Power Supply) systems, as important facilities to ensure continuous power supply of equipment, are widely used in scenarios with extremely high requirements for power supply stability. In recent years, with the gradual replacement of lead-acid batteries by lithium ion batteries, especially iron phosphate batteries, as the mainstream energy storage units of UPS systems due to their high energy density, high rate discharge capability and excellent cycle life, higher requirements for accurate perception and real-time collection of their operating state are put forward.

[0003] In the prior art, the UPS lithium battery pack operation data collection method is usually performed in a "fixed frequency, uniform sampling point, unified uploading" manner, mainly collecting basic information such as cell voltage, current and temperature, and lacks layered perception ability for local hot spot areas at the module level. At the same time, the existing system uses a unified frequency for data sampling and uploading, which cannot flexibly adjust the collection granularity and communication frequency according to key dynamic factors such as load state and thermal abnormality trend, resulting in difficulties in timely capturing high-risk signs in the early stages of battery thermal runaway or capacity degradation, and problems such as excessive collection or delayed response, which not only increases data redundancy and system pressure, but also has potential safety hazards.

[0004] In addition, in terms of battery thermal management, although some schemes introduce thermal simulation assisted design, there is no data collection point optimization strategy driven by simulation results, and the enhancement of point deployment still relies on manual experience, which has problems such as insufficient coverage of thermal abnormalities, repeated layout or unreachability, and cannot meet the dual requirements of refinement and self-adaptation for operation monitoring of large-capacity high-power UPS battery systems.

[0005] It is necessary to propose a UPS high-voltage lithium battery pack operation data collection method and system to at least solve some of the above problems. SUMMARY

[0006] A series of simplified concepts are introduced in the summary section, which will be further described in detail in the specific embodiments section. The summary section of the present application does not mean to attempt to limit the key features and essential technical features of the claimed technical solutions, nor to determine the protection scope of the claimed technical solutions.

[0007] In the first aspect, the present application proposes a UPS high-voltage lithium battery pack operation data collection method, which comprises:

[0008] obtain cell operation parameter information of each cell;

[0009] obtain module first operation parameter information of basic point positions and module second operation parameter information of enhanced point positions;

[0010] generate collection data of a target battery pack according to the cell operation parameter information, the module first operation parameter information and the module second operation parameter information;

[0011] store the collection data of the target battery pack in a local storage end based on a fixed frequency and upload the collection data to a cloud management platform based on a dynamic frequency.

[0012] In a feasible implementation, the cell operation parameter information includes cell voltage information, cell power information, cell temperature information and cell internal resistance information.

[0013] The first operation parameter information includes total voltage information and total current information.

[0014] The second operation parameter information includes point position temperature information.

[0015] In a feasible implementation, the specific determination of the enhanced point positions includes:

[0016] obtain thermal simulation result information and heat dissipation fluid simulation information of the target battery pack;

[0017] determine the enhanced point positions according to the thermal simulation result information and the heat dissipation fluid simulation information.

[0018] In a feasible implementation, the determination of the enhanced point positions according to the thermal simulation result information and the heat dissipation fluid simulation information includes:

[0019] perform spatial grid division on the target battery pack to establish a three-dimensional sampling candidate model including a plurality of spatial units;

[0020] map the thermal simulation result information and the heat dissipation fluid simulation information to the three-dimensional sampling candidate model respectively to construct a corresponding temperature-airflow joint atlas;

[0021] calculate a thermal anomaly potential score for each spatial unit based on the temperature-airflow joint atlas, wherein the thermal anomaly potential score is determined according to a temperature deviation, a temperature gradient, a wind speed factor and a heat dissipation boundary distance.

[0022] screen candidate enhanced point positions in the spatial units with the thermal anomaly potential score exceeding a set threshold value.

[0023] The candidate enhanced point sites are optimized based on structural accessibility, wiring feasibility and spatial symmetry constraints of the point site layout, and the optimized candidate enhanced point sites are taken as the enhanced point sites corresponding to the second operation parameter information of the module.

[0024] In a possible implementation, the candidate enhanced point sites are screened in the spatial units with the thermal anomaly potential scores exceeding a set threshold value.

[0025] An initial candidate point set is constructed for all spatial units with the thermal anomaly potential scores exceeding a preset score.

[0026] The potential score difference between each candidate spatial unit and adjacent units is calculated in the initial candidate point set.

[0027] If the potential score difference exceeds a set gradient threshold value, the unit is marked as a potential thermal mutation boundary point.

[0028] In combination with the spatial topological structure and temperature variation trend, a regional growth algorithm is applied to identify potential thermal anomaly high-risk area clusters.

[0029] In each of the thermal anomaly high-risk area clusters, the candidate enhanced point sites are dynamically screened according to local score extreme points, centrality and historical temperature fluctuation stability.

[0030] For an isolated distributed anomaly score point, whether it constitutes a latent heat source is determined by predicting the temperature rise trend and a neighborhood flow velocity decay model.

[0031] If yes, the candidate enhanced point site is also included.

[0032] In a possible implementation, the regional growth algorithm is applied to identify potential thermal anomaly high-risk area clusters in combination with the spatial topological structure and temperature variation trend, and the method comprises the following steps.

[0033] The target battery pack space is divided into three-dimensional grids, and each spatial unit is bound with its current temperature, temperature variation rate, thermal anomaly potential score and adjacent unit index.

[0034] Spatial units with a thermal anomaly potential score greater than or equal to a first threshold value and a temperature variation rate greater than or equal to a second threshold value are screened from all spatial units as regional growth seed points.

[0035] A regional growth criterion is defined, wherein the regional growth criterion comprises that a candidate expansion unit is spatially adjacent to an existing boundary unit, the thermal anomaly potential score is greater than or equal to a third threshold value, the adjacent temperature difference is less than a set gradient threshold value, and the structure partition region is not crossed.

[0036] From the seed point, the region is iteratively expanded based on the region growing criterion until no new spatial unit meets the criterion;

[0037] All grown region clusters are screened for multiple clusters, and regions with insufficient points or insufficient risk scores are removed. The edge region is corrected by a heat diffusion model to identify potential high-risk region clusters of thermal anomalies.

[0038] In a feasible implementation, the structure accessibility, wiring feasibility and spatial symmetry constraints based on the point layout optimize the candidate enhanced point, and the optimized candidate enhanced point is used as the enhanced point corresponding to the second operating parameter information of the module, including:

[0039] Construct a topological mapping of the candidate enhanced point in the battery module structure to obtain the physical location and structural association information of each candidate enhanced point in the module;

[0040] Based on the structure accessibility rule, calculate the accessible path length of each candidate enhanced point relative to the preset maintenance opening or detection window. If the path exceeds the preset maximum access distance or crosses the non-dismantling structure, the point is removed or replaced with a new point with thermal representation in its neighborhood.

