Lithium battery energy storage safety management system and method

Abstract

This application relates to the field of lithium battery safety technology, and in particular to a lithium battery energy storage safety management system and method. The method includes: acquiring real-time heterogeneous intrusion characteristics of the environment in which the lithium battery is located; analyzing heterogeneous contaminant intrusion characteristics based on these real-time heterogeneous intrusion characteristics, and integrating them to form a multi-source intrusion dataset; performing multi-level safety risk coupling modeling based on the multi-source intrusion dataset, and outputting a comprehensive risk level; adaptively activating a defense mechanism based on the comprehensive risk level, and generating a cross-dimensional defense strategy; evaluating the battery status after defense according to the cross-dimensional defense strategy, and generating and outputting a safety defense effectiveness report. This application combines the dynamic feedback mechanism of contaminant intrusion characteristics and multi-level safety risks to identify the safety vulnerabilities and evolution trends of lithium battery energy storage systems on underground inspection robots, enabling rapid response to high-risk intrusion scenarios, timely mitigation of potential risks, and prevention of local anomalies from escalating into systemic safety accidents.

Description

Technical Field

[0001] This 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 Technology

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

[0003] However, when existing lithium batteries are used in inspection robots, the lithium battery energy storage safety management system mainly focuses on monitoring and balancing the basic parameters at the cell level. Its protection strategies are mostly static presets or single-dimensional responses. When facing unique, complex, and heterogeneous environments, the existing system has significant shortcomings and cannot guarantee the continuous, safe, and efficient operation of the inspection robot. Summary of the Invention

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

[0005] In a first aspect, this application provides a lithium battery energy storage safety management method, the method comprising:

[0006] Real-time heterogeneous invasion characteristics of the lithium battery's environment are acquired. Based on these characteristics, the invasion features of heterogeneous contaminants are analyzed, and a multi-source invasion dataset is formed. Based on this dataset, multi-level safety risk coupling modeling is performed, and a comprehensive risk level is output. Based on the comprehensive risk level, an adaptive defense mechanism is activated to generate a cross-dimensional defense strategy. According to the cross-dimensional defense strategy, the battery status after defense is evaluated, and a safety defense effectiveness report is generated and output.

[0007] This solution utilizes a multimodal feature separation engine to analyze the spatiotemporal characteristics of heterogeneous intrusion, addressing the misjudgment problem of collaborative intrusion by composite pollutants (such as salt spray and metal dust) in existing methods, thus improving feature recognition accuracy. By associating comprehensive risk levels and dominant risk types, an adaptive mechanism that matches the optimal combination of defense mechanisms drives the defense strategy to continuously and dynamically iterate and upgrade based on actual performance data. This significantly enhances the system's intelligent response and adaptive defense capabilities when facing unknown or mutated pollutant intrusions. Furthermore, by accurately matching defense intensity and risk levels, it achieves efficient optimization of defense resources. 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. The cross-dimensional defense strategy simultaneously suppresses material-level erosion and system-level failures, reducing the probability of thermal runaway and providing technical support for the high-safety-requirement energy storage scenarios of lithium batteries in mining inspection robots.

[0008] Optionally, the step of acquiring real-time heterogeneous invasion characteristics of the lithium battery's environment, analyzing heterogeneous contaminant invasion characteristics, and integrating these characteristics to form a multi-source invasion dataset includes:

[0009] Establish a multi-physics field collaborative sensing system, construct an invasion factor sensing network, and synchronously collect spatially distributed environmental invasion parameters;

[0010] The time interval of the lithium battery being attacked by foreign matter is obtained, and a standardized time data sequence is generated by eliminating the clock monitoring deviation between intervals through a distributed node time alignment mechanism.

[0011] Based on the spatially distributed environmental invasion parameters and standardized time data series, the real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located are analyzed and confirmed.

[0012] Based on the real-time heterogeneous invasion features, the terrain undulation distribution information and the temporal distribution information of heterogeneous invasion are analyzed by a multimodal feature separation engine.

[0013] Based on the terrain undulation distribution information and the time series distribution information of heterogeneous invasion, combined with the real-time heterogeneous invasion feature quantity, a multimodal feature separation engine is used to determine the pollutant characteristics;

[0014] Analyze the characteristics of the pollutants to determine the invasion features of several types of heterogeneous pollutants;

[0015] Based on the aforementioned analysis of heterogeneous pollutant invasion characteristics, the multi-source invasion dataset is integrated to form the dataset.

[0016] This scheme utilizes spatially distributed environmental invasion parameters and standardized time data sequences generated by a distributed node time alignment mechanism to comprehensively perceive and quantify the dynamic heterogeneous invasion situation of the microenvironment in which lithium batteries are located. Through a two-stage decoupling process of a multimodal feature separation engine (first separating the terrain undulation distribution information and the heterogeneous invasion time sequence distribution information, and then combining the original feature quantities to analyze the pollutant characteristics), it deeply explores the spatial structural constraints and temporal evolution patterns behind invasion events, and accurately traces the core pollutant types and their specific invasion behaviors. Finally, by integrating the invasion features of several types of heterogeneous pollutants, a multi-source invasion dataset that can comprehensively characterize the complex environmental invasion scenarios in mines is constructed.

[0017] Optionally, based on the terrain undulation distribution information and the temporal distribution information of heterogeneous invasion, combined with the real-time heterogeneous invasion feature quantities, a multimodal feature separation engine is used to determine the pollutant characteristics, including:

[0018] Based on the terrain undulation distribution information, the temporal distribution information of the heterogeneous invasion, and the real-time heterogeneous invasion feature quantity, a multimodal feature tensor is constructed.

[0019] Based on the multimodal feature separation engine, the multimodal feature tensor is analyzed to obtain static feature components and dynamic evolution feature components;

[0020] The pollutant characteristics are constructed based on the static feature components and the dynamic evolution feature components.

[0021] This approach utilizes a technical chain of "multimodal feature tensor construction → dynamic and static feature separation → characteristic fusion" 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 attributes of pollutants, avoiding over-defense against temporary low-risk pollutants; dynamic evolution feature components predict diffusion paths, achieving preventive protection; and the pollutant characteristics generated by the fusion of the two reduce the prediction error rate of subsequent pollutant-battery material interaction models.

[0022] Optionally, the multimodal feature separation engine includes:

[0023] Based on the multimodal feature tensor, the inherent properties of pollutants are separated and analyzed using a tensor feature decoupling algorithm to obtain static feature components.

[0024] Based on the multimodal feature tensor and combined with spatiotemporal evolution modeling techniques, pollutant diffusion and migration behavior is separated, and the pollutant diffusion and migration behavior is analyzed to obtain dynamic evolution feature components.

[0025] This solution constructs a dual-modal risk assessment system for lithium batteries, encompassing both static and dynamic aspects. At the risk identification level, it decouples the static and dynamic components of multi-source attack data; the static feature analysis module quantifies chronic contaminant damage and predicts electrode corrosion rates; and the dynamic feature monitoring module provides early warnings of sudden risks, forming a dual-protection mechanism. At the defense strategy level, it implements tiered resource allocation based on the dual-modal architecture—deploying low-cost, long-term protection (such as slow-release coatings) for static corrosion risks and employing millisecond-level response mechanisms (such as directional airflow isolation) for dynamic migration risks. This system accurately distinguishes between acidic gas corrosion and dynamic risks from suspended particles, reducing false alarm rates. At the optimization level, it shortens system latency through the synergy of single-time static feature calculations and lightweight dynamic feature updates. This system effectively solves the problem of misjudgment caused by multi-source data coupling, providing a precise and economical solution for the full lifecycle safety protection of lithium batteries.

[0026] Optionally, based on the multi-source intrusion dataset, multi-level security risk coupling modeling is performed to output a comprehensive risk level, including:

[0027] 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.

[0028] Based on several risk indices, a multi-level early warning indicator is derived through an integrated multi-scale risk assessment model;

[0029] The overall risk level is determined based on multi-level early warning indicators.

[0030] This solution significantly enhances the safety and protection performance of energy storage systems by constructing a multi-dimensional technical system. It achieves digital reconstruction of the pollutant invasion process based on a dynamic interactive model, reducing the risk index error rate and effectively capturing hidden, progressive damage characteristics. Furthermore, it enables millimeter-level spatial positioning of high-risk battery areas through multi-level early warning indicators, driving the precise activation of active interception mechanisms and reducing ineffective defense energy consumption. The simultaneously established spatiotemporal 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 defense strategies. By overcoming existing limitations through coupled modeling technology, it constructs a digital twin system compatible with multiple types of pollutants (gas, particulate, liquid) and complex battery material systems, fully adapting to the environmental requirements of lithium battery scenarios and providing an intelligent solution for lithium battery energy storage safety protection.

[0031] Optionally, based on the pollutant characteristics and battery material properties in the multi-source attack 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:

[0032] Based on the characteristics of the pollutants, the static feature components and the dynamic evolution feature components in the pollutants are extracted by intelligent analysis of the pollutant characteristics through multi-source data fusion.

[0033] Based on the battery material properties, the electrode surface energy level distribution, electrolyte chemical stability threshold, and membrane pore structure parameters in the battery material are analyzed.

