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

Abstract

The invention discloses a UPS high-voltage lithium battery pack operation data acquisition method and system, and relates to the field of data acquisition, and the method comprises the steps: obtaining the cell operation parameter information of each cell; obtaining module first operation parameter information of the basic point location and module second operation parameter information of the enhanced point location; according to the cell operation parameter information, the module first operation parameter information and the module second operation parameter information, generating collection data of the target battery pack; and storing the acquired data of the target battery pack in a local storage end based on a fixed frequency, and uploading the acquired data to a cloud management platform based on a dynamic frequency. The scheme of the invention not only improves the early sensing ability of the thermal safety and performance change of the UPS lithium battery pack, but also effectively reduces the data processing pressure and deployment difficulty, and has significant advantages in the aspects of system stability, maintainability and safety 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: Obtaining cell operation parameter information of each cell; Obtaining module first operation parameter information of basic point positions and module second operation parameter information of enhanced point positions; Generating 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; Storing the collection data of the target battery pack in a local storage end based on a fixed frequency and uploading the collection data to a cloud management platform based on a dynamic frequency.

[0008] In a feasible implementation, the cell operation parameter information includes cell voltage information, cell power information, cell temperature information and cell internal resistance information; The first operation parameter information includes total voltage information and total current information; The second operation parameter information includes point position temperature information.

[0009] In a feasible implementation, the specific determination of the enhanced point positions includes: Obtaining thermal simulation result information and heat dissipation fluid simulation information of the target battery pack; Determining the enhanced point positions according to the thermal simulation result information and the heat dissipation fluid simulation information.

[0010] 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: Performing spatial grid division on the target battery pack to establish a three-dimensional sampling candidate model including a plurality of spatial units; Mapping 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; 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; Screening candidate enhanced point positions in the spatial units whose thermal anomaly potential scores exceed a set threshold; 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.

[0011] In a feasible implementation, the screening of the candidate enhanced point positions in the spatial units whose thermal anomaly potential scores exceed a set threshold includes: Constructing an initial candidate point set for all spatial units whose thermal anomaly potential scores exceed a preset score; Calculate the potential score difference of each candidate space unit with its adjacent units in the initial candidate point set above; If the potential score difference above exceeds a set gradient threshold, mark the unit as a potential thermal mutation boundary point; Combine the spatial topology structure and temperature change trend, and apply the region growing algorithm to identify potential thermal anomaly high-risk area clusters; In each of the above thermal anomaly high-risk area clusters, according to the local score extreme point, centrality and historical temperature fluctuation stability, dynamically screen the above candidate enhancement point; For isolated distribution of abnormal score points, through the prediction of temperature rise trend and neighborhood flow velocity decay model, judge whether it constitutes a latent heat source; If true, also included in the above candidate enhancement point.

[0012] In a possible implementation, the above combining the spatial topology structure and temperature change trend, and applying the region growing algorithm to identify potential thermal anomaly high-risk area clusters, comprises: Perform three-dimensional grid division on the target battery pack space, and bind the current temperature, temperature change rate, thermal anomaly potential score and adjacent unit index of each space unit; Screen out the space units with thermal anomaly potential score greater than or equal to a first threshold and temperature change rate greater than or equal to a second threshold from all space units as region growing seed points; Define region growing criteria, wherein the region growing criteria include that the candidate expansion unit is adjacent to the existing boundary unit space, the thermal anomaly potential score is greater than or equal to a third threshold, the adjacent temperature difference is less than a set gradient threshold, and does not cross the structural partition area; Starting from the seed point, iteratively expand the region based on the region growing criteria until there is no new space unit that meets the criteria; Perform multi-cluster screening on all grown region clusters, remove regions with insufficient points or insufficient risk scores, and modify the thermal diffusion model for the edge region to identify potential thermal anomaly high-risk area clusters.

[0013] In a possible implementation, the above optimization of the candidate enhancement point based on the structure accessibility, wiring feasibility and spatial symmetry constraint conditions of the point layout, takes the optimized candidate enhancement point as the above enhancement point corresponding to the above module second operation parameter information, comprising: Construct the topological mapping of the candidate enhancement point in the battery module structure to obtain the physical position and structural association information of each candidate enhancement point in the module; Based on the structural accessibility rule, the reachable path length of each of the above-mentioned candidate enhanced point relative to the preset maintenance opening or detection window is calculated, and if the path exceeds the preset maximum access distance or passes through the non-dissociable structure, the point is removed or replaced with a new point with thermal representation in its neighborhood; Based on the wiring feasibility rule, it is judged whether the wiring path between each point and the nearest collection interface passes through the high-voltage isolation area, the ventilation channel or the electromagnetic interference area, and if the wiring requirement is not met, the current point is removed; According to the symmetry characteristics of the module structure, the candidate points located in the symmetric region are grouped, and only the representative points with higher thermal anomaly potential or better signal coverage in each group are retained to avoid redundant deployment; The points that meet the above structural accessibility, wiring feasibility and symmetry constraints are determined as the optimized enhanced points, which are the collection positions of the module second operating parameter information.

[0014] In a feasible implementation, the determination of the fixed frequency includes: The basic frequency information is determined according to the first operating parameter information; The environmental correction frequency information is generated according to the use environment information of the target battery pack; The fixed frequency is generated according to the basic frequency information and the environmental correction frequency information.

[0015] In a feasible implementation, the determination of the dynamic frequency includes: According to the temperature change trend in the cell operating parameter information and the module second operating parameter information, the temperature rise rate and temperature fluctuation amplitude of each enhanced point are calculated; A multi-factor dynamic frequency control mechanism is introduced according to the temperature rise rate and temperature fluctuation amplitude, and the upload frequency grade of the point is determined 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 fluctuation frequency in the historical sampling record of the point; The dynamic frequency is determined according to the upload frequency grade and the frequency grade table.