[0041] Based on the wiring feasibility rule, determine whether the wiring path between each point and the nearest collection interface crosses the high-voltage isolation area, ventilation channel or electromagnetic interference area. If it does not meet the wiring requirements, the current point is removed.

[0042] According to the symmetry characteristics of the module structure, group the candidate points located in the symmetric region, and only keep the representative points with higher thermal anomaly potential or better signal coverage in each group to avoid redundant deployment.

[0043] The points that meet the structure accessibility, wiring feasibility and symmetry constraints are determined as the optimized enhanced points, which are the collection locations of the second operating parameter information of the module.

[0044] In a feasible implementation, the determination of the fixed frequency includes:

[0045] Determine the base frequency information according to the first operating parameter information;

[0046] Generate environment correction frequency information according to the use environment information of the target battery pack;

[0047] Generate the fixed frequency according to the base frequency information and the environment correction frequency information.

[0048] In a feasible implementation, the determination of the dynamic frequency includes:

[0049] According to the temperature change trend in the above cell operation parameter information and the above module second operation parameter information, the temperature rise rate and the temperature fluctuation amplitude of each enhanced point are calculated;

[0050] According to the temperature rise rate and the temperature fluctuation amplitude, a multi-factor dynamic frequency regulation mechanism is introduced, and the upload frequency level of the point is determined according to specific indexes, wherein the specific indexes include the distance between the current temperature value and the set safety threshold, the increase amplitude of the temperature rise rate per unit time, the heat anomaly potential score of the region where the point is located, and the fluctuation frequency in the historical sampling record of the point;

[0051] According to the upload frequency level and the frequency level table, the dynamic frequency is determined.

[0052] In a second aspect, the application provides a UPS high-voltage lithium battery pack operation data acquisition system, comprising:

[0053] A first acquisition unit is configured to acquire cell operation parameter information of each cell;

[0054] A second acquisition unit is configured to acquire module first operation parameter information of basic points and module second operation parameter information of enhanced points;

[0055] A generation unit is configured to generate acquisition data of a target battery pack according to the above cell operation parameter information, the above module first operation parameter information, and the above module second operation parameter information;

[0056] A data processing unit is configured to store the acquisition data of the target battery pack in a local storage based on a fixed frequency, and upload the acquisition data to a cloud management platform based on a dynamic frequency.

[0057] In summary, the present application proposes a UPS high-voltage lithium battery pack operation data acquisition method, which integrates full-level parameter perception, hot simulation driving point optimization, and frequency intelligent regulation and other innovative mechanisms, significantly improving the accuracy, timeliness and safety of system operation state monitoring. First, the present application adopts a layered acquisition strategy at the cell level and module level, covering not only traditional basic parameters such as voltage, current, temperature, and internal resistance, but also introducing an "enhanced point" mechanism. Based on the results of thermal simulation and heat dissipation fluid simulation, a temperature-airflow joint map is constructed to accurately identify local hot risk areas and deploy additional temperature acquisition points in key areas to achieve high-sensitivity monitoring of potential thermal runaway areas. Second, compared to the traditional uniform frequency sampling mode, the present application proposes a "fixed frequency + dynamic frequency" dual-channel data strategy. Fixed frequency is used for periodic storage of cell and module basic parameters to ensure data traceability throughout the operation process. Dynamic frequency is based on a multi-factor fusion model of temperature rise rate, thermal score, and historical volatility to adaptively adjust the upload frequency of enhanced points, effectively improving response speed in high-risk situations while reducing data redundancy in low-risk scenarios, balancing real-time performance and system load. In the selection of enhanced points, the present application uses region growing algorithm combined with spatial topology and temperature gradient change trend to automatically identify clusters of thermal abnormal high-risk areas, and further introduces engineering constraints such as structural accessibility, wiring feasibility, and spatial symmetry to optimize the selection of candidate enhanced points, improving the implementability and information coverage efficiency of point deployment and breaking through the limitations of existing solutions relying on manual point deployment. In addition, the present application can also be coordinated with edge computing and cloud platforms to support remote upgrades, real-time fault prediction, and off-network data disaster management, providing a safe, intelligent, and efficient operation data acquisition solution for large-scale deployment of high-power UPS systems. In summary, the present application not only improves the early perception ability of UPS lithium battery pack thermal safety and performance changes, but also effectively reduces data processing pressure and deployment difficulty, with significant advantages in system stability, maintainability, and safety assurance. The UPS high-voltage lithium battery pack operation data acquisition method proposed by the present application, other advantages, objectives and features of the present application will be embodied in part through the following description, and will be understood by those skilled in the art through research and practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of preferred embodiments, and are not meant to limit the present specification. Moreover, the same reference numerals are used throughout the various drawings to designate identical elements. In the drawings:

[0059] Figure 1 A flowchart of a UPS high-voltage lithium battery pack operation data acquisition method according to an embodiment of the present application;

[0060] Figure 2 A method for determining an enhanced point provided by an embodiment of the present application is shown in the flow chart;

[0061] Figure 3 A method for determining an enhanced point according to thermal simulation result information and heat dissipation fluid simulation information provided by an embodiment of the present application is shown in the flow chart;

[0062] Figure 4 A method for screening a candidate enhanced point provided by an embodiment of the present application is shown in the flow chart;

[0063] Figure 5 A method for identifying a potential thermal abnormality high-risk area cluster provided by an embodiment of the present application is shown in the flow chart;

[0064] Figure 6 A method for optimizing a candidate enhanced point provided by an embodiment of the present application is shown in the flow chart;

[0065] Figure 7 A method for determining a fixed frequency provided by an embodiment of the present application is shown in the flow chart;

[0066] Figure 8 A method for determining a dynamic frequency provided by an embodiment of the present application is shown in the flow chart;

[0067] Figure 9 A structure schematic diagram of a UPS high-voltage lithium battery pack operation data acquisition system provided by an embodiment of the present application is shown in the flow chart. DETAILED DESCRIPTION

[0068] The terms "first", "second", "third", "fourth" and the like in the description and claims of the present application and above-mentioned drawings (if there are) are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments.

[0069] Please refer to Figure 1A UPS high-voltage lithium battery pack operation data acquisition method provided by the embodiment of the application can specifically include:

[0070] S110, cell operation parameter information of each cell is acquired;

[0071] S120, module first operation parameter information of a basic point and module second operation parameter information of an enhanced point are acquired;

[0072] S130, the acquisition data of the target battery pack is generated according to the above cell operation parameter information, the above module first operation parameter information and the above module second operation parameter information;

[0073] S140, the acquisition data of the target battery pack is stored in a local storage end based on a fixed frequency and uploaded to a cloud management platform based on a dynamic frequency.

[0074] Exemplarily, the embodiment of the application provides an operation data acquisition method for a UPS high-voltage lithium battery pack. The method realizes comprehensive perception and efficient uploading of the operation state of the battery pack through a multi-level data acquisition strategy, guarantees system safety and takes into account data processing efficiency.