[0034] 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 membrane pore structure parameters, a multi-parameter coupled analysis is performed to establish a pollutant-battery material interaction model.

[0035] 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 coupling effect of battery materials, and several risk indices are quantified and generated.

[0036] This approach utilizes static and dynamic evolutionary components of pollutants, electrode surface energy level distribution, electrolyte chemical stability threshold, and membrane pore structure parameters to comprehensively analyze multi-dimensional information on pollutant characteristics and battery material properties. This allows for a deeper characterization of the complex mechanisms of pollutant-battery material interaction, enhancing the comprehensiveness and precision of the analysis of pollutant invasion risk characteristics within the battery. Furthermore, by employing a multi-source data fusion framework to perform logical space unification and coupling analysis on the aforementioned parameters, the nonlinear correlations between static properties and dynamic evolution, and between inherent material properties and real-time environmental conditions, are effectively revealed. This improves the accuracy of invasion risk prediction and assessment under complex multi-factor coupling conditions.

[0037] Optionally, 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:

[0038] Based on several risk indices, spatial positioning features and temporal duration features are extracted by performing spatiotemporal dimension separation and analysis on several risk indices.

[0039] Based on the time duration characteristics, the duration of heterogeneous invasion corresponding to several risk indices is determined, and the cumulative time of lithium battery invasion is recorded.

[0040] Based on the spatial positioning features and combined with the environmental intrusion parameters, the environmental intrusion parameters are mapped to the battery area corresponding to the spatial positioning features to obtain the lithium battery intrusion location features.

[0041] Based on the cumulative time of lithium battery intrusion and the location characteristics of lithium battery intrusion, a cross-spatiotemporal scale fusion analysis mechanism is used to perform spatiotemporal coupling correction on each risk index to determine several risk classification reference coefficients.

[0042] Based on several risk grading reference coefficients, preset early warning level information is matched to determine the early warning level identifier corresponding to each risk grading reference coefficient, so as to construct the multi-level early warning identifier.

[0043] This solution effectively addresses the challenge of quantifying dynamic safety risks in lithium batteries by utilizing a spatiotemporal coupling mechanism, providing a decision-making basis for defense strategy generation. The risk assessment system built upon a spatiotemporal fusion model improves early warning accuracy and effectively resolves the false alarm dilemma of existing solutions through multi-dimensional data coupling. A location-feature-driven dynamic resource allocation mechanism reduces resource consumption for defense measures, forming a closed-loop optimization of risk assessment and defense response. Furthermore, an algorithm linking cumulative duration and location sensitivity coefficient is introduced to provide early warnings of hidden risks such as slow erosion, transforming passive response into proactive defense. At the execution level, the deep integration of multi-level early warning indicators and differentiated defense mechanisms constitutes a cross-dimensional collaborative defense system: red warnings trigger hard protection such as physical melting, while yellow warnings activate soft maintenance such as electrode coating repair, generating targeted risk mitigation strategies.

[0044] Optionally, based on the comprehensive risk level, an adaptive defense mechanism is activated to generate a cross-dimensional defense strategy, including:

[0045] Based on the multi-source invasion dataset, the characteristics of the pollutants and the properties of the battery materials are extracted;

[0046] Based on the characteristics of the pollutants and the properties of the battery materials, an interaction analysis is performed to identify several types of risks caused by the pollutants.

[0047] Based on the aforementioned risk types, analyze and identify the current dominant risk type;

[0048] Obtain a historical pollutant impact dataset, and construct a risk type decision matrix based on the historical pollutant impact dataset;

[0049] Based on several dominant risk types and combined with several risk classification reference parameters, the risk type decision matrix is ​​matched to adaptively activate several defense mechanisms corresponding to different defense mechanism types.

[0050] The defense mechanisms include maintaining basic protection, triggering active interception, and executing physical circuit breakers.

[0051] Based on several of the aforementioned defense mechanisms, a targeted cross-dimensional defense strategy is generated.

[0052] This solution constructs a multi-dimensional collaborative security defense system, achieving dual optimization of defense effectiveness and resource management. Based on the dominant risk type identification and dynamic decision matrix matching mechanism, it effectively avoids false triggering of defense mechanisms, accurately focusing limited resources on high-priority threats and improving the response speed to critical threats. The layered defense architecture reduces system energy consumption in low-risk scenarios and extends the lifespan of the battery management system through differentiated resource allocation strategies. Combined with the targeted activation mechanism, it further reduces the ineffective consumption of consumables such as neutralizers. In terms of environmental adaptability, a cross-dimensional strategy engine is adopted to achieve dynamic coordination of spatial protection, temporal response, and defense mechanisms to cope with the collaborative invasion of complex pollution scenarios in underground mining environments. Relying on the historical data backtracking mechanism of the risk decision matrix, a closed-loop iteration of "identification-response-optimization" is formed, continuously strengthening the long-term robustness of the system in extreme environments and providing a verifiable evolutionary path for security decisions.

[0053] Optionally, based on the aforementioned cross-dimensional defense strategy, the battery status after defense is evaluated, and a security defense effectiveness report is generated and output, including:

[0054] Based on the cross-dimensional defense strategy, after executing several defense mechanisms corresponding to different defense mechanism types in the cross-dimensional defense strategy, the battery state after lithium battery defense is obtained according to the multi-physics field collaborative sensing system.

[0055] Based on the battery state after the lithium battery defense, combined with the battery state before the defense, the operating states of the lithium battery before and after the defense are compared to obtain the differences in states before and after the defense.

[0056] Based on the differences in the states before and after the defense, the battery material properties before and after the implementation of the defense measures are dynamically compared in multiple dimensions, and a structured safety defense performance report containing multi-dimensional performance indicators and correction suggestions is output.

[0057] This solution constructs a data-driven defense performance evaluation system, achieving closed-loop management across the entire chain from strategy verification to operation and maintenance optimization. Based on multi-physics dynamic data comparison technology, a visualized verification mechanism for defense performance is established, accurately evaluating the safety protection effect through quantitative indicators. The structured performance report generated by the system not only includes real-time performance parameters but also drives adaptive defense mechanisms to iterate strategies through analytical models such as residual risk area identification. For example, it 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 life cycle. By continuously accumulating performance data, it constructs a battery health evolution map, providing a scientific basis for predictive maintenance and extending the service life of the energy storage system. The standardized report enables precise fault unit location, reducing the frequency of manual inspections. While avoiding over-maintenance, it also reduces operation and maintenance decision-making costs, ultimately forming a synergistic efficiency system of safety protection, targeted measures, and cost control.

[0058] Secondly, this application provides a lithium battery energy storage safety management system, the system comprising:

[0059] The data processing module is used to 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 dataset.

[0060] The risk modeling module is used to perform multi-level security risk coupling modeling based on the multi-source intrusion dataset and output a comprehensive risk level.

[0061] The strategy optimization module is used to adaptively activate the defense mechanism and generate a cross-dimensional defense strategy based on the comprehensive risk level.

[0062] The performance verification module is used to evaluate the battery status after defense based on the cross-dimensional defense strategy, and generate and output a security defense performance report. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a schematic diagram of an application scenario provided in an embodiment of this application;

[0065] Figure 2 A flowchart illustrating a lithium battery energy storage safety management method provided in an embodiment of this application;

[0066] Figure 3 This is a schematic diagram of the structure of a lithium battery energy storage safety management system provided in an embodiment of this application. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0068] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0069] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0070] When lithium batteries are used in inspection robots, existing lithium battery energy storage safety management systems mainly focus on monitoring and balancing the basic parameters of the battery cells themselves. Their protection strategies are mostly static presets or single-dimensional responses. When facing unique, complex, and heterogeneous environments, existing systems have significant shortcomings and cannot guarantee the continuous, safe, and efficient operation of inspection robots.

[0071] Based on this, this application provides a lithium battery energy storage safety management system and method. It utilizes a multimodal feature separation engine to analyze the spatiotemporal characteristics of heterogeneous intrusion, solving the problem of misjudging the collaborative intrusion of composite pollutants (such as salt spray + metal dust) in existing methods, improving feature recognition accuracy. By associating comprehensive risk levels and dominant risk types, an adaptive mechanism drives the defense strategy to continuously and dynamically iterate and upgrade based on actual performance data, significantly enhancing the system's intelligent response and adaptive defense capabilities when facing unknown or mutated pollutant intrusions. Furthermore, by accurately matching defense strength and risk levels, it achieves efficient optimization of defense resources. 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. The cross-dimensional defense strategy simultaneously suppresses material-level erosion and system-level failures, reducing the probability of thermal runaway, and providing technical support for the high-safety-requirement energy storage scenarios of lithium batteries in mining inspection robots.

[0072] Figure 1 This is a schematic diagram illustrating an application scenario provided by this application. During the automated safety hazard inspection of underground mines by inspection robots, the method provided in this application improves the long-term reliability of lithium batteries used in mining inspection robots. A cross-dimensional defense strategy simultaneously suppresses material-level erosion 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.