[0016] The second aspect, the application provides a UPS high-voltage lithium battery pack operating data acquisition system, comprising: The first acquisition unit is used for acquiring the cell operating parameter information of each cell; The second acquisition unit is used for acquiring the module first operating parameter information of the basic point and the module second operating parameter information of the enhanced point; The generating unit is configured to generate collection 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. The data processing unit is configured to store the collection data of the target battery pack in the local storage end based on a fixed frequency and upload the collection data to a cloud management platform based on a dynamic frequency.

[0017] In summary, the UPS high-voltage lithium battery pack operation data acquisition method proposed in the present application integrates a plurality of innovative mechanisms such as full-level parameter perception, thermal simulation driving point optimization and frequency intelligent regulation, significantly improving the accuracy, timeliness and safety of system operation state monitoring. Firstly, the present application adopts a layered acquisition strategy at the cell level and the module level, which not only covers 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-airflow joint map is constructed to accurately identify local thermal risk areas, and additional temperature collection points are arranged in key areas to achieve high-sensitivity monitoring of potential thermal runaway areas. Secondly, compared with the traditional uniform frequency sampling mode, the present 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 traceability throughout the operation 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 performance and system load. In the aspect of enhanced point selection, the present application automatically identifies clusters of thermal abnormal high-risk areas through a 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 solutions relying on manual point deployment. In addition, the present solution 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 the data processing pressure and deployment difficulty, having significant advantages in system stability, maintainability and safety protection. The UPS high-voltage lithium battery pack operation data acquisition method proposed in the present application, other advantages, objectives and features of the present application will be partially embodied through the following description, and partially understood by those skilled in the art through research and practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0018] 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 better understanding of the preferred embodiments, and are not intended to be a limitation of the present description. Moreover, in the drawings, like reference numerals denote same or similar components. In the drawings: Figure 1 A flow chart of a method for collecting operation data of a UPS high-voltage lithium battery pack according to an embodiment of the present application is shown in FIG. 1. Figure 2 A flow chart of a method for determining an enhanced point according to an embodiment of the present application is shown in FIG. 2. Figure 3 A flow chart of a method for determining an enhanced point according to thermal simulation result information and heat dissipation fluid simulation information according to an embodiment of the present application is shown in FIG. 3. Figure 4 A flow chart of a method for screening a candidate enhanced point according to an embodiment of the present application is shown in FIG. 4. Figure 5 A flow chart of a method for identifying a potential thermal abnormality high-risk area cluster according to an embodiment of the present application is shown in FIG. 5. Figure 6 A flow chart of a method for optimizing a candidate enhanced point according to an embodiment of the present application is shown in FIG. 6. Figure 7 A flow chart of a method for determining a fixed frequency according to an embodiment of the present application is shown in FIG. 7. Figure 8 A flow chart of a method for determining a dynamic frequency according to an embodiment of the present application is shown in FIG. 8. Figure 9 A structure diagram of a system for collecting operation data of a UPS high-voltage lithium battery pack according to an embodiment of the present application is shown in FIG. 9. DETAILED DESCRIPTION

[0019] The terms "first", "second", "third", "fourth" and the like in the description and in the claims of the present application, and above-described drawings (if there are) are used to distinguish similar objects, and do not necessarily have to be used to describe a particular sequential or chronological order. 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 have to be limited to only 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 a part of the embodiments of the present application, not all.

[0020] Please refer to Figure 1 A flow chart of a UPS high-voltage lithium battery pack operation data acquisition method provided by the embodiments of the present application can specifically include: S110, obtaining cell operation parameter information of each cell; S120, obtaining module first operation parameter information of a basic point and module second operation parameter information of an enhanced point; S130, generating acquisition data of a target battery pack according to the above-mentioned cell operation parameter information, the above-mentioned module first operation parameter information and the above-mentioned module second operation parameter information; S140, storing the acquisition data of the target battery pack in a local storage end based on a fixed frequency, and uploading to a cloud management platform based on a dynamic frequency.

[0021] For example, the embodiments of the present application provide a running data acquisition method for a UPS high-voltage lithium battery pack. The method realizes comprehensive perception and efficient uploading of the running state of the battery pack through multi-level data acquisition strategies, which not only guarantees system safety, but also takes into account data processing efficiency.

[0022] First, in step S110, the system obtains the operation parameter information of the cell level. This step collects the key operation parameters of each lithium cell in real time through the BMS system, including but not limited to cell voltage, cell current, cell temperature and cell internal resistance, etc. Through these basic information, the working state, load response and aging characteristics of the cell can be comprehensively monitored, and the basic data support for battery safety, performance diagnosis and capacity prediction is provided.

[0023] Then, in step S120, the system further acquires module-level operating parameter information, including two parts: one is the first operating parameter information of the basic point of the module, such as the total voltage and total current of each module, which are macro parameters for monitoring the overall power supply state of the module; the second is the second operating parameter information of the enhanced point corresponding to the module determined by simulation analysis and thermal anomaly perception mechanism, mainly collecting temperature information of key local areas. This layered collection strategy combining basic and enhanced points significantly improves the system's perception ability of potential thermal risk areas.

[0024] Then, in step S130, the system fuses the above-mentioned acquired cell operating parameters, module first operating parameters, and second operating parameters of the enhanced point to generate complete target battery pack collection data. This step constructs a multi-dimensional operating feature set on the sampling time axis through data structuring processing, facilitating subsequent state analysis, anomaly identification, and historical trend comparison.

[0025] Finally, in step S140, the system performs a frequency division processing strategy on the collected data: on the one hand, all data are stored locally at a predetermined fixed frequency, ensuring that critical historical information can still be recorded completely in offline state; on the other hand, for temperature indicators of specific points (especially enhanced points), the upload frequency is dynamically adjusted based on a multi-factor dynamic judgment mechanism, achieving high-frequency response upload in high-risk state and low-frequency energy-saving upload in low-risk state, thereby improving the real-time, sensitivity, and cloud processing efficiency of overall data upload.

