Remote box opening detection method and system for fire extinguishing of lithium battery container

Abstract

The invention discloses a remote box opening detection method and system for lithium battery container fire extinguishment, and the method is used for judging the box opening risk of a target lithium battery container with a fire behavior, and comprises the steps: carrying out the real-time monitoring of the target lithium battery container after physical isolation, and obtaining the real-time parameters of the fire behavior and the type of a lithium battery; inputting the acquired data into a risk assessment model to obtain a real-time quantitative result; determining whether to open the target lithium battery container according to the real-time quantitative result; the risk assessment model comprehensively considers a temperature risk index, a gas risk index, a fire behavior risk index and a structure risk index, calculates quantitative risk values and grades the quantitative risk values, and guides different box opening decisions; the fire risk of the lithium battery container can be scientifically evaluated, the deflagration risk possibly caused by blind box opening is avoided, the safety and effectiveness of fire extinguishing rescue are improved, and technical support is provided for storage and transportation safety of lithium batteries.

Description

Technical Field

[0001] The present invention belongs to the technical field of fire safety, and in particular relates to a remote box opening detection method and system for lithium battery container fire extinguishing. Background Art

[0002] As the main electrochemical energy storage device, lithium batteries are widely used in electric vehicles, portable devices and energy storage systems. However, lithium batteries have safety hazards during transportation and storage, especially when fire accidents occur during container transportation. How to safely and effectively extinguish fires has become an urgent problem to be solved.

[0003] Traditional methods of dealing with lithium battery fires are usually to open the container directly to extinguish the fire or to immerse the entire container in water for cooling. The former is prone to rapid spread of fire or even explosion due to sudden air intake, endangering the safety of rescuers; the latter may cause waste of resources and environmental pollution. At present, there is a lack of systematic methods for scientifically evaluating lithium battery container fires and guiding unpacking decisions, which increases the difficulty of rescue and safety risks. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a remote unpacking detection method and system for lithium battery container fire extinguishing, so as to achieve the purpose of safely assessing the fire risk of lithium battery containers, scientifically deciding on the unpacking method, reducing the safety hazards of rescue personnel, and improving the fire extinguishing efficiency.

[0005] The specific technical solutions are as follows: A remote opening detection method for lithium battery container fire extinguishing is used to determine the opening risk of a target lithium battery container where a fire occurs. The method comprises the following steps: Step S1, real-time monitoring of the physically isolated target lithium battery container is performed to obtain the real-time fire parameters of the target lithium battery container and the type of lithium batteries in the container.

[0006] The real-time fire parameters include at least: container surface temperature distribution data and the highest temperature value in the hot spot area, internal gas component concentration data, fire coverage area, flame intensity, number of fire sources and container structure integrity data.

[0007] Step S2, establishing a risk assessment model, wherein the risk assessment model is used to calculate the risk of uncontrolled explosion and combustion of the target lithium battery container after unpacking, and inputting the collected data into the risk assessment model to obtain real-time quantitative results.

[0008] Step S3, determining whether to open the target lithium battery container according to the real-time quantitative result.

[0009] Furthermore, in step S1, obtaining the real-time fire parameters of the target lithium battery container includes: Use a thermal imaging system to monitor the surface temperature distribution of the target lithium battery container and record the highest temperature value in the hot spot area and temperature distribution data.

[0010] Insert a gas sampling tube into the container through the detection port of the container to collect internal gas samples, analyze the collected gas samples in real time, and detect the hydrogen concentration. , Carbon monoxide concentration , CO2 concentration and characteristic volatile organic compound concentrations The characteristic organic volatiles are: for lithium iron phosphate batteries, methyl cyclohexane C 7 H 14 Concentration, for ternary lithium batteries, analysis of dimethylcyclohexane C 8 H 16 concentration.

[0011] The integrity of the container structure is tested through the image monitoring system to identify areas of structural deformation, bulging, leakage or welding failure.

[0012] Furthermore, the risk assessment model calculates the quantitative risk value according to the following mathematical expression: : ;in, is the temperature risk indicator, is the gas risk indicator, is a fire risk indicator. It is a structural risk indicator, ranging from 0 to 1.