[0073] Specifically, the method of this application is applied to any server that communicates with a sensor network and a battery performance database. The server obtains real-time heterogeneous invasion characteristics provided by the sensor network and battery material properties from the battery performance database. By constructing a multi-physics collaborative sensing system, spatially distributed environmental parameters are collected synchronously. A distributed node time alignment mechanism is used to eliminate clock deviations and generate standardized time series data. By combining terrain undulation information with the time series, static pollution sources and dynamic diffusion behaviors are separated, thereby obtaining quantified heterogeneous invasion characteristics. The real-time heterogeneous invasion characteristics are combined with the characteristic information of the battery material action mode to generate and output a safety defense effectiveness report to safety maintenance personnel.

[0074] For specific implementation details, please refer to the following examples.

[0075] Figure 2 This is a flowchart illustrating a lithium battery energy storage safety management method according to an embodiment of this application. The method of this embodiment can be applied to servers in the above-described scenarios. Figure 2 As shown, the method includes:

[0076] S201. Obtain real-time heterogeneous invasion characteristics of the environment where 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 dataset.

[0077] Real-time heterogeneous invasion characteristics can refer to the quantitative parameters of various types of invasion factors in the external environment of a lithium battery, which are collected in real time through a sensor network and originate from the sensor network.

[0078] The characteristics of heterogeneous contaminant invasion can be characteristic information that characterizes the interaction modes of contaminants with different physical / chemical properties on battery materials.

[0079] Multi-source invasion datasets can be structured collections that integrate environmental parameters, pollutant characteristics, and spatiotemporal distribution information.

[0080] Specifically, due to the complex terrain of underground mines and the presence of various uncertain safety hazards in their wide areas, existing underground mine operations typically employ mining inspection robots with remote communication capabilities to conduct automated safety hazard inspections of the terrain. This is to proactively identify potential safety hazards that are difficult to detect within the mine, ensuring the normal and safe production of personnel underground. In the aforementioned scenarios, lithium batteries, as the core power source of mining inspection robots, face multiple intertwined challenges. For example, physical and mechanical aggression can cause structural damage and internal short circuit risks to batteries due to severe vibrations, collisions, and drops; dust and moisture can form conductive bridges through adsorption and corrode components, threatening safety; extreme temperature differences can exacerbate battery aging and performance fluctuations; and chemical corrosive gases and oil stains can erode protective layers. Existing technologies only monitor single parameters and cannot distinguish the types of pollutants and their dynamic migration behavior. For example, conductive dust may cause battery short circuits, while corrosive gases may slowly degrade electrode materials. If the intrusion characteristics are not accurately identified, the defense strategy will lack specificity, leading to excessive defense and increased energy consumption, while insufficient defense may cause thermal runaway. This step constructs a multi-physics field collaborative sensing system to synchronously collect spatially distributed environmental parameters and uses a distributed node time alignment mechanism to eliminate clock deviations and generate standardized time series data. By combining terrain undulation information with the time series, static pollution sources and dynamic diffusion behavior are separated, thereby accurately quantifying heterogeneous intrusion characteristics. This design is the basis for subsequent risk modeling and avoids the defense blind spots caused by neglecting the spatiotemporal coupling effect in existing methods.

[0081] S202. Based on the multi-source intrusion dataset, perform multi-level security risk coupling modeling and output the comprehensive risk level;

[0082] Multi-level safety risk coupling modeling can be a modeling process that quantifies material-level risks through interaction models and then upgrades them to system-level risks by combining spatiotemporal scale fusion mechanisms to obtain a mathematical model. The aforementioned mathematical model is constructed based on simulation libraries and historical fault databases.

[0083] The overall risk level can be a hierarchical label representing the overall safety status of the battery, generated by aggregating multiple warning indicators.

[0084] Specifically, lithium battery safety risks exhibit multi-scale characteristics: microscopically, contaminants corrode electrode materials, and macroscopically, they cause system failures. Existing risk assessment models only focus on a single scale and do not couple the spatiotemporal evolution of safety risks. For example, localized high concentrations of contaminants may be ignored in the short term, but long-term accumulation will lead to membrane perforation. This step designs a contaminant-battery material interaction model, combining contaminant characteristics with battery material properties to simulate the impact of contaminant penetration on materials. Furthermore, through spatiotemporal separation and analysis, the risk index is mapped to specific battery regions and associated with the duration of invasion. Finally, a cross-spatiotemporal scale fusion mechanism is used to couple and analyze the multi-scale characteristics, correcting the risk level. The aforementioned separation and coupling mechanism solves the problem of misjudging localized cumulative risks in existing methods, providing a basis for precise defense.

[0085] S203. Based on the comprehensive risk level, adaptively activate the defense mechanism to generate a cross-dimensional defense strategy;

[0086] An adaptive activation defense mechanism can refer to a decision-making process that dynamically selects and triggers the optimal combination of defense measures based on the real-time risk level and the dominant risk type.

[0087] The dominant risk type can be the main induced risk type that affects the current comprehensive risk level assessment.

[0088] Cross-dimensional defense strategies can be multi-dimensional collaborative defense schemes that integrate physical isolation (spatial dimension), chemical neutralization (material dimension), and electrical melting (energy dimension).

[0089] Specifically, existing defense methods employ fixed response patterns, which are inadequate to address the complexity of heterogeneous attacks. If the defense mechanism does not match the overall risk level, existing hazards cannot be effectively addressed. This step constructs a risk type decision matrix, linking the overall risk level and the dominant risk type to match the optimal combination of defense mechanisms. For example, when the overall risk level is "medium risk" and the dominant type is "chemical corrosion," corrosion inhibitor release is triggered; if the risk is "physical short circuit," localized airtight isolation is initiated. This adaptive mechanism significantly improves defense efficiency and avoids resource waste.

[0090] S204. Based on the cross-dimensional defense strategy, assess the battery status after defense, and generate and output a security defense effectiveness report.

[0091] The security defense effectiveness report can be a structured report comparing key parameters before and after the defense, generated from multi-physics sensing data and battery state models.

[0092] Specifically, the lack of quantitative assessment of defense effectiveness in existing technologies leads to a lag in strategy iteration, making adjustments to defense strategies reliant on human experience and post-fault analysis, which fails to promptly stop the accumulation of gradual risks. This step, through multi-dimensional dynamic comparison of battery status before and after defense, combined with changes in material properties, outputs actionable correction suggestions to safety maintenance personnel, driving the defense strategy to dynamically iterate and upgrade based on actual performance data. This significantly improves the system's adaptive defense capability against new or mutated contaminants, optimizes resource allocation, and ultimately achieves continuous reduction of safety risks throughout the lithium battery's life cycle and effective control of operating costs.

[0093] This solution utilizes a multimodal feature separation engine to analyze the spatiotemporal characteristics of heterogeneous intrusion, addressing the misjudgment problem of collaborative intrusion by composite pollutants (such as salt spray and metal dust) in existing methods, thus improving feature recognition accuracy. By associating comprehensive risk levels and dominant risk types, an adaptive mechanism that matches the optimal combination of defense mechanisms drives the defense strategy to continuously and dynamically iterate and upgrade based on actual performance data. This significantly enhances the system's intelligent response and adaptive defense capabilities when facing unknown or mutated pollutant intrusions. Furthermore, by accurately matching defense intensity and risk levels, it achieves efficient optimization of defense resources. 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. The cross-dimensional defense strategy simultaneously suppresses material-level erosion and system-level failures, reducing the probability of thermal runaway and providing technical support for the high-safety-requirement energy storage scenarios of lithium batteries in mining inspection robots.

[0094] In some embodiments, a multi-physics collaborative sensing system is established, an invasion factor sensing network is constructed, and spatially distributed environmental invasion parameters are collected synchronously; the time interval of the lithium battery being invaded by heterogeneous substances is obtained, and clock monitoring deviations between intervals are eliminated through a distributed node time alignment mechanism to generate a standardized time data sequence; based on the spatially distributed environmental invasion parameters and the standardized time data sequence, 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 characteristic quantities, the terrain undulation distribution information and the heterogeneous invasion time sequence distribution information are analyzed through a multi-modal feature separation engine; based on the terrain undulation distribution information and the heterogeneous invasion time sequence distribution information, combined with the real-time heterogeneous invasion characteristic quantities, the pollutant characteristics are determined using a multi-modal feature separation engine; the pollutant characteristics are analyzed to determine several types of heterogeneous pollutant invasion characteristics; based on the analysis of several heterogeneous pollutant invasion characteristics, the multi-source invasion dataset is integrated to form the dataset.

[0095] A multi-physics collaborative sensing system can be a distributed monitoring architecture that integrates multi-dimensional sensing nodes to synchronously capture the spatial distribution characteristics of environmental intrusion parameters.

[0096] An invasion factor sensing network can be a topological network composed of sensing nodes deployed at key locations in the battery pack, used to sense the type, intensity, and diffusion path of environmental invasion factors in real time.

[0097] Environmental invasion parameters can be physical quantities that quantify the intensity of environmental pollutant invasion.

[0098] Distributed node time alignment mechanisms can be algorithms that eliminate acquisition time discrepancies between multiple nodes through clock synchronization protocols, ensuring the consistency of time-series data.

