Depth defense protection method for charging station

Abstract

The invention discloses a deep defense protection method for a charging station, relates to the technical field of fire early warning and safety protection, and aims to solve the problems of high false alarm rate of fire early warning, delayed fire isolation and insufficient credibility of accident traceability data in the prior art. According to the method, a monitoring-disposal-traceability complete-cycle protection system is constructed, a temperature field, electrical parameters and environmental data are collected in a fusion mode through a multi-source sensor before a disaster, a self-adaptive safety threshold value is generated based on a dynamic game model, and a risk path is pre-judged in combination with digital twinborn deduction; a three-level response mechanism is adopted in the disaster; after the disaster, operation logs are stored in a fragmented mode through the block chain space-time anchoring technology, an accident evolution chain is reversely reconstructed through digital twinning, and tamper-resistant judicial evidence is generated. The early warning precision is improved through dynamic threshold optimization, the response time is shortened through multi-stage linkage control, the traceability credibility is guaranteed through the quantum evidence storage technology, and a closed-loop optimization system for charging station fire protection is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of fire warning and safety protection, and particularly to a depth defense protection method for a charging station. Background Art

[0002] Currently, the fire protection systems of charging stations generally adopt a single-dimensional monitoring mechanism based on fixed thresholds, and achieve fire warning through basic sensors such as smoke sensors and temperature sensors. The fire extinguishing strategies mostly rely on the execution of preset linkage programs. Most of the existing technologies analyze fire risk factors through a linear regression model, predict periodic risks using time series methods, and use solenoid valve control to achieve fixed-point spraying of fire extinguishing agents. Although such solutions can achieve basic risk warning, there are still significant limitations in terms of adaptability, disposal accuracy, and system self-optimization ability under complex working conditions.

[0003] Chinese invention patent authorization document CN118491019B proposes a fire protection and extinguishing system and optimization method for a charging station, but this invention has the following technical defects: (1) The risk modeling relies on linear regression analysis and cannot capture the non-linear correlations of multi-factor couplings such as battery thermal runaway and arc ignition, resulting in a sharp decline in the model prediction accuracy as the complexity of the working conditions increases; (2) The fire extinguishing strategy is fixed in the opening and closing control of the solenoid valve, lacking a precise spraying mechanism based on dynamic fire source positioning, and it is easy to have blind areas in the coverage of fire extinguishing agents in scenarios with smoke shielding or multiple fire points occurring simultaneously; (3) The data closed-loop feedback only adjusts parameters through simple historical data analysis, and does not build an anti-tampering evidence storage system with legal effect, resulting in a lack of reliable data support for accident traceability; (4) The time series analysis is limited to the identification of periodic trends, and does not introduce a real-time environmental parameter correction mechanism, making it difficult to cope with sudden risks such as sudden changes in charging load and cooling failure. The above defects restrict the improvement of the safety protection ability of charging facilities. Summary of the Invention

[0004] The present invention aims to solve three major technical problems in the existing fire protection systems of charging stations: insufficient accuracy of fire warning, poor timeliness of fire isolation, and low credibility of accident traceability data. Specifically, it includes: (1) By coupling multi-dimensional environmental parameters for analysis and optimizing the dynamic threshold model, improving the accuracy of fire warning under complex working conditions and avoiding delays in disposal caused by false alarms; (2) Constructing a multi-spectral feature verification and hierarchical response mechanism to achieve centimeter-level positioning of the fire source and millisecond-level deployment of the fire prevention barrier, blocking the chain diffusion of lithium battery thermal runaway; (3) Establishing an anti-tampering data evidence storage and digital twin reverse deduction system, solidifying the evidence chain of the entire accident cycle based on blockchain spatio-temporal anchoring technology, providing high-confidence traceability support for liability determination, and finally forming a closed-loop optimized protection system for warning-disposal-traceability.

[0005] The present invention proposes a defense-in-depth protection method for charging stations, which includes: real-time monitoring of the temperature field distribution in the target area through a thermal imager, while collecting multi-dimensional environmental parameters to generate a dynamic threshold judgment benchmark, and triggering an environment adaptive algorithm for threshold optimization when it is determined that the threshold needs to be updated; when the monitoring data exceeds the optimized threshold, starting a multi-spectral verification module to confirm the fire characteristics, locating the fire source based on the verified characteristics and predicting the direction of disaster spread, and dynamically adopting a three-level fire extinguishing strategy according to the spread direction; after the fire extinguishing is completed, storing the data of the disposal process in slices in a tamper-proof database, retroactively tracing the accident cause chain based on the stored data to form a closed-loop feedback, and driving the continuous iterative optimization of the monitoring threshold and response strategy.