[0026] Through the above four steps, this method realizes full-level parameter acquisition from the cell to the module, accurate perception from the basic state to the high-risk area, intelligent control from static collection to dynamic upload, and is suitable for the fine management needs of modern data centers and high-reliability UPS systems for lithium battery pack operating state.

[0027] The UPS high-voltage lithium battery pack operation data acquisition method provided by the present application 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 the 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 thermal 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 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.

[0028] In an exemplary embodiment, 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.

[0029] Exemplarily, the UPS high-voltage lithium battery pack operation data acquisition method provided by the present application achieves fine monitoring and safety warning of the entire battery system operation state through layered perception of cell, module basic parameters, and enhanced point temperature. The collected operating parameter information is divided into three categories according to data dimensions, namely cell operating parameter information, module first operating parameter information, and module second operating parameter information.

[0030] The battery cell operating parameter information is the core data foundation for building the underlying status perception of the system, which includes: battery cell voltage information, which is used to determine the charge state and voltage balance of the single battery cell; battery cell power information, that is, the battery cell power output or absorption obtained by calculating the voltage and current, reflecting the instantaneous energy conversion state; battery cell temperature information, which is used to monitor the heating condition of the battery cell during the charging and discharging process and is an important basis for evaluating thermal safety; battery cell internal resistance information, which can be used to evaluate the health status and aging degree of the battery cell. Increased internal resistance is often related to performance degradation or potential failure.

[0031] The primary module operating parameter information primarily refers to global module-level operating indicators, including total module voltage, which reflects the overall module voltage output capability; and total module current, which monitors the module's current workload and is closely related to the battery pack's output power. These parameters typically serve as key inputs for the battery management system (BMS) to control power and schedule charge and discharge strategies.

[0032] The module's second operating parameter information mainly refers to the ambient temperature data of the enhanced points, namely: point temperature information. This information does not cover the entire module, but selects temperature sensors deployed at key locations in high-risk areas or heat accumulation areas determined through thermal simulation and airflow analysis to capture local temperature rise or precursors to thermal runaway.

[0033] In actual applications, the system acquires these three parameters in real time through a high-precision sampling mechanism and integrates them into a unified data structure for subsequent fixed-frequency local recording and dynamic-frequency intelligent upload. This creates a closed-loop data collection system consisting of "bottom-level precision acquisition, mid-level thermal sensing enhancement, and top-level intelligent frequency modulation upload." This multi-dimensional, multi-granular data acquisition approach significantly improves the comprehensiveness of system operating status identification and responsiveness, effectively supporting the highly secure and stable operation of UPS high-voltage lithium battery packs in critical scenarios such as data centers and telecom base stations.

[0034] In one possible implementation, Figure 2 As shown, Figure 2 This is a schematic diagram of a specific process for determining an enhancement point provided by an embodiment of the present invention. The specific steps for determining the enhancement point include: S210, obtaining thermal simulation result information and heat dissipation fluid simulation information of the target battery pack; S220: Determine the enhancement points according to the thermal simulation result information and the heat dissipation fluid simulation information.

[0035] For example, in order to achieve accurate perception of key thermal anomaly areas in a UPS high-voltage lithium battery pack, the present invention improves the spatial resolution and risk response capability of temperature monitoring by enhancing the intelligent layout of points.

[0036] First, in step S210, the system obtains the thermal simulation result information and 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, workload, battery cell thermal characteristics, and environmental boundary conditions. This simulation model can output the temperature distribution, heat accumulation location, and temperature rise trend inside the battery pack under different operating conditions. The heat dissipation fluid simulation information calculates the flow field characteristics in the air cooling system or natural convection cooling system to obtain data such as airflow velocity, flow direction, and pressure drop distribution, reflecting the spatial variation characteristics of cooling efficiency and air circulation capacity within the battery pack. The combination of these two types of information provides a comprehensive reference for static heat source distribution and dynamic heat dissipation capacity, and serves as the basic supporting data for enhanced point screening.

[0037] In step S220, the system conducts a comprehensive analysis of the battery pack space based on the above-mentioned thermal simulation and fluid simulation data, and determines the location with the greatest thermal risk perception value as the enhanced point. Specifically, the system first divides the battery pack space into a plurality of regular grid units, and calculates a thermal anomaly potential score for each unit, which is composed of weighted indicators such as temperature deviation, temperature gradient, wind speed, and heat dissipation boundary distance. Then, areas with higher scores and weaker thermal diffusion capabilities are identified as candidate points. Furthermore, the regional growing algorithm is used to identify thermal clusters, and 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 enhanced points.

[0038] These enhanced points are primarily deployed in battery module heat dissipation blind spots, air duct boundaries, cell-dense areas, or historically high-temperature regions. They focus on collecting temperature change information in these areas, providing precise input for the system's real-time monitoring, anomaly warnings, and dynamic sampling strategies. This significantly improves the thermal management and operational safety of the entire UPS lithium battery system. This implementation not only enables intelligent and visual point placement but also effectively avoids issues such as insufficient blind spot coverage, information redundancy, and physical inaccessibility inherent in traditional manual point placement, demonstrating significant engineering practical value and potential for widespread adoption.