[0075] First, in step S110, the system acquires cell-level operation parameter information. This step realizes real-time acquisition of key operation parameters of each lithium cell through a BMS system, including but not limited to cell voltage, cell current, cell temperature and cell internal resistance. Through these basic information, comprehensive monitoring of the working state, load response and aging characteristics of the cell can be realized, and basic data support is provided for battery safety, performance diagnosis and capacity prediction.

[0076] Next, in step S120, the system further acquires module-level operation parameter information, including two parts: one is module first operation parameter information of a basic point, such as the total voltage and total current of each module and other macro parameters, which are used to monitor the overall power supply state of the module; the other is module second operation parameter information corresponding to an enhanced point determined through simulation analysis and thermal anomaly perception mechanism, mainly collecting temperature information of a key local area. This layered acquisition strategy combining basic and enhanced points significantly improves the perception ability of the system to potential thermal risk areas.

[0077] Then, in step S130, the system fuses the above-mentioned acquired cell operation parameters, module first operation parameters and second operation parameters of the enhanced point to generate complete target battery pack acquisition data. This step realizes structured processing of data, constructs a multi-dimensional operation feature set on a sampling time axis, and facilitates subsequent state analysis, anomaly identification and historical trend comparison.

[0078] Finally, in step S140, the system performs a frequency division processing strategy on the collected data: on the one hand, all data are locally stored at a preset fixed frequency, ensuring that key historical information can still be recorded completely in an offline state; on the other hand, for temperature indicators of specific points (especially enhanced points), the uploading frequency is dynamically adjusted based on a multi-factor dynamic judgment mechanism, realizing high-frequency response uploading in a high-risk state and low-frequency energy-saving uploading in a low-risk state, thereby improving the real-time performance, sensitivity of overall data uploading, and cloud processing efficiency.

[0079] Through the above four steps, the method realizes full-level parameter acquisition from the battery cell to the module, accurate perception from the basic state to the high-risk area, intelligent control from static collection to dynamic uploading, and is suitable for the fine management needs of modern data centers and high-reliability UPS systems for the operating state of lithium battery packs.

[0080] The application provides a UPS high-voltage lithium battery pack operation data acquisition method, which combines full-level parameter perception, hot simulation driving point optimization and frequency intelligent regulation and other innovative mechanisms, significantly improves the accuracy, timeliness and safety of system operation state monitoring, firstly, the application adopts a layered acquisition strategy at the cell level and the module level, which not only covers the basic parameters such as voltage, current, temperature and internal resistance, but also introduces an "enhanced point" mechanism, based on the results of thermal simulation and heat dissipation fluid simulation, a temperature-air flow joint atlas is constructed to accurately identify local thermal risk areas, and additional temperature acquisition points are arranged in key areas to achieve high sensitivity monitoring of potential thermal runaway areas. Secondly, compared with the traditional unified frequency sampling mode, the application proposes a "fixed frequency + dynamic frequency" dual-channel data strategy. The fixed frequency is used for periodic storage of cell and module basic parameters to ensure data trace in the whole process. The dynamic frequency is based on a multi-factor fusion model of temperature rise rate, thermal score and historical volatility to adaptively adjust the upload frequency of the enhanced point, effectively improving the response speed in high-risk states, while reducing data redundancy in low-risk scenarios, balancing real-time and system load. In the aspect of enhanced point selection, the application automatically identifies clusters of thermal abnormal high-risk areas through region growing algorithm combined with spatial topology and temperature gradient change trend, and further introduces engineering constraints such as structural accessibility, wiring feasibility and spatial symmetry to optimize the selection of candidate enhanced points, improving the implementability and information coverage efficiency of point deployment, and breaking through the limitations of existing schemes relying on manual point deployment. In addition, the scheme can also be coordinated with edge computing and cloud platform to support remote upgrade, real-time fault prediction and off-network data disaster management, providing a safe, intelligent and efficient operation data acquisition solution for large-scale deployment of high-power UPS systems. In summary, the application not only improves the early perception ability of UPS lithium battery pack thermal safety and performance changes, but also effectively reduces the data processing pressure and deployment difficulty, and has significant advantages in system stability, maintainability and safety guarantee.

[0081] In a feasible implementation manner, the cell operation parameter information includes cell voltage information, cell power information, cell temperature information and cell internal resistance information.

[0082] The first operation parameter information includes total voltage information and total current information.

[0083] The second operation parameter information includes point temperature information.

[0084] Exemplarily, the UPS high-voltage lithium battery pack operation data acquisition method provided by the application realizes fine monitoring and safety warning of the entire battery system operation state through layered perception of cell, module basic parameters and enhanced point temperature. Among them, the collected operation parameter information is divided into three categories according to the data dimension, which are cell operation parameter information, module first operation parameter information and module second operation parameter information.

[0085] The cell operation parameter information is the core data basis for constructing the system bottom layer state perception, which includes: cell voltage information, which is used to judge the state of charge and voltage balance degree of the single cell; cell power information, which is obtained by calculating the voltage and current, and reflects the instantaneous energy conversion state; cell temperature information, which is used to monitor the heating condition of the cell in the charging and discharging process, and is an important basis for evaluating thermal safety; cell internal resistance information, which can be used to evaluate the health state and aging degree of the cell, and the increase of internal resistance is often related to performance degradation or potential failure.

[0086] The module first operation parameter information mainly refers to the global operation index at the module level, which includes: module total voltage information, which is used to reflect the voltage output capability of the entire module; module total current information, which is used to monitor the current work load of the module, and is closely related to the output power of the battery pack. These parameters are usually used as important inputs for power control, charging and discharging strategy scheduling of the battery management system (BMS).

[0087] The module second operation parameter information mainly refers to the environmental temperature data of the enhanced point, i.e. point temperature information. This information does not cover the entire module, but selects the temperature sensors deployed at the key positions of the high-risk areas or heat accumulation areas determined through thermal simulation and airflow analysis, to capture the local temperature rise or premonition of thermal runaway.

[0088] In actual application, the system obtains the above three types of parameters in real time through high-precision sampling mechanism, and fuses them into a unified data structure, which is used for subsequent fixed frequency local recording and dynamic frequency intelligent uploading, to build a closed-loop data acquisition system of "bottom layer precision acquisition-middle layer thermal perception enhancement-upper layer intelligent frequency adjustment upload". This multi-dimensional and multi-granularity data acquisition method greatly improves the comprehensiveness of system operation state recognition and the responsiveness, and effectively supports the high safety and high stability operation of the UPS high-voltage lithium battery pack in key scenarios such as data center and telecommunication base station.

[0089] In a feasible implementation manner, as shown in Figure 2 , a specific determination method flow chart of the enhanced point provided by the embodiment of the application is shown in Figure 2 The specific determination steps of the enhanced point include:

[0090] S210, obtaining thermal simulation result information and heat dissipation fluid simulation information of the target battery pack;

[0091] S220, determining the enhanced point based on the thermal simulation result information and the heat dissipation fluid simulation information.