[0006] Preferably, this method constructs a dynamic optimization function based on a game theory model by integrating the heat dissipation efficiency factor and the environmental parameter fluctuation characteristics, generates an adaptive threshold interval according to the real-time working conditions of the cooling system and the chemical characteristics difference of the battery pack, and triggers a multi-level warning mechanism matching the battery type when it is detected that the temperature rise rate of the battery pack exceeds the preset chemical system sensitivity threshold.

[0007] Preferably, this method loads the corresponding temperature rise history model according to the chemical system type of the battery pack, calculates the threshold correction coefficient through the spatial distribution deviation degree between the real-time monitoring data and the historical model, and automatically reduces the temperature anomaly determination threshold and increases the monitoring frequency when it is detected that the state of charge deviation degree of the lithium iron phosphate battery pack reaches the preset safety margin boundary.

[0008] Preferably, this method generates a dynamic temperature field distribution based on the thermal radiation signals collected by a multi-spectral sensing array, synchronously analyzes the evolution trend of thermal anomalies through a preset temperature gradient threshold and a time series prediction model of the temperature rise rate. When it is detected that the radiation spectrum characteristics of the hot spot area match the preset lithium battery thermal runaway characteristic pattern, a three-dimensional fire source model is constructed by integrating ultra-wideband positioning data and visual positioning coordinates, and the disaster spread path is simulated in real time by loading the environmental fluid parameters through a digital twin engine.

[0009] Preferably, this three-level fire extinguishing strategy includes: the first-level response, when it is detected that the sudden change amount of the local temperature exceeds the preset threshold, starting a high-precision positioning system to control the sprinkler device to perform spatial directional fire extinguishing, and adjusting the spraying angle and flow rate in real time to match the dynamic position of the fire source; the second-level response, when the deflagration spectrum characteristics are identified, synchronously activating a high-reflectivity fire protection cover to cover the fire source area and generating an electromagnetic isolation barrier to block the heat radiation propagation path; the third-level response, when the monitoring system determines the thermal runaway risk, triggering a linkage protection mechanism, cutting off the energy input according to the emergency stop instruction of the charging pile, activating an audible and visual alarm device to emit a warning signal, starting an asphyxiating fire extinguishing system to cover the target area according to the fire source position, and synchronously executing a gradient cooling process.

[0010] Preferably, the high-precision positioning system for controlling the spraying device includes: constructing a dynamic flow regulation system based on multi-sensor fusion, realizing real-time calibration of the spraying angle through the collaborative control of the solenoid valve array and the millimeter-wave radar, and dynamically matching the two-phase mixing ratio of fine water mist and gas fire extinguishing agent based on the energy density of the fire source.

[0011] Preferably, the high-reflectivity fireproof cover includes a multi-layer composite protection structure, including a high-thermal-conductivity composite coating on the outer layer, an adaptive deformation skeleton in the middle layer, and an energy absorption layer on the inner layer. The centimeter-level positioning system is used to drive the robotic arm to perform rapid encapsulation of the fireproof cover, and at the same time trigger the magnetic seal array to construct an oxygen-deficient inhibition environment.

[0012] Preferably, the linkage protection mechanism triggers the fire extinguishing blanket deployment mechanism according to the emergency stop instruction, so that the flame-retardant isolation layer completely covers the fire source area; starts the cooling process according to the fire source coverage status, and sequentially performs preliminary water mist spraying, corrosion inhibitor cooling, and pulse-maintained temperature reduction; constructs a closed-loop control signal according to the real-time fire situation data, and dynamically coordinates the action timing of the fire extinguishing blanket deployment and the cooling system; activates the emergency power supply unit according to the energy input interruption state to maintain the continuous operation of the key protection components.

[0013] Preferably, this method constructs a multi-modal data sharding storage architecture, binds the sensing data and operation records through three-dimensional feature binding by using the spatio-temporal anchoring technology, generates sharding keys by using anti-quantum encryption algorithms, and writes the verification hash values into the blockchain nodes based on the consensus mechanism to form an immutable evidence chain with legal effect.