[0099] A standardized time data series can be a time-series dataset formed by reordering asynchronously collected multi-source environmental parameters according to a unified time base.

[0100] Topographic relief distribution information can be characteristic data describing the surface of the battery pack and the surrounding spatial geometry.

[0101] The temporal distribution information of heterogeneous invasion can be a dynamic curve that records the change of the invasion intensity of different pollutants over time.

[0102] A 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.

[0103] Pollutant characteristics can be a set of features that include the inherent properties of pollutants.

[0104] Specifically, in the field of lithium battery safety monitoring, to address the need for precise prevention and control of multi-source heterogeneous invasion in complex underground mining scenarios, it is necessary to construct a technical system encompassing multi-physics field collaborative sensing, spatiotemporal information fusion, and intelligent feature decoupling. Existing monitoring methods suffer from risk misjudgment due to the limitations of single sensors, specifically manifested in the inability to accurately identify coupling effects, such as the linkage between increased humidity and accelerated electrochemical corrosion. By deploying temperature, humidity, gas concentration, and particulate matter sensors around the lithium battery pack to form an invasion factor sensing network, each node collects spatially distributed environmental invasion parameters at a preset frequency and uploads them to a central server via a wireless network. Subsequently... Based on the time interval of lithium battery heterogeneous attack, a distributed node time alignment mechanism is invoked to generate a standardized time data sequence that eliminates clock skew. This standardized data is then overlaid with a 3D battery model to analyze the spatial distribution of parameters and temporal series fluctuation patterns, outputting real-time heterogeneous attack features. These features are input into a multimodal feature separation engine, which extracts static and dynamic features, fusing terrain undulation distribution information with heterogeneous attack temporal distribution information. Ultimately, pollutant characteristics are determined, and heterogeneous pollutant attack features are classified and labeled. Static attributes, dynamic behaviors, and spatial distribution features are integrated to form a structured multi-source attack dataset. This effectively integrates the processing chain of spatiotemporal alignment → multiphysics field fusion → static and dynamic feature separation, solving the problem of fragmented environmental monitoring data. Compared to existing solutions, it improves the accuracy of pollutant characteristic identification and provides a structured, multi-dimensional, and highly consistent multi-source attack dataset for subsequent multi-level safety risk coupling modeling.

[0105] This scheme utilizes spatially distributed environmental invasion parameters and standardized time data sequences generated by a distributed node time alignment mechanism to comprehensively perceive and quantify the dynamic heterogeneous invasion situation of the microenvironment in which lithium batteries are located. Through a two-stage decoupling process of a multimodal feature separation engine (first separating the terrain undulation distribution information and the heterogeneous invasion time sequence distribution information, and then combining the original feature quantities to analyze the pollutant characteristics), it deeply explores the spatial structural constraints and temporal evolution patterns behind invasion events, and accurately traces the core pollutant types and their specific invasion behaviors. Finally, by integrating the invasion features of several types of heterogeneous pollutants, a multi-source invasion dataset that can comprehensively characterize the complex environmental invasion scenarios in mines is constructed.

[0106] In some embodiments, a multimodal feature tensor is constructed based on topographic relief distribution information, time-series distribution information of heterogeneous invasion, and real-time heterogeneous invasion feature quantities; a multimodal feature separation engine is used to analyze the multimodal feature tensor to obtain static feature components and dynamic evolution feature components; and pollutant characteristics are constructed based on the static feature components and dynamic evolution feature components.

[0107] Multimodal feature tensors can be three-dimensional data structures that integrate terrain, temporal, and environmental parameters, with dimensions including spatial location, time series, and physical field features.

[0108] Static characteristic components can be a set of features that characterize the inherent and unchanging properties of pollutants.

[0109] Dynamic evolution characteristic components can be time-varying features that describe the migration and diffusion behavior of pollutants.

[0110] Specifically, existing lithium battery environmental monitoring on mining inspection robots relies on a single sensor, resulting in data fragmentation and an inability to depict terrain-driven spatial migration patterns and temporal correlations. This paper addresses these shortcomings by integrating terrain undulation distribution information with the temporal distribution information of heterogeneous intrusion, constructing a spatiotemporal dual-dimensional intrusion profile to avoid misjudgment risks caused by missing dimensions. Facing the bottleneck in identifying heterogeneous pollutant characteristics, existing methods, relying on real-time concentration data, struggle to distinguish between instantaneous fluctuations and essential characteristics. A multimodal feature separation engine is introduced to decouple static feature components from dynamic evolution feature components, achieving penetrating quantification of the chronic corrosion risk of pollutants. To meet the precision requirements of defense strategies, a third-order multimodal feature tensor is constructed based on terrain × historical × real-time features, establishing an implicit mapping between characteristics and data. For example, steep slopes + high-frequency droplet temporal label "high-flow liquid," and flat areas + aerosol temporal label "suspended." "Particle deposition" provides a targeted basis for defense mechanisms (such as chemical neutralization) to avoid mismatch between risks and strategies. Addressing the challenge of strong coupling of multiple pollutants in complex underground mining scenarios involving lithium batteries on inspection robots, it predicts terrain aggregation and time accumulation effects through spatiotemporal collaborative analysis, constructing dynamic risk prediction capabilities (such as predicting electrolyte leakage paths). It converts terrain information into an elevation gradient matrix, encodes temporal information into a time-event matrix, and organizes real-time features into feature vectors. Tensor expansion and alignment generate a three-dimensional tensor of space × time × features. 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 fused to output a structured set of characteristic parameters: chemical properties (redox activity / pH sensitivity), physical properties (sedimentation rate / adhesion coefficient), and behavioral properties (electrode coverage growth curve / diaphragm permeation threshold).

[0111] This approach utilizes a technical chain of "multimodal feature tensor construction → dynamic and static feature separation → characteristic fusion" 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 attributes of pollutants, avoiding over-defense against temporary low-risk pollutants; dynamic evolution feature components predict diffusion paths, achieving preventive protection; and the pollutant characteristics generated by the fusion of the two reduce the prediction error rate of subsequent pollutant-battery material interaction models.

[0112] In some embodiments, based on the multimodal feature tensor, the inherent properties of pollutants are separated and analyzed using a tensor feature decoupling algorithm to obtain static feature components; based on the multimodal feature tensor, combined with spatiotemporal evolution modeling techniques, the diffusion and migration behavior of pollutants is separated and analyzed to obtain dynamic evolution feature components.

[0113] Tensor feature decoupling algorithms can be mathematical methods that use high-order tensor decomposition to remove the inherent properties of pollutants.

[0114] The inherent properties of pollutants can be their inherent physicochemical characteristics (such as molecular structure, corrosivity, and thermal stability).

[0115] Spatiotemporal evolution modeling techniques can be algorithms that combine time series prediction and spatial diffusion simulation to quantify the migration patterns of pollutants.

[0116] Pollutant diffusion and migration behavior can be described as the dynamic propagation path and concentration changes of pollutants driven by environmental factors (wind speed, temperature, and humidity).

[0117] The dynamic evolution characteristic components can be time-series characteristic matrices that reflect the diffusion rate, direction, and concentration fluctuations of pollutants.

[0118] Specifically, lithium batteries face the challenge of multiple heterogeneous contaminant attacks in complex environments, with fundamentally different risk mechanisms. For example, static properties dominate long-term hazards; the molecular stability of corrosive gases (such as SO2) directly determines the oxidation rate of electrode materials, requiring quantification of cumulative damage through inherent properties. Dynamic behavior, on the other hand, dominates short-term risks; for instance, the localized accumulation of conductive dust driven by airflow can trigger instantaneous short circuits, necessitating diffusion models to predict sudden risks. However, existing solutions have significant drawbacks. Feature confusion leads to mutual interference between static properties and dynamic behavior in a single model. For example, transient features of dust diffusion are misjudged as inherent corrosivity, resulting in over-defense. Response lag forces the system to wait for the complete diffusion process to end before assessing risk due to unseparated features, missing critical meltdown opportunities caused by dust accumulation. Resource waste is also prominent; high-frequency monitoring of static properties is meaningless, while dynamic behavior requires real-time tracking. Addressing the characteristics of heterogeneous contaminants is crucial. The problem of misjudgment caused by coupling needs to be addressed by decoupling static attributes and dynamic behaviors at the mechanism level to achieve more accurate risk assessment and more efficient defense strategies. Precise tiered prevention and control of lithium battery environmental risks can be achieved through multimodal feature decoupling. Its technical path and advantages can be summarized as follows: First, at the data input layer, a three-dimensional feature tensor of spatial location × timestamp × environmental parameters is received. This data originates from the fusion of standardized time series and spatial distribution parameters. In the feature extraction stage, a tensor feature decoupling algorithm is used to decompose the original data into static and dynamic components. The static component extracts the inherent attribute matrix of pollutants (such as corrosivity index, hygroscopicity, etc.) to determine long-term material-level threats (such as electrolyte decomposition), while the dynamic component uses a ConvLSTM network to model the spatiotemporal migration patterns of pollutants, generating a location probability heatmap and concentration gradient change rate to capture instantaneous environmental risks (such as local electric arcs). Finally, the two components are output in parallel to the pollutant characteristic database to support the risk modeling module.