[0092] For example, in order to realize accurate perception of key thermal abnormal areas in the UPS high-voltage lithium battery pack, the application improves the spatial resolution and risk response capability of temperature monitoring by intelligent layout of the enhanced point.

[0093] First, in step S210, the system obtains the thermal simulation result information and the heat dissipation fluid simulation information of the target battery pack. The thermal simulation result information is a temperature field distribution model constructed based on the battery pack structure, working load, cell thermal characteristics and environmental boundary conditions. The simulation model can output the temperature distribution, thermal aggregation position and temperature rise trend inside the battery pack under different operating conditions. The heat dissipation fluid simulation information is obtained by calculating the flow field characteristics in the air cooling system or natural convection cooling system, such as air flow velocity, flow direction and pressure drop distribution, which reflects the spatial variation characteristics of cooling efficiency and air flow capacity inside the battery pack. The combination of these two types of information provides comprehensive reference for static heat source distribution and dynamic heat dissipation capacity, which is the basic support data for the selection of enhanced points.

[0094] In step S220, the system performs comprehensive analysis on the battery pack space based on the thermal simulation and fluid simulation data, and determines the position with the highest thermal risk perception value as the enhanced point. Specifically, the system first divides the battery pack space into multiple regular grid units, and calculates the thermal abnormality potential score for each unit. The score is composed of temperature deviation, temperature gradient, wind speed, heat dissipation boundary distance and other indicators. Then, the regions with high score and weak heat diffusion capacity are identified as candidate points. Further, the thermal aggregation clusters are identified by region growing algorithm, and the engineering constraints such as structural accessibility, wiring feasibility and spatial symmetry are introduced to filter and optimize the candidate points, and finally a group of points with reasonable spatial distribution, physical deployability and high information value are determined as the enhanced points.

[0095] These enhanced points are mainly deployed in the heat dissipation dead angle, air duct boundary, cell dense area or historical high temperature area of the battery module, focusing on collecting temperature change information in these areas to provide accurate input for real-time monitoring, abnormal warning and dynamic sampling strategy of the system, thereby significantly improving the thermal management level and operation safety of the entire UPS lithium battery system. This implementation not only realizes the intelligentization and visualization of point layout, but also effectively avoids the problems of insufficient coverage in blind area, information redundancy and physical inaccessibility in traditional manual point layout, which has significant engineering practical value and promotion prospect.

[0096] In a feasible implementation, as shown inFigure 3 as shown, Figure 3 A method for determining an enhanced point based on thermal simulation result information and heat dissipation fluid simulation information is provided for an embodiment of the application. The step S220 of determining the enhanced point based on the thermal simulation result information and the heat dissipation fluid simulation information includes:

[0097] S2201, performing spatial grid division on the target battery pack to establish a three-dimensional sampling candidate model including a plurality of spatial units;

[0098] S2202, mapping the thermal simulation result information and the heat dissipation fluid simulation information to the three-dimensional sampling candidate model respectively, and constructing a corresponding temperature-airflow joint atlas;

[0099] S2203, calculating a thermal anomaly potential score for each spatial unit based on the temperature-airflow joint atlas, wherein the thermal anomaly potential score is determined according to a temperature deviation, a temperature gradient, a wind speed factor, and a heat dissipation boundary distance;

[0100] S2204, screening candidate enhanced point positions in the spatial units whose thermal anomaly potential scores exceed a set threshold value;

[0101] S2205, optimizing the candidate enhanced point positions based on structure accessibility, wiring feasibility, and spatial symmetry constraint conditions of point position layout, and taking the optimized candidate enhanced point positions as the enhanced point positions corresponding to the module second operation parameter information.

[0102] For example, to achieve fine monitoring of the operating state of a UPS high-voltage lithium battery pack, the application provides an optimization method for determining an enhanced point based on simulation data. This method drives the intelligent layout of enhanced sampling points through thermal simulation and fluid simulation information, effectively improving the temperature perception ability of local thermal risk areas.

[0103] First, in step S2201, the system performs spatial grid division on the target battery pack, divides it into a plurality of three-dimensional sampling units with spatial coordinates, and constructs a three-dimensional sampling candidate model covering the entire internal structure of the battery system. Each spatial unit represents a possible location for deploying a temperature sensor and records its corresponding physical properties, such as the module it belongs to, the relationship between the cells, the air duct boundary conditions, etc., providing a spatial indexing basis for subsequent analysis.

[0104] Then, in step S2202, the system maps the acquired thermal simulation result information and the heat dissipation fluid simulation information to the three-dimensional sampling candidate model respectively, to form a temperature-airflow joint atlas containing temperature distribution and airflow characteristics. Specifically, the thermal simulation result is used to assign each unit with a simulated temperature value and its gradient characteristics, and the fluid simulation information provides data such as airflow velocity, direction, and pressure loss at each unit, and the two can comprehensively reflect the heat accumulation tendency and cooling capacity of a certain region after being fused.

[0105] Subsequently, in step S2203, the system calculates the thermal abnormality potential score of each space unit based on the joint atlas.

[0106] Specifically, the system first maps the thermal simulation result information and the heat dissipation fluid simulation information to the three-dimensional space grid of the battery pack, and assigns each space unit with key attributes such as the current temperature, airflow velocity, temperature gradient between adjacent units, and the distance to the heat dissipation boundary. Then, the system calculates the thermal abnormality potential score of each space unit based on the following scoring formula: Thermal abnormality potential score of the i-th space unit = (T i - T avg) * w 1 + (T i - T avg) * w 2 + (T i - T avg) * w 3 + (T i - T avg) * w 4 + (1 / v) * w 5 + (D i) * w 6

[0107]

[0108] wherein, represents the thermal abnormality potential score of the i-th space unit; T i represents the temperature of the i-th space unit; T avg represents the average temperature of the battery system as a whole; represents the temperature gradient length between the i-th space unit and its adjacent units, which is used to identify the thermal mutation boundary; v represents the local airflow velocity of the i-th space unit; 1 / v represents the wind speed factor, i.e., the reciprocal of the wind speed, which is used to reflect the risk of insufficient cooling efficiency; and D i represents the spatial distance of the i-th space unit to the nearest heat dissipation boundary (such as the outlet of the air duct or the metal shell), which is used to depict the feature that the heat diffusion of the point is hindered by the physical structure. w i represents the weight factor, which can be set according to the actual scene or the experience model, and common settings are, for example,

[0109] The scoring formula combines four key factors, not only focusing on the high and low of the temperature itself, but also taking into account the temperature change trend, cooling capacity difference, and structural thermal resistance, which are important factors that are easily induced in actual operation. Secondly, the reciprocal of the wind speed is introduced as an indication of insufficient cooling, which strengthens the risk identification ability of the low-efficiency heat dissipation area and makes up for the defects of the traditional temperature value judgment method. In addition, by introducing the distance boundary factor, the point placement is avoided in the area that already has good heat dissipation conditions, thereby improving the risk coverage efficiency and deployment value of the point placement.