[0014] Preferably, this method reconstructs the multi-dimensional evolution model of the accident scene through the digital twin engine, identifies the threshold failure nodes and response strategy defects based on the causal reasoning algorithm, converts the analysis results into training samples and injects them into the defense model, and generates an upgrade instruction package containing the optimized parameter set and disposal plan and pushes it to the associated devices.

[0015] The present invention has the following beneficial effects: 1. Through the dynamic threshold optimization model and multi-dimensional parameter fusion analysis, the false alarm and missed alarm risks in complex scenarios are significantly reduced, the early accurate identification and rapid response of fire hazards are realized, and the spread of the fire is effectively curbed.

[0016] 2. Based on the multi-spectral feature verification and fire source dynamic positioning technology, the rapid deployment of the fireproof barrier and the intelligent matching of the fire extinguishing strategy are realized, and the blocking efficiency of the thermal runaway diffusion of lithium batteries and the accuracy of fire extinguishing agent coverage are significantly improved.

[0017] 3. Through the tamper-proof data evidence storage and digital twin reverse deduction, a complete and verifiable accident evidence chain is formed, providing judicial-level credible data support for liability determination and greatly simplifying the dispute resolution process.

[0018] 4. Based on real-time data feedback and causal reasoning mechanism, dynamically correct the monitoring thresholds and response strategies, and continuously improve the system's adaptability to new risk patterns and defense effectiveness.

[0019] 5. Integrate multiple protection mechanisms and redundant designs to ensure the stable operation of critical equipment under extreme conditions. At the same time, achieve data security compliance through privacy protection technology to meet the requirements of high-level functional safety. Brief Description of the Drawings

[0020] Figure 1 is a schematic diagram of the method flow of the present invention; Figure 2 is a schematic diagram of the fire classification response process of the present invention. Detailed Embodiments

[0021] Embodiment 1 According to Figure 1 as shown, the present invention proposes a method for in-depth defense of charging stations, which specifically includes three parts: pre-disaster early warning, in-disaster fire fighting, and post-disaster evidence preservation.

[0022] The pre-disaster prediction system of the present invention constructs a multi-dimensional and three-dimensional monitoring network, and realizes continuous state perception of charging equipment, vehicle batteries, and environmental parameters through a distributed sensor array deployed in the charging area. The core monitoring module includes a thermal imaging scanning device, a multi-spectral sensing unit, and a high-precision electrical parameter acquisition system, forming a composite sensing ability covering temperature field distribution, energy transmission characteristics, and gas composition changes. The sensor network adopts a redundant topology design to ensure blind-free acquisition of monitoring data in key areas and provide a complete raw data basis for subsequent analysis.

[0023] The system introduces an adaptive threshold generation mechanism based on deep learning, breaking through the limitations of traditional fixed thresholds. By constructing a multi-dimensional parameter correlation model, it analyzes the dynamic coupling relationship between the temperature change rate, voltage fluctuation pattern, heat dissipation efficiency, and environmental factors in real time. The threshold generation engine continuously receives input parameters such as the operating state of the heat dissipation system, battery chemical characteristics, and historical failure modes, and dynamically adjusts the warning range of each monitoring index in combination with the digital twin simulation results to form an elastic safety boundary matching the environmental conditions and equipment status.

[0024] The collected raw data enters a multi-level processing pipeline. First, noise suppression and feature enhancement are performed to eliminate signal distortions caused by factors such as electromagnetic interference and mechanical vibration. The discrete sensor data is fused into a multi-dimensional state matrix in a unified spatio-temporal coordinate system through a spatio-temporal alignment algorithm to accurately characterize the three-dimensional thermal field distribution, dynamic electrical parameter characteristics, and surrounding environment characteristics of the charging equipment. The data fusion module particularly strengthens the tracking ability of the formation process of local hot spots and captures the gradual temperature rise signal caused by micro-short circuits inside the battery pack.

[0025] The system constructs a high-fidelity digital twin model to establish an accurate mapping of charging equipment, vehicle batteries, and environmental elements in the virtual space. The twin engine combines physical modeling with machine learning to real-time simulate the propagation path of thermal runaway, the evolution trend of electrical faults, and the multi-device chain reaction scenario. During the deduction process, real-time monitoring data is dynamically loaded as boundary conditions to generate a disaster development prediction model containing parameters such as the fault diffusion speed, energy release intensity, and environmental impact range.