[0013] Furthermore, the temperature risk indicator The calculation method is: ;in, The safety temperature threshold is determined according to the type of lithium battery. The temperature for lithium iron phosphate battery is 80°C, and the temperature for ternary lithium battery is 60°C. The dangerous temperature threshold is 150°C for lithium iron phosphate batteries and 120°C for ternary lithium batteries.

[0014] Further, gas risk indicators The calculation method is: ;in, is the lower explosion limit concentration of hydrogen, taking 4% by volume; is the allowable exposure limit concentration of carbon monoxide, which is 50ppm; The exposure limit concentration of characteristic volatile organic compounds is 200 ppm.

[0015] Furthermore, the fire risk index The calculation method is: ;in, Cover the container surface area for fire; is the total surface area of ​​the container; The flame intensity is extracted from the real-time fire image through image processing algorithm; is the maximum reference value of flame intensity; is the number of fire sources detected; For reference, the maximum number of fire sources; , , are weight coefficients, and their values ​​are 0.5, 0.3, and 0.2 respectively.

[0016] Furthermore, structural risk indicators The calculation method is to take the average value of deformation, cracking and sealing failure: ,in, , and They are all extracted and quantified from real-time monitoring images through image recognition technology.

[0017] It is the visible deformation degree of the container, ranging from 0 to 1. , is the maximum bulge or depression volume; is the standard volume of a container; is the surface area where deformation occurs.

[0018] It is the degree of cracks on the container surface, which is quantitatively described according to the number of cracks. If there is no crack, it is 0; if there are 1-5 cracks, it is 0.1, 0.2, 0.4, 0.6, 0.9 respectively; if there are more than 5 cracks, it is 1.

[0019] The degree of failure of the container seal, no visible smoke leakage and When 0.3 is taken; there is no visible smoke leakage and When there is visible smoke leakage, take 0.5; When , it takes 0.5; otherwise, it takes 1.

[0020] Furthermore, the changing trend of each parameter is analyzed and the risk growth rate is calculated: ;when >0 and continues to grow, the system automatically raises the risk level to level one and issues an early warning; represents the risk value at the current time t; Indicates the previous moment The risk value of is the sampling time interval, which is 5-10 seconds; the continuous growth is within three consecutive sampling cycles All of them are positive and show an increasing trend.

[0021] Furthermore, the quantitative risk value output by the risk assessment model The levels are as follows: when When the risk is low, manual unpacking and inspection is allowed; when The risk is medium, and robots are needed to assist in unpacking while maintaining a safe distance; when It is a high risk, unpacking is prohibited, and further physical isolation and cooling measures are required; when It is an extremely high risk and emergency measures must be taken, including: sinking the container into water or using a fire extinguishing agent to inject it.

[0022] Based on the same inventive concept, the present invention provides a remote opening detection system for lithium battery container fire extinguishing, which is used to execute the method of the present invention. The system includes: a monitoring module, a processing module, a decision module and an execution module.

[0023] Furthermore, the monitoring module is used to perform real-time monitoring of the target lithium battery container after physical isolation, and obtain the real-time fire parameters of the target lithium battery container and the type of lithium batteries in the container; the monitoring module includes a thermal imaging system, a gas sampling and analysis system, and an image monitoring system.

[0024] Furthermore, the processing module is used to run the risk assessment model, input the real-time fire parameters and the lithium battery type into the risk assessment model, and obtain a quantitative risk value. , the risk assessment model calculates the quantitative risk value according to the following mathematical expression: ;in, is the temperature risk indicator, is the gas risk indicator, is a fire risk indicator. It is an indicator of structural risk.

[0025] Furthermore, the decision module is used to determine the quantitative risk value. Determine whether to open the target lithium battery container; when When the decision module issues an instruction to allow manual unpacking and inspection; When , the decision module issues an instruction that the robot needs to assist in unpacking and maintain a safe distance; when When , the decision module issues an instruction to prohibit opening the box and take further physical isolation and cooling measures; when When an emergency occurs, the decision module issues an instruction to take emergency measures.