[0110] ​Next, in step S2204, the system filters out spatial units whose thermal anomaly potential scores exceed a set threshold and constructs a candidate enhancement point set. These points are often located in areas with high incidence of thermal anomalies, airflow dead zones, or structural dead zones, and are potential risk points for thermal runaway. Sensors should be deployed in these areas for high-frequency temperature monitoring.

[0111] Finally, in step S2205, the system performs multi-constraint optimization on the candidate points based on the actual structure and engineering deployment conditions. First, based on structural accessibility rules, points located inside non-removable structures or inaccessible by manual maintenance are eliminated. Second, based on wiring feasibility analysis, sensor paths that need to cross high-voltage or interference areas are filtered out. Third, based on spatial symmetry constraints, only representative points are retained within the structurally symmetrical area to avoid redundant placement. Ultimately, a set of optimized enhanced points that meet deployment feasibility, reasonable spatial distribution, and high thermal sensing value is obtained, and these are used as sampling locations for the module's second operating parameter information.

[0112] Through this implementation method, the UPS system can achieve key monitoring and differentiated sampling strategies for high-risk areas. Without significantly increasing the number of sensors and system complexity, it can greatly improve the accuracy of thermal risk perception and data value density, and significantly enhance the system's thermal safety monitoring and intelligent response capabilities.

[0113] In one feasible implementation, such as Figure 4 As shown, Figure 4 This is a flowchart illustrating a method for screening candidate enhancement sites according to an embodiment of the present invention. Step S2204, which screens candidate enhancement sites in spatial cells where the thermal anomaly potential score exceeds a set threshold, includes:

[0114] S22041. Construct an initial candidate point set for all spatial units whose thermal anomaly potential scores exceed the preset scores;

[0115] S22042. Calculate the difference in potential score between each candidate spatial cell and its neighboring cells in the above initial candidate point set.

[0116] S22043. If the difference in the above potential scores exceeds the set gradient threshold, then the unit is marked as a potential thermal mutation boundary point.

[0117] S22044. Combining spatial topology and temperature change trends, a region growing algorithm is used to identify potential high-risk thermal anomaly regions.

[0118] S22045. In each of the above-mentioned high-risk thermal anomaly areas, candidate enhancement points are dynamically selected based on local score extreme points, centrality, and historical temperature fluctuation stability.

[0119] S22046, For the isolated distribution of abnormal score points, by predicting the temperature rise trend and the neighborhood flow velocity attenuation model, judge whether it constitutes a latent heat source;

[0120] S22047, if true, also included in the above candidate enhancement point.

[0121] Exemplary, in step S22041, the system filters all space units with thermal anomaly potential score exceeding the preset threshold, and constructs the initial candidate point set. These points represent the areas with high temperature, steep gradient or limited heat dissipation in the battery pack, and have strong local heat accumulation characteristics, which are the precursor points of thermal runaway.

[0122] In step S22042, the system calculates the difference between the potential scores of each point in the initial candidate point set and its spatial adjacent units to obtain the local score difference of each point, so as to quantify whether the point is in the "mutation zone" with rapid heat change.

[0123] In step S22043, the system marks the points with potential score difference exceeding the set gradient threshold as potential thermal mutation boundary points. These points are usually located at the junction of thermal anomaly area and normal area, and are the positions with rapid temperature change and narrow heat diffusion path, which are important for capturing edge runaway trend.

[0124] In step S22044, the system further combines the spatial topological structure and temperature change trend, and uses region growing algorithm to identify multiple potential thermal anomaly high-risk area clusters. This process starts from the mutation boundary point, and expands the region according to the similarity of thermal score between adjacent units, temperature connectivity and spatial connectivity, etc. until the growing condition is not met, so as to construct the risk area boundary with obvious heat accumulation trend.

[0125] Then, in step S22045, the system determines one or more points in each thermal anomaly area cluster as the candidate enhancement point of the cluster according to the local score extreme point (the highest thermal score), the spatial centrality (the propagation center in the region), and the historical temperature fluctuation stability (large fluctuation and rapid change), etc. These points not only represent the strongest area of thermal risk, but also have the value of continuous monitoring and prediction.

[0126] In step S22046, the system specially processes the "isolated high score points" that failed to cluster into any area cluster. By analyzing the temperature change trend (such as temperature rise rate, second derivative) of these points and the airflow velocity attenuation characteristics of its neighborhood, it is judged whether it is a latent heat source. For example, although the current temperature is not significantly high, there are characteristics such as fast temperature rise rate and weak heat dissipation, which suggest that this area has the possibility of forming a hot spot in the short term.

[0127] Finally, in step S22047, if the above analysis confirms that the isolated point has a strong temperature rise trend or a structural heat dissipation bottleneck, it is also included in the candidate reinforcement point set to ensure that the entire system takes into account both explicit and implicit thermal risks when deploying points.

[0128] Through the aforementioned continuous analysis and screening steps, this embodiment can dynamically construct a well-structured and highly efficient cluster of enhanced monitoring points, supported by multi-source simulation data. This mechanism not only improves the early identification capability of thermal risks but also avoids ineffective redundant deployment and resource waste, demonstrating strong engineering practicality and intelligence, and has broad application value in large-scale UPS lithium battery systems.

[0129] In one feasible implementation, such as Figure 5 As shown, Figure 5 This is a flowchart illustrating a method for identifying potential high-risk thermal anomaly regions according to an embodiment of the present invention. Step S22044, which combines spatial topology and temperature change trends, applies a region growing algorithm to identify potential high-risk thermal anomaly regions, including:

[0130] S220441. Divide the target battery pack space into three-dimensional meshes and bind each spatial cell to its current temperature, temperature change rate, thermal anomaly potential score and adjacent cell index.

[0131] S220442. Select spatial cells from all spatial cells that have a thermal anomaly potential score greater than or equal to the first threshold and a temperature change rate greater than or equal to the second threshold, and use them as seed points for regional growth.

[0132] S220443. Define the region growth criteria, wherein the above region growth criteria include: the candidate expansion unit is spatially adjacent to the existing boundary unit, the thermal anomaly potential score is greater than or equal to the third threshold, the adjacent temperature difference is less than the set gradient threshold, and it does not cross the structural partition region.

[0133] S220444. Starting from the above seed point, iteratively expand the region based on the above region growth criterion until no new spatial unit satisfies the criterion.

[0134] S220445. Perform multi-cluster screening on all completed growth area clusters, remove areas with insufficient points or insufficient risk scores, and correct the thermal diffusion model for edge areas to identify potential high-risk thermal anomaly area clusters.

[0135] For example, to accurately identify the space area with potential thermal runaway risk inside the UPS high-voltage lithium battery pack, the present application proposes a region growing algorithm combining spatial topology and temperature change trend in step S22044 for constructing a thermal anomaly high-risk region cluster. This method is based on simulation perception and spatial evolution, and realizes intelligent identification of thermal aggregation area by gradually aggregating high-risk space units.