[0119] This solution constructs a dual-modal risk assessment system for lithium batteries, encompassing both static and dynamic aspects. At the risk identification level, it decouples the static and dynamic components of multi-source attack data; the static feature analysis module quantifies chronic contaminant damage and predicts electrode corrosion rates; and the dynamic feature monitoring module provides early warnings of sudden risks, forming a dual-protection mechanism. At the defense strategy level, it implements tiered resource allocation based on the dual-modal architecture—deploying low-cost, long-term protection (such as slow-release coatings) for static corrosion risks and employing millisecond-level response mechanisms (such as directional airflow isolation) for dynamic migration risks. This system accurately distinguishes between acidic gas corrosion and dynamic risks from suspended particles, reducing false alarm rates. At the optimization level, it shortens system latency through the synergy of single-time static feature calculations and lightweight dynamic feature updates. This system effectively solves the problem of misjudgment caused by multi-source data coupling, providing a precise and economical solution for the full lifecycle safety protection of lithium batteries.

[0120] In some embodiments, 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 the risk index; based on several risk indices, a multi-level early warning indicator is obtained through an integrated multi-scale risk assessment model; and based on the multi-level early warning indicator, the comprehensive risk level is determined.

[0121] Pollutant-battery material interaction models can be mathematical models that characterize the physical / chemical interactions between heterogeneous pollutants and lithium battery materials (electrodes, electrolytes, separators). By quantifying the impact of pollutant penetration, corrosion, deposition, and other behaviors on battery materials, battery safety risks can be predicted.

[0122] The risk index can be a numerical indicator that quantifies the degree of damage caused by pollutants to different material components of lithium batteries.

[0123] An integrated multi-scale risk assessment model can be a dynamic risk assessment architecture at the material, component, and system levels.

[0124] Multi-level early warning indicators can be markers that couple risk levels across different spatial locations and time periods.

[0125] The overall risk level can be the result of a global risk assessment after integrating multiple levels of early warning indicators.

[0126] Specifically, existing lithium battery safety management methods exhibit systemic flaws when dealing with heterogeneous contaminant intrusion, creating a closed-loop loophole in risk prevention and control. Based on contaminant characteristics (including static and dynamic evolution components) and battery material properties (covering electrode surface energy level distribution, electrolyte chemical stability threshold, and separator pore structure parameters) from multi-source intrusion datasets, and using kinetic simulation algorithms (such as kinetic Monte Carlo), a contaminant-battery material interaction model is constructed by integrating physical / chemical, battery material, and contaminant inherent properties. This model dynamically simulates the contaminant intrusion process within the lithium battery (such as adsorption, reaction, migration, and secondary damage effects) and quantifies key risk indices (e.g., indices reflecting localized corrosion rates, indices identifying electrolyte instability critical points, and indices assessing separator penetration risk). Based on these risk indices, through integration... A multi-scale risk assessment model (which incorporates microscopic material-level risks, single-cell battery-level risks, and system-level risks into a unified analytical framework) is used. Cross-scale correlation algorithms (such as mapping electrode corrosion rate to single-cell capacity decay rate, or associating membrane blockage risk to system thermal runaway probability) are employed for comprehensive deduction. This results in multi-level early warning indicators with clear physical meaning and actionable direction (such as "material degradation early warning", "single-cell failure early warning", and "system safety early warning"). Finally, based on the superposition effect and urgency of the above multi-level early warning indicators, a comprehensive risk level (such as "low risk", "medium risk", "high risk", and "emergency risk") is determined through weighted adaptive fusion rules (such as assigning higher weights to high-frequency, cross-scale early warning signals). This provides a quantitative decision-making basis for the precise implementation of risk intervention strategies.

[0127] This solution significantly enhances the safety and protection performance of energy storage systems by constructing a multi-dimensional technical system. It achieves digital reconstruction of the pollutant invasion process based on a dynamic interactive model, reducing the risk index error rate and effectively capturing hidden, progressive damage characteristics. Furthermore, it enables millimeter-level spatial positioning of high-risk battery areas through multi-level early warning indicators, driving the precise activation of active interception mechanisms and reducing ineffective defense energy consumption. The simultaneously established spatiotemporal 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 defense strategies. By overcoming existing limitations through coupled modeling technology, it constructs a digital twin system compatible with multiple types of pollutants (gas, particulate, liquid) and complex battery material systems, fully adapting to the environmental requirements of lithium battery scenarios and providing an intelligent solution for lithium battery energy storage safety protection.

[0128] In some embodiments, based on pollutant characteristics, the characteristics of pollutants are intelligently analyzed through multi-source data fusion to extract static and dynamic evolutionary characteristic components from the pollutants; based on battery material properties, the electrode surface energy level distribution, electrolyte chemical stability threshold, and membrane pore structure parameters in the battery material are analyzed; based on the static and dynamic evolutionary characteristic components of the pollutants, combined with the electrode surface energy level distribution, electrolyte chemical stability threshold, and membrane pore structure parameters, 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 quantified and generated.

[0129] Battery material properties can be inherent performance parameters of key components of lithium batteries, derived from battery performance databases.

[0130] The energy level distribution on the electrode surface can be a quantitative parameter characterizing the electronic energy state on the surface of lithium battery electrode materials.

[0131] Electrolyte chemical stability thresholds can be considered as a set of critical parameters for an electrolyte to withstand the chemical attack of contaminants.

[0132] The pore structure parameters of the separator can be the outer protective separator of the lithium battery.

[0133] Historical pollutant impacts can be a quantitative record of how different types of pollutants cause battery performance degradation under specific environmental conditions.

[0134] The process of pollutant invasion can be a physicochemical chain reaction in which pollutants penetrate from the environment into the battery and trigger cascade failure.

[0135] Specifically, the static characteristic components of pollutants are the core basis for identifying potential threat sources and fundamental mechanisms of action. 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 "behavioral trajectory" of pollutants in complex electrochemical environments) are ignored, it is impossible to predict the more aggressive secondary products that may be generated after dissolution or decomposition, or their alteration of local ion transport characteristics. At the same time, the inherent properties of battery materials constitute a key line of defense against attack. The energy level distribution on the electrode surface determines the "target" activity of pollutant adsorption or reaction, and the electrolyte chemical stability threshold identifies the "danger" that induces failure. The membrane pore structure parameters act as a "window," 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 couple the static and dynamic characteristic components of pollutants, electrode energy level distribution, electrolyte stability thresholds, and membrane pore parameters into the same logical space for analysis. Within this framework, "feature mapping correlation" is used to perform topological matching between static functional groups of pollutants and highly active defect sites on electrodes to assess the initial adsorption risk. "Evolutionary path superposition" is applied to map the dynamic decomposition sequence of pollutants onto the electrolyte stability threshold spectrum to identify critical points that trigger chain side reactions. Boundary conditions; Implementing "transmission barrier simulation" combines the dynamic migration tendency of pollutants with the pore structure of the separator to predict their diffusion bottlenecks and enrichment regions inside the battery. This deep coupling analysis, which transcends the limitations of single parameters, ultimately constructs a pollutant-battery material interaction model that comprehensively reflects the pollutant's intrusive ability, the battery material's resistance, and the interaction pathways between the two. Based on this model, by integrating historical pollutant impact data with real-time monitoring of battery material states (such as electrode passivation layer thickness, electrolyte composition micro-changes, and the degree of local blockage in the separator), the invasion process of specific pollutants in a specific battery microenvironment can be dynamically simulated. For example, when simulating pollutant migration to the electrode... When the surface is exposed, the model calculates the reaction probability and products based on the real-time electrode state (energy level changes) and the current morphology of the contaminants (dynamic evolution results). When the contaminants approach the electrolyte stability threshold boundary, the model dynamically adjusts the threshold sensitivity according to the slight changes in the current electrolyte composition. Through this refined dynamic simulation process, key risk indices are finally quantified, including: the "local corrosion rate index" which reflects the real-time corrosion rate of a specific electrode site; the "electrolyte instability critical index" which predicts the possibility and time scale of contaminant-induced electrolyte decomposition; and the "diaphragm penetration risk index" which assesses the risk of contaminant accumulation in the diaphragm pores leading to blockage or short circuit.

[0136] This approach utilizes static and dynamic evolutionary components of pollutants, electrode surface energy level distribution, electrolyte chemical stability threshold, and membrane pore structure parameters to comprehensively analyze multi-dimensional information on pollutant characteristics and battery material properties. This allows for a deeper characterization of the complex mechanisms of pollutant-battery material interaction, enhancing the comprehensiveness and precision of the analysis of pollutant invasion risk characteristics within the battery. Furthermore, by employing a multi-source data fusion framework to perform logical space unification and coupling analysis on the aforementioned parameters, the nonlinear correlations between static properties and dynamic evolution, and between inherent material properties and real-time environmental conditions, are effectively revealed. This improves the accuracy of invasion risk prediction and assessment under complex multi-factor coupling conditions.