[0026] Furthermore, the execution module is used to perform corresponding operations according to the instructions of the decision module, including manual unpacking operations, robot-assisted unpacking operations, and emergency disposal operations.

[0027] Compared with the prior art, the present invention has the following beneficial effects: The present invention achieves accurate assessment and risk grading of fire conditions in lithium battery containers by establishing a quantitative assessment model that integrates four major risk indicators: temperature, gas, fire intensity, and structure. It makes intelligent decisions on the unpacking method based on different risk levels, effectively solving the risk of explosion caused by blind unpacking in the traditional fire-fighting process, significantly improving the safety and effectiveness of fire-fighting and rescue. At the same time, differentiated parameter standards are formulated according to the characteristics of different types of lithium batteries, which improves the accuracy and adaptability of the assessment model and provides important technical support for the safe storage and transportation of lithium batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of the remote box opening detection method for lithium battery container fire extinguishing of the present invention; Figure 2 The figure is a schematic diagram of the composition of the remote box opening detection system for lithium battery container fire extinguishing of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described implementation mode is a part of the present invention, not all implementation modes. Based on the implementation modes of the present invention, all other implementation modes obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Example 1 like Figure 1 As shown, it is a flow chart of the remote opening detection method for lithium battery container fire extinguishing of the present invention, which is used to judge the opening risk of the target lithium battery container where the fire occurs. The method comprises the following steps: Step S1, real-time monitoring of the physically isolated target lithium battery container is performed to obtain the real-time fire parameters of the target lithium battery container and the type of lithium batteries in the container.

[0031] Physical isolation means keeping the burning lithium battery container at a safe distance from other surrounding containers or combustibles. It is usually recommended to keep the distance at least 10 meters. Fire walls or fire blankets can be set up for isolation when necessary. The purpose of physical isolation is to prevent the spread of fire and create a relatively safe environment for rescue operations. Determining the type of lithium battery (lithium iron phosphate battery or ternary lithium battery) is crucial for subsequent risk assessment, because different types of lithium batteries have different thermal runaway characteristics and temperature thresholds.

[0032] The real-time fire parameters include at least: container surface temperature distribution data and the highest temperature value in the hot spot area, internal gas component concentration data, fire coverage area, flame intensity, number of fire sources and container structure integrity data.

[0033] Surface temperature distribution data is obtained through infrared thermal imaging, which can detect hot spots that are invisible to the naked eye; internal gas composition concentration data reflects the degree of thermal runaway of the battery and the potential risk of explosion; the fire coverage area, flame intensity and number of fire sources characterize the severity and spread of the fire; and structural integrity data is related to the physical stability of the container and the potential risk of structural collapse.

[0034] Step S2, establishing a risk assessment model, wherein the risk assessment model is used to calculate the risk of uncontrolled explosion and combustion of the target lithium battery container after unpacking, and inputting the collected data into the risk assessment model to obtain real-time quantitative results.

[0035] The model design takes into account the particularity of thermal runaway of lithium batteries, especially the explosion phenomenon that may be caused by the sudden introduction of oxygen after unpacking. The quantitative results transform risk assessment from traditional empirical judgment to a quantifiable and comparable scientific decision-making basis, greatly improving the accuracy and reliability of rescue decisions.

[0036] Step S3, determining whether to open the target lithium battery container according to the real-time quantitative result.

[0037] The system automatically generates unpacking suggestions based on the calculated risk value R, avoiding the risks that may be caused by human subjective judgment.

[0038] In step S1, obtaining the real-time fire parameters of the target lithium battery container includes: Use a thermal imaging system to monitor the surface temperature distribution of the target lithium battery container and record the highest temperature value in the hot spot area and temperature distribution data.

[0039] The thermal imaging system uses an infrared thermal imager with a resolution of no less than 320×240 pixels, a temperature measurement range of -20°C to 500°C, and an accuracy of ±2°C or ±2% of the reading. The system scans the surface of the container every 5 seconds, generates a temperature heat map, and automatically identifies areas with abnormal temperatures. The temperature change trend in the hot spot area is an important indicator for predicting the progress of battery thermal runaway. When the temperature rise rate exceeds 10°C / minute, the system will trigger an early warning.