[0136] In step S220441, the system divides the entire battery pack space into multiple structurally consistent space units by three-dimensional grid division, and each unit represents a fixed volume of physical area. To realize subsequent analysis, each unit needs to bind four key attributes: first, the current temperature value, reflecting its thermal state; second, the temperature change rate, used to identify fast heating points; third, the thermal anomaly potential score based on multiple factors, for comprehensive assessment of the risk level of the point; fourth, the index information of the adjacent units of the unit, used to establish an expandable path relationship in the spatial topology structure.

[0137] In step S220442, the system selects regions with strong thermal risk characteristics from all space units as seed points for region growing. The specific screening conditions include: the thermal anomaly potential score of the unit is not less than the set first threshold (for example, 0.7), and the temperature change rate is not less than the second threshold (for example, 1.5°C / min). Only units that meet both the score and the temperature rise trend have the potential to become a potential thermal aggregation source and have growth value.

[0138] Then, in step S220443, the system defines the criteria for region growing to limit the logical boundaries of region expansion. Specifically, before a space unit is included in the region cluster, it must meet the following four conditions:

[0139] (1) It has at least a first-order spatial adjacent relationship with the current growing boundary;

[0140] (2) Its thermal anomaly potential score is not less than the set third threshold (such as 0.5), to ensure that the region thermal intensity does not drop sharply;

[0141] (3) The temperature difference between it and the adjacent boundary unit should not exceed the set gradient threshold (such as 2.5°C), to avoid region mutation;

[0142] (4) Its spatial connection path should not cross structural barriers such as metal shells, thermal insulation layers, air ducts, and other non-thermal regions.

[0143] This multi-condition criterion guarantees the coherence, continuity and physical reasonableness of region growing.

[0144] In step S220444, the system starts with all seed points and gradually expands the space using a region growing algorithm. In each iteration, the system traverses all current region boundary cells and performs growth criterion checks on all its adjacent cells. If the criteria are met, the cells are included in the region cluster, and the boundary is updated, until no new cells are added in a consecutive iteration. Ultimately, each seed point will grow one or more connected hot-risk region clusters.

[0145] In step S220445, the system evaluates and filters all generated region clusters. Region clusters with too few points (e.g., below a set threshold) or whose overall thermal anomaly score is below the global average are considered local noise or atypical anomaly regions and are removed. For region edges with blurred boundaries or drastic gradient changes, a fitting correction based on a thermal diffusion physics model is performed to enhance the thermophysical continuity of the region boundaries. After filtering, the system marks the final high-quality region clusters as potential high-risk thermal anomaly region clusters, providing direct input for subsequent enhanced point selection and dynamic sampling control strategies.

[0146] Through the aforementioned sequential process, this invention constructs a region growth identification mechanism with spatial structure perception and temperature trend prediction capabilities. This mechanism can efficiently extract key thermal risk areas within UPS battery packs, significantly improving the initiative, intelligence, and safety assurance capabilities of deployment strategies. This method is applicable to thermal safety monitoring of heterogeneous module layouts, high heat dissipation complexity, and large-scale UPS systems, and has broad application prospects.

[0147] In one feasible implementation, such as Figure 6 As shown, Figure 6 This is a flowchart illustrating a method for optimizing candidate enhancement points according to an embodiment of the present invention. Step S2205 optimizes the candidate enhancement points based on constraints related to the structural accessibility, wiring feasibility, and spatial symmetry of the point layout. The optimized candidate enhancement points are then used as the enhancement points corresponding to the second operating parameter information of the module. The method includes:

[0148] S22051. Construct a topological mapping of candidate enhancement points in the battery module structure to obtain the physical location and structural association information of each of the above candidate enhancement points in the module.

[0149] S22052. Based on the structural accessibility rule, calculate the reachable path length of each of the above candidate enhancement points relative to the preset maintenance opening or detection window. If the path exceeds the preset maximum access distance or crosses a non-removable structure, then remove the point or replace it with a new point with thermal representativeness in its neighborhood.

[0150] S22053, based on the wiring feasibility rules, determine whether the wiring path between each point and the nearest collection interface crosses the high-voltage isolation area, ventilation channel or electromagnetic interference area, if it does not meet the wiring requirements, the current point is removed;

[0151] S22054, according to the symmetry characteristics of the module structure, group the candidate points located in the symmetric area, only keep the representative points with higher thermal anomaly potential or better signal coverage in each group to avoid redundant deployment;

[0152] S22055, the point meeting the above structural accessibility, the above wiring feasibility and the above symmetry constraint condition is determined as the optimized enhanced point, as the collection position of the second operation parameter information of the module.

[0153] To ensure the implementability and effectiveness of the enhanced sampling points in the UPS high-voltage lithium battery pack in actual deployment, the application designs a set of point optimization method based on engineering constraint conditions, which mainly evaluates and selects the candidate enhanced points from three aspects of structural accessibility, wiring feasibility and spatial symmetry, and finally forms a group of optimized points with high thermal sensing value and convenient deployment. Specifically, it includes the following five sub-steps:

[0154] In step S22051, the system constructs the topological mapping of all candidate enhanced points in the battery module structure based on the three-dimensional space model. The mapping not only records the spatial coordinates and the module number of each point, but also associates its surrounding structure information, such as cell arrangement, air duct path, cooling fin position, shell boundary, etc., providing a structural semantic basis for subsequent accessibility and wiring analysis.

[0155] In step S22052, the system evaluates all candidate enhanced points according to the structural accessibility rules. Specifically, the system calculates the shortest path length of each point to the nearest maintenance opening or detection window, if the path exceeds the preset maximum maintenance distance, or crosses the non-detachable structure (such as metal shell, thermal insulation layer, structural adhesive, etc.), the point is considered as unreachable, and is not suitable as an enhanced point. For the accessibility critical point, the system can find a substitute point with similar thermal score in its neighborhood to maintain the continuity of thermal sensing coverage.

[0156] In step S22053, the system judges the physical connection path between each point and the collection main control board according to the wiring feasibility rules. If the path needs to cross the high-voltage isolation area, the core ventilation channel, the electromagnetic shielding layer or the high-frequency noise area, it will face safety or signal stability problems, the system will mark the point as "wiring not feasible" and remove or replace it. At the same time, the system can also refer to the wiring slot and cable length margin in the actual module to perform engineering-level path evaluation on point layout.

[0157] In step S22054, the system groups the candidate points based on the structural design file or symmetry rule of the module. If the left and right structures or the up and down layouts of the module are completely symmetrical, they can be regarded as symmetrical blocks. For the candidate points in each symmetrical region, only the representative point with higher thermal anomaly potential score, closer to the center, or better signal quality is retained, and the remaining points are regarded as information redundancy and excluded, thereby simplifying the wiring and reducing the redundant perception.