[0039] In one possible implementation, Figure 3 As shown, Figure 3 A schematic flow chart of a method for determining enhancement points based on thermal simulation result information and heat dissipation fluid simulation information provided in an embodiment of the present invention. Step S220 of determining the enhancement points based on the thermal simulation result information and heat dissipation fluid simulation information includes: S2201, performing spatial grid division on the target battery pack to establish a three-dimensional sampling candidate model including multiple spatial units; S2202, map the thermal simulation result information and the heat dissipation fluid simulation information to the three-dimensional sampling candidate model respectively, and construct a corresponding temperature-air flow joint atlas; S2203, calculate a thermal anomaly potential score for each space unit based on the temperature-air flow 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; S2204, screen candidate enhanced point positions in the space unit whose thermal anomaly potential score exceeds a set threshold value; S2205, optimize the candidate enhanced point positions based on structure accessibility, wiring feasibility, and spatial symmetry constraint conditions of point position layout, and take the optimized candidate enhanced point positions as the enhanced point positions corresponding to the module second operation parameter information.

[0040] To achieve fine monitoring of the operating state of a UPS high-voltage lithium battery pack, an optimization method for determining enhanced point positions based on simulation data is provided. The method drives intelligent deployment of enhanced sampling point positions through thermal simulation and fluid simulation information, effectively improving the temperature sensing capability of local thermal risk areas.

[0041] 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 set of three-dimensional sampling candidate models covering the entire internal structure of the battery system. Each space unit represents a possible position of a temperature sensor, and records its corresponding physical properties, such as the module to which it belongs, the relationship between the cells, the wind channel boundary condition, etc., providing a spatial indexing basis for subsequent analysis.

[0042] Next, in step S2202, the system maps the obtained thermal simulation result information and heat dissipation fluid simulation information to the three-dimensional sampling candidate model, forming a temperature-air flow joint atlas containing temperature distribution and air flow 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 air flow speed, direction, and pressure loss data at each unit. After the fusion of the two, the heat accumulation tendency and cooling capacity of a certain area can be comprehensively reflected.

[0043] Then, in step S2203, the system calculates the thermal anomaly potential score of each space unit based on the joint atlas.

[0044] Specifically, the system first maps the thermal simulation result information and the heat dissipation fluid simulation information to the three-dimensional spatial grid of the battery pack, and assigns key attributes such as the current temperature, airflow velocity, temperature gradient between adjacent units, and distance to the heat dissipation boundary to each spatial unit. Then, the system calculates the thermal anomaly potential score of each spatial unit based on the following scoring formula: Distance to heat dissipation boundary

[0045] in, Indicates the Thermal anomaly potential score of each spatial unit; The temperature deviation between the cell temperature and the overall average temperature of the battery system, reflecting the degree of local overheating; Represents the temperature gradient modulus between it and the adjacent unit, which is used to identify the thermal mutation boundary; is the local airflow velocity of the unit, is the wind speed factor, i.e. the inverse of wind speed, which is used to reflect the risk of insufficient cooling efficiency; It is the spatial distance between the unit and the nearest heat dissipation boundary (such as the air duct outlet or metal shell), which is used to describe the heat diffusion obstruction characteristics of this point due to the physical structure. is a weight factor, which can be set according to the actual scenario or empirical model. Common settings include .

[0046] This scoring formula integrates four key factors. It not only focuses on the temperature itself, but also takes into account important factors that can easily induce thermal runaway in actual operation, such as temperature change trends, differences in cooling capacity, and structural thermal resistance. Secondly, the introduction of the inverse wind speed as an indicator of insufficient cooling strengthens the risk identification ability of areas with inefficient heat dissipation, and makes up for the shortcomings of the traditional judgment method that only looks at temperature values. In addition, by introducing the distance boundary factor, it avoids placing enhanced points in areas that already have good heat dissipation conditions, thereby improving the risk coverage efficiency and deployment value of the points.

[0047] Next, in step S2204, the system selects spatial units with thermal anomaly potential scores exceeding a set threshold and constructs a set of candidate enhancement points. These points are often located in areas with high thermal anomaly risk, wind blind spots, or structural dead zones, and are potential risk points for thermal runaway, requiring priority deployment of sensors for high-frequency temperature monitoring.

[0048] Finally, in step S2205, the system performs multi-constraint optimization on the candidate points, combining the actual structure and engineering deployment conditions. First, based on structural accessibility rules, points located inside non-removable structures or inaccessible to manual maintenance are eliminated. Second, combined with wiring feasibility analysis, sensor paths that need to pass through high-voltage or interference areas are filtered out. Third, based on spatial symmetry constraints, only representative points are retained within the structural symmetry area to avoid redundant points. Ultimately, a set of optimized enhanced points that meet deployment feasibility, reasonable spatial distribution, and high thermal sensing value are obtained, and these are used as sampling locations for the module's second operating parameter information.

[0049] Through this implementation method, the UPS system can achieve focused monitoring and differentiated sampling strategies for high-risk areas. Without significantly increasing the number of sensors and system complexity, it greatly improves the thermal risk perception accuracy and data value density, and significantly enhances the system's thermal safety monitoring capabilities and intelligent response capabilities.

[0050] In one possible implementation, Figure 4 As shown, Figure 4 This is a schematic diagram of a process for screening candidate enhancement points provided by an embodiment of the present invention. Step S2204 of screening candidate enhancement points in the spatial units whose thermal anomaly potential scores exceed a set threshold includes: S22041. Construct an initial candidate point set for all spatial units whose thermal anomaly potential scores exceed a preset score; S22042. Calculate the potential score difference between each candidate spatial unit and its adjacent units in the initial candidate point set; S22043. When the potential score difference exceeds the set gradient threshold, the unit is marked as a potential thermal mutation boundary point; S22044. Combining spatial topology and temperature change trends, the region growing algorithm is used to identify potential high-risk thermal anomaly clusters. S22045. In each of the above-mentioned high-risk thermal anomaly clusters, dynamically select the above-mentioned candidate enhancement points based on local scoring extreme points, centrality, and historical temperature fluctuation stability; S22046: For isolated abnormal scoring points, determine whether they constitute potential heat sources by predicting temperature rise trends and neighborhood flow velocity attenuation models; S22047, if true, also includes the above candidate enhancement points.