[0040] Insert a gas sampling tube into the container through the detection port of the container to collect internal gas samples, analyze the collected gas samples in real time, and detect the hydrogen concentration. , Carbon monoxide concentration , CO2 concentration and characteristic volatile organic compound concentrations The characteristic organic volatiles are: for lithium iron phosphate batteries, methyl cyclohexane C 7 H 14 Concentration, for ternary lithium batteries, analysis of dimethylcyclohexane C 8 H 16 concentration.

[0041] The gas sampling system adopts an explosion-proof design. The sampling tube is made of high-temperature resistant materials (can withstand temperatures above 800°C) and is no less than 3 meters long to ensure that the operator maintains a safe distance from the fire source when inserting the gas sampling tube into the container through the container's built-in detection port for the first time. The gas analyzer uses a combination of infrared spectroscopy and electrochemical sensors to detect multiple gas components at the same time, with a response time of less than 10 seconds. Characteristic organic volatiles are the products of thermal decomposition of lithium battery electrolytes. Their concentration directly reflects the degree of thermal decomposition inside the battery and is an important indicator for warning that the battery is about to explode. The system completes a comprehensive analysis of the gas composition every 10 seconds.

[0042] The image monitoring system is used to detect the integrity of the container structure and identify areas of structural deformation, bulging, leakage or welding failure. The image monitoring system uses a high-definition camera array with a resolution of no less than 1080P and is equipped with a zoom lens, which can achieve detailed observation at a long distance (more than 50 meters).

[0043] The risk assessment model calculates the quantitative risk value according to the following mathematical expression : ;in, is the temperature risk indicator, is the gas risk indicator, is a fire risk indicator. The structural risk index, the value range of each index is 0 to 1, 0 means no risk, 1 means the highest risk. On the one hand, structural integrity is the basic premise of unpacking operation. If the structure is severely damaged or the temperature is too high but the structure is not obviously deformed ( If the value is close to 1, there is a risk of deflagration). Even if other indicators are low, the overall risk is still very high. On the other hand, any one of the three indicators of temperature, gas and fire reaching a high risk level may lead to uncontrolled deflagration after unpacking, so the maximum value is taken as the basis for evaluation.

[0044] Temperature risk indicators The calculation method is: ;in, The safety temperature threshold is determined according to the type of lithium battery. The temperature for lithium iron phosphate battery is 80°C, and the temperature for ternary lithium battery is 60°C. The dangerous temperature threshold is 150°C for lithium iron phosphate batteries and 120°C for ternary lithium batteries. The safe temperature threshold represents the temperature point at which the battery begins to accelerate thermal decomposition. Below this temperature, the battery is in a relatively stable state; the dangerous temperature threshold represents the critical point at which the battery is about to enter irreversible thermal runaway. Above this temperature, the battery will quickly decompose and release a large amount of heat and gas.

[0045] Gas risk indicators The calculation method is: ;in, is the lower explosion limit concentration of hydrogen, taking 4% by volume; is the allowable exposure limit concentration of carbon monoxide, which is 50ppm; The concentration of characteristic organic volatiles is 200ppm. Hydrogen is one of the most dangerous gases produced during the thermal runaway of lithium batteries. It is extremely flammable and explosive. When its concentration reaches the lower explosion limit (LEL), it is very likely to explode once oxygen is introduced into the box.

[0046] Fire risk index The calculation method is: ;in, Cover the container surface area for fire; is the total surface area of ​​the container; The flame intensity is extracted from the real-time fire image through image processing algorithm; is the maximum reference value of flame intensity; is the number of fire sources detected; For reference, the maximum number of fire sources; , , are weight coefficients, and their values ​​are 0.5, 0.3, and 0.2 respectively. The value is the maximum flame brightness value based on historical data statistics, corresponding to complete combustion at a temperature of 2000K; The value is 10.

[0047] Structural risk indicators The calculation method is to take the average value of deformation, cracking and sealing failure: ,in, , and They are all extracted and quantified from real-time monitoring images through image recognition technology.

