Lithium battery energy storage safety management system and method

Abstract

The invention relates to the technical field of lithium battery safety, in particular to a lithium battery energy storage safety management system and method. The method comprises the following steps: acquiring a real-time heterogeneous invasion characteristic quantity of an environment where a lithium battery is located, analyzing heterogeneous pollutant invasion characteristics based on the real-time heterogeneous invasion characteristic quantity, and integrating to form a multi-source invasion data set; based on the multi-source invasion data set, performing multi-level security risk coupling modeling, and outputting a comprehensive risk level; based on the comprehensive risk level, a defense mechanism is activated in a self-adaptive mode, and a cross-dimension defense strategy is generated; and according to the cross-dimensional defense strategy, evaluating the state of the battery after defense, and generating and outputting a security defense efficiency report. According to the method, a dynamic mutual feedback mechanism of pollutant invasion characteristics and multi-level safety risks is combined, safety weak points and evolution trends of the lithium battery energy storage system on the underground inspection robot are identified, rapid response to high-risk invasion scenes is realized, potential risks are timely solved, and local abnormity is prevented from being upgraded into systematic safety accidents.

Description

Technical Field

[0001] The present application relates to the field of lithium battery safety technology, and in particular to a lithium battery energy storage safety management system and method. Background Art

[0002] As inspection robots become key equipment for improving safety, efficiency, and intelligence, their autonomous operation relies heavily on a stable and reliable energy supply system. Lithium batteries, with their high energy density, long cycle life, and lack of memory effect, have become the preferred power battery.

[0003] However, when existing lithium batteries are used in inspection robots, their energy storage safety management systems primarily focus on monitoring and balancing basic parameters at the cell level. Their protection strategies are often static or single-dimensional. This presents significant deficiencies in complex, heterogeneous environments, making it difficult to ensure the continuous, safe, and efficient operation of inspection robots. Summary of the Invention

[0004] The present application provides a lithium battery energy storage safety management system and method to solve the above technical problems.

[0005] In a first aspect, the present application provides a lithium battery energy storage safety management method, the method comprising: Acquire real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located, analyze the invasion characteristics of heterogeneous pollutants based on the real-time heterogeneous invasion characteristics, and integrate them to form a multi-source invasion data set; based on the multi-source invasion data set, perform multi-level security risk coupling modeling and output a comprehensive risk level; based on the comprehensive risk level, adaptively activate the defense mechanism and generate a cross-dimensional defense strategy; according to the cross-dimensional defense strategy, evaluate the battery status after defense, and generate and output a security defense effectiveness report.

[0006] Through this solution, a multimodal feature separation engine is used to analyze the spatiotemporal characteristics of heterogeneous invasions, solving the problem of misjudgment of coordinated invasions of composite pollutants (such as salt spray + metal dust) by existing methods, improving feature recognition accuracy, and matching the optimal defense mechanism combination through an adaptive mechanism that drives the defense strategy to undergo continuous dynamic iteration and upgrade based on actual performance data by associating the comprehensive risk level and the dominant risk type. This significantly enhances the system's intelligent response and adaptive defense capabilities when facing invasions of unknown or mutated pollutants, and achieves efficient and optimized allocation of defense resources by accurately matching defense strength and risk level. Defense performance reports drive strategy iteration, shortening the system's response cycle to new pollutants and improving the long-term reliability of lithium batteries used in mining inspection robots in complex industrial environments. Cross-dimensional defense strategies simultaneously suppress material-level corrosion and system-level failures, reducing the probability of thermal runaway, and providing technical support for energy storage scenarios with high safety requirements for lithium batteries in mining inspection robots.

[0007] Optionally, the acquiring of real-time heterogeneous invasion characteristic quantities of the environment in which the lithium battery is located, the analyzing of heterogeneous pollutant invasion characteristics, and the integrating of the analyzing of heterogeneous pollutant invasion characteristics to form a multi-source invasion data set include: Establish a multi-physics field collaborative perception system, build an invasion factor perception network, and synchronously collect spatially distributed environmental invasion parameters; Obtain the time intervals during which lithium batteries are attacked by heterogeneous substances, eliminate clock monitoring deviations between intervals through a distributed node time alignment mechanism, and generate standardized time data series; Based on the spatially distributed environmental attack parameters and the standardized time data series, analyzing and confirming the real-time heterogeneous attack characteristics of the environment in which the lithium battery is located; Based on the real-time heterogeneous invasion feature quantity, the terrain undulation distribution information and the heterogeneous invasion time series distribution information are analyzed and obtained through a multimodal feature separation engine; Based on the terrain undulation distribution information and the heterogeneous invasion time series distribution information, combined with the real-time heterogeneous invasion feature quantity, a multimodal feature separation engine is used to determine the pollutant characteristics; Analyze the characteristics of the pollutants and determine the invasion characteristics of several types of heterogeneous pollutants; Based on several of the analyzed heterogeneous pollutant invasion features, the multi-source invasion dataset is integrated to form the multi-source invasion dataset.

[0008] Through this solution, the dynamic heterogeneous invasion situation of the lithium battery microenvironment is comprehensively perceived and quantified by utilizing spatially distributed environmental invasion parameters and their standardized time data series generated by a distributed node time alignment mechanism. Through the two-level decoupling processing of the multimodal feature separation engine (first separating the terrain undulation distribution information from the heterogeneous invasion time series distribution information, and then combining the original feature quantities to analyze the pollutant characteristics), the spatial structural constraints and temporal evolution patterns behind the invasion events are deeply explored, and the core pollutant types and their specific invasion behaviors are accurately traced. Finally, by integrating the invasion characteristics of several types of heterogeneous pollutants, a multi-source invasion dataset is constructed that can comprehensively characterize the complex environmental invasion scenarios under the mine.

[0009] Optionally, based on the terrain undulation distribution information and the heterogeneous invasion time series distribution information, combined with the real-time heterogeneous invasion feature quantity, a multimodal feature separation engine is used to determine the pollutant characteristics, including: Constructing a multimodal feature tensor based on the terrain undulation distribution information, the heterogeneous invasion time series distribution information, and the real-time heterogeneous invasion feature quantity; Analyzing the multimodal feature tensor based on the multimodal feature separation engine to obtain a static feature component and a dynamic evolution feature component; Based on the static characteristic components and the dynamic evolution characteristic components, pollutant characteristics are constructed.

[0010] Through this solution, the technical chain of "multimodal feature tensor construction → dynamic and static feature separation → feature fusion" is utilized to solve the problems of missing dimensions and attribute confusion in the analysis of heterogeneous pollutants; compared with existing methods, static feature components accurately identify the essential properties of pollutants, avoiding excessive defense against temporary low-risk pollutants; dynamic evolution feature components predict diffusion paths to achieve preventive protection; the pollutant characteristics generated by the fusion of the two reduce the prediction error rate of subsequent pollutant-battery material interaction models.

[0011] Optionally, the multimodal feature separation engine includes: Separating the inherent properties of the pollutants and analyzing the inherent properties of the pollutants to obtain static characteristic components based on the multimodal characteristic tensor using a tensor characteristic decoupling algorithm; According to the multimodal feature tensor, combined with the spatiotemporal evolution modeling technology, the pollutant diffusion and migration behaviors are separated and analyzed to obtain the dynamic evolution feature components.

[0012] This solution has established a static-dynamic dual-modal risk assessment system for lithium batteries. At the risk identification level, the static and dynamic components of multi-source attack data are decoupled. The static feature analysis module quantifies chronic contaminant damage and predicts electrode corrosion rates. The dynamic feature monitoring module warns of sudden risks, providing dual protection. At the defense strategy level, a gradient resource allocation is implemented based on the dual-modal architecture—low-cost, long-term protection (such as slow-release coatings) is deployed for static corrosion risks, while millisecond-level response mechanisms (such as directional airflow isolation) are enabled for dynamic migration risks. This system can accurately distinguish between acid gas corrosion and dynamic suspended particle risks, reducing false trigger rates. At the optimization level, system latency is shortened through the coordinated calculation of single static features and lightweight updates of dynamic features. This system effectively solves the problem of misjudgment caused by the coupling of multi-source data, providing a precise and economical solution for the safety protection of lithium batteries throughout their life cycle.

[0013] Optionally, based on the multi-source intrusion dataset, multi-level security risk coupling modeling is performed to output a comprehensive risk level, including: Based on the pollutant characteristics and battery material properties in the multi-source invasion data set, a pollutant-battery material interaction model is established to simulate the impact of pollutants on lithium batteries and quantitatively generate several risk indices; Based on several risk indices, a multi-level warning indicator is obtained through an integrated multi-scale risk assessment model; Determine the comprehensive risk level based on multi-level early warning signs.

[0014] Through this solution, the safety protection efficiency of the energy storage system is significantly improved by building a multi-dimensional technical system; based on the dynamic interactive model, the digital reconstruction of the pollutant invasion process is realized, the error rate of the risk index is reduced, and the hidden progressive damage characteristics are effectively captured; then, the high-risk areas of the battery are spatially positioned at the millimeter level through multi-level early warning signs, driving the active interception mechanism to be accurately activated, reducing ineffective defense energy consumption; the synchronously established time-space separation analysis mechanism can dynamically track the risk evolution trajectory (such as the diffusion path of corrosion from the edge to the center), improving the millisecond-level response of the defense strategy; through the coupling modeling technology, the existing limitations are broken through, and a digital twin system compatible with gaseous / particulate / liquid multi-type pollutants and complex battery material systems is constructed, which fully adapts to the environmental requirements of lithium battery scenarios and provides an intelligent solution for the safety protection of lithium battery energy storage.