[0137] In some embodiments, based on several risk indices, spatial location features and temporal duration features are extracted by performing spatiotemporal dimension separation and analysis on the risk indices; based on the temporal duration features, the duration of heterogeneous invasion corresponding to the several risk indices is determined, and the cumulative time of lithium battery invasion is recorded; based on the spatial location features, combined with the environmental invasion parameters, the environmental invasion parameters are mapped to the battery area corresponding to the spatial location features to obtain the lithium battery invasion location features; based on the cumulative time of lithium battery invasion, combined with the lithium battery invasion location features, a spatiotemporal coupling correction is performed on each of the risk indices through a cross-spatiotemporal scale fusion analysis mechanism to determine several risk level reference coefficients; according to the several risk level reference coefficients, preset warning level information is matched to determine the warning level identifier corresponding to each of the risk level reference coefficients, so as to construct a multi-level warning identifier.

[0138] Spatiotemporal dimension separation analysis can be an analytical process that decomposes the risk index into independent spatial features (distribution of invasion locations) and temporal features (invasion persistence trend).

[0139] Spatial location characteristics can be a key parameter for describing the spatial distribution of contaminants inside or on the surface of lithium batteries.

[0140] The duration of time can be a characteristic of how pollutant invasion evolves over time.

[0141] The cumulative duration of heterogeneous invasion can be the cumulative time during which a specific contaminant continues to act on a specific area of ​​a lithium battery.

[0142] The location characteristics of lithium battery intrusion can identify the specific physical location of contaminants inside the battery.

[0143] The cross-spatiotemporal scale fusion analysis mechanism can be an algorithmic framework for dynamically associating and correcting spatiotemporally separated features.

[0144] The risk grading reference coefficient can be a comprehensive risk quantification value after being corrected by spatiotemporal coupling.

[0145] Specifically, existing methods for dynamic assessment of lithium battery safety risks suffer from spatiotemporal fragmentation. Current technologies only classify warning levels based on static risk values, neglecting the spatiotemporal characteristics of contaminant intrusion. Spatially, different areas of the battery exhibit significant differences in their sensitivity to contaminants; for example, the electrolyte is susceptible to sulfide corrosion while the separator is easily clogged by particulate matter. Without location feature analysis, high-risk areas will be missed during monitoring. Temporally, the severity of damage from short-term high-concentration intrusion differs drastically from that from long-term low-concentration intrusion. For instance, one hour of acid mist exposure only causes surface corrosion, while 24 hours of continuous exposure leads to electrolyte decomposition. Existing methods cannot quantify such cumulative effects. Contaminant migration exhibits spatiotemporal correlation; for example, after moisture erosion leads to structural loosening, chemical contaminants are more likely to penetrate the same area. Single-dimensional analysis will severely underestimate the combined risks. Based on this, a precise early warning mechanism based on spatiotemporal coupling effects is proposed: by inputting multiple generated risk indices (such as corrosion index and thermal runaway index), each index is analyzed... Spatiotemporal decoupling: In the spatial dimension, the influence coordinates inside the battery are determined based on environmental invasion parameters (such as pollutant concentration gradient) (e.g., "positive electrode active material layer - edge region"). In the temporal dimension, the duration of the index's continuous effect is extracted (e.g., "sulfide exposure lasts for 2.8 hours"). Subsequently, multiple temporal duration features under the same spatial location feature are superimposed to generate the cumulative heterogeneous invasion time (e.g., "cumulative invasion at the positive electrode edge lasts for 3.5 hours"). Simultaneously, combined with the battery's 3D model, the environmental invasion parameters are mapped to the corresponding locations, and the lithium battery invasion location features are labeled (e.g., "separator pore region - number A7"). Furthermore, a spatiotemporal weight matrix is ​​constructed for coupling correction: Given the differences in sensitivity in different regions, the long-term invasion index of high-risk locations is subject to differentiated weighting (e.g., for highly sensitive areas such as the electrolyte interface, the risk value increases by 30% for every 1 hour of cumulative time; for low-sensitive areas such as the outer casing, it only increases by 5% for the same duration). Finally, the corrected risk classification reference coefficient is output. Finally, based on preset threshold rules (such as a red alert if the coefficient is >0.8), multi-level warning labels associated with specific locations are automatically generated (e.g., "diaphragm pore area A7: red high risk").

[0146] This solution effectively addresses the challenge of quantifying dynamic safety risks in lithium batteries by utilizing a spatiotemporal coupling mechanism, providing a decision-making basis for defense strategy generation. The risk assessment system built upon a spatiotemporal fusion model improves early warning accuracy and effectively resolves the false alarm dilemma of existing solutions through multi-dimensional data coupling. A location-feature-driven dynamic resource allocation mechanism reduces resource consumption for defense measures, forming a closed-loop optimization of risk assessment and defense response. Furthermore, an algorithm linking cumulative duration and location sensitivity coefficient is introduced to provide early warnings of hidden risks such as slow erosion, transforming passive response into proactive defense. At the execution level, the deep integration of multi-level early warning indicators and differentiated defense mechanisms constitutes a cross-dimensional collaborative defense system: red warnings trigger hard protection such as physical melting, while yellow warnings activate soft maintenance such as electrode coating repair, generating targeted risk mitigation strategies.

[0147] In some embodiments, based on a multi-source invasion dataset, pollutant characteristics and battery material properties are extracted; based on the pollutant characteristics and 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 analyzed and identified; a historical pollutant impact dataset is obtained, and a risk type decision matrix is ​​constructed based on the historical pollutant impact dataset; based on several dominant risk types and combined with several risk classification reference parameters, the risk type decision matrix is ​​matched to adaptively activate several defense mechanisms corresponding to different defense mechanism types; the defense mechanism types include maintaining basic protection, triggering active interception, and executing physical circuit breaking; and a targeted cross-dimensional defense strategy is generated according to several defense mechanisms.

[0148] The risk type decision matrix can be a mapping table built based on historical pollutant impact datasets, linking risk types with defense mechanisms.

[0149] Defense mechanisms can be of the following types: maintaining basic protection: low-power monitoring mode (e.g., maintaining an ion barrier); triggering active interception: dynamic neutralization reaction (e.g., releasing a passivating agent); and performing physical fuse: emergency isolation circuit.

[0150] Specifically, the lithium batteries on inspection robots in underground mining environments exhibit significant variability in their susceptibility to heterogeneous contaminants. For instance, the same contaminant can induce drastically different reactions under varying environmental conditions, such as high temperature or high humidity. This variability directly leads to the difficulty in effectively covering complex risk scenarios, such as the coexistence of chemical corrosion and thermal runaway, if a fixed defense mechanism is adopted. Existing methods typically trigger defenses based solely on a single comprehensive risk level threshold, neglecting the priority differences between different risk types (for example, dendrite-induced short circuits may be more urgent than localized corrosion). Directly implementing physical melting or other methods based on this would be problematic. While high-level defenses can effectively block risks, they can easily cause unnecessary system downtime. To address this issue and optimize resource utilization, this solution targets and activates defense mechanisms based on the location characteristics (spatial dimension) and cumulative time of lithium battery intrusion (temporal dimension). For example, for specific electrode areas that have been continuously corroded and have accumulated high risk, the system will prioritize the implementation of powerful measures such as active interception; while for newly detected intrusion signals or new areas with low risk, only basic monitoring is required. The battery safety defense system, built upon a multi-source intrusion dataset, utilizes multi-dimensional feature extraction and dynamic... The system establishes a closed-loop protection mechanism based on risk assessment. First, it extracts key parameters from the characteristics of pollutants, such as chemical composition, concentration gradient, and intrusion rate, as well as the properties of battery materials, such as composition and interface stability. It then constructs a pollutant-material interaction model to simulate adsorption, diffusion, and chemical reaction trajectories, identifying potential risk types such as chemical corrosion, dendrite penetration, and ion channel blockage. By monitoring the impact of interaction intensity on performance parameters such as voltage and temperature in real time, the system determines the current dominant risk level. For example, the risk of rapid corrosion caused by high-concentration pollutants is prioritized. A risk decision matrix constructed using historical data maps risk types, pollutant characteristic thresholds, and material state parameters to validated defense response modes, forming an adaptive triggering mechanism. For instance, when risk parameters are below the warning value, it maintains basic protection by fine-tuning the charge-discharge strategy; when the critical threshold is exceeded, it initiates active interception such as neutralizer injection or isolation layer generation; and if signs of thermal runaway appear, it immediately executes circuit melting or physical isolation. Finally, through a cross-dimensional collaborative strategy, it integrates response actions such as electrolyte modification, interface repair, enhanced thermal management, and power limiting to construct a multi-level protection barrier from electrochemical regulation to physical isolation, achieving dynamic matching between risk warning and defense response.