[0051] Exemplarily, in step S22041, the system screens all the spatial units with a thermal anomaly potential score exceeding a preset threshold, and constructs an initial candidate point set. These points represent areas with high temperature, sharp temperature gradient, or limited heat dissipation within the battery pack, and have strong local heat accumulation characteristics, which are precursory points of possible thermal runaway.

[0052] In step S22042, the system calculates the difference between the potential scores of each point in the initial candidate point set and its spatially adjacent units, to obtain the local score difference of each point, so as to quantify whether the point is in a “mutation zone” with sharp thermal change.

[0053] In step S22043, the system marks the points with a potential score difference exceeding a set gradient threshold as potential thermal mutation boundary points. These points are usually located at the junction of abnormal and normal areas, and are positions with rapid temperature change and narrowed heat diffusion path, which are of great significance for capturing edge runaway trends.

[0054] In step S22044, the system further combines the spatial topological structure and temperature change trend, and uses a region growing algorithm to identify multiple potential thermal anomaly high-risk area clusters. This process starts from the mutation boundary points, and expands the region according to the thermal score similarity between adjacent units, temperature connectivity, and spatial connectivity, until the growing condition is not met, thereby constructing the risk area boundary with obvious heat accumulation trend.

[0055] Then, in step S22045, the system determines one or more points in each thermal anomaly area cluster as candidate enhancement points of the cluster according to multiple-dimensional indexes such as local score extreme points (highest thermal score), spatial centrality (propagation center in the region), and historical temperature fluctuation stability (large fluctuation and sharp change). These points not only represent the strongest area of thermal risk, but also have continuous monitoring and prediction value.

[0056] In step S22046, the system specially processes “isolated high-score points” that fail to be clustered into any area cluster. By analyzing the temperature change trend (such as temperature rise rate, second derivative) of these points and the airflow velocity decay characteristics of their neighborhood, it is determined whether they are latent heat sources. For example, although the current temperature is not significantly high, there is a fast temperature rise rate and weak heat dissipation ability, which indicates that this area has the possibility of forming a hot spot in the short term.

[0057] Finally, in step S22047, if the above analysis confirms that the isolated point has a strong temperature rise trend or structural heat dissipation bottleneck, it is also included in the candidate enhancement point set, to ensure that the entire system considers both explicit and implicit thermal risks when deploying points.

[0058] Through the above continuous analysis and screening steps, the embodiment can dynamically construct a cluster of enhanced points with reasonable structure and high thermal perception efficiency under the support of multi-source simulation data. This mechanism not only improves the early identification ability of thermal risk, but also avoids invalid and redundant point distribution and resource waste, has strong engineering practicability and intelligence, and has wide popularization value in large-scale UPS lithium battery systems.

[0059] In a feasible implementation manner, as shown in Figure 5 , Figure 5 A flowchart of a method for identifying a potential thermal anomaly high-risk area cluster is provided in the embodiment of the application. The step S22044 combines spatial topology and temperature change trend and applies a region growing algorithm to identify a potential thermal anomaly high-risk area cluster, including: S220441, performing three-dimensional grid division on the target battery pack space, and binding the current temperature, temperature change rate, thermal anomaly potential score and adjacent unit index of each space unit; S220442, selecting, from all space units, a space unit with a thermal anomaly potential score greater than or equal to a first threshold value and a temperature change rate greater than or equal to a second threshold value as a region growing seed point; S220443, defining a region growing criterion, wherein the region growing criterion includes that a candidate expansion unit is adjacent to an existing boundary unit space, 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; S220444, starting from the seed point, iteratively expanding the region based on the region growing criterion until there is no new space unit meeting the criterion; S220445, performing multi-cluster screening on all grown region clusters, removing regions with insufficient points or insufficient risk scores, and performing thermal diffusion model correction on the edge region to identify a potential thermal anomaly high-risk area cluster.

[0060] For example, in order to accurately identify the space area with potential thermal runaway risk in the UPS high-voltage lithium battery pack, the application proposes a region growing algorithm combining spatial topology and temperature change trend in step S22044, which is used to construct a thermal anomaly high-risk area 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.

[0061] In step S220441, the system performs three-dimensional grid division on the entire battery pack space, dividing it into a plurality of structurally consistent spatial units, each unit representing a fixed volume of physical area. To enable subsequent analysis, each unit needs to be bound to four key attributes: first, the current temperature value, reflecting its thermal state; second, the temperature change rate, used to identify rapid heating points; third, the thermal anomaly potential score based on multiple factors, which comprehensively assesses the risk level of the point; and fourth, the adjacency unit index information of the unit, used to establish an expandable path relationship in the spatial topology.

[0062] In step S220442, the system selects areas with strong thermal risk characteristics from all spatial units as seed points for region growing. The specific screening conditions include: the thermal anomaly potential score of the unit is not less than a set first threshold (e.g., 0.7), and its temperature change rate is not less than a second threshold (e.g., 1.5°C / min). Only units that meet both the score and temperature rise trend criteria have the potential to become potential heat aggregation sources and have growth value.

[0063] Then, in step S220443, the system defines the criteria for region growing to limit the logical boundaries of region expansion. Specifically, before a spatial unit is included in the region cluster, it must meet the following four conditions: (1) It has at least a first-order spatial adjacency relationship with the current growing boundary; (2) Its thermal anomaly potential score is not less than a set third threshold (e.g., 0.5), ensuring that the region's thermal intensity does not drop sharply; (3) The temperature difference between it and the adjacent boundary unit should not exceed a set gradient threshold (e.g., 2.5°C), to avoid abrupt changes in the region; (4) Its spatial connection path should not cross structural barriers such as metal shells, thermal insulation layers, air ducts, and other non-thermal areas.

[0064] This multi-condition criterion ensures the coherence, continuity, and physical reasonableness of region growing.