[0015] Optionally, based on the pollutant characteristics and battery material properties in the multi-source invasion dataset, a pollutant-battery material interaction model is established to simulate the impact of pollutants on lithium batteries and quantify and generate several risk indices, including: Based on the pollutant characteristics, intelligently analyzing the pollutant characteristics through multi-source data fusion, extracting the static characteristic components and the dynamic evolution characteristic components in the pollutants; Based on the battery material properties, the electrode surface energy level distribution, electrolyte chemical stability threshold, and diaphragm pore structure parameters in the battery material are analyzed; Based on the static characteristic components and the dynamic evolution characteristic components of the pollutants, combined with the electrode surface energy level distribution, the electrolyte chemical stability threshold, and the diaphragm pore structure parameters, a multi-parameter coupling analysis is performed to establish a pollutant-battery material interaction model; Based on the pollutant-battery material interaction model, the pollutant invasion process is dynamically simulated according to the historical pollutant impact and the real-time battery material coupling impact, and several risk indices are quantitatively generated.

[0016] Through this scheme, the static characteristic components and dynamic evolution characteristic components of pollutants, the electrode surface energy level distribution, the electrolyte chemical stability threshold and the pore structure parameters of the diaphragm are used to comprehensively analyze the multi-dimensional information of pollutant characteristics and battery material properties, so as to deeply characterize the complex mechanism of the interaction between pollutants and battery materials, and improve the comprehensiveness and precision of the analysis of the risk characteristics of pollutant invasion inside the battery; at the same time, the logical space unification and coupling analysis of the above parameters are carried out through the multi-source data fusion framework, which effectively reveals the nonlinear correlation between static properties and dynamic evolution, and between the inherent characteristics of materials and the real-time state of the environment, and improves the accuracy of invasion risk prediction and assessment under complex multi-factor coupling conditions.

[0017] Optionally, based on the risk indices, a multi-level warning indicator is obtained through an integrated multi-scale risk assessment model, including: Based on the risk indices, extracting spatial positioning features and temporal duration features by performing spatiotemporal separation and analysis on the risk indices; Based on the time duration feature, determining the duration of the heterogeneous attacks corresponding to the risk indices, and recording the cumulative duration of the lithium battery attacks; Based on the spatial positioning feature and in combination with the environmental invasion parameter, the environmental invasion parameter is mapped to the battery area corresponding to the spatial positioning feature to obtain the lithium battery invasion position feature; Based on the cumulative time of the lithium battery attack and the location characteristics of the lithium battery attack, a cross-spatiotemporal fusion analysis mechanism is used to perform spatiotemporal coupling correction on each risk index to determine a number of risk grading reference coefficients; According to the plurality of risk grading reference coefficients, the preset warning level information is matched and the warning level identifier corresponding to each of the risk grading reference coefficients is determined to construct the multi-level warning identifier.

[0018] Through this solution, the spatiotemporal coupling mechanism is utilized to effectively solve the problem of quantifying the dynamic safety risks of lithium batteries, providing a decision-making basis for the generation of defense strategies. The risk assessment system constructed based on the spatiotemporal fusion model has achieved the goal of improving the accuracy of early warnings, and effectively solved the false alarm dilemma of existing solutions through multi-dimensional data coupling. Through the dynamic resource allocation mechanism driven by location characteristics, the resource consumption of defense measures is reduced, forming a closed-loop optimization of risk assessment and defense response. Furthermore, an algorithm correlating the cumulative duration with the location sensitivity coefficient is introduced to provide early warnings for hidden risks such as slow erosion, transforming passive responses into active defenses. At the execution level, the deep integration of multi-level early warning signs and differentiated defense mechanisms constitutes a cross-dimensional collaborative defense system: red warnings trigger hard protections such as physical fuses, and yellow warnings activate soft maintenance such as electrode coating repairs, generating targeted risk resolution strategies.

[0019] Optionally, based on the comprehensive risk level, a defense mechanism is adaptively activated to generate a cross-dimensional defense strategy, including: extracting the pollutant characteristics and the battery material properties based on the multi-source invasion data set; Based on the characteristics of the pollutants and the properties of the battery materials, an interaction analysis is performed to identify several risk types caused by the pollutants; Based on several risk types, analyze and identify the current dominant risk type; Obtaining a historical pollutant impact dataset, and constructing a risk type decision matrix based on the historical pollutant impact dataset; Based on the plurality of dominant risk types, combined with a plurality of risk grading reference parameters, the risk type decision matrix is ​​matched, and the plurality of defense mechanisms corresponding to different defense mechanism types are adaptively activated; The types of defense mechanisms include maintaining basic protection, triggering active interception, and performing physical fusing; Based on several of the aforementioned defense mechanisms, a targeted cross-dimensional defense strategy is generated.

[0020] Through this solution, a multi-dimensional collaborative security defense system is constructed, which achieves dual optimization of defense effectiveness and resource management. Based on the dominant risk type identification and dynamic decision matrix matching mechanism, it effectively avoids the false triggering of the defense mechanism, accurately focuses limited resources on high-priority threats, and improves the response speed of key threats. The layered defense architecture reduces the energy consumption of the system in low-risk scenarios through differentiated resource allocation strategies, extends the service life of the battery management system, and cooperates with the targeted activation mechanism to reduce the ineffective consumption of consumables such as neutralizers. At the environmental adaptability level, a cross-dimensional strategy engine is adopted to achieve dynamic coordination of spatial protection, temporal response and defense mechanisms to cope with the coordinated invasion of complex pollution scenarios in underground mine environments. Relying on the historical data backtracking mechanism of the risk decision matrix, a closed-loop iteration of "identification-response-optimization" is formed to continuously enhance the long-term robustness of the system in extreme environments and provide a verifiable evolutionary path for security decision-making.

[0021] Optionally, based on the cross-dimensional defense strategy, the battery status after defense is evaluated, and a security defense effectiveness report is generated and output, including: Based on the cross-dimensional defense strategy, after executing several of the defense mechanisms corresponding to the different defense mechanism types in the cross-dimensional defense strategy, the battery status of the lithium battery after defense is obtained according to the multi-physical field collaborative sensing system; Based on the battery status of the lithium battery after defense, combined with the battery status before defense, the operating status of the lithium battery before and after defense is compared to obtain the difference between the status before and after defense; Based on the difference in status before and after the defense, the battery material properties before and after the defense measures are implemented are dynamically compared in multiple dimensions, and a structured security defense effectiveness report including multi-dimensional performance indicators and correction suggestions is output.

[0022] This solution builds a data-driven defense effectiveness evaluation system, achieving closed-loop management from policy verification to operation and maintenance optimization. Based on multi-physics field dynamic data comparison technology, a visual verification mechanism for defense effectiveness is established, accurately assessing safety protection effectiveness through quantitative indicators. The structured effectiveness reports generated by the system not only include real-time performance parameters but also drive adaptive defense mechanisms to iterate their policies through analytical models such as residual risk area identification. For example, this system can specifically enhance the physical fuse protection level, forming a dynamic risk suppression closed loop of "evaluation-optimization-re-evaluation." This closed-loop management mechanism generates compound benefits across the entire battery lifecycle. By constructing a battery health evolution map through continuously accumulated performance data, it provides a scientific basis for predictive maintenance and extends the service life of the energy storage system. Standardized reports enable precise location of faulty units, reducing the frequency of manual inspections, avoiding excessive maintenance while lowering operation and maintenance decision-making costs, ultimately forming a synergistic and efficient system that combines safety protection, targeted response, and cost control.

[0023] In a second aspect, the present application provides a lithium battery energy storage safety management system, the system comprising: A data processing module is used to obtain real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located, analyze the invasion characteristics of heterogeneous pollutants based on the real-time heterogeneous invasion characteristics, and integrate them to form a multi-source invasion data set; A risk modeling module, configured to perform multi-level security risk coupling modeling based on the multi-source intrusion data set and output a comprehensive risk level; A strategy optimization module, configured to adaptively activate defense mechanisms based on the comprehensive risk level and generate cross-dimensional defense strategies; The effectiveness verification module is used to evaluate the battery status after defense according to the cross-dimensional defense strategy, and generate and output a security defense effectiveness report. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 A schematic diagram of an application scenario provided in one embodiment of the present application; Figure 2 A flowchart of a lithium battery energy storage safety management method provided in one embodiment of the present application; Figure 3 A schematic diagram of the structure of a lithium battery energy storage safety management system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] To make the purpose, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0028] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0029] When used in patrol robots, existing lithium-ion battery energy storage safety management systems primarily focus on monitoring and balancing basic cell-level parameters. Their protection strategies are often static or single-dimensional. These systems present significant deficiencies in complex, heterogeneous environments, making it difficult to ensure the continuous, safe, and efficient operation of patrol robots.

[0030] Based on this, the present application provides a lithium battery energy storage safety management system and method. A multimodal feature separation engine is used to analyze the spatiotemporal characteristics of heterogeneous invasions, addressing the existing method's misjudgment of the coordinated invasion of composite pollutants (such as salt spray + metal dust), improving feature recognition accuracy, and matching the optimal defense mechanism combination with an adaptive mechanism by associating the comprehensive risk level and the dominant risk type. The adaptive mechanism drives the defense strategy to undergo continuous dynamic iteration and upgrade based on actual performance data, significantly enhancing the system's intelligent response and adaptive defense capabilities when facing invasions from unknown or mutated pollutants. By accurately matching defense strength with risk level, efficient and optimized allocation of defense resources is achieved. Defense performance reports drive strategy iteration, shortening the system's response cycle to new pollutants and improving the long-term reliability of lithium batteries used in mining inspection robots in complex industrial environments. Cross-dimensional defense strategies simultaneously suppress material-level corrosion and system-level failures, reducing the probability of thermal runaway, and providing technical support for energy storage scenarios with high safety requirements for lithium batteries used in mining inspection robots.

[0031] Figure 1This is a schematic diagram of an application scenario provided by this application. During automated safety hazard inspections by inspection robots in underground mines, the method provided in this application is applied to improve the long-term reliability of lithium batteries used in mining inspection robots. This cross-dimensional defense strategy simultaneously suppresses material-level corrosion and system-level failures, reducing the probability of thermal runaway and providing technical support for energy storage scenarios with high safety requirements for lithium batteries used in mining inspection robots.