[0151] This solution constructs a multi-dimensional collaborative security defense system, achieving dual optimization of defense effectiveness and resource management. Based on the dominant risk type identification and dynamic decision matrix matching mechanism, it effectively avoids false triggering of defense mechanisms, accurately focusing limited resources on high-priority threats and improving the response speed to critical threats. The layered defense architecture reduces system energy consumption in low-risk scenarios and extends the lifespan of the battery management system through differentiated resource allocation strategies. Combined with the targeted activation mechanism, it further reduces the ineffective consumption of consumables such as neutralizers. In terms of environmental adaptability, a cross-dimensional strategy engine is adopted to achieve dynamic coordination of spatial protection, temporal response, and defense mechanisms to cope with the collaborative invasion of complex pollution scenarios in underground mining environments. Relying on the historical data backtracking mechanism of the risk decision matrix, a closed-loop iteration of "identification-response-optimization" is formed, continuously strengthening the long-term robustness of the system in extreme environments and providing a verifiable evolutionary path for security decisions.

[0152] 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 state after the lithium battery defense is obtained according to the multi-physics field collaborative sensing system; based on the battery state after the lithium battery defense, combined with the battery state before the defense, the operating state of the lithium battery before and after the defense is compared to obtain the state difference before and after the defense; based on the state difference before and after the 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 performance report containing multi-dimensional performance indicators and correction suggestions is output.

[0153] The difference in state before and after defense can be the change in key material properties of lithium batteries before and after the implementation of the defense mechanism, including the shift in electrode surface energy levels and the rate of electrolyte stability degradation.

[0154] A structured security defense effectiveness report can be a standardized output document that includes quantitative indicators of defense efficiency, material damage maps, and recommendations for modifying defense strategies.

[0155] Specifically, lithium batteries face the threat of heterogeneous contaminants (such as corrosive gases and conductive dust) in complex underground mining environments. Existing safety management systems suffer from assessment lag and a lack of decision-making closure. Assessment lag manifests in the lack of real-time effectiveness verification after defensive measures are implemented, making it impossible to determine whether the defense has truly mitigated the risk. For example, chemical neutralizers may leave byproducts that cause secondary corrosion, but existing systems only record "defense implemented" without quantifying the actual effect. The lack of decision-making closure manifests in the disconnect between defense strategies and subsequent optimization. If localized overheating of the battery after defense is not detected, the system cannot automatically trigger supplementary measures, leading to risk accumulation. By constructing a dynamic feedback loop, implementing multi-dimensional collaborative analysis, and driving proactive operation and maintenance with standardized output, a closed-loop battery safety defense system is formed. Its core necessity lies in coping with complex and ever-changing operating environments, verifying defense effectiveness in real time through an "execution-assessment-correction" closed-loop mechanism. This includes comparing key parameters such as changes in electrolyte chemical stability threshold and membrane pore blockage rate, accurately identifying defense blind spots such as localized arc residue after physical melting, and based on… The residual risk heatmap dynamically adjusts the strategy intensity to avoid one-way defense failure caused by the migration of pollutant diffusion paths (such as the focusing and transfer of corrosive gases due to changes in wind speed). A cross-dimensional coupled analysis framework is established to integrate physical dimension shell stress monitoring, chemical dimension electrode surface pollutant spectral analysis, and electrical dimension internal resistance fluctuation tracking, breaking through the limitations of single-dimensional detection and effectively preventing hidden risks such as microscopic perforation of the separator. The technical indicator of decreasing temperature rise rate is transformed into operable operation and maintenance instructions to guide safety maintenance personnel to accurately locate high-risk battery clusters. In terms of implementation path, the solution executes defensive actions such as removing electrode dust with active interception devices and injecting chemical neutralizing agents to degrade acidic gases. It uses a multi-physics field collaborative sensing system to collect neutralization reaction temperature and electric field intensity data. Through comparative analysis of the state before and after defense (such as voltage fluctuation convergence time and improvement in pH spatial distribution), a structured performance report is generated, which includes the proportion of electrochemical stability improvement, thermal runaway suppression effect, and residual risk level. Corrective suggestions such as adjusting the neutralizing agent concentration and adding cooling channels are proposed, forming a complete technical closed loop from strategy execution to effect optimization.

[0156] This solution constructs a data-driven defense performance evaluation system, achieving closed-loop management across the entire chain from strategy verification to operation and maintenance optimization. Based on multi-physics dynamic data comparison technology, a visualized verification mechanism for defense performance is established, accurately evaluating the safety protection effect through quantitative indicators. The structured performance report generated by the system not only includes real-time performance parameters but also drives adaptive defense mechanisms to iterate strategies through analytical models such as residual risk area identification. For example, it 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 life cycle. By continuously accumulating performance data, it constructs a battery health evolution map, providing a scientific basis for predictive maintenance and extending the service life of the energy storage system. The standardized report enables precise fault unit location, reducing the frequency of manual inspections. While avoiding over-maintenance, it also reduces operation and maintenance decision-making costs, ultimately forming a synergistic efficiency system of safety protection, targeted measures, and cost control.

[0157] Figure 3 This is a schematic diagram of the structure of a lithium battery energy storage safety management system provided in an embodiment of this application, as shown below. 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 efficiency verification module 304.

[0158] Data processing module 301 is used to 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 dataset.

[0159] The risk modeling module 302 is used to perform multi-level security risk coupling modeling based on the multi-source intrusion dataset and output a comprehensive risk level.

[0160] The strategy optimization module 303 is used to adaptively activate the defense mechanism and generate a cross-dimensional defense strategy based on the comprehensive risk level.

[0161] The performance verification module 304 is used to evaluate the battery status after defense based on the cross-dimensional defense strategy, and generate and output a security defense performance report.

[0162] Optionally, when the data processing module 301 acquires the real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located, and analyzes the heterogeneous contaminant invasion characteristics to integrate them into a multi-source invasion dataset, it is specifically used for:

[0163] Establish a multi-physics field collaborative sensing system, construct an invasion factor sensing network, and synchronously collect spatially distributed environmental invasion parameters;

[0164] The time interval of the lithium battery being attacked by foreign matter is obtained, and a standardized time data sequence is generated by eliminating the clock monitoring deviation between intervals through a distributed node time alignment mechanism.

[0165] Based on the spatially distributed environmental invasion parameters and standardized time data series, the real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located are analyzed and confirmed.

[0166] Based on the real-time heterogeneous invasion features, the terrain undulation distribution information and the temporal distribution information of heterogeneous invasion are analyzed by a multimodal feature separation engine.

[0167] Based on the terrain undulation distribution information and the time series distribution information of heterogeneous invasion, combined with the real-time heterogeneous invasion feature quantity, a multimodal feature separation engine is used to determine the pollutant characteristics;

[0168] Analyze the characteristics of the pollutants to determine the invasion features of several types of heterogeneous pollutants;

[0169] Based on the aforementioned analysis of heterogeneous pollutant invasion characteristics, the multi-source invasion dataset is integrated to form the dataset.

[0170] Optionally, when the data processing module 301 determines the pollutant characteristics based on the terrain undulation distribution information and the time-series distribution information of heterogeneous invasion, combined with the real-time heterogeneous invasion feature quantities, and using a multimodal feature separation engine, it is specifically used for:

[0171] Based on the terrain undulation distribution information, the temporal distribution information of the heterogeneous invasion, and the real-time heterogeneous invasion feature quantity, a multimodal feature tensor is constructed.

[0172] Based on the multimodal feature separation engine, the multimodal feature tensor is analyzed to obtain static feature components and dynamic evolution feature components;

[0173] The pollutant characteristics are constructed based on the static feature components and the dynamic evolution feature components.

[0174] Optionally, in the data processing module 301, the multimodal feature separation engine is specifically used for:

[0175] Based on the multimodal feature tensor, the inherent properties of pollutants are separated and analyzed using a tensor feature decoupling algorithm to obtain static feature components.

[0176] Based on the multimodal feature tensor and combined with spatiotemporal evolution modeling techniques, pollutant diffusion and migration behavior is separated, and the pollutant diffusion and migration behavior is analyzed to obtain dynamic evolution feature components.

[0177] Optionally, when the risk modeling module 302 performs multi-level security risk coupling modeling based on the multi-source attack dataset and outputs a comprehensive risk level, it is specifically used for:

[0178] 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.

[0179] Based on several risk indices, a multi-level early warning indicator is derived through an integrated multi-scale risk assessment model;

[0180] The overall risk level is determined based on multi-level early warning indicators.

[0181] 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 quantifies and generates several risk indices, it is specifically used for:

[0182] Based on the characteristics of the pollutants, the static feature components and the dynamic evolution feature components in the pollutants are extracted by intelligent analysis of the pollutant characteristics through multi-source data fusion.

[0183] Based on the battery material properties, the electrode surface energy level distribution, electrolyte chemical stability threshold, and membrane pore structure parameters in the battery material are analyzed.

[0184] 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 membrane pore structure parameters, a multi-parameter coupled analysis is performed to establish a pollutant-battery material interaction model.

[0185] 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 coupling effect of battery materials, and several risk indices are quantified and generated.

[0186] Optionally, when the risk modeling module 302 derives multi-level early warning indicators based on several risk indices and through an integrated multi-scale risk assessment model, it is specifically used for:

[0187] Based on several risk indices, spatial positioning features and temporal duration features are extracted by performing spatiotemporal dimension separation and analysis on several risk indices.

[0188] Based on the time duration characteristics, the duration of heterogeneous invasion corresponding to several risk indices is determined, and the cumulative time of lithium battery invasion is recorded.