[0065] In step S220444, the system starts with all seed points and gradually expands the space through the region growing algorithm. In each iteration, the system traverses all current region boundary units and performs growth criterion judgment on all their adjacent units. If it meets the criteria, it is included in the region cluster and the boundary is updated until no new units are added in consecutive iterations. Finally, each seed point will grow into one or more connected thermal risk region clusters.

[0066] In step S220445, the system evaluates and screens all generated regional clusters. For regional clusters with too few point locations (e.g., less than a set threshold) or with a total thermal anomaly score lower than the global average, they are considered as local noise or non-typical abnormal regions and are eliminated. For regional edges with fuzzy boundaries or dramatic gradient changes, fitting correction will also be performed based on the thermal diffusion physical model to enhance the thermal physical continuity of the regional boundaries. After screening, the system marks the final retained high-quality regional clusters as potential high-risk thermal anomaly regional clusters, providing direct input for subsequent enhanced point location selection and dynamic sampling control strategies.

[0067] Through the above continuous process, the present application constructs a regional growth recognition mechanism with spatial structure perception and temperature trend prediction capabilities, which can efficiently extract key thermal risk regions inside the UPS battery pack, significantly improving the initiative, intelligence, and safety guarantee capability of the point layout strategy. This method is suitable for thermal safety monitoring of heterogeneous module layout, high heat dissipation complexity, and large-scale UPS systems, and has a wide application prospect.

[0068] In a feasible implementation manner, as shown in Figure 6 , Figure 6 A method for optimizing candidate enhanced point locations is provided for an embodiment of the present application. The above step S2205 optimizes the above candidate enhanced point locations based on the structural accessibility, wiring feasibility, and spatial symmetry constraints of the point layout to optimize the candidate enhanced point locations as the above enhanced point locations corresponding to the above module second operating parameter information, which includes: S22051, a topological mapping of the candidate enhanced point locations in the battery module structure is constructed to obtain the physical location and structural association information of each of the candidate enhanced point locations in the module; S22052, based on the structural accessibility rules, the accessible path length of each of the candidate enhanced point locations relative to the preset maintenance opening or detection window is calculated, and if the path exceeds the preset maximum access distance or passes through an indecomposable structure, the point location is eliminated or replaced with a new point location with thermal representation in its neighborhood; S22053, based on the wiring feasibility rules, it is judged whether the wiring path between each point location and the nearest collection interface passes through a high-voltage isolation area, a ventilation channel, or an electromagnetic interference area, and if it does not meet the wiring requirements, the current point location is eliminated; S22054, according to the symmetry features of the module structure, the candidate point locations located in the symmetric regions are grouped, and only the representative point locations with higher thermal anomaly potential or better signal coverage in each group are retained to avoid redundant deployment; S22055, the point locations that meet the above structural accessibility, the above wiring feasibility, and the above symmetry constraints are determined as the optimized enhanced point locations as the collection locations of the module second operating parameter information.

[0069] To ensure the practicability 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, mainly evaluates and screens the candidate enhanced points from the aspects of structural accessibility, wiring feasibility and spatial symmetry, and finally forms a set of optimized points with high heat sensing value and convenient deployment. Specifically, the following five sub-steps are included: 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.

[0070] In step S22052, the system evaluates all candidate enhanced points according to the structural accessibility rules. Specifically, the system calculates the shortest path length from each point to the nearest maintenance opening or detection window. If the path exceeds the preset maximum maintenance distance or passes through non-detachable structures (such as metal shell, thermal insulation layer, structural adhesive, etc.), the point is considered as unreachable and not suitable as an enhanced point. For the accessibility critical points, the system can find alternative points with similar thermal scores in their neighborhood to maintain the continuity of thermal sensing coverage.

[0071] In step S22053, the system judges the physical connection path between each point and the acquisition main control board according to the wiring feasibility rules. If the path needs to pass through high-voltage isolation area, core ventilation channel, electromagnetic shielding layer or high-frequency noise area, it will face safety or signal stability problems, and the system will mark the point as "wiring not feasible" and exclude or replace it. At the same time, the system can also refer to the actual module wiring slot and cable length margin to perform engineering-level path evaluation on point placement.

[0072] In step S22054, the system groups the candidate points based on the structural design file or symmetry rules of the module. For example, if the left and right structures or the upper and lower layouts of the module are completely symmetrical, they can be considered as symmetrical blocks. For the candidate points in each symmetrical region, only the representative points with higher thermal anomaly potential score, closer to the center or better signal quality are retained, and the rest of the points can be considered as information redundancy and excluded, thereby simplifying the wiring and reducing redundant sensing.

[0073] In step S22055, the system aggregates all points that simultaneously meet the structural accessibility, routing feasibility, and spatial symmetry constraints and marks them as the final set of optimized and enhanced points. These points will serve as sampling locations for the module's second operating parameter information and will be used to deploy highly sensitive temperature sensors to dynamically detect and quickly respond to local thermal anomalies within the module.

[0074] Through the above continuous optimization process, this embodiment significantly improves the deployment feasibility of enhanced points and the adaptability of the system structure while ensuring the thermal sensing coverage capability, avoiding data loss, wiring interference or structural intervention problems caused by unreasonable sensor positioning, and effectively achieving the goal of "high-value deployment + low engineering cost + safe and controllable", providing a feasible intelligent monitoring foundation for UPS battery systems.

[0075] In one possible implementation, Figure 7 As shown, Figure 7 A schematic diagram of a process for determining a fixed frequency provided by an embodiment of the present invention. The steps of determining the fixed frequency include: S310: Determine basic frequency information according to the first operating parameter information; S320, generating environment correction frequency information according to the power usage environment information of the target battery pack; S330: Generate the fixed frequency according to the basic frequency information and the environment correction frequency information.