[0026] Based on the multi-agent reinforcement learning framework, the system establishes an attack-defense game model to simulate potential risk scenarios. The protection strategy generator and the virtual attack agent conduct adversarial training in the digital twin environment, and continuously optimize the protection decision through millions of game iterations. The game process focuses on solving complex threat scenarios such as the utilization of monitoring blind spots, threshold avoidance attacks, and sensor deception, and dynamically generates a multi-level strategy library containing preventive control, early intervention, and emergency response.

[0027] When the monitoring data touches the dynamic security threshold, the system activates the hierarchical warning mechanism. In the primary warning stage, preventive control is implemented through flexible means such as adjusting the charging power and enhancing the heat dissipation intensity; the intermediate warning triggers a deep diagnosis of the device state, and locates potential fault sources in combination with the digital twin deduction results; in the advanced warning stage, the emergency protocol preloading is started to lay a foundation for subsequent rapid response. The warning information is pushed to the operation and maintenance terminal in real time through the multi-channel redundant transmission system, and the standby state of the protection device is activated synchronously.

[0028] The system establishes a continuous evolution mechanism, and continuously absorbs newly generated monitoring data and disposal cases through the online learning module. The warning model is incrementally trained regularly to optimize the balance between the sensitivity and specificity of the feature extraction network. The adaptability of the model to new battery technologies and special environmental conditions is particularly strengthened, and the existing knowledge is quickly adapted to new scenarios through transfer learning technology to ensure the forward-looking and universality of the protection system.

[0029] To ensure the reliability of monitoring, the system integrates multiple self-check function modules. Periodically execute self-check processes such as sensor calibration, communication link verification, and computing resource health diagnosis, and automatically switch to standby units in case of abnormalities. The consensus algorithm is used to cross-verify key monitoring data to identify and eliminate false alarms caused by single-point failures. A data credibility evaluation system is established, and a manual review process is initiated for monitoring information with contradictions to minimize the risk of misjudgment.

[0030] In the fire confirmation stage, the system establishes a three-dimensional fire source positioning model by integrating thermal imaging, gas composition analysis, and acoustic feature recognition technologies through a heterogeneous sensor network. The multi-spectral sensor array captures the radiation characteristics of the flame in specific frequency bands, and combines with deep learning algorithms to exclude interference factors such as environmental reflection, accurately determining the coordinates of the ignition point and the direction of fire spread. The positioning information is synchronized to the dynamic protection decision-making engine in real time, triggering the parallel loading of multi-modal emergency response protocols.

[0031] The protection system activates a hierarchical response mechanism, dynamically adjusting the response strategy according to the development stage of the fire. In the primary response stage, directional suppression measures are deployed. A local cooling zone is formed by high-pressure fine water mist to block the heat conduction path, and at the same time, the pre-protection mechanism of adjacent equipment is activated. In the intermediate response stage, a composite physical isolation is enabled. The intelligent fireproof cover quickly closes along the preset track under the drive of the servo mechanism. Inert gas is injected into the sealed structure to change the combustion environment, and the electromagnetic field restraint device simultaneously forms an energy shielding layer. In the advanced response stage, a linkage protection mechanism is executed. First, the risk is determined in real time through intelligent monitoring of the charging pile, and the charging circuit is immediately cut off and the audible and visual alarm device is activated. The asphyxiating fire extinguishing system is started synchronously, driving the flame retardant covering layer to accurately block the fire source area, and at the same time, a hierarchical cooling process is deployed to implement temperature control, forming a collaborative disposal mode of physical isolation and chemical inhibition.

[0032] The dynamic fire extinguishing system adopts a multi-parameter feedback adjustment mechanism. The fire extinguishing agent spraying device dynamically adjusts the action range and intensity according to the real-time fire field scanning data. The flow control unit combines the heat radiation intensity and the smoke diffusion speed, and intelligently matches the particle size distribution of the fine water mist and the concentration gradient of the chemical inhibitor. The spraying angle is automatically calibrated according to the movement trajectory of the fire source, ensuring that the fire extinguishing medium always covers the core combustion area, while avoiding secondary damage to unaffected equipment.

[0033] The physical isolation system constructs a multi-layer protection structure. The reflective layer of the nano-composite material conducts the thermal radiation back to the fire field, the buffer layer absorbs the explosion shock wave through controllable deformation, and the shape memory alloy skeleton changes its structural form under high-temperature triggering to enhance the sealing performance. The isolation barrier integrates an environmental reconstruction module inside, quickly reducing the oxygen concentration below the combustion critical value through gas replacement, and the negative pressure maintenance system prevents the leakage of toxic smoke.

[0034] The energy management and control system implements an intelligent power coordination strategy. While cutting off the main power supply circuit, it switches to the standby energy module to maintain the operation of key protection components. The power distribution unit analyzes the working conditions of the fire extinguishing system and the cooling device in real time, dynamically optimizing the power supply path and power distribution ratio, ensuring that the core disposal unit continuously obtains stable power supply. The safety protection module synchronously reconstructs the grounding network topology, guiding the fault energy to be discharged along the preset safe path, and constructing a safe operation boundary for personnel.

[0035] The entire process of disposal synchronously records multi-dimensional operation logs, and data such as the action sequence of protection equipment, the change curve of environmental parameters, and the decision logic deduction path are encrypted and written into a tamper-proof memory. The quantum communication module uploads key disposal node information to the distributed evidence storage network in real time, providing verifiable operation chain evidence for subsequent liability tracing. After the fire is suppressed, the system automatically switches to the aftermath monitoring mode, continuously scanning for the risk of re-ignition until the environmental parameters return to the safe threshold.

[0036] After the fire is completely extinguished and the environmental parameters are stable, the system starts the multi-modal data solidification process. The complete record of the disposal process is transmitted back through the distributed sensor network, including thermal imaging sequences, fire extinguishing agent spraying trajectories, equipment operation logs, and environmental parameter change curves. The data preprocessing module aligns heterogeneous information in space and time, eliminates the timing deviation caused by transmission delay, and constructs a multi-dimensional event timeline including physical characteristics, operation instructions, and environmental states. After feature extraction, all data generates standardized evidence units, and each unit is embedded with a unique spatio-temporal identification code to ensure the integrity and traceability of the data chain.

[0037] The system establishes a three-dimensional data anchoring mechanism, deeply binding the collected raw data with geographical location and accurate timestamps. The spatial anchoring module obtains the absolute coordinates of the equipment through the satellite positioning system and establishes a mapping relationship of the local coordinate system in combination with UWB positioning data; the time anchoring adopts a high-precision clock synchronization protocol to accurately locate the operation nodes to the millisecond level; the data anchoring engine iteratively calculates the feature hash value of key events to generate a unique Merkle root value. The anchored data packets generate shard keys through anti-quantum encryption algorithms and perform cross-media distributed storage.

[0038] The blockchain evidence storage network starts the intelligent contract execution process to perform multi-node consensus verification on the solidified data. The verification process includes data integrity verification, operation compliance review, and physical law compliance analysis. Through an improved proof-of-authority mechanism, the verified evidence units are written into the consortium chain nodes with legal effect, forming an immutable evidence record. The evidence information is synchronously pushed to the regulatory agency nodes, supporting zero-knowledge proof technology to achieve data authenticity verification under privacy protection.

[0039] The digital twin engine loads the complete accident data set and reconstructs the full-cycle evolution model of the disaster occurrence and disposal. The reconstruction process uses multi-scale fusion technology to couple and analyze macroscopic environmental parameters and microscopic equipment states, and aligns the time series data of each subsystem through the dynamic time warping algorithm. The reverse deduction module, based on an improved causal reasoning model, locates the initial node where the threshold fails, identifies deviations in the execution of protection strategies, and marks the performance degradation characteristics of key equipment.

[0040] The responsibility determination system initiates a multi-source evidence cross-verification process and integrates the offline analysis results of physical and chemical detection equipment. The mobile detection unit collects samples of combustion residues and confirms the composition of the ignition substances through spectral feature comparison; the electrical safety diagnosis module analyzes the equipment communication logs and reconstructs the control instruction sequence before the fault occurs; the environmental simulation unit reproduces the boundary conditions such as temperature, humidity, and electromagnetic interference during the accident to verify the rationality of the protection strategy. All verification results are subjected to correlation analysis through the knowledge graph engine to generate a multi-dimensional determination report including the allocation of responsibility weights.

[0041] The system self-optimization mechanism initiates the process of updating the federated learning model and disassembles the accident cases into a training sample set. The optimization module locates the model defects and calculates the parameter correction amount by comparing the deviation between the expected protection effect and the actual disposal result. The incremental training process adopts a curriculum learning strategy, preferentially learning typical fault modes with high confidence and gradually expanding to marginal cases. The updated policy library is pushed to devices of the same model through a secure channel to synchronously refresh the inference rule library of the digital twin model.

[0042] The judicial evidence encapsulation module performs structured processing on the full-cycle data to generate an electronic file in line with international standards. The file includes copies of the original data, evidence preservation verification reports, responsibility determination letters, and system self-optimization records, and ensures legal effect through digital signature technology. The evidence package adopts a hierarchical access permission design, supports different roles such as regulatory authorities, operators, and manufacturers to retrieve and verify data as needed, and protects commercially sensitive information through homomorphic encryption technology.

[0043] The system final acceptance module performs a full-link review and detection to verify the judicial compliance of the evidence preservation data and the integrity of the restoration of the equipment status. The anti-tampering ability of the blockchain network is verified through simulated attack tests, and the effectiveness of the system self-optimization mechanism is checked by means of fault injection. After passing the final acceptance, the system resets the standby status of all protection devices, updates the dynamic threshold parameter library, and generates an operation and maintenance guidance document containing improvement suggestions to complete the full closed-loop process of post-disaster disposal.

[0044] Embodiment 2 According to Figure 2 As shown, in the intelligent disposal system for charging station fires, the present invention constructs a three-level progressive response mechanism, and realizes the precise matching of the disaster development stage and the disposal strategy through the Dynamic Game-based Protection (DGAP) model. Each level of response includes independent triggering conditions, disposal processes, and effectiveness verification links, forming a closed-loop control chain of "monitoring - decision-making - execution - feedback" to ensure that the fire can be targeted controlled during the germination period, development period, and outbreak period.

[0045] The first-level response is initiated when the system detects that the local temperature difference breaks through the dynamic threshold. The positioning subsystem constructs a three-dimensional space coordinate system through an ultra-wideband base station array, combines thermal imaging data with the gas concentration gradient to lock the core area of the fire source, and drives the servo mechanism to adjust the azimuth and elevation angles of the sprinkler device. The sprinkler control module dynamically adjusts the coverage density and action range of the fine water mist based on the real-time temperature field distribution model, forming a continuous cooling zone in the target area. At the same time, adjacent charging units receive pre-alarm signals, activate the enhanced mode of the heat dissipation system, and reduce the output power, forming a preventive control area with a radius of 3 meters. The system collects data on the change in heat radiation flux every 200 milliseconds after the disposal is initiated. If the temperature rise rate does not drop below the safety threshold within 5 seconds, a strategy upgrade assessment is triggered.

[0046] The collaborative control mechanism of the first-level response is achieved through multi-bus data fusion. The sprinkler flow controller receives the spatial coordinate deviation compensation value from the positioning subsystem and dynamically corrects the opening combination of the nozzle array; the environmental monitoring unit synchronously analyzes the influence of air humidity on the evaporation rate of the fine water mist and optimizes the injection duration through the PID algorithm. When the system detects the risk of electrical arc flash in a local area, it automatically superimposes the high-frequency pulse fire extinguishing agent delivery mode, mixes nano-scale perfluorinated hexanone particles into the fine water mist, and forms a dual suppression effect.

[0047] The second-level response is automatically activated when the fire characteristics conform to the deflagration precursor model. The physical isolation system is executed in two stages: first, a high-precision servo mechanism drives the fireproof cover to move along the slide rail, and the shape memory alloy skeleton triggers deformation when it contacts the high-temperature area, making the edge of the protective cover form a curved surface seal with the ground; then the electromagnetic restraint device activates the superconducting coil to generate a circular magnetic field within a range of 0.5 meters around the fire source to inhibit the movement of charged particles. The inert gas injection system deployed inside the isolation space replaces oxygen at a specific flow rate gradient, reducing the oxygen concentration from 21% to below 12% within 4 seconds, and simultaneously activating the negative pressure maintenance device to prevent the leakage of toxic smoke.

[0048] The effectiveness verification link of the second-level response includes dynamic parameter calibration. The gas analysis module detects the change in gas components in the isolation area every 100 milliseconds. When it detects a sudden increase in methane concentration or an abnormal oxygen concentration recovery rate, the system automatically adds the active cooling function of the aerogel thermal insulation layer. The distributed strain sensors outside the fireproof cover monitor the structural deformation in real time. If the detected impact pressure exceeds the design threshold, the controllable collapse mechanism of the honeycomb structure of the buffer layer is immediately triggered, and more than 80% of the explosion energy is absorbed through material deformation.

[0049] The third-level response activates the linkage protection mechanism for the thermal runaway chain reaction. The intelligent monitoring system of the charging pile continuously collects battery temperature data at a cycle of 100 ms. When an abnormal temperature rise rate is identified, a remote emergency stop instruction is triggered within 200 ms through the industrial Ethernet, and the audible and visual alarm device is activated. The asphyxiating fire extinguishing system is started synchronously. Driven by compressed air, the fiberglass fire blanket unfolds within 15 seconds to form a 6m×9m flame-retardant isolation layer, and its oxygen barrier rate exceeds 95%. The chassis cooling system performs three-level gradient cooling: first, warm water mist is sprayed through a 7-cm branch pipe for 5 seconds for rapid cooling, then water mist containing corrosion inhibitor is switched for targeted cooling, and finally, pulse spraying at an interval of 0.5 s is used to maintain thermal stability.

[0050] The system dynamically adjusts the water mist flow through a pressure sensor with an accuracy of ±0.1 bar to ensure that the full-load operation time is not less than 30 minutes. The emergency power supply unit immediately takes over the power supply of key equipment after the emergency stop, giving priority to ensuring the continuous operation of the positioning sensor and the communication module. The safety protection module synchronously reconstructs the grounding network topology, guides the fault energy to be safely released along the preset path, and automatically generates an effectiveness evaluation report containing parameters such as the integrity of the fire blanket coverage and the coolant consumption after the disposal, providing data support for system optimization.

[0051] The multi-level response system realizes dynamic coordination through the data bus, continuously collects key parameters such as the change rate of the temperature field and the gas concentration gradient, and evaluates the disposal effectiveness every 500 milliseconds. When the previous-level measures fail to effectively contain the fire, the system automatically upgrades the response level and optimizes the strategy combination. For example, during the second-level response stage, if the oxygen concentration in the isolation area is detected to rise, the sprinkling intensity is increased and the gas replacement time is extended; during the third-level response, if the integrity of the fire blanket coverage is insufficient or the cooling water pressure is abnormal, the system immediately starts the dynamic compensation mechanism: additional auxiliary spraying points are added according to the infrared positioning data, or the standby pump group is switched to maintain the water mist pressure. All operation instructions are transmitted in real time through the industrial Ethernet, with a delay of less than 1 ms, ensuring the precise synchronization of the audible and visual alarm, the unfolding of the fire blanket, and the cooling spray, forming a complete "monitoring - isolation - cooling" disposal chain. During the disposal process, the oxygen barrier rate and the temperature reduction rate indicators are continuously verified. When the thresholds are not reached, a secondary disposal sequence is automatically triggered until a stable and controlled state is formed.

Claims

1. A charging station defense-in-depth protection method, characterized in that: The method comprises: The temperature field distribution of the target area is monitored in real time by a thermal imager, and multi-dimensional environmental parameters are collected at the same time to generate a dynamic threshold judgment benchmark. When it is determined that the threshold needs to be updated, the environmental adaptive algorithm is triggered to optimize the threshold. When the monitoring data exceeds the optimization threshold, the multispectral verification module is activated to confirm the fire characteristics, locate the fire source based on the verification characteristics and predict the direction of disaster spread, and dynamically adopt a three-level fire extinguishing strategy according to the spread direction; After the fire is extinguished, the disposal process data will be stored in shards in a tamper-proof database. The cause chain of the accident will be traced back based on the stored data to form a closed-loop feedback, driving the continuous iterative optimization of monitoring thresholds and response strategies.

2. A charging station defense-in-depth protection method according to claim 1, characterized in that: The method constructs a dynamic optimization function based on a game theory model by integrating the heat dissipation efficiency factor and the fluctuation characteristics of environmental parameters, generates an adaptive threshold interval according to the real-time cooling system operating conditions and the chemical characteristics of the battery pack, and triggers a multi-level early warning mechanism matching the battery type when the temperature rise rate of the battery pack is monitored to exceed the preset chemical system sensitivity threshold.

3. A charging station defense-in-depth protection method according to claim 1 or 2, characterized in that: The method loads the corresponding temperature rise history model according to the chemical system type of the battery pack, calculates the threshold correction coefficient through the spatial distribution deviation between the real-time monitoring data and the historical model, and automatically lowers the temperature anomaly judgment threshold and increases the monitoring frequency when it is detected that the charge state deviation of the lithium iron phosphate battery pack reaches a preset safety margin boundary.

4. A charging station defense-in-depth protection method according to claim 1, characterized in that: The method generates a dynamic temperature field distribution based on the thermal radiation signal collected by a multi-spectral sensor array, and synchronously analyzes the evolution trend of thermal anomalies through a preset temperature gradient threshold and a time series prediction model of the temperature rise rate. When the radiation spectrum characteristics of the hot spot area are detected to match the preset lithium battery thermal runaway characteristic pattern, the ultra-wideband positioning data and visual positioning coordinates are integrated to construct a three-dimensional model of the fire source, and the environmental fluid parameters are loaded through the digital twin engine to simulate the disaster diffusion path in real time.

5. A charging station defense-in-depth protection method according to claim 1, characterized in that: The three-level fire extinguishing strategy includes: first-level response, when it is detected that the local temperature mutation exceeds the preset threshold, the high-precision positioning system is activated to control the sprinkler device to perform spatial directional fire extinguishing, and the spray angle and flow are adjusted in real time to match the dynamic position of the fire source; second-level response, when the spectral characteristics of the deflagration are identified, the high-reflectivity fireproof cover is synchronously activated to cover the fire source area and an electromagnetic isolation barrier is generated to block the thermal radiation propagation path; third-level response, when the monitoring system determines the risk of thermal runaway, the linkage protection mechanism is triggered, the energy input is cut off according to the emergency stop command of the charging pile, the sound and light alarm device is linked to send a warning signal, the asphyxiation fire extinguishing system is started according to the location of the fire source to cover the target area, and the gradient cooling process is synchronously executed.

6. A charging station defense-in-depth protection method according to claim 5, characterized in that: The high-precision positioning system controls the spray device, including: building a dynamic flow regulation system based on multi-sensor fusion, realizing real-time calibration of the spray angle through coordinated control of the solenoid valve array and the millimeter-wave radar, and dynamically matching the two-phase mixing ratio of fine water mist and gas fire extinguishing agent based on the energy density of the fire source.

7. A charging station defense-in-depth protection method according to claim 5, characterized in that: The high-reflectivity fireproof cover comprises a multi-layer composite protective structure, including an outer layer of high thermal conductivity composite coating, a middle layer of adaptive deformation skeleton and an inner layer of energy absorption layer. The centimeter-level positioning system drives the robotic arm to perform rapid packaging of the fireproof cover, while triggering the magnetic sealing array to construct an oxygen-deficient suppression environment.

8. A charging station defense-in-depth protection method according to claim 5, characterized in that: The linkage protection mechanism triggers the fire blanket deployment mechanism according to the emergency stop command, so that the flame retardant isolation layer completely covers the fire source area; starts the cooling process according to the fire source coverage status, and executes preliminary water mist injection, corrosion inhibitor cooling and pulse maintenance cooling in sequence; builds a closed-loop control signal according to real-time fire data, and dynamically coordinates the action timing of the fire blanket deployment and the cooling system; activates the emergency power supply unit according to the energy input interruption status to maintain the continuous operation of key protection components.

9. A charging station defense-in-depth protection method according to claim 1, characterized in that: The method constructs a multimodal data sharding storage architecture, binds sensor data and operation records with three-dimensional features through spatiotemporal anchoring technology, generates sharding keys using a quantum-resistant encryption algorithm, and writes verification hash values ​​into blockchain nodes based on a consensus mechanism, thereby forming an unalterable evidence chain with judicial validity.

10. A charging station defense-in-depth protection method according to claim 1 or 9, characterized in that: The method reconstructs a multi-dimensional evolution model of the accident scenario through a digital twin engine, identifies threshold failure nodes and response strategy defects based on a causal reasoning algorithm, converts the analysis results into training samples and injects them into the defense model, and generates an upgrade instruction package containing an optimized parameter set and a disposal plan and pushes it to associated devices.

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