Tunnel fire prevention and control method, system, medium and equipment for hydrogen fuel cell vehicle

Abstract

The invention provides a tunnel fire prevention and control method and system for a hydrogen fuel cell vehicle, a medium and equipment, and relates to the technical field of tunnel safety. According to the method, traffic accident event data in a tunnel, a vehicle type recognition result and multi-point real-time hydrogen concentration data are collected, and pre-stored tunnel environment parameters are combined; and quantitatively calculating an individual risk value and a social risk value through a quantitative risk assessment model. And when the personal risk value exceeds a preset personal risk intervention threshold value, the system automatically starts a tunnel ventilation regulation and control strategy and an emergency response strategy, dynamically adjusts the urgency level of an emergency response measure according to the social risk value, and continuously operates until the personal risk value is reduced to an acceptable level. According to the method, early quantitative evaluation and active and accurate prevention and control of the fire risk of the hydrogen fuel cell vehicle in the tunnel are realized, the scientificity of emergency decision and the timeliness of response measures are effectively improved, and the severity of casualties and accident consequences is remarkably reduced.

Description

Technical Field

[0001] This application relates to the field of tunnel safety technology, and in particular to a method, system, medium and equipment for preventing tunnel fires in hydrogen fuel cell vehicles. Background Technology

[0002] The application of hydrogen fuel cell vehicles has expanded from public transportation demonstrations to commuter buses, urban delivery, cold chain transportation, long-haul logistics, municipal sanitation, and shared mobility. However, hydrogen molecules are small and prone to leakage, and have a wide flammability range in air and a low ignition energy, making them susceptible to jet flames or even deflagration after leakage. Hydrogen storage tanks operate at pressures as high as 70 MPa; if a fire breaks out in an accident and the hydrogen is not released in time, it could trigger an explosion, producing fireballs, shock waves, and high-speed debris with severe consequences. Especially in semi-enclosed spaces such as tunnels, hydrogen accumulation can easily lead to explosions, posing a significant risk. Current technologies have significant shortcomings: they mostly employ passive response mechanisms, typically activating fire suppression only after an open flame or high temperature is detected, missing the optimal time to control the hydrogen cloud; monitoring methods are limited, relying on traditional fire detectors or individual hydrogen sensors, making early comprehensive risk assessment difficult; and there is a lack of quantitative risk assessment capabilities, making it impossible to scientifically quantify the probability of an explosion and the severity of its consequences, with prevention and control decisions often based on experience and lacking supporting evidence. Summary of the Invention

[0003] This application provides a method, system, medium, and equipment for preventing and controlling tunnel fires in hydrogen fuel cell vehicles, in order to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions that enable early identification, dynamic assessment, and proactive intervention of hydrogen leakage and fire risks, significantly improving the safety and emergency response efficiency of hydrogen fuel cell vehicles operating in tunnels.

[0004] On the one hand, this application provides a method for preventing tunnel fires in hydrogen fuel cell vehicles, including the following steps: Collect fire prevention and control data inside the tunnel, including traffic accident data, vehicle type identification results, and multi-point real-time hydrogen concentration data. Based on the fire prevention and control data and pre-stored tunnel environmental parameters, a quantitative risk assessment model is used to calculate individual risk values ​​and social risk values. The individual risk value represents the probability of death of an individual at any location in the tunnel within a unit of time, and the social risk value represents the cumulative frequency of multiple casualties caused by a single accident. The tunnel environmental parameters include, but are not limited to, tunnel geometry, longitudinal ventilation speed, wind direction, ambient temperature, and humidity. When the personal risk value exceeds the preset personal risk intervention threshold, the tunnel ventilation control strategy and emergency response strategy are activated and executed, and the urgency level of the emergency response strategy is dynamically adjusted according to the social risk value until the personal risk value drops below the preset acceptable personal risk threshold.

[0005] Furthermore, the collection of fire prevention and control data within the tunnel includes the following steps: The accident perception unit detects vehicle collisions, sudden stops, or abnormal parking events to obtain traffic accident event data. The visual confirmation unit performs image recognition on the accident vehicle to confirm that it is a hydrogen fuel cell vehicle, thereby obtaining the vehicle type identification result. The real-time hydrogen concentration data at multiple points is obtained through a hydrogen monitoring unit; the hydrogen monitoring unit includes hydrogen sensors distributed along the longitudinal and transverse directions of the tunnel.

[0006] Furthermore, the quantitative risk assessment model includes a consequence quantification sub-model, a leakage frequency quantification sub-model, and a risk calculation unit; Based on the fire prevention and control data and pre-stored tunnel environmental parameters, the individual risk value and social risk value are calculated using a quantitative risk assessment model, including the following steps: Based on the vehicle type identification results and traffic accident event data, the pre-stored hydrogen leak scenario library is called to determine the hydrogen leak rate and hydrogen leak location corresponding to the current accident. Based on the hydrogen leakage rate, hydrogen leakage location, multi-point real-time hydrogen concentration data, and tunnel environmental parameters, the consequence quantification sub-model is used to predict at least one of the hydrogen diffusion concentration field, potential explosion overpressure field, or thermal radiation field in the tunnel, as the consequence quantification result; the consequence quantification sub-