[0076] In one possible implementation, Figure 8 As shown, Figure 8 A schematic diagram of a process for determining a dynamic frequency provided by an embodiment of the present invention. The steps of determining the dynamic frequency include: S410: Calculate the temperature rise rate and temperature fluctuation amplitude of each enhanced point based on the battery cell operating parameter information and the temperature change trend in the module second operating parameter information; S420: Introduce a multi-factor dynamic frequency control mechanism based on the temperature rise rate and temperature fluctuation amplitude, and determine the upload frequency level of the point based on specific indicators, where the specific indicators include the distance between the current temperature value and the set safety threshold, the increase in the temperature rise rate per unit time, the thermal anomaly potential score of the area where the point is located, and the frequency of fluctuations in the historical sampling records of the point; S430: Determine the dynamic frequency according to the upload frequency level and the frequency level comparison table.

[0077] To achieve the dynamic balance of the accurate collection and uploading efficiency of the UPS high-voltage lithium battery pack operation data, the application introduces the power consumption environment perception and temperature dynamic change regulation, and the system can flexibly adjust the sampling frequency according to the operation condition, 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, and specifically as follows: Firstly, in the determination of the fixed frequency, the system forms the basic data collection rhythm through three steps: In step S310, the system analyzes the working strength and electrical load stability of the current battery pack based on the first operation 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 operation state, the voltage and current fluctuation is small, and a lower basic sampling frequency can be set; while in frequent charging and discharging or power fluctuation, the basic sampling frequency is correspondingly increased. The system maps different electrical states to different sampling frequency baselines such as 0.5Hz, 1Hz or 2Hz by setting corresponding interval threshold values.

[0078] 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, workplace temperature and humidity level, etc. For example, in the high-density operation of data center or industrial power consumption scene, due to the large heat dissipation pressure and high failure cost, the system can appropriately increase the sampling frequency to enhance the safety protection capability; while in some light load scenes, the frequency can be reduced to save resources.

[0079] 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 operation state and external risk environment jointly drive the sampling intensity. The fixed frequency is mainly used for collecting cell basic parameters and module first parameters, supporting local storage and periodic uploading, and guaranteeing the full-time period complete record of system operation state.

[0080] Next, in the determination of the dynamic frequency, the system mainly aims at enhancing point collection task, and dynamically adjusts the sampling and uploading frequency according to the thermal risk change, which specifically includes the following three steps: In step S410, the system analyzes the temperature sensor data of 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.

[0081] In step S420, the system introduces a multi-factor dynamic frequency regulation mechanism to comprehensively evaluate the thermal risk level of each enhanced point. This mechanism assigns a uploading frequency level to the point based on the following four specific indicators: (1) The distance between the current temperature value and the set safety upper limit (the smaller the distance, the higher the risk); (2) The increase in temperature rise rate per unit time (whether there is an accelerating temperature rise trend); (3) The thermal anomaly potential score of the region where the point is located (obtained based on simulation and spatial modeling calculation); (4) The fluctuation frequency and mutation times in the historical sampling records of the point (reflecting instability).

[0082] The system weights and integrates these four indicators to form a risk level score and maps it to an uploading frequency level, such as low, medium, and high.

[0083] Finally, in step S430, the system determines the specific sampling uploading frequency based on the uploading frequency level and the built-in frequency level comparison table. For example: Low risk level → uploading frequency 1.0 Hz (upload once every second); Medium risk level → uploading frequency 2.0 Hz (once every 0.5 seconds); High risk level → uploading 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 enhanced points to an emergency frequency of 10 Hz to ensure data continuity and real-time monitoring during emergencies.

[0084] In summary, this embodiment combines fixed frequency with dynamic frequency to build a stable and reliable yet flexible and responsive operation data acquisition system, balancing global perception and local thermal risk capture, and having significant advantages in ensuring the thermal safety and operational efficiency of UPS lithium battery packs.

[0085] In a second aspect, as shown in Figure 9 The present application proposes a UPS high-voltage lithium battery pack operation data acquisition system, comprising: A first acquisition unit 21 for acquiring cell operating parameter information of each cell; A second acquisition unit 22 for acquiring module first operating parameter information of basic points and module second operating parameter information of enhanced points; A generation unit 23 for generating acquisition data of a target battery pack based on the above cell operating parameter information, the above module first operating parameter information, and the above module second operating parameter information; The data processing unit 24 is configured to store the collected data of the target battery pack in the local storage based on a fixed frequency and upload the collected data to the cloud management platform based on a dynamic frequency.

[0086] It can be understood that the UPS high-voltage lithium battery pack operation data acquisition system can also perform the steps of any method described in the first aspect.

[0087] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit it; although the present 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 present application.

Claims

1. A method for collecting operating data of a UPS high-voltage lithium battery pack, characterized in that: include: Obtain the battery cell operating parameter information of each battery cell; Obtaining first operating parameter information of the module at the basic point and second operating parameter information of the module at the enhanced point; Generate collected 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; The collected data of the target battery pack is stored in a local storage terminal based on a fixed frequency, and uploaded to a cloud management platform based on a dynamic frequency.

2. The UPS high-voltage lithium battery pack operation data acquisition method according to claim 1, characterized in that: The battery cell operating parameter information includes battery cell voltage information, battery cell power information, battery cell temperature information and battery 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 UPS high-voltage lithium battery pack operation data acquisition method according to claim 1, characterized in that: The specific steps of determining the enhancement points include: Obtaining thermal simulation result information and heat dissipation fluid simulation information of the target battery pack; The enhancement points are determined according to the thermal simulation result information and the heat dissipation fluid simulation information.