[0158] In step S22055, the system summarizes all points that meet the structural accessibility, wiring feasibility, and spatial symmetry constraint conditions at the same time, and marks them as the final optimized enhanced point set. These points will be used as the sampling positions of the second running parameter information of the module, for deploying high-sensitivity temperature sensors to realize dynamic perception and rapid response to local thermal anomalies of the module.

[0159] Through the above continuous optimization process, the embodiment significantly improves the deployment feasibility of the enhanced point and the system structure adaptation degree under the premise of ensuring thermal perception coverage, avoids data loss, wiring interference, or structural intervention problems caused by unreasonable sensor location, effectively realizes the goal of "high-value point + low engineering cost + safe and controllable", and provides a landable intelligent monitoring foundation for the UPS battery system.

[0160] In a feasible implementation manner, as shown in Figure 7 , a flowchart of a method for determining a fixed frequency is provided for the embodiment of the present application. The determination step of the fixed frequency includes: Figure 7 S310, determining the basic frequency information according to the first running parameter information;

[0161]

[0162] S320, generating the environment correction frequency information according to the use environment information of the target battery pack;

[0163] S330, generating the fixed frequency according to the basic frequency information and the environment correction frequency information.

[0164] In a feasible implementation manner, as shown in Figure 8 , a flowchart of a method for determining a dynamic frequency is provided for the embodiment of the present application. The determination step of the dynamic frequency includes: Figure 8 S410, calculating the temperature rise rate and temperature fluctuation amplitude of each enhanced point according to the temperature change trend in the cell running parameter information and the second running parameter information of the module;

[0165]

[0166] ​​S420, a multi-factor dynamic frequency regulation mechanism is introduced according to the temperature rise rate and the temperature fluctuation amplitude, and a specific index is used to determine the uploading frequency level of the point, wherein the specific index includes the distance between the current temperature value and the set safety threshold, the increase amplitude of the temperature rise rate per unit time, the thermal anomaly potential score of the region where the point is located, and the fluctuation frequency in the historical sampling record of the point;

[0167] S430, the dynamic frequency is determined according to the uploading frequency level and the frequency level table.

[0168] For example, in order to realize the accurate collection and uploading efficiency of the UPS high-voltage lithium battery pack operation data, the system can flexibly adjust the sampling frequency according to the operation condition by introducing the power consumption environment perception and temperature dynamic change regulation, so as to guarantee the data integrity and optimize the communication and storage resources. The embodiment includes a determination step of the fixed frequency and a determination step of the dynamic frequency, which are as follows:

[0169] Firstly, in the determination of the fixed frequency, the system forms the basic data collection rhythm through three steps:

[0170] In step S310, the system analyzes the working strength and electrical load stability of the current battery pack based on the first running parameter information of the module (such as total voltage and total current). For example, when the UPS is in standby state or long-time constant load running state, the voltage and current fluctuation is small, and a lower basic sampling frequency can be set; when the power fluctuation is frequent or severe, the basic sampling frequency is correspondingly increased. The system maps different electrical states to different sampling frequency baselines, such as 0.5 Hz, 1 Hz or 2 Hz, by setting corresponding interval thresholds.

[0171] In step S320, the system further introduces a power consumption environment perception mechanism. This mechanism can dynamically generate environment correction frequency information based on environmental parameters such as on-site load type, grid-connected / off-grid mode, power supply switching frequency, and workplace temperature and humidity level. For example, in the high-density operation of data centers or industrial power consumption scenarios, due to the high heat dissipation pressure and high failure cost, the system can appropriately increase the sampling frequency to enhance the safety protection capability; in some light load scenarios, the frequency can be reduced to save resources.

[0172] In step S330, the system fuses the above-mentioned basic frequency and environment correction frequency to generate the final fixed frequency value. The fusion method can adopt weighted average, priority maximum value and other strategies to ensure that the basic running state and external risk environment jointly drive the sampling intensity. The fixed frequency is mainly used to collect the basic parameters of the battery cell and the first parameters of the module, support local storage and periodic uploading, and guarantee the full-time complete record of the system running state.

[0173] Next, in terms of dynamic frequency determination, the system mainly targets the enhancement point collection task and dynamically adjusts the sampling upload frequency according to the thermal risk changes, including the following three steps:

[0174] In step S410, the system analyzes the temperature sensor data corresponding to each enhancement point in real time, calculates the temperature rise rate (the change slope of temperature per unit time) and the temperature fluctuation amplitude (the difference between the maximum and minimum values of temperature in a period of time). These two indicators jointly reflect whether the current thermal dynamics of the point is abnormal, such as rapid temperature rise or frequent fluctuation.

[0175] In step S420, the system introduces a multi-factor dynamic frequency regulation mechanism to comprehensively evaluate the thermal risk level of each enhancement point. This mechanism assigns upload frequency levels to points according to the following four specific indicators:

[0176] (1) The distance between the current temperature value and the set safety upper limit (the smaller the distance, the higher the risk);

[0177] (2) The increase in temperature rise rate per unit time (whether there is an accelerating temperature rise trend);

[0178] (3) The thermal anomaly potential score of the region where the point is located (obtained according to simulation and spatial modeling calculation);

[0179] (4) The fluctuation frequency and mutation times in the historical sampling records of the point (reflecting instability).

[0180] The system weights and integrates these four indicators to form a risk level score and maps it to an upload frequency level, such as low, medium, and high.

[0181] Finally, in step S430, the system determines the specific sampling upload frequency according to the upload frequency level and the built-in frequency level comparison table. For example:

[0182] Low risk level → upload frequency 1.0 Hz (upload once every second);

[0183] Medium risk level → upload frequency 2.0 Hz (once every 0.5 seconds);

[0184] High risk level → upload frequency 5.0 Hz (once every 0.2 seconds); In special states (such as UPS startup, BMS alarm, city power outage, etc.), the system can also uniformly raise all enhancement points to an emergency frequency of 10 Hz to ensure data continuity and monitoring real-time during emergencies.

[0185] In summary, the embodiment combines fixed frequency and dynamic frequency to construct a stable and reliable operation data acquisition system with flexible response, which takes into account global perception and local hot risk capture, and has significant advantages in ensuring the thermal safety and operation efficiency of the UPS lithium battery pack.

[0186] In a second aspect, as shown by the accompanying drawings, Figure 9 The application provides a UPS high-voltage lithium battery pack operation data acquisition system, which comprises:

[0187] The first acquisition unit 21 is configured to acquire the cell operation parameter information of each cell;

[0188] The second acquisition unit 22 is configured to acquire the module first operation parameter information of the basic point and the module second operation parameter information of the enhanced point;

[0189] The generation unit 23 is configured to generate the acquisition data of the target battery pack according to the cell operation parameter information, the module first operation parameter information and the module second operation parameter information;

[0190] The data processing unit 24 is configured to store the acquisition data of the target battery pack in the local storage end based on the fixed frequency and upload the acquisition data to the cloud management platform based on the dynamic frequency.