[0032] Specifically, the method of the present application is applied to any server, which communicates with the sensor network and the battery performance database respectively, and obtains the real-time heterogeneous invasion feature quantity provided by the sensor network and the battery material properties of the battery performance database through the server. By constructing a multi-physical field collaborative perception system, spatially distributed environmental parameters are synchronously collected, and the distributed node time alignment mechanism is used to eliminate clock deviation, generate standardized time series data, combine terrain undulation information with time series, separate static pollution sources and dynamic diffusion behaviors, and thus obtain quantitative heterogeneous invasion characteristics. The real-time heterogeneous invasion feature quantity is combined with the characteristic information of the battery material action mode to generate and output a security defense effectiveness report to the security maintenance personnel.

[0033] For specific implementation methods, please refer to the following embodiments.

[0034] Figure 2 This is a flow chart of a lithium battery energy storage safety management method provided in one embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes: S201, obtaining real-time heterogeneous invasion feature quantities of the environment in which the lithium battery is located, analyzing the invasion characteristics of heterogeneous pollutants based on the real-time heterogeneous invasion feature quantities, and integrating them to form a multi-source invasion data set; The real-time heterogeneous invasion feature quantity may refer to the quantitative parameters of various types of invasion factors in the external environment of the lithium battery collected in real time through the sensor network, and is derived from the sensor network.

[0035] The invasion characteristics of heterogeneous pollutants can be characteristic information that characterizes the action mode of pollutants with different physical / chemical properties on battery materials.

[0036] The multi-source invasion dataset can be a structured data set that integrates environmental parameters, pollutant characteristics, and spatiotemporal distribution information.

[0037] Specifically, due to the complex terrain of underground mines and the fact that there are many uncertain safety hazards in the wide areas they cover, existing underground mines usually use mining inspection robots with remote communication functions to conduct automated safety hazard inspections of the terrain and mines during operation, so as to detect safety hazards that are not easy to detect in the mines in advance and ensure the normal safety production of personnel underground. In the above scenarios, lithium batteries, as the core power source of mining inspection robots, face multiple intertwined challenges. For example, physical and mechanical invasion causes the battery to cause structural damage and internal short circuit risks due to severe vibration, collision and falling; dust and moisture form conductive bridges through adsorption and corrode components, threatening safety; extreme temperature differences aggravate battery aging and performance fluctuations; chemical corrosive gases and oil stains erode the protective layer. Existing technologies only monitor a single parameter and cannot distinguish the type of pollutant and its dynamic migration behavior. For example, conductive dust may cause battery short circuits, while corrosive gases will slowly degrade electrode materials. If the invasion characteristics are not accurately identified, the defense strategy will lack specificity, resulting in excessive defense and increased energy consumption, and insufficient defense will cause thermal runaway. This step constructs a multi-physical field collaborative perception system, synchronously collects spatially distributed environmental parameters, and uses a distributed node time alignment mechanism to eliminate clock deviations, generate standardized time series data, and combine terrain undulation information with time series to separate static pollution sources and dynamic diffusion behaviors, thereby accurately quantifying heterogeneous invasion characteristics. This design is the basis for subsequent risk modeling and avoids the defense blind spots caused by existing methods ignoring the spatiotemporal coupling effect.

[0038] S202. Based on the multi-source intrusion data set, perform multi-level security risk coupling modeling and output a comprehensive risk level; Multi-level safety risk coupling modeling can be a modeling process that quantifies material-level risks through interaction models, and then combines the spatiotemporal scale fusion mechanism to upgrade to system-level risks to construct a mathematical model. The above mathematical model is constructed based on a simulation library and a historical fault database.

[0039] The comprehensive risk level can be a hierarchical label that represents the overall safety status of the battery, which is generated by aggregating multiple levels of warning signs.

[0040] Specifically, lithium battery safety risks have multi-scale characteristics: pollutants corrode electrode materials at the microscopic level and cause system failures at the macroscopic level. Existing risk assessment models only focus on a single scale and do not couple the spatiotemporal evolution of safety risks. For example, local high concentrations of pollutants may be ignored in the short term, but long-term accumulation will lead to diaphragm perforation. This step designs a pollutant-battery material interaction model, combines pollutant characteristics with battery material properties, simulates the impact of pollutant penetration on materials, and further maps the risk index to specific battery areas through spatiotemporal separation and analysis, and associates it with the duration of invasion. Finally, the multi-scale characteristics are coupled and analyzed through a cross-spatiotemporal scale fusion mechanism to correct the risk level. The above-mentioned separation coupling mechanism solves the problem of misjudgment of local cumulative risks by existing methods, and provides a basis for precise defense.

[0041] S203. Based on the comprehensive risk level, adaptively activate the defense mechanism and generate a cross-dimensional defense strategy; The adaptive activation defense mechanism may refer to a decision-making process of dynamically selecting and triggering the optimal combination of defense measures based on the real-time risk level and dominant risk type.

[0042] The dominant risk type may be the main induced risk type that affects the current comprehensive risk level assessment.

[0043] A cross-dimensional defense strategy can be a multi-dimensional collaborative defense solution that integrates physical isolation (spatial dimension), chemical neutralization (material dimension), and electrical fusing (energy dimension).

[0044] Specifically, existing defense methods employ fixed response patterns, unable to cope with the complexity of heterogeneous attacks. If the defense mechanism doesn't match the overall risk level, the existing danger won't be addressed specifically. This step constructs a risk type decision matrix, linking the overall risk level with the dominant risk type to match the optimal defense mechanism combination. For example, when the overall risk level is "medium risk" and the dominant risk type is "chemical corrosion," the release of the corrosion inhibitor is triggered; if the risk is "physical short circuit," local airtight isolation is initiated. This adaptive mechanism significantly improves defense efficiency and avoids wasted resources.

[0045] S204. Evaluate the battery status after defense based on the cross-dimensional defense strategy, and generate and output a security defense effectiveness report.

[0046] The security defense effectiveness report can be a structured report comparing key parameters before and after defense, generated by multi-physics field sensing data and battery status model.

[0047] Specifically, the lack of quantitative evaluation of defense effects in existing technologies will lead to delayed strategy iteration, making the adjustment of defense strategies dependent on manual experience and post-fault analysis, and unable to timely block the gradual accumulation of risks; this step dynamically compares the battery status before and after defense in multiple dimensions, combines changes in material properties, and outputs actionable correction suggestions to security maintenance personnel, driving the dynamic iteration and upgrade of defense strategies based on actual performance data, significantly improving the system's adaptive defense capabilities against new or mutated pollutants, and optimizing resource allocation, ultimately achieving a continuous reduction in safety risks throughout the life cycle of lithium batteries and effective control of operating costs.

[0048] Through this solution, a multimodal feature separation engine is used to analyze the spatiotemporal characteristics of heterogeneous invasions, solving the problem of misjudgment of coordinated invasions of composite pollutants (such as salt spray + metal dust) by existing methods, improving feature recognition accuracy, and matching the optimal defense mechanism combination through an adaptive mechanism that drives the defense strategy to undergo continuous dynamic iteration and upgrade based on actual performance data by associating the comprehensive risk level and the dominant risk type. This significantly enhances the system's intelligent response and adaptive defense capabilities when facing invasions of unknown or mutated pollutants, and achieves efficient and optimized allocation of defense resources by accurately matching defense strength and risk level. Defense performance reports drive strategy iteration, shortening the system's response cycle to new pollutants and improving the long-term reliability of lithium batteries used in mining inspection robots in complex industrial environments. Cross-dimensional defense strategies simultaneously suppress material-level corrosion and system-level failures, reducing the probability of thermal runaway, and providing technical support for energy storage scenarios with high safety requirements for lithium batteries in mining inspection robots.

[0049] In some embodiments, a multi-physical field collaborative perception system is established, an invasion factor perception network is constructed, and spatially distributed environmental invasion parameters are synchronously collected; the time intervals when the lithium battery is subjected to heterogeneous invasion are obtained, and the clock monitoring deviation between intervals is eliminated through a distributed node time alignment mechanism to generate a standardized time data series; based on the spatially distributed environmental invasion parameters and the standardized time data series, the real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located are analyzed and confirmed; based on the real-time heterogeneous invasion feature quantity, the terrain undulation distribution information and the heterogeneous invasion time series distribution information are analyzed through a multimodal feature separation engine; based on the terrain undulation distribution information and the heterogeneous invasion time series distribution information, combined with the real-time heterogeneous invasion feature quantity, a multimodal feature separation engine is used to determine the pollutant characteristics; the pollutant characteristics are analyzed to determine several types of heterogeneous pollutant invasion characteristics; based on several analyzed heterogeneous pollutant invasion characteristics, the multi-source invasion data set is integrated to form the multi-source invasion data set.

[0050] The multi-physics field collaborative perception system can be a distributed monitoring architecture that integrates multi-dimensional sensor nodes to synchronously capture the spatial distribution characteristics of environmental invasion parameters.

[0051] The invasion factor sensing network can be a topological network composed of sensor nodes deployed at key locations of the battery pack, which is used to perceive the type, intensity and diffusion path of environmental invasion factors in real time.

[0052] Environmental attack parameters can be physical quantities that quantify the attack intensity of environmental pollutants.

[0053] The distributed node time alignment mechanism can be an algorithm that eliminates the acquisition time deviation between multiple nodes through a clock synchronization protocol to ensure the consistency of time series data.

[0054] The standardized time data series can be a time series data set formed by reordering asynchronously collected multi-source environmental parameters according to a unified time base.

[0055] The terrain undulation distribution information may be characteristic data describing the geometric structure of the battery pack surface and the surrounding space.

[0056] The temporal distribution information of heterogeneous invasion time can be a dynamic curve that records the change of invasion intensity of different pollutants over time.

[0057] The multimodal feature separation engine can be an algorithm module that integrates tensor decomposition and spatiotemporal evolution modeling to separate the static properties and dynamic diffusion behavior of pollutants.

[0058] Pollutant characteristics can be a set of features that contain inherent properties of pollutants.

[0059] Specifically, in the field of lithium battery safety monitoring, in order to meet the demand for precise prevention and control of multi-source heterogeneous invasions in complex underground mine scenarios, it is necessary to build a technical system covering multi-physical field collaborative perception, spatiotemporal information fusion, and feature intelligent decoupling. The existing monitoring methods lead to risk misjudgment due to the limitations of a single sensor, which is specifically manifested in the inability to accurately identify coupling effects, such as the linkage relationship in which increased humidity exacerbates electrochemical corrosion. By deploying temperature, humidity, gas concentration, and particulate matter sensors around the lithium battery pack to form an invasion factor perception network, each node collects spatially distributed environmental invasion parameters at a preset frequency and uploads them to the central server via a wireless network. Subsequently, Based on the time intervals when lithium batteries are attacked by heterogeneous substances, a distributed node time alignment mechanism is invoked to generate a standardized time data series that eliminates clock bias. The standardized data is then superimposed on the three-dimensional battery model to analyze the spatial distribution of parameters and the time series fluctuation pattern, outputting real-time heterogeneous attack feature quantities. This feature quantity is then fed into a multimodal feature separation engine, which extracts static and dynamic features, fuses terrain undulation distribution information with heterogeneous attack temporal distribution information, and ultimately determines pollutant characteristics and classifies and labels heterogeneous pollutant attack features. This system integrates static attributes, dynamic behavior, and spatial distribution features to form a structured multi-source attack dataset. This effectively integrates the processing chain of spatiotemporal alignment → multi-physics field fusion → static and dynamic feature separation, resolving the fragmentation problem of environmental monitoring data. Compared to existing solutions, this system improves the accuracy of pollutant feature identification and provides a structured, multi-dimensional, and highly consistent multi-source attack dataset for subsequent multi-level safety risk coupling modeling.

[0060] Through this solution, the dynamic heterogeneous invasion situation of the lithium battery microenvironment is comprehensively perceived and quantified by utilizing spatially distributed environmental invasion parameters and their standardized time data series generated by a distributed node time alignment mechanism. Through the two-level decoupling processing of the multimodal feature separation engine (first separating the terrain undulation distribution information from the heterogeneous invasion time series distribution information, and then combining the original feature quantities to analyze the pollutant characteristics), the spatial structural constraints and temporal evolution patterns behind the invasion events are deeply explored, and the core pollutant types and their specific invasion behaviors are accurately traced. Finally, by integrating the invasion characteristics of several types of heterogeneous pollutants, a multi-source invasion dataset is constructed that can comprehensively characterize the complex environmental invasion scenarios under the mine.

[0061] In some embodiments, a multimodal feature tensor is constructed based on terrain undulation distribution information, heterogeneous invasion time series distribution information, and real-time heterogeneous invasion feature quantities; based on a multimodal feature separation engine, the multimodal feature tensor is analyzed to obtain static feature components and dynamic evolution feature components; based on the static feature components and dynamic evolution feature components, pollutant characteristics are constructed.

[0062] The multimodal feature tensor can be a three-dimensional data structure that integrates terrain, time series, and environmental parameters. The dimensions include spatial position, time series, and physical field characteristics.

[0063] The static feature component can be a set of features that characterize the inherent and unchanging properties of the pollutant.

[0064] The dynamic evolution characteristic component can be a time-varying characteristic that describes the migration and diffusion behavior of pollutants.

[0065] Specifically, the lithium battery environmental monitoring on the existing mining inspection robot relies on a single sensor, which leads to data fragmentation and the inability to characterize the spatial migration law and time correlation driven by terrain. By integrating the terrain undulation distribution information and the time series distribution information of heterogeneous invasion, a spatiotemporal dual-dimensional invasion portrait is constructed to avoid the risk of misjudgment caused by missing dimensions; facing the bottleneck of heterogeneous pollutant characteristic identification, the existing method is difficult to distinguish between instantaneous fluctuations and essential characteristics due to its reliance on real-time concentration data. A multimodal feature separation engine is introduced to achieve penetrating quantification of the chronic corrosion risk of pollutants by decoupling static feature components and dynamic evolution feature components; in order to meet the precision requirements of defense strategies, a third-order multimodal feature tensor is constructed based on terrain × history × real-time features, and an implicit mapping of features and data is established, such as steep slope terrain + high-frequency droplet time series is marked as "highly mobile liquid", flat area + aerosol time series is marked as "suspended "Particle deposition" provides a targeted basis for defense mechanisms (such as chemical neutralization) to avoid mismatches between risks and strategies. To address the challenge of multiple factors strongly coupled with pollutants in lithium batteries on inspection robots in complex underground mining scenarios, spatiotemporal collaborative analysis is used to predict terrain aggregation effects and time accumulation effects, building dynamic risk prediction capabilities (such as predicting electrolyte leakage paths). Topographic information is converted into an elevation gradient matrix, temporal information is encoded into a time-event matrix, and real-time feature quantities are organized into feature vectors. Tensor expansion and alignment are then performed to generate a three-dimensional tensor of space × time × feature. Static features are then extracted through tensor decomposition, and dynamic evolution laws are learned using an LSTM network. Finally, the static and dynamic components are integrated to output a structured characteristic parameter set: chemical properties (redox activity / pH sensitivity), physical properties (sedimentation rate / adhesion coefficient), and behavioral properties (electrode coverage growth curve / diaphragm permeation threshold).

[0066] Through this solution, the technical chain of "multimodal feature tensor construction → dynamic and static feature separation → feature fusion" is utilized to solve the problems of missing dimensions and attribute confusion in the analysis of heterogeneous pollutants; compared with existing methods, static feature components accurately identify the essential properties of pollutants, avoiding excessive defense against temporary low-risk pollutants; dynamic evolution feature components predict diffusion paths to achieve preventive protection; the pollutant characteristics generated by the fusion of the two reduce the prediction error rate of subsequent pollutant-battery material interaction models.

[0067] In some embodiments, based on the multimodal feature tensor, the tensor feature decoupling algorithm is used to separate the inherent properties of pollutants, analyze the inherent properties of pollutants, and obtain static feature components; based on the multimodal feature tensor, combined with the spatiotemporal evolution modeling technology, the diffusion and migration behavior of pollutants is separated, analyzed, and dynamic evolution feature components are obtained.

[0068] The tensor feature decoupling algorithm can be a mathematical method to strip away the inherent properties of pollutants through high-order tensor decomposition.

[0069] The inherent properties of pollutants can be the inherent physical and chemical characteristics of pollutants (such as molecular structure, corrosiveness, and thermal stability).

[0070] Spatiotemporal evolution modeling technology can be an algorithm that combines time series prediction and spatial diffusion simulation to quantify the migration patterns of pollutants.

[0071] Pollutant diffusion and migration behavior can be the dynamic propagation path and concentration changes of pollutants driven by environmental factors (wind speed, temperature and humidity).

[0072] The dynamic evolution characteristic component can be a time series characteristic matrix reflecting the diffusion rate, direction and concentration fluctuation of pollutants.

[0073] Specifically, the risk mechanisms of lithium batteries facing the invasion of multiple heterogeneous pollutants in complex environments show essential differences. For example, static properties dominate long-term hazards. For example, the molecular stability of chemically corrosive gases (such as SO2) directly determines the oxidation rate of electrode materials, and its cumulative damage needs to be quantified through inherent properties; while dynamic behavior dominates short-term risks. For example, the local accumulation of conductive dust driven by airflow may cause instantaneous short circuits, and the diffusion model needs to be used to predict sudden risks; however, existing solutions have significant defects. Feature confusion causes static properties and dynamic behaviors to interfere with each other in a single model. For example, the transient characteristics of dust diffusion are misjudged as inherent corrosiveness, which leads to excessive defense; response lag forces the system to wait for the end of the complete diffusion process before assessing the risk due to the failure to separate features, and miss the critical fusing opportunity for dust accumulation; resource waste is also a prominent problem. It is meaningless to implement high-frequency monitoring of static properties, while dynamic behavior needs to be tracked in real time; solving the problem of heterogeneous pollutant characteristics To solve the misjudgment problem caused by coupling, it is necessary to decouple static properties and dynamic behaviors at the mechanism level to achieve more accurate risk assessment and more efficient defense strategies. Through multimodal feature decoupling, accurate stratified prevention and control of lithium battery environmental risks can be achieved. Its technical path and advantages can be summarized as follows: first, the three-dimensional feature tensor of spatial position × timestamp × environmental parameters is received at the data input layer. This data comes from the fusion of standardized time series and spatial distribution parameters; in the feature extraction stage, the tensor feature decoupling algorithm is used to decompose the original data into static and dynamic dual components - the static component determines the long-term threat at the material level (such as electrolyte decomposition) by extracting the inherent property matrix of the pollutant (such as corrosive index, moisture absorption rate, etc.), while the dynamic component uses the ConvLSTM network to model the spatiotemporal migration law of pollutants, generate position probability heat maps and concentration gradient change rates, and capture environmental-level instantaneous risks (such as local electric arcs); finally, the two components are output in parallel to the pollutant characteristic database to support the call of the risk modeling module.

[0074] This solution has established a static-dynamic dual-modal risk assessment system for lithium batteries. At the risk identification level, the static and dynamic components of multi-source attack data are decoupled. The static feature analysis module quantifies chronic contaminant damage and predicts electrode corrosion rates. The dynamic feature monitoring module warns of sudden risks, providing dual protection. At the defense strategy level, a gradient resource allocation is implemented based on the dual-modal architecture—low-cost, long-term protection (such as slow-release coatings) is deployed for static corrosion risks, while millisecond-level response mechanisms (such as directional airflow isolation) are enabled for dynamic migration risks. This system can accurately distinguish between acid gas corrosion and dynamic suspended particle risks, reducing false trigger rates. At the optimization level, system latency is shortened through the coordinated calculation of single static features and lightweight updates of dynamic features. This system effectively solves the problem of misjudgment caused by the coupling of multi-source data, providing a precise and economical solution for the safety protection of lithium batteries throughout their life cycle.

[0075] In some embodiments, based on the pollutant characteristics and battery material properties in the multi-source invasion data set, a pollutant-battery material interaction model is established to simulate the impact of pollutants on lithium batteries and quantitatively generate a risk index; based on several risk indices, a multi-level warning sign is obtained through an integrated multi-scale risk assessment model; based on the multi-level warning sign, the comprehensive risk level is determined.

[0076] The pollutant-battery material interaction model can be a mathematical model that characterizes the physical / chemical interactions between heterogeneous pollutants and lithium battery materials (electrodes, electrolytes, and separators). It predicts battery safety risks by quantifying the impact of pollutant penetration, corrosion, deposition, and other behaviors on battery materials.

[0077] The risk index can be a numerical indicator that quantifies the degree of damage caused by pollutants to different material components of lithium batteries.

[0078] The integrated multi-scale risk assessment model can be a dynamic risk assessment architecture at the material level, component level, and system level.

[0079] Multi-level warning signs can be markers for coupling risk levels at different spatial locations and time stages.

[0080] The comprehensive risk level can be the result of a global risk assessment after integrating multiple levels of warning indicators.

[0081] Specifically, the existing lithium battery safety management methods expose systematic defects when dealing with the invasion of heterogeneous pollutants, forming a closed-loop loophole for risk prevention and control; based on the pollutant characteristics (including their static characteristic components and dynamic evolution characteristic components) and battery material properties (covering the electrode surface energy level distribution, electrolyte chemical stability threshold and diaphragm pore structure parameters) in the multi-source invasion data set, based on dynamic simulation algorithms (such as kinetic Monte Carlo), integrating physics / chemistry, battery material properties, and inherent properties of pollutants, a pollutant-battery material interaction model is constructed to dynamically simulate the invasion process of pollutants in the lithium battery (such as adsorption, reaction, migration and secondary damage effects), and quantify the generation of key risk indices (such as an index reflecting the local corrosion rate, an index indicating the critical point of electrolyte instability, and an index for evaluating the risk of diaphragm penetration); relying on these risk indices, through integration A multi-scale risk assessment model (which incorporates microscopic material-level risks, single-cell-level risks, and system-level risks into a unified analysis framework) is developed. Cross-scale correlation algorithms (such as mapping electrode corrosion rate to single-cell capacity decay rate, or correlating diaphragm blockage risk to system thermal runaway probability) are used for comprehensive deduction, ultimately deriving multi-level warning signs with clear physical meaning and action orientation (such as "material degradation warning," "single-cell failure warning," and "system safety warning"). Finally, based on the superposition effect and urgency of the above-mentioned multi-level warning signs, weighted adaptive fusion rules are used (for example, assigning higher weights to high-frequency, cross-scale warning signals) to determine comprehensive risk levels (such as "low risk," "medium risk," "high risk," and "emergency risk") that can reflect both the intensity of immediate threats and the prediction of long-term evolution trends, providing a quantitative decision-making basis for the precise implementation of risk intervention strategies.

[0082] Through this solution, the safety protection efficiency of the energy storage system is significantly improved by building a multi-dimensional technical system; based on the dynamic interactive model, the digital reconstruction of the pollutant invasion process is realized, the error rate of the risk index is reduced, and the hidden progressive damage characteristics are effectively captured; then, the high-risk areas of the battery are spatially positioned at the millimeter level through multi-level early warning signs, driving the active interception mechanism to be accurately activated, reducing ineffective defense energy consumption; the synchronously established time-space separation analysis mechanism can dynamically track the risk evolution trajectory (such as the diffusion path of corrosion from the edge to the center), improving the millisecond-level response of the defense strategy; through the coupling modeling technology, the existing limitations are broken through, and a digital twin system compatible with gaseous / particulate / liquid multi-type pollutants and complex battery material systems is constructed, which fully adapts to the environmental requirements of lithium battery scenarios and provides an intelligent solution for the safety protection of lithium battery energy storage.

[0083] In some embodiments, based on the characteristics of the pollutants, the characteristics of the pollutants are intelligently analyzed through multi-source data fusion to extract the static characteristic components and dynamic evolution characteristic components of the pollutants; based on the properties of the battery materials, the electrode surface energy level distribution, the electrolyte chemical stability threshold, and the diaphragm pore structure parameters are analyzed; based on the static characteristic components and dynamic evolution characteristic components of the pollutants, combined with the electrode surface energy level distribution, the electrolyte chemical stability threshold, and the diaphragm pore structure parameters, the multi-parameter coupling analysis is performed to establish a pollutant-battery material interaction model; based on the pollutant-battery material interaction model, the pollutant invasion process is dynamically simulated according to the historical pollutant impact and the real-time battery material coupling impact, and a number of risk indices are quantitatively generated.

[0084] Battery material properties can be the inherent performance parameters of key components of lithium batteries, which are derived from the battery performance database.

[0085] The electrode surface energy level distribution can be a quantitative parameter to characterize the energy state of electrons on the surface of lithium battery electrode materials.

[0086] The electrolyte chemical stability threshold may be a set of critical parameters for the electrolyte to withstand chemical attack by contaminants.

[0087] The pore structure parameters of the diaphragm can be the outer protective diaphragm of the lithium battery.

[0088] Historical pollutant impacts can be a quantitative record library of battery performance degradation caused by different types of pollutants under specific environmental conditions.

[0089] The contaminant attack process can be a process in which contaminants penetrate from the environment into the battery and trigger a physical and chemical chain reaction of cascading failure.

[0090] Specifically, the static characteristic components of pollutants are the core basis for identifying potential threat sources and basic action mechanisms. For example, pollutants containing specific functional groups may preferentially attack highly active sites on the electrode surface. However, if their dynamic evolution characteristic components (i.e., the "behavior trajectory" of pollutants in complex electrochemical environments) are ignored, it is impossible to predict the more corrosive secondary products that may be generated after their dissolution or decomposition, or their changes in local ion transport characteristics. At the same time, the inherent properties of battery materials constitute a key line of defense against invasion. The energy level distribution on the electrode surface determines the "target" activity of pollutant adsorption or reaction, and the chemical stability threshold of the electrolyte identifies the "dangerous" sites that induce failure. The pore structure parameters of the membrane act like a "molecular sieve", regulating the physical accessibility and rate of pollutant invasion into key areas. To deeply analyze these complex interactions, a multi-source data fusion framework is used to constrain the static and dynamic characteristic components of pollutants, electrode energy level distribution, electrolyte stability threshold and membrane pore parameters to the same logical space for coupling analysis. Within this framework, "feature mapping association" is used to topologically match the static functional groups of pollutants with highly active defect sites of the electrode to assess the initial adsorption risk. "Evolutionary path superposition" is applied to map the dynamic decomposition sequence of pollutants onto the electrolyte stability threshold map to identify the critical factors that trigger chain side reactions. boundary conditions; implement "transport barrier simulation" to combine the dynamic migration tendency of pollutants with the pore structure of the diaphragm to predict their diffusion bottlenecks and enrichment areas inside the battery. This deep coupling analysis that goes beyond the limitations of a single parameter ultimately constructs a pollutant-battery material interaction model that can comprehensively reflect the pollutant invasion ability, battery material resistance, and the interaction pathway between the two; based on this model, the historical pollutant impact data and the real-time monitoring of battery material status (such as the thickness of the electrode passivation layer, slight changes in electrolyte components, and the degree of local blockage of the diaphragm) are integrated to dynamically simulate the invasion process of specific pollutants in a specific battery microenvironment. For example, when the simulated pollutants migrate to the electrode When the surface is exposed to the atmosphere, the model calculates the reaction probability and products based on the real-time electrode state (energy level change) and the current form of the pollutant (dynamic evolution result); when the pollutant approaches the threshold boundary of the electrolyte stability, the model dynamically adjusts the threshold sensitivity according to the slight change of the current electrolyte composition; through this refined dynamic simulation process, the key risk indices are finally quantified and generated, including: the "local corrosion rate index" that reflects the real-time corrosion rate of a specific electrode site; the "electrolyte instability critical index" that predicts the possibility and time scale of pollutant-induced electrolyte decomposition; and the "membrane penetration risk index" that evaluates the risk of blockage or short circuit caused by the enrichment of pollutants in the diaphragm pores.

[0091] Through this scheme, the static characteristic components and dynamic evolution characteristic components of pollutants, the electrode surface energy level distribution, the electrolyte chemical stability threshold and the pore structure parameters of the diaphragm are used to comprehensively analyze the multi-dimensional information of pollutant characteristics and battery material properties, so as to deeply characterize the complex mechanism of the interaction between pollutants and battery materials, and improve the comprehensiveness and precision of the analysis of the risk characteristics of pollutant invasion inside the battery; at the same time, the logical space unification and coupling analysis of the above parameters are carried out through the multi-source data fusion framework, which effectively reveals the nonlinear correlation between static properties and dynamic evolution, and between the inherent characteristics of materials and the real-time state of the environment, and improves the accuracy of invasion risk prediction and assessment under complex multi-factor coupling conditions.

[0092] In some embodiments, based on several risk indices, spatial positioning features and time duration features are extracted by separating and analyzing the risk indices in time and space dimensions; based on the time duration features, the duration of heterogeneous invasions corresponding to the several risk indices is determined, and the cumulative time of lithium battery invasions is recorded; based on the spatial positioning features, combined with the environmental invasion parameters, the environmental invasion parameters are mapped to the battery area corresponding to the spatial positioning features to obtain the lithium battery invasion location features; based on the cumulative time of lithium battery invasions, combined with the lithium battery invasion location features, each of the risk indexes is subjected to time and space coupling correction through a cross-time and space scale fusion analysis mechanism to determine several risk grading reference coefficients; according to the several risk level reference coefficients, the preset warning level information is matched to determine the warning level identification corresponding to each of the risk level reference coefficients to construct a multi-level warning identification.

[0093] Spatiotemporal dimension separation analysis can be an analytical process that breaks down the risk index into independent spatial characteristics (invasion location distribution) and temporal characteristics (invasion persistence trend).

[0094] Spatial localization characteristics can be key parameters for describing the spatial distribution of pollutants inside or on the surface of lithium batteries.

[0095] The time-duration characteristic may be a characteristic of the evolution of the contaminant attack over time.

[0096] The cumulative time of heterogeneous invasion can be the cumulative length of time that a specific pollutant continuously acts on a specific area of ​​a lithium battery.

[0097] The lithium battery invasion location feature can identify the specific physical location where the contaminants invade the battery.

[0098] The cross-spatiotemporal scale fusion analysis mechanism can be an algorithmic framework for dynamically correlating and correcting spatiotemporally separated features.

[0099] The risk grading reference coefficient can be a comprehensive risk quantification value corrected by time-space coupling.

[0100] Specifically, the existing methods in the dynamic assessment of lithium battery safety risks have the problem of time and space separation. The existing technology only divides the warning level by static risk values, but ignores the time and space characteristics of pollutant invasion. In the spatial dimension, there are significant differences in the sensitivity of different areas of the battery to pollutants. For example, the electrolyte is easily corroded by sulfides and the diaphragm is easily blocked by particulate matter. The lack of location feature analysis will lead to omission of monitoring in high-risk areas. In the time dimension, the degree of harm caused by short-term high concentration and long-term low concentration invasion is completely different. For example, 1 hour of acid mist exposure only causes surface corrosion, while 24 hours will cause electrolyte decomposition. Existing methods cannot quantify such cumulative effects. Pollutant migration has temporal and spatial correlation. For example, after moisture erosion causes the structure to become loose, chemical pollutants are more likely to penetrate into the same area. Single-dimensional analysis will seriously underestimate the compound risk. Based on this, a precise early warning mechanism based on the spatiotemporal coupling effect is proposed: by inputting multiple risk indices generated (such as corrosion index, thermal runaway index), each index is analyzed Decoupling of the spatiotemporal dimensions: In the spatial dimension, the environmental attack parameters (such as the pollutant concentration gradient) are used to determine their impact coordinates within the battery (for example, "positive electrode active material layer-edge area"). In the temporal dimension, the time period during which the index persists is extracted (for example, "sulfide exposure lasts 2.8 hours"). Subsequently, multiple time duration features under the same spatial positioning feature are superimposed to generate the cumulative heterogeneous attack time (for example, "the positive electrode edge is cumulatively attacked for 3.5 hours"). Simultaneously, combined with the battery 3D model, the environmental attack parameters are mapped to the corresponding locations, and the lithium battery attack location features are annotated (for example, "diaphragm pore area-number A7"). Furthermore, a spatiotemporal weight matrix is ​​constructed for coupling correction: Given the different sensitivities of different regions, the long-term attack index of high-risk locations is differentially weighted (for example, in highly sensitive areas such as the electrolyte interface, the risk value increases by 30% for every additional hour of cumulative time; in less sensitive areas such as the outer shell, the risk value increases by only 5% for the same duration). Finally, the corrected risk grading reference coefficient is output. Finally, based on preset threshold rules (e.g., a coefficient > 0.8 is a red warning), a multi-level warning label associated with the specific location is automatically generated (e.g., "Diaphragm Pore Area A7: Red High Risk").

[0101] Through this solution, the spatiotemporal coupling mechanism is utilized to effectively solve the problem of quantifying the dynamic safety risks of lithium batteries, providing a decision-making basis for the generation of defense strategies. The risk assessment system constructed based on the spatiotemporal fusion model has achieved the goal of improving the accuracy of early warnings, and effectively solved the false alarm dilemma of existing solutions through multi-dimensional data coupling. Through the dynamic resource allocation mechanism driven by location characteristics, the resource consumption of defense measures is reduced, forming a closed-loop optimization of risk assessment and defense response. Furthermore, an algorithm correlating the cumulative duration with the location sensitivity coefficient is introduced to provide early warnings for hidden risks such as slow erosion, transforming passive responses into active defenses. At the execution level, the deep integration of multi-level early warning signs and differentiated defense mechanisms constitutes a cross-dimensional collaborative defense system: red warnings trigger hard protections such as physical fuses, and yellow warnings activate soft maintenance such as electrode coating repairs, generating targeted risk resolution strategies.

[0102] In some embodiments, based on a multi-source intrusion data set, pollutant characteristics and the battery material properties are extracted; based on the pollutant characteristics and the battery material properties, interaction analysis is performed to identify several risk types caused by pollutants; based on several risk types, the current dominant risk type is parsed and identified; a historical pollutant impact data set is obtained, and a risk type decision matrix is ​​constructed based on the historical pollutant impact data set; based on several dominant risk types, combined with several risk grading reference parameters, the risk type decision matrix is ​​matched, and several defense mechanisms corresponding to different defense mechanism types are adaptively activated; defense mechanism types include maintaining basic protection, triggering active interception, and executing physical fuses; based on several defense mechanisms, targeted cross-dimensional defense strategies are generated.

[0103] The risk type decision matrix can be a mapping table constructed based on the historical pollutant impact dataset, associating risk types with defense mechanisms.

[0104] The types of defense mechanisms can be: maintaining basic protection: low-power monitoring mode (such as ion barrier maintenance); triggering active interception: dynamic neutralization reaction (such as releasing passivation agent); performing physical fusing: emergency isolation circuit.

[0105] Specifically, the lithium batteries on inspection robots in underground mine environments are subject to significant variability when attacked by heterogeneous pollutants. For example, the same pollutant may induce completely different reactions under different environmental conditions such as high temperature or high humidity. This variability directly leads to the fact that if a fixed defense mechanism is adopted, it will be difficult to effectively cover complex risk scenarios such as the coexistence of chemical corrosion and thermal runaway. Existing methods usually trigger defense based on a single comprehensive risk level threshold, but ignore the priority differences between different risk types (for example, dendrite-induced short circuits may be more urgent than local corrosion). If physical fusing is directly performed for this reason, Although high-level defense can block risks, it is very easy to cause unnecessary system downtime. In order to solve the above problems and optimize resource utilization, this solution activates the defense mechanism based on the location characteristics (spatial dimension) and the cumulative time of the invasion (time dimension) of lithium battery invasion. For example, for specific electrode areas that have been continuously corroded and have a high risk accumulation, the system will give priority to active interception and other strong measures; for new areas where invasion signals have just been detected or the risk is low, only basic monitoring is required. The battery safety defense system built based on multi-source invasion data sets extracts multi-dimensional features and dynamically detects them. A closed-loop protection mechanism is formed based on dynamic risk assessment. The system first extracts key parameters from contaminant chemical composition, concentration gradient, intrusion rate, and other properties of battery materials, such as composition and interface stability. It then constructs a contaminant-material interaction model to simulate adsorption, diffusion, and chemical reaction trajectories. This model then identifies potential risk types such as chemical corrosion, dendrite penetration, and ion channel blockage. By real-time monitoring the impact of interaction intensity on performance parameters such as voltage and temperature, the system determines the current dominant risk level. For example, the risk of rapid corrosion caused by high-concentration contaminants is prioritized. A risk decision matrix constructed using historical data maps risk types, contaminant characteristic thresholds, and material state parameters to validated defense response patterns, forming an adaptive triggering mechanism. For example, when risk parameters fall below warning values, basic protection with fine-tuned charge and discharge strategies is maintained. Active interception measures such as neutralizer injection or isolation layer formation are initiated when critical thresholds are exceeded. If precursors to thermal runaway occur, circuit fusing or physical isolation is immediately implemented. Finally, a cross-dimensional collaborative strategy integrates response actions such as electrolyte modification, interface repair, thermal management enhancement, and power limiting, creating a multi-level protection barrier from electrochemical regulation to physical isolation, achieving dynamic matching of risk warning and defense response.

[0106] Through this solution, a multi-dimensional collaborative security defense system is constructed, which achieves dual optimization of defense effectiveness and resource management. Based on the dominant risk type identification and dynamic decision matrix matching mechanism, it effectively avoids the false triggering of the defense mechanism, accurately focuses limited resources on high-priority threats, and improves the response speed of key threats. The layered defense architecture reduces the energy consumption of the system in low-risk scenarios through differentiated resource allocation strategies, extends the service life of the battery management system, and cooperates with the targeted activation mechanism to reduce the ineffective consumption of consumables such as neutralizers. At the environmental adaptability level, a cross-dimensional strategy engine is adopted to achieve dynamic coordination of spatial protection, temporal response and defense mechanisms to cope with the coordinated invasion of complex pollution scenarios in underground mine environments. Relying on the historical data backtracking mechanism of the risk decision matrix, a closed-loop iteration of "identification-response-optimization" is formed to continuously enhance the long-term robustness of the system in extreme environments and provide a verifiable evolutionary path for security decision-making.

[0107] In some embodiments, based on a cross-dimensional defense strategy, after executing several defense mechanisms corresponding to different defense mechanism types in the cross-dimensional defense strategy, the battery status of the lithium battery after defense is obtained according to the multi-physical field collaborative perception system; based on the battery status of the lithium battery after defense, combined with the battery status before defense, the operating status of the lithium battery before and after defense is compared to obtain the difference in status before and after defense; based on the difference in status before and after defense, the battery material properties before and after the implementation of the defense measures are dynamically compared in multiple dimensions, and a structured security defense effectiveness report containing multi-dimensional performance indicators and correction suggestions is output.

[0108] The difference in state before and after the defense can be the change in the key material properties of the lithium battery before and after the defense mechanism is implemented, including the electrode surface energy level offset, electrolyte stability decay rate, etc.

[0109] A structured security defense effectiveness report can be a standardized output document that includes quantitative indicators of defense efficiency, material damage maps, and defense strategy correction suggestions.

[0110] Specifically, lithium batteries face the invasion of heterogeneous pollutants (such as corrosive gases and conductive dust) in the complex environment of underground mines. The existing safety management system has the defects of evaluation lag and lack of decision-making closed loop. The evaluation lag is manifested in the lack of real-time effectiveness verification after the implementation of defense measures, and it is impossible to judge whether the defense has truly eliminated the risk. For example, chemical neutralizers may leave residual byproducts and cause secondary corrosion, but the existing system only records "defense has been executed" and does not quantify the actual effect. The defect of the lack of decision-making closed loop is manifested in the disconnection between defense strategy and subsequent optimization. If local overheating of the battery is not detected after defense, the system cannot automatically trigger supplementary measures, resulting in risk accumulation. By building a dynamic feedback closed loop, implementing multi-dimensional collaborative analysis and standardized output-driven active operation and maintenance, a closed-loop battery safety defense system has been formed. Its core necessity lies in coping with complex and changing operating environments, and verifying the defense effectiveness in real time through the "execution-evaluation-correction" closed-loop mechanism. For example, by comparing the changes in the electrolyte chemical stability threshold and key parameters such as the diaphragm pore blockage rate, it accurately identifies defense blind spots such as local arc residue after physical melting, and based on The residual risk heat map dynamically adjusts the strategy intensity to avoid unidirectional defense failures caused by the migration of pollutant diffusion paths (such as the focus and shift of corrosive gas due to wind speed changes). A cross-dimensional coupled analysis framework is established, integrating shell stress monitoring in the physical dimension, spectral analysis of electrode surface contaminants in the chemical dimension, and internal resistance fluctuation tracking in the electrical dimension. This framework overcomes the limitations of single-dimensional detection and effectively prevents hidden risks such as microscopic perforation of the diaphragm. The temperature rise rate technical indicator is converted into actionable operation and maintenance instructions to guide safety maintenance personnel in accurately locating high-risk battery clusters. In terms of implementation path, the solution implements defensive actions such as active interception devices to remove electrode dust and injecting chemical neutralizers to degrade acidic gases. The solution utilizes a multi-physical field collaborative sensing system to collect neutralization reaction temperature and electric field strength data. After comparing and analyzing the states before and after the defense (such as the voltage fluctuation convergence time and the improvement of the pH value spatial distribution), a structured performance report is generated, which includes the electrochemical stability improvement ratio, thermal runaway suppression effect, and residual risk level. Corrective suggestions such as adjusting the neutralizer concentration and adding cooling channels are also proposed, forming a complete technical closed loop from strategy execution to effect optimization.

[0111] This solution builds a data-driven defense effectiveness evaluation system, achieving closed-loop management from policy verification to operation and maintenance optimization. Based on multi-physics field dynamic data comparison technology, a visual verification mechanism for defense effectiveness is established, accurately assessing safety protection effectiveness through quantitative indicators. The structured effectiveness reports generated by the system not only include real-time performance parameters but also drive adaptive defense mechanisms to iterate their policies through analytical models such as residual risk area identification. For example, this system can specifically enhance the physical fuse protection level, forming a dynamic risk suppression closed loop of "evaluation-optimization-re-evaluation." This closed-loop management mechanism generates compound benefits across the entire battery lifecycle. By constructing a battery health evolution map through continuously accumulated performance data, it provides a scientific basis for predictive maintenance and extends the service life of the energy storage system. Standardized reports enable precise location of faulty units, reducing the frequency of manual inspections, avoiding excessive maintenance while lowering operation and maintenance decision-making costs, ultimately forming a synergistic and efficient system that combines safety protection, targeted response, and cost control.

[0112] Figure 3 This is a structural diagram of a lithium battery energy storage safety management system provided in one embodiment of the present application, such as Figure 3 As shown, a lithium battery energy storage safety management system 300 of this embodiment includes: a data processing module 301 , a risk modeling module 302 , a strategy optimization module 303 and an effectiveness verification module 304 .

[0113] The data processing module 301 is used to obtain real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located, analyze the invasion characteristics of heterogeneous pollutants based on the real-time heterogeneous invasion characteristics, and integrate them to form a multi-source invasion data set; A risk modeling module 302 is configured to perform multi-level security risk coupling modeling based on the multi-source intrusion dataset and output a comprehensive risk level; A strategy optimization module 303 is configured to adaptively activate defense mechanisms based on the comprehensive risk level and generate a cross-dimensional defense strategy; The effectiveness verification module 304 is used to evaluate the battery status after defense according to the cross-dimensional defense strategy, and generate and output a security defense effectiveness report.

[0114] Optionally, when the data processing module 301 obtains the real-time heterogeneous invasion characteristic quantity of the environment in which the lithium battery is located, analyzes the invasion characteristics of heterogeneous pollutants, and integrates them to form a multi-source invasion data set, it is specifically used to: Establish a multi-physics field collaborative perception system, build an invasion factor perception network, and synchronously collect spatially distributed environmental invasion parameters; Obtain the time intervals during which lithium batteries are attacked by heterogeneous substances, eliminate clock monitoring deviations between intervals through a distributed node time alignment mechanism, and generate standardized time data series; Based on the spatially distributed environmental attack parameters and the standardized time data series, analyzing and confirming the real-time heterogeneous attack characteristics of the environment in which the lithium battery is located; Based on the real-time heterogeneous invasion feature quantity, the terrain undulation distribution information and the heterogeneous invasion time series distribution information are analyzed and obtained through a multimodal feature separation engine; Based on the terrain undulation distribution information and the heterogeneous invasion time series distribution information, combined with the real-time heterogeneous invasion feature quantity, a multimodal feature separation engine is used to determine the pollutant characteristics; Analyze the characteristics of the pollutants and determine the invasion characteristics of several types of heterogeneous pollutants; Based on several of the analyzed heterogeneous pollutant invasion features, the multi-source invasion dataset is integrated to form the multi-source invasion dataset.

[0115] Optionally, when the data processing module 301 determines the pollutant characteristics based on the terrain undulation distribution information and the heterogeneous invasion time series distribution information in combination with the real-time heterogeneous invasion feature quantity and adopts a multimodal feature separation engine, it is specifically configured to: Constructing a multimodal feature tensor based on the terrain undulation distribution information, the heterogeneous invasion time series distribution information, and the real-time heterogeneous invasion feature quantity; Analyzing the multimodal feature tensor based on the multimodal feature separation engine to obtain a static feature component and a dynamic evolution feature component; Based on the static characteristic components and the dynamic evolution characteristic components, pollutant characteristics are constructed.

[0116] Optionally, in the data processing module 301, the multimodal feature separation engine is specifically configured to: Separating the inherent properties of the pollutants and analyzing the inherent properties of the pollutants to obtain static characteristic components based on the multimodal characteristic tensor using a tensor characteristic decoupling algorithm; According to the multimodal feature tensor, combined with the spatiotemporal evolution modeling technology, the pollutant diffusion and migration behaviors are separated and analyzed to obtain the dynamic evolution feature components.

[0117] Optionally, when the risk modeling module 302 performs multi-level security risk coupling modeling based on the multi-source intrusion dataset and outputs a comprehensive risk level, it is specifically configured to: Based on the pollutant characteristics and battery material properties in the multi-source invasion data set, a pollutant-battery material interaction model is established to simulate the impact of pollutants on lithium batteries and quantitatively generate several risk indices; Based on several risk indices, a multi-level warning indicator is obtained through an integrated multi-scale risk assessment model; Determine the comprehensive risk level based on multi-level early warning signs.

[0118] Optionally, when the risk modeling module 302 establishes a pollutant-battery material interaction model based on the pollutant characteristics and battery material properties in the multi-source invasion dataset, simulates the impact of pollutants on lithium batteries, and quantitatively generates a number of risk indices, it is specifically configured to: Based on the pollutant characteristics, intelligently analyzing the pollutant characteristics through multi-source data fusion, extracting the static characteristic components and the dynamic evolution characteristic components in the pollutants; Based on the battery material properties, the electrode surface energy level distribution, electrolyte chemical stability threshold, and diaphragm pore structure parameters in the battery material are analyzed; Based on the static characteristic components and the dynamic evolution characteristic components of the pollutants, combined with the electrode surface energy level distribution, the electrolyte chemical stability threshold, and the diaphragm pore structure parameters, a multi-parameter coupling analysis is performed to establish a pollutant-battery material interaction model; Based on the pollutant-battery material interaction model, the pollutant invasion process is dynamically simulated according to the historical pollutant impact and the real-time battery material coupling impact, and several risk indices are quantitatively generated.

[0119] Optionally, when the risk modeling module 302 derives a multi-level warning indicator based on the plurality of risk indices through an integrated multi-scale risk assessment model, it is specifically configured to: Based on the risk indices, extracting spatial positioning features and temporal duration features by performing spatiotemporal separation and analysis on the risk indices; Based on the time duration feature, determining the duration of the heterogeneous attacks corresponding to the risk indices, and recording the cumulative duration of the lithium battery attacks; Based on the spatial positioning feature and in combination with the environmental invasion parameter, the environmental invasion parameter is mapped to the battery area corresponding to the spatial positioning feature to obtain the lithium battery invasion position feature; Based on the cumulative time of the lithium battery attack and the location characteristics of the lithium battery attack, a cross-spatiotemporal fusion analysis mechanism is used to perform spatiotemporal coupling correction on each risk index to determine a number of risk grading reference coefficients; According to the plurality of risk grading reference coefficients, the preset warning level information is matched and the warning level identifier corresponding to each of the risk grading reference coefficients is determined to construct the multi-level warning identifier.

[0120] Optionally, when the strategy optimization module 303 adaptively activates the defense mechanism based on the comprehensive risk level and generates a cross-dimensional defense strategy, it is specifically configured to: extracting the pollutant characteristics and the battery material properties based on the multi-source invasion data set; Based on the characteristics of the pollutants and the properties of the battery materials, an interaction analysis is performed to identify several risk types caused by the pollutants; Based on several risk types, analyze and identify the current dominant risk type; Obtaining a historical pollutant impact dataset, and constructing a risk type decision matrix based on the historical pollutant impact dataset; Based on the plurality of dominant risk types, combined with a plurality of risk grading reference parameters, the risk type decision matrix is ​​matched, and the plurality of defense mechanisms corresponding to different defense mechanism types are adaptively activated; The types of defense mechanisms include maintaining basic protection, triggering active interception, and performing physical fusing; Based on several of the aforementioned defense mechanisms, a targeted cross-dimensional defense strategy is generated.

[0121] Optionally, when the effectiveness verification module 304 evaluates the battery status after defense according to the cross-dimensional defense strategy and generates and outputs a security defense effectiveness report, it is specifically configured to: Based on the cross-dimensional defense strategy, after executing several of the defense mechanisms corresponding to the different defense mechanism types in the cross-dimensional defense strategy, the battery status of the lithium battery after defense is obtained according to the multi-physical field collaborative sensing system; Based on the battery status of the lithium battery after defense, combined with the battery status before defense, the operating status of the lithium battery before and after defense is compared to obtain the difference between the status before and after defense; Based on the difference in status before and after the defense, the battery material properties before and after the defense measures are implemented are dynamically compared in multiple dimensions, and a structured security defense effectiveness report including multi-dimensional performance indicators and correction suggestions is output.

[0122] The system of this embodiment can be used to execute the method of any of the above embodiments. Its implementation principles and technical effects are similar and will not be described in detail here.

Claims

1. A lithium battery energy storage safety management method, characterized in that: include: Acquire real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located, analyze the invasion characteristics of heterogeneous pollutants based on the real-time heterogeneous invasion characteristics, and integrate them to form a multi-source invasion data set; Based on the multi-source intrusion data set, multi-level security risk coupling modeling is performed to output a comprehensive risk level; Based on the comprehensive risk level, adaptively activate defense mechanisms and generate cross-dimensional defense strategies; According to the cross-dimensional defense strategy, the battery status after defense is evaluated, and a security defense effectiveness report is generated and output.

2. The method according to claim 1, characterized in that The acquisition of real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located, the analysis of heterogeneous pollutant invasion characteristics, and integration to form a multi-source invasion data set include: Establish a multi-physics field collaborative perception system, build an invasion factor perception network, and synchronously collect spatially distributed environmental invasion parameters; Obtain the time intervals during which lithium batteries are attacked by heterogeneous substances, eliminate clock monitoring deviations between intervals through a distributed node time alignment mechanism, and generate standardized time data series; Based on the spatially distributed environmental attack parameters and the standardized time data series, analyzing and confirming the real-time heterogeneous attack characteristics of the environment in which the lithium battery is located; Based on the real-time heterogeneous invasion feature quantity, the terrain undulation distribution information and the heterogeneous invasion time series distribution information are analyzed and obtained through a multimodal feature separation engine; Based on the terrain undulation distribution information and the heterogeneous invasion time series distribution information, combined with the real-time heterogeneous invasion feature quantity, a multimodal feature separation engine is used to determine the pollutant characteristics; Analyze the characteristics of the pollutants and determine the invasion characteristics of several types of heterogeneous pollutants; Based on several of the analyzed heterogeneous pollutant invasion features, the multi-source invasion dataset is integrated to form the multi-source invasion dataset.

3. The method according to claim 2, characterized in that Based on the terrain undulation distribution information and the heterogeneous invasion time series distribution information, combined with the real-time heterogeneous invasion feature quantity, a multimodal feature separation engine is used to determine the pollutant characteristics, including: Constructing a multimodal feature tensor based on the terrain undulation distribution information, the heterogeneous invasion time series distribution information, and the real-time heterogeneous invasion feature quantity; Analyzing the multimodal feature tensor based on the multimodal feature separation engine to obtain a static feature component and a dynamic evolution feature component; Based on the static characteristic components and the dynamic evolution characteristic components, pollutant characteristics are constructed.

4. The method according to claim 3, characterized in that The multimodal feature separation engine includes: Separating the inherent properties of the pollutants and analyzing the inherent properties of the pollutants to obtain static characteristic components based on the multimodal characteristic tensor using a tensor characteristic decoupling algorithm; According to the multimodal feature tensor, combined with the spatiotemporal evolution modeling technology, the pollutant diffusion and migration behaviors are separated and analyzed to obtain the dynamic evolution feature components.

5. The method according to claim 4, characterized in that Based on the multi-source attack dataset, a multi-level security risk coupling model is performed to output a comprehensive risk level, including: Based on the pollutant characteristics and battery material properties in the multi-source invasion data set, a pollutant-battery material interaction model is established to simulate the impact of pollutants on lithium batteries and quantitatively generate several risk indices; Based on several risk indices, a multi-level warning indicator is obtained through an integrated multi-scale risk assessment model; Determine the comprehensive risk level based on multi-level early warning signs.

6. The method according to claim 5, characterized in that Based on the pollutant characteristics and battery material properties in the multi-source invasion dataset, a pollutant-battery material interaction model is established to simulate the impact of pollutants on lithium batteries and quantify several risk indices, including: Based on the pollutant characteristics, intelligently analyzing the pollutant characteristics through multi-source data fusion, extracting the static characteristic components and the dynamic evolution characteristic components in the pollutants; Based on the battery material properties, the electrode surface energy level distribution, electrolyte chemical stability threshold, and diaphragm pore structure parameters in the battery material are analyzed; Based on the static characteristic components and the dynamic evolution characteristic components of the pollutants, combined with the electrode surface energy level distribution, the electrolyte chemical stability threshold, and the diaphragm pore structure parameters, a multi-parameter coupling analysis is performed to establish a pollutant-battery material interaction model; Based on the pollutant-battery material interaction model, the pollutant invasion process is dynamically simulated according to the historical pollutant impact and the real-time battery material coupling impact, and several risk indices are quantitatively generated.

7. The method according to claim 5, characterized in that Based on several of the aforementioned risk indices, a multi-level early warning indicator is derived through an integrated multi-scale risk assessment model, including: Based on the risk indices, extracting spatial positioning features and temporal duration features by performing spatiotemporal separation and analysis on the risk indices; Based on the time duration feature, determining the duration of the heterogeneous attacks corresponding to the risk indices, and recording the cumulative duration of the lithium battery attacks; Based on the spatial positioning feature and in combination with the environmental invasion parameter, the environmental invasion parameter is mapped to the battery area corresponding to the spatial positioning feature to obtain the lithium battery invasion position feature; Based on the cumulative time of the lithium battery attack and the location characteristics of the lithium battery attack, a cross-spatiotemporal fusion analysis mechanism is used to perform spatiotemporal coupling correction on each risk index to determine several risk grading reference coefficients; According to the plurality of risk grading reference coefficients, the preset warning level information is matched and the warning level identifier corresponding to each of the risk grading reference coefficients is determined to construct the multi-level warning identifier.

8. The method according to claim 5, characterized in that Based on the comprehensive risk level, defense mechanisms are adaptively activated to generate a cross-dimensional defense strategy, including: extracting the pollutant characteristics and the battery material properties based on the multi-source invasion data set; Based on the characteristics of the pollutants and the properties of the battery materials, an interaction analysis is performed to identify several risk types caused by the pollutants; Based on several risk types, analyze and identify the current dominant risk type; Obtaining a historical pollutant impact dataset, and constructing a risk type decision matrix based on the historical pollutant impact dataset; Based on the plurality of dominant risk types, combined with a plurality of risk grading reference parameters, the risk type decision matrix is ​​matched, and the plurality of defense mechanisms corresponding to different defense mechanism types are adaptively activated; The types of defense mechanisms include maintaining basic protection, triggering active interception, and performing physical fusing; Based on several of the aforementioned defense mechanisms, a targeted cross-dimensional defense strategy is generated.

9. The method according to claim 8, characterized in that According to the cross-dimensional defense strategy, the battery status after defense is evaluated, and a security defense effectiveness report is generated and output, including: Based on the cross-dimensional defense strategy, after executing several of the defense mechanisms corresponding to the different defense mechanism types in the cross-dimensional defense strategy, the battery status of the lithium battery after defense is obtained according to the multi-physical field collaborative sensing system; Based on the battery status of the lithium battery after defense, combined with the battery status before defense, the operating status of the lithium battery before and after defense is compared to obtain the difference between the status before and after defense; Based on the difference in status before and after the defense, the battery material properties before and after the defense measures are implemented are dynamically compared in multiple dimensions, and a structured security defense effectiveness report including multi-dimensional performance indicators and correction suggestions is output.

10. A lithium battery energy storage safety management system, characterized in that: include: A data processing module is used to obtain real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located, analyze the invasion characteristics of heterogeneous pollutants based on the real-time heterogeneous invasion characteristics, and integrate them to form a multi-source invasion data set; A risk modeling module, configured to perform multi-level security risk coupling modeling based on the multi-source intrusion data set and output a comprehensive risk level; A strategy optimization module, configured to adaptively activate defense mechanisms based on the comprehensive risk level and generate a cross-dimensional defense strategy; The effectiveness verification module is used to evaluate the battery status after defense according to the cross-dimensional defense strategy, and generate and output a security defense effectiveness report.

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