4. The UPS high-voltage lithium battery pack operation data acquisition method according to claim 3, characterized in that: The determining of the enhancement points according to the thermal simulation result information and the heat dissipation fluid simulation information includes: Performing spatial grid division on the target battery pack to establish a three-dimensional sampling candidate model including a plurality of spatial units; Mapping the thermal simulation result information and the heat dissipation fluid simulation information to the three-dimensional sampling candidate models respectively to construct corresponding temperature-airflow joint maps; Calculating a thermal anomaly potential score for each spatial unit based on the temperature-airflow joint map, wherein the thermal anomaly potential score is determined based on temperature deviation, temperature gradient, wind speed factor, and heat dissipation boundary distance; Screening candidate enhancement points in the spatial units where the thermal anomaly potential score exceeds a set threshold; The candidate enhancement points are optimized based on the structural accessibility, wiring feasibility and spatial symmetry constraints of the point layout, and the optimized candidate enhancement points are used as the enhancement points corresponding to the second operating parameter information of the module.

5. The UPS high-voltage lithium battery pack operation data acquisition method according to claim 4, characterized in that: The screening of candidate enhancement points in the spatial units where the thermal anomaly potential score exceeds a set threshold comprises: Construct an initial candidate point set for all spatial units whose thermal anomaly potential scores exceed the preset scores; Calculating the potential score difference between each candidate spatial unit and its adjacent units in the initial candidate point set; When the potential score difference exceeds the set gradient threshold, the unit is marked as a potential thermal mutation boundary point; Combining spatial topology with temperature change trends, a region growing algorithm is applied to identify potential high-risk thermal anomaly clusters. In each of the high-risk thermal anomaly area clusters, the candidate enhancement points are dynamically screened based on local scoring extreme points, centrality, and historical temperature fluctuation stability; For isolated abnormal scoring points, whether they constitute potential heat sources is determined by predicting the temperature rise trend and the neighborhood flow velocity attenuation model; If true, the candidate enhancement point is also included.

6. The UPS high-voltage lithium battery pack operation data acquisition method according to claim 5, characterized in that: The spatial topology and temperature change trends are combined to apply the region growing algorithm to identify potential high-risk thermal anomaly clusters, including: Divide the target battery pack space into a three-dimensional grid and bind each spatial unit to its current temperature, temperature change rate, thermal anomaly potential score, and adjacent unit index; Screen out the spatial units whose thermal anomaly potential score is greater than or equal to the first threshold and whose temperature change rate is greater than or equal to the second threshold from all spatial units as regional growth seed points; Define region growing criteria, wherein the region growing criteria include that the candidate expansion unit is spatially adjacent to the existing boundary unit, the thermal anomaly potential score is greater than or equal to a third threshold, the adjacent temperature difference is less than a set gradient threshold, and does not cross the structural isolation area; Starting from the seed point, iteratively expanding the region based on the region growing criterion until no new spatial units meet the criterion; Multi-cluster screening is performed on all regional clusters that have completed growth, and areas with insufficient points or risk scores are eliminated. The thermal diffusion model is corrected in the edge areas to identify potential high-risk regional clusters of thermal anomalies.

7. The UPS high-voltage lithium battery pack operation data acquisition method according to claim 4, characterized in that: The optimizing the candidate enhancement points based on the structural accessibility, wiring feasibility, and spatial symmetry constraints of the point layout, and using the optimized candidate enhancement points as the enhancement points corresponding to the second operating parameter information of the module, includes: Constructing a topological map of the candidate enhancement points in the battery module structure to obtain the physical location and structural association information of each candidate enhancement point in the module; Based on the structural accessibility rule, the reachable path length of each candidate enhancement point relative to the preset maintenance opening or detection window is calculated. If the path exceeds the preset maximum access distance or passes through an irremovable structure, the point is eliminated or replaced with a new point with thermal representativeness in its neighborhood. Based on the wiring feasibility rules, determine whether the wiring path between each point and the nearest acquisition interface passes through the high-voltage isolation area, ventilation channel or electromagnetic interference area. If it does not meet the wiring requirements, the current point is eliminated; Based on the symmetry of the module structure, candidate points in symmetrical areas are grouped, and only representative points with higher thermal anomaly potential or better signal coverage are retained in each group to avoid redundant deployment; The points that meet the structural accessibility, the wiring feasibility and the symmetry constraint conditions are determined as optimized enhanced points, which serve as the collection locations for the second operating parameter information of the module.

8. The UPS high-voltage lithium battery pack operation data acquisition method according to claim 1, characterized in that: The step of determining the fixed frequency includes: Determining basic frequency information according to the first operating parameter information; generating environment correction frequency information according to the power usage environment information of the target battery pack; The fixed frequency is generated according to the basic frequency information and the environment correction frequency information.

9. The UPS high-voltage lithium battery pack operation data acquisition method according to claim 1, characterized in that: The step of determining the dynamic frequency includes: Calculate the temperature rise rate and temperature fluctuation amplitude of each enhanced point according to the temperature change trend in the battery cell operating parameter information and the second operating parameter information of the module; A multi-factor dynamic frequency control mechanism is introduced based on the temperature rise rate and temperature fluctuation amplitude, and the upload frequency level of the point is determined according to specific indicators, where the specific indicators include the distance between the current temperature value and the set safety threshold, the increase in the temperature rise rate per unit time, the thermal anomaly potential score of the area where the point is located, and the frequency of fluctuations in the historical sampling records of the point; The dynamic frequency is determined according to the upload frequency level and a frequency level comparison table.

10. A UPS high-voltage lithium battery pack operation data acquisition system, characterized in that: include: A first acquiring unit is used to acquire cell operating parameter information of each cell; a second acquiring unit, configured to acquire first operating parameter information of the module at the basic point and second operating parameter information of the module at the enhanced point; a generating unit, configured to generate collected 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; The data processing unit is used to store the collected data of the target battery pack in a local storage terminal based on a fixed frequency, and upload it to the cloud management platform based on a dynamic frequency.

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