[0191] It can be understood that the UPS high-voltage lithium battery pack operation data acquisition system can also perform the steps of any method according to the first aspect.

[0192] The above embodiments are only used to illustrate the technical solutions of the application, but not limit the application; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A method for collecting operating data of a UPS high-voltage lithium battery pack, characterized in that, The method comprises the following steps: obtaining cell operating parameter information of each cell; obtaining module first operating parameter information of a basic point and module second operating parameter information of an enhanced point; generating acquisition data of a target battery pack according to the cell operating parameter information, the module first operating parameter information and the module second operating parameter information; storing the acquisition data of the target battery pack in a local storage end based on a fixed frequency and uploading the acquisition data to a cloud management platform based on a dynamic frequency; the specific determination step of the enhanced point comprises: obtaining thermal simulation result information and heat dissipation fluid simulation information of the target battery pack; determining the enhanced point according to the thermal simulation result information and the heat dissipation fluid simulation information; the determination of the enhanced point according to the thermal simulation result information and the heat dissipation fluid simulation information comprises: spatial grid division is performed on the target battery pack to establish a three-dimensional sampling candidate model comprising a plurality of spatial units; the thermal simulation result information and the heat dissipation fluid simulation information are respectively mapped to the three-dimensional sampling candidate model to construct a corresponding temperature-airflow joint atlas; a thermal anomaly potential score is calculated for each spatial unit based on the temperature-airflow joint atlas, wherein the thermal anomaly potential score is determined according to a temperature deviation, a temperature gradient, a wind speed factor and a heat dissipation boundary distance; candidate enhanced points are screened in the spatial units whose thermal anomaly potential scores exceed a set threshold value; the candidate enhanced points are optimized based on structure accessibility, wiring feasibility and spatial symmetry constraint conditions of point layout, and the optimized candidate enhanced points are taken as the enhanced points corresponding to the module second operating parameter information; the screening of the candidate enhanced points in the spatial units whose thermal anomaly potential scores exceed the set threshold value comprises: an initial candidate point set is constructed for all spatial units whose thermal anomaly potential scores exceed a preset score; a potential score difference between each candidate spatial unit and adjacent units is calculated in the initial candidate point set; if the potential score difference exceeds a set gradient threshold value, the unit is marked as a potential thermal mutation boundary point; potential thermal anomaly high-risk area clusters are identified by applying a region growing algorithm in combination with spatial topological structure and temperature variation trend; in each thermal anomaly high-risk area cluster, the candidate enhanced points are dynamically screened according to local score extreme points, centrality and historical temperature fluctuation stability; for an isolated distributed abnormal score point, whether it constitutes a latent heat source is judged by a temperature rise trend prediction and a neighborhood flow velocity decay model; if yes, the abnormal score point is also included in the candidate enhanced points; the identification of the potential thermal anomaly high-risk area clusters by applying the region growing algorithm in combination with the spatial topological structure and the temperature variation trend comprises: three-dimensional grid division is performed on the target battery pack space, and a current temperature, a temperature variation rate, a thermal anomaly potential score and an adjacent unit index are bound for each spatial unit; in all spatial units, spatial units whose thermal anomaly potential scores are greater than or equal to a first threshold value and whose temperature variation rates are greater than or equal to a second threshold value are selected as region growing seed points; Defining a region growing criterion, wherein the region growing criterion comprises that a candidate expansion unit is spatially adjacent to an existing boundary unit, a thermal anomaly potential score is greater than or equal to a third threshold value, an abutment temperature difference is less than a set gradient threshold value, and a region does not cross a structural partition region; Starting from the seed point, iteratively expanding the region based on the region growing criterion until no new spatial unit meets the criterion; Performing multi-cluster screening on all grown region clusters, eliminating regions with insufficient points or insufficient risk scores, and performing thermal diffusion model correction on edge regions to identify potential high-risk thermal anomaly region clusters.

2. The method of claim 1, wherein, The cell operating parameter information includes cell voltage information, cell power information, cell temperature information, and cell internal resistance information; The first operating parameter information includes total voltage information and total current information; The second operating parameter information includes point temperature information.

3. The method of claim 1, wherein the UPS high-voltage lithium battery pack operating data is collected by, The candidate enhanced point is optimized based on the structure accessibility, wiring feasibility, and spatial symmetry constraint conditions of the point layout, and the optimized candidate enhanced point is used as the enhanced point corresponding to the second operating parameter information of the module, comprising: Constructing a topological mapping of the candidate enhanced point in the battery module structure to obtain the physical position and structural association information of each candidate enhanced point in the module; Based on the structure accessibility rule, calculate the accessible path length of each candidate enhanced point relative to the preset maintenance opening or detection window. If the path exceeds the preset maximum access distance or crosses the non-dismantling structure, eliminate the point or replace it with a new point with thermal representation in its neighborhood. Based on the wiring feasibility rule, determine whether the wiring path between each point and the nearest collection interface crosses the high-voltage isolation area, ventilation channel, or electromagnetic interference area. If it does not meet the wiring requirements, the current point is removed. According to the symmetry characteristics of the module structure, group the candidate points located in the symmetric region, and only keep the representative points with higher thermal anomaly potential or better signal coverage in each group to avoid redundant deployment. The point that meets the structure accessibility, wiring feasibility, and symmetry constraint conditions is determined as the optimized enhanced point, which is used as the collection location of the second operating parameter information of the module.

4. The method of claim 1, wherein the UPS high-voltage lithium battery pack operating data is collected by, The determination step of the fixed frequency includes: Determine the base frequency information according to the first operating parameter information; Generate environment correction frequency information according to the use environment information of the target battery pack; Generate the fixed frequency according to the base frequency information and the environment correction frequency information.

5. The method of claim 1, wherein, The determination step of the dynamic frequency includes: According to the temperature change trend in the cell operating parameter information and the second operating parameter information of the module, calculate the temperature rise rate and temperature fluctuation amplitude of each enhanced point; Introduce a multi-factor dynamic frequency control mechanism according to the temperature rise rate and temperature fluctuation amplitude, and determine the upload frequency level of the point according to specific indicators, wherein the specific indicators include the distance between the current temperature value and the set safety threshold, the increase amplitude of the temperature rise rate per unit time, the thermal anomaly potential score of the region where the point is located, and the frequent fluctuation degree in the historical sampling record of the point. determining the dynamic frequency according to the upload frequency level and the frequency level reference table.

6. A UPS high-voltage lithium battery pack operation data acquisition system, characterized in that, The method is executed, and the system comprises: A first acquisition unit is configured to acquire cell operating parameter information of each cell. A second acquisition unit is configured to acquire module first operating parameter information of a basic point and module second operating parameter information of an enhanced point. A generation unit is configured to generate collection data of a target battery pack according to the cell operating parameter information, the module first operating parameter information and the module second operating parameter information. A data processing unit is configured to store the collection data of the target battery pack in a local storage end based on a fixed frequency and upload the collection data to a cloud management platform based on a dynamic frequency.

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