[0189] Based on the spatial positioning features and combined with the environmental intrusion parameters, the environmental intrusion parameters are mapped to the battery area corresponding to the spatial positioning features to obtain the lithium battery intrusion location features.

[0190] Based on the cumulative time of lithium battery intrusion and the location characteristics of lithium battery intrusion, a cross-spatiotemporal scale fusion analysis mechanism is used to perform spatiotemporal coupling correction on each risk index to determine several risk classification reference coefficients.

[0191] Based on several risk grading reference coefficients, preset early warning level information is matched to determine the early warning level identifier corresponding to each risk grading reference coefficient, so as to construct the multi-level early warning identifier.

[0192] Optionally, when the strategy optimization module 303 adaptively activates the defense mechanism and generates a cross-dimensional defense strategy based on the comprehensive risk level, it is specifically used for:

[0193] Based on the multi-source invasion dataset, the characteristics of the pollutants and the properties of the battery materials are extracted;

[0194] Based on the characteristics of the pollutants and the properties of the battery materials, an interaction analysis is performed to identify several types of risks caused by the pollutants.

[0195] Based on the aforementioned risk types, analyze and identify the current dominant risk type;

[0196] Obtain a historical pollutant impact dataset, and construct a risk type decision matrix based on the historical pollutant impact dataset;

[0197] Based on several dominant risk types and combined with several risk classification reference parameters, the risk type decision matrix is ​​matched to adaptively activate several defense mechanisms corresponding to different defense mechanism types.

[0198] The defense mechanisms include maintaining basic protection, triggering active interception, and executing physical circuit breakers.

[0199] Based on several of the aforementioned defense mechanisms, a targeted cross-dimensional defense strategy is generated.

[0200] Optionally, when the performance verification module 304 evaluates the battery status after defense based on the cross-dimensional defense strategy and generates and outputs a security defense performance report, it is specifically used for:

[0201] Based on the cross-dimensional defense strategy, after executing several defense mechanisms corresponding to different defense mechanism types in the cross-dimensional defense strategy, the battery state after lithium battery defense is obtained according to the multi-physics field collaborative sensing system.

[0202] Based on the battery state after the lithium battery defense, combined with the battery state before the defense, the operating states of the lithium battery before and after the defense are compared to obtain the differences in states before and after the defense.

[0203] Based on the differences in the states before and after the defense, the battery material properties before and after the implementation of the defense measures are dynamically compared in multiple dimensions, and a structured safety defense performance report containing multi-dimensional performance indicators and correction suggestions is output.

[0204] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A method for safe management of lithium battery energy storage, characterized in that, include: The real-time heterogeneous invasion characteristics of the environment in which the lithium battery is located are obtained. Based on the real-time heterogeneous invasion characteristics, the invasion characteristics of heterogeneous pollutants are analyzed and integrated to form a multi-source invasion dataset. Based on the multi-source intrusion dataset, multi-level security risk coupling modeling is performed, and a comprehensive risk level is output. Based on the comprehensive risk level, the defense mechanism is adaptively activated to generate a cross-dimensional defense strategy. Based on the cross-dimensional defense strategy, assess the battery status after defense, and generate and output a security defense effectiveness report. The process of acquiring real-time heterogeneous invasion characteristics of the lithium battery's environment, analyzing heterogeneous contaminant invasion characteristics, and integrating them to form a multi-source invasion dataset includes: Establish a multi-physics field collaborative sensing system, construct an invasion factor sensing network, and synchronously collect spatially distributed environmental invasion parameters; The time interval of the lithium battery being attacked by foreign matter is obtained, and a standardized time data sequence is generated by eliminating the clock monitoring deviation between intervals through a distributed node time alignment mechanism. Based on the spatially distributed environmental invasion parameters and 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 features, the terrain undulation distribution information and the temporal distribution information of heterogeneous invasion are analyzed by a multimodal feature separation engine. Based on the terrain undulation distribution information and the time series distribution information of heterogeneous invasion, 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 to determine the invasion features of several types of heterogeneous pollutants; Based on the aforementioned analysis of heterogeneous pollutant invasion characteristics, the multi-source invasion dataset is integrated to form the aforementioned dataset. Based on the comprehensive risk level, an adaptive activation defense mechanism is used to generate a cross-dimensional defense strategy, including: Based on the multi-source invasion dataset, the characteristics of the pollutants and the properties of the battery materials are extracted; Based on the characteristics of the pollutants and the properties of the battery materials, an interaction analysis is performed to identify several types of risks caused by the pollutants. Based on the aforementioned risk types, analyze and identify the current dominant risk type; Obtain a historical pollutant impact dataset, and construct a risk type decision matrix based on the historical pollutant impact dataset; Based on several dominant risk types and combined with several risk classification reference parameters, the risk type decision matrix is ​​matched to adaptively activate several defense mechanisms corresponding to different defense mechanism types. The defense mechanisms include maintaining basic protection, triggering active interception, and executing physical circuit breakers. Based on several of the aforementioned defense mechanisms, a targeted cross-dimensional defense strategy is generated.

2. The method according to claim 1, characterized in that, Based on the terrain undulation distribution information and the temporal distribution information of heterogeneous invasion, combined with the real-time heterogeneous invasion feature quantities, a multimodal feature separation engine is used to determine the pollutant characteristics, including: Based on the terrain undulation distribution information, the temporal distribution information of the heterogeneous invasion, and the real-time heterogeneous invasion feature quantity, a multimodal feature tensor is constructed. Based on the multimodal feature separation engine, the multimodal feature tensor is analyzed to obtain static feature components and dynamic evolution feature components; The pollutant characteristics are constructed based on the static feature components and the dynamic evolution feature components.

3. The method according to claim 2, characterized in that, The multimodal feature separation engine includes: Based on the multimodal feature tensor, the inherent properties of pollutants are separated and analyzed using a tensor feature decoupling algorithm to obtain static feature components. Based on the multimodal feature tensor and combined with spatiotemporal evolution modeling techniques, pollutant diffusion and migration behavior is separated, and the pollutant diffusion and migration behavior is analyzed to obtain dynamic evolution feature components.

4. The method according to claim 3, characterized in that, Based on the aforementioned multi-source intrusion dataset, multi-level security risk coupling modeling is performed, and a comprehensive risk level is output, including: 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. Based on several risk indices, a multi-level early warning indicator is derived through an integrated multi-scale risk assessment model; The overall risk level is determined based on multi-level early warning indicators.

5. The method according to claim 4, 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 and generate several risk indices, including: Based on the characteristics of the pollutants, the static feature components and the dynamic evolution feature components in the pollutants are extracted by intelligent analysis of the pollutant characteristics through multi-source data fusion. Based on the battery material properties, the electrode surface energy level distribution, electrolyte chemical stability threshold, and membrane pore structure parameters in the battery material 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 membrane pore structure parameters, a multi-parameter coupled 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 coupling effect of battery materials, and several risk indices are quantified and generated.

6. The method according to claim 4, characterized in that, Based on several risk indices, a multi-level early warning system is derived through an integrated multi-scale risk assessment model, including: Based on several risk indices, spatial positioning features and temporal duration features are extracted by performing spatiotemporal dimension separation and analysis on several risk indices. Based on the time duration characteristics, the duration of heterogeneous invasion corresponding to several risk indices is determined, and the cumulative time of lithium battery invasion is recorded. Based on the spatial positioning features and combined with the environmental intrusion parameters, the environmental intrusion parameters are mapped to the battery area corresponding to the spatial positioning features to obtain the lithium battery intrusion location features. Based on the cumulative time of lithium battery intrusion and the location characteristics of lithium battery intrusion, a cross-spatiotemporal scale fusion analysis mechanism is used to perform spatiotemporal coupling correction on each risk index to determine several risk classification reference coefficients. Based on several risk grading reference coefficients, preset early warning level information is matched to determine the early warning level identifier corresponding to each risk grading reference coefficient, so as to construct the multi-level early warning identifier.

7. The method according to claim 6, characterized in that, Based on the aforementioned 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 defense mechanisms corresponding to different defense mechanism types in the cross-dimensional defense strategy, the battery state after lithium battery defense is obtained according to the multi-physics field collaborative sensing system. Based on the battery state after the lithium battery defense, combined with the battery state before the defense, the operating states of the lithium battery before and after the defense are compared to obtain the differences in states before and after the defense. Based on the differences in the states before and after the defense, the battery material properties before and after the implementation of the defense measures are dynamically compared in multiple dimensions, and a structured safety defense performance report containing multi-dimensional performance indicators and correction suggestions is output.

8. A lithium battery energy storage safety management system, characterized in that, Applied to the method as described in any one of claims 1-7, comprising: The data processing module is used to 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 dataset. The risk modeling module is used to perform multi-level security risk coupling modeling based on the multi-source intrusion dataset and output a comprehensive risk level. The strategy optimization module is used to adaptively activate the defense mechanism and generate a cross-dimensional defense strategy based on the comprehensive risk level. The performance verification module is used to evaluate the battery status after defense based on the cross-dimensional defense strategy, and generate and output a security defense performance report.

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