[0048] It is the visible deformation degree of the container, ranging from 0 to 1. , is the maximum bulge or depression volume; is the standard volume of a container; is the surface area where deformation occurs. About 33.1 cubic meters, The 0.1 and 0.2 in the formula are normalization coefficients, which means that when the maximum deformation volume reaches 10% of the container volume or the deformation area reaches 20% of the total surface area, the deformation index reaches the maximum value of 1.

[0049] It is the degree of cracks on the container surface, which is quantitatively described according to the number of cracks. If there is no crack, it is 0; if there are 1-5 cracks, it is 0.1, 0.2, 0.4, 0.6, 0.9 respectively; if there are more than 5 cracks, it is 1.

[0050] The degree of failure of the container seal, no visible smoke leakage and When 0.3 is taken; there is no visible smoke leakage and When there is visible smoke leakage, take 0.5; When , it takes 0.5; otherwise, it takes 1.

[0051] Analyze the changing trend of each parameter and calculate the risk growth rate: ;when >0 and continues to grow, the system automatically raises the risk level to level one and issues an early warning; represents the risk value at the current time t; Indicates the previous moment The risk value of is the sampling time interval, which is 5-10 seconds; the continuous growth is within three consecutive sampling cycles All of them are positive and show an increasing trend.

[0052] Quantitative risk value output by risk assessment model The levels are as follows: when When the risk is low, manual unpacking and inspection is allowed; when The risk is medium, and robots are needed to assist in unpacking while maintaining a safe distance; when It is a high risk, unpacking is prohibited, and further physical isolation and cooling measures are required; when It is an extremely high risk and emergency measures must be taken, including: sinking the container into water or using a fire extinguishing agent to inject it.

[0053] Fire development often has nonlinear characteristics and may deteriorate rapidly in a short period of time. By monitoring its trend, the system can predict the direction of fire development and respond in advance. When it detects that the risk value continues to rise and the rate of increase continues to accelerate within three consecutive sampling cycles (about 15-30 seconds), the system determines that the fire is accelerating, automatically increases the risk level and issues an early warning. For example, if the current risk value is 0.25 (low risk), but the risk growth rate continues to increase, the system will raise the risk level to medium risk (0.3-0.6) in advance to reserve more safe evacuation time for rescue personnel.

[0054] In order to verify the practical application effect of the present invention, the research team conducted system testing and simulation exercises in a port cargo area. The test environment is a standard 20-foot lithium battery transport container, which contains 720 lithium iron phosphate battery packs (model LFP100AH). The simulation exercise uses a controlled ignition method to induce thermal runaway of a single battery pack at a specific location in the container, and then the remote unpacking detection method and system of the present invention are used for monitoring and decision-making.

[0055] After the test started, the monitoring module quickly captured the initial fire signal. The thermal imaging system showed that the temperature near the ignition location rose rapidly. After 10 minutes, the maximum temperature Tmax in the hot spot area reached 92°C. The temperature risk index The calculated value is 0.17. The gas sampling and analysis system detects the hydrogen concentration 0.8% (20% of the lower explosion limit), carbon monoxide concentration The concentration of methylcyclohexane is 12ppm 45ppm, gas risk indicator The calculated value is 0.23.

[0056] The image monitoring system showed that the fire covered an area of ​​about 0.5 square meters (1.3% of the container surface area), the flame intensity was medium, and one obvious fire source was detected. The fire risk index The calculated value is 0.16. Structural monitoring shows that the container has no obvious deformation and cracks, but there is a small amount of smoke leakage at the door gap. The structural risk index The calculated value is 0.36.

[0057] The processing module comprehensively calculates the risk value to be 0.083, which is a low risk level. The decision-making module recommends manual unpacking and inspection, but at the same time the system detects that the risk growth rate has been on an upward trend for three consecutive cycles: the first cycle ΔR=0.002 / s, the second cycle ΔR=0.003 / s, and the current cycle ΔR=0.005 / s. Based on the risk growth trend analysis, the system automatically increases the first-level risk level to medium risk, issues a warning, and recommends the use of robot-assisted unpacking.

[0058] Following the system's advice, the rescue team dispatched a firefighting robot to open the container 30 meters away. After opening the container, thermal imaging revealed that the temperature of multiple battery packs inside the container rose rapidly, with the highest temperature rising from 92°C to 135°C within 5 minutes. At the same time, three new battery packs began to thermally run away. However, due to advance preparation, the robot immediately sprayed water mist and special fire extinguishing agents into the container, successfully controlling the spread of the fire and preventing a chain reaction of thermal runaway. In the end, the fire only destroyed 12 battery packs in the container (1.7% of the total), and the remaining 708 battery packs were preserved.

[0059] In comparison, a similar lithium battery container fire accident occurred in the same port one month before the system test. Due to the lack of scientific assessment methods, rescue workers directly opened the container to put out the fire, which caused the sudden inflow of air to cause the battery packs in the container to collectively thermally run away and explode, resulting in minor burns to two firefighters, and all the battery packs in the container were scrapped, with economic losses exceeding 2 million yuan.

[0060] Through comparative analysis, the remote unpacking detection method of the present invention has significant technical effects: through scientific evaluation and graded disposal, it effectively prevents the risk of explosion caused by blind unpacking and ensures the personal safety of rescue personnel; accurate risk assessment and timely early warning mechanism enable the rescue team to adopt appropriate unpacking methods and fire extinguishing strategies to minimize fire losses (from 100% loss to 1.7%); compared with traditional empirical judgment, this system provides quantitative risk assessment based on multi-parameter real-time monitoring, provides a scientific basis for rescue decision-making, and reduces human judgment errors; the system analyzes the risk growth trend and issues early warning, which wins valuable preparation time for rescue operations and allows calm response even when the fire situation deteriorates rapidly.

[0061] The actual application verification shows that the present invention successfully solves the key technical problems in the fire extinguishing process of lithium battery containers, and has important practical value and promotion significance.

[0062] Example 2 like Figure 2 , which is a schematic diagram of the composition of the remote opening detection system for lithium battery container fire extinguishing of the present invention, and is used to execute the method of the present invention. The system includes: a monitoring module, a processing module, a decision module and an execution module.

[0063] The monitoring module is used to monitor the target lithium battery container after physical isolation in real time, and obtain the real-time fire parameters of the target lithium battery container and the type of lithium batteries in the container; the monitoring module includes a thermal imaging system, a gas sampling and analysis system and an image monitoring system.

[0064] The monitoring module is deployed in a distributed manner, and data sharing and collaboration are achieved between subsystems through industrial-grade wireless networks. The thermal imaging system adopts dual-spectrum technology, integrating visible light and infrared thermal imagers, and can provide clear images under various lighting conditions with thermal sensitivity better than 0.05°C. The gas sampling and analysis system adopts a modular design, including an electrochemical sensor group, an infrared spectrometer and a micro gas chromatograph, which can detect at least 10 gas components at the same time. The image monitoring system consists of multiple high-definition cameras, covering all angles of the container, equipped with an automatic zoom function, which can achieve seamless switching from panoramic monitoring to detailed close-ups. All monitoring equipment adopts an explosion-proof design and can work stably in harsh environments such as high temperature, high humidity, and toxic gases.

[0065] The processing module is used to run the risk assessment model, input the real-time fire parameters and the lithium battery type into the risk assessment model, and obtain a quantitative risk value. , the risk assessment model calculates the quantitative risk value according to the following mathematical expression: ;in, is the temperature risk indicator, is the gas risk indicator, is a fire risk indicator. It is an indicator of structural risk.

[0066] The processing module uses a high-performance edge computing server equipped with an industrial-grade GPU accelerator card to support real-time image processing and deep learning reasoning. The system runs a risk assessment algorithm designed specifically for lithium battery fires, adopts a multi-threaded parallel processing architecture, can process multiple data streams simultaneously, and has a calculation delay of less than 100 milliseconds. The risk assessment model is based on a hybrid reasoning method combining Bayesian networks and fuzzy logic, which can process multi-source heterogeneous data and scientifically model uncertain factors. The system also integrates an adaptive learning module that can continuously optimize model parameters based on new case data to improve assessment accuracy. All calculation processes and intermediate results are saved to the database to support post-analysis and model verification.

[0067] The decision module is used to determine the quantitative risk value Determine whether to open the target lithium battery container; when When the decision module issues an instruction to allow manual unpacking and inspection; When , the decision module issues an instruction that the robot needs to assist in unpacking and maintain a safe distance; when When , the decision module issues an instruction to prohibit opening the box and take further physical isolation and cooling measures; when When an emergency occurs, the decision module issues an instruction to take emergency measures.

[0068] The decision-making module adopts an expert system architecture and integrates a rule-based reasoning engine and a decision support system. In addition to automatically generating decision recommendations based on the risk value R, the system also provides a detailed risk factor analysis report, indicating the dominant risk factors and their contribution ratios. The decision-making module is equipped with a human-computer interaction interface, which displays real-time monitoring data, risk assessment results and decision recommendations on a large screen to support on-site commanders in making informed decisions. The system is also equipped with an emergency plan library, which provides standard operating procedures (SOPs) and resource allocation recommendations for different risk levels and scenarios. In extreme cases of communication interruption, the decision-making module can run autonomously and execute preset safety assurance procedures to ensure the continuity and safety of rescue operations.

[0069] The execution module is used to perform corresponding operations according to the instructions of the decision module, including manual unpacking operations, robot-assisted unpacking operations, and emergency disposal operations. The execution module integrates a variety of rescue equipment and technical means, and can deal with fires of different risk levels. Manual unpacking operations are equipped with a full set of personal protective equipment, including high-temperature resistant protective clothing, positive pressure air respirators and vital signs monitoring systems; robot-assisted unpacking operations use special firefighting robots with functions such as remote control, explosion-proof design, high-pressure water guns and mechanical arms, with a maximum working radius of up to 100 meters; emergency disposal operations include large-scale lifting equipment, special fire extinguishing agent injection systems and container rapid immersion devices; the execution module is also equipped with a drone aerial monitoring system to provide a bird's-eye view to assist in command and decision-making.

[0070] All execution equipment maintains real-time data links with the decision-making center, supports remote monitoring and emergency stop functions, and maximizes the safety of rescue personnel.

[0071] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A remote unpacking detection method for lithium battery container fire extinguishing, which determines the unpacking risk of a target lithium battery container where a fire occurs, and is characterized in that: The method comprises the following steps: Step S1, real-time monitoring of the physically isolated target lithium battery container to obtain the real-time fire parameters of the target lithium battery container and the type of lithium batteries in the container; The real-time fire parameters include at least: container surface temperature distribution data and the highest temperature value of the hot spot area, internal gas component concentration data, fire coverage area, flame intensity, number of fire sources and container structural integrity data; Step S2, establishing a risk assessment model, wherein the risk assessment model is used to calculate the risk of uncontrolled explosion and combustion of the target lithium battery container after unpacking, and inputting the collected data into the risk assessment model to obtain real-time quantitative results; Step S3, determining whether to open the target lithium battery container according to the real-time quantitative result.

2. The method according to claim 1, characterized in that In step S1, obtaining the real-time fire parameters of the target lithium battery container includes: Use a thermal imaging system to monitor the surface temperature distribution of the target lithium battery container and record the highest temperature value in the hot spot area and temperature distribution data; Insert a gas sampling tube into the container through the detection port of the container to collect internal gas samples, analyze the collected gas samples in real time, and detect the hydrogen concentration. , Carbon monoxide concentration , CO2 concentration and characteristic volatile organic compound concentrations The characteristic organic volatiles are: for lithium iron phosphate batteries, methylcyclohexane C7H 14 Concentration, for ternary lithium batteries, analysis of dimethylcyclohexane C8H 16 concentration; The integrity of the container structure is tested through the image monitoring system to identify areas of structural deformation, bulging, leakage or welding failure.

3. The method according to claim 2, characterized in that The risk assessment model calculates the quantitative risk value according to the following mathematical expression : ;in, is the temperature risk indicator, is the gas risk indicator, is a fire risk indicator. It is a structural risk indicator, ranging from 0 to 1.

4. The method according to claim 3, characterized in that Temperature risk indicators The calculation method is: ;in, The safety temperature threshold is determined according to the type of lithium battery. The temperature for lithium iron phosphate battery is 80°C, and the temperature for ternary lithium battery is 60°C. The dangerous temperature threshold is 150°C for lithium iron phosphate batteries and 120°C for ternary lithium batteries.

5. The method according to claim 3, characterized in that: Gas risk indicators The calculation method is: ;in, is the lower explosion limit concentration of hydrogen, taking 4% by volume; is the allowable exposure limit concentration of carbon monoxide, which is 50ppm; The exposure limit concentration of characteristic volatile organic compounds is 200 ppm.

6. The method according to claim 3, characterized in that Fire risk index The calculation method is: ;in, Cover the container surface area for fire; is the total surface area of ​​the container; The flame intensity is extracted from the real-time fire image through image processing algorithm; is the maximum reference value of flame intensity; is the number of fire sources detected; For reference, the maximum number of fire sources; , , are weight coefficients, and their values ​​are 0.5, 0.3, and 0.2 respectively.

7. The method according to claim 3, characterized in that Structural risk indicators The calculation method is to take the average value of deformation, cracking and sealing failure: ,in, , and They are all extracted and quantified from real-time monitoring images using image recognition technology; It is the visible deformation degree of the container, ranging from 0 to 1. , is the maximum bulge or depression volume; is the standard volume of a container; is the surface area where deformation occurs; The degree of cracks on the container surface is quantitatively described according to the number of cracks. If there is no crack, it is 0; if there are 1-5 cracks, it is 0.1, 0.2, 0.4, 0.6, 0.9 respectively; if there are more than 5 cracks, it is 1; The degree of failure of the container seal, no visible smoke leakage and When 0.3 is taken; there is no visible smoke leakage and When there is visible smoke leakage, take 0.5; When , it takes 0.5; otherwise, it takes 1.

8. The method according to any one of claims 1 to 7, characterized in that: Analyze the changing trend of each parameter and calculate the risk growth rate: ;when >0 and continues to grow, the system automatically raises the risk level to level one and issues an early warning; represents the risk value at the current time t; Indicates the previous moment The risk value of is the sampling time interval, the value is 5-10 seconds; The continuous growth is within three consecutive sampling periods. All of them are positive and show an increasing trend.

9. The method according to any one of claims 1 to 7, characterized in that: Quantitative risk value output by risk assessment model The levels are as follows: when When the risk is low, manual unpacking and inspection is allowed; when The risk is medium, and robots are needed to assist in unpacking while maintaining a safe distance; when It is a high risk, unpacking is prohibited, and further physical isolation and cooling measures are required; when It is an extremely high risk and emergency measures must be taken, including: sinking the container into water or using a fire extinguishing agent to inject it.

10. A remote opening detection system for lithium battery container fire extinguishing, used to execute the method according to any one of claims 1 to 9, characterized in that: The system includes: a monitoring module, a processing module, a decision module and an execution module; The monitoring module is used to monitor the target lithium battery container after physical isolation in real time, and obtain the real-time fire parameters of the target lithium battery container and the type of lithium batteries in the container; the monitoring module includes a thermal imaging system, a gas sampling and analysis system and an image monitoring system; The processing module is used to run the risk assessment model, input the real-time fire parameters and the lithium battery type into the risk assessment model, and obtain a quantitative risk value. , the risk assessment model calculates the quantitative risk value according to the following mathematical expression: ;in, is the temperature risk indicator, is the gas risk indicator, is a fire risk indicator. It is a structural risk indicator; The decision module is used to determine the quantitative risk value Determine whether to open the target lithium battery container; when When the decision module issues an instruction to allow manual unpacking and inspection; When , the decision module issues an instruction that the robot needs to assist in unpacking and maintain a safe distance; when When , the decision module issues an instruction to prohibit opening the box and take further physical isolation and cooling measures; when When the emergency situation occurs, the decision module issues an instruction to take emergency measures; The execution module is used to perform corresponding operations according to the instructions of the decision module, including manual unpacking operations, robot-assisted unpacking operations, and emergency handling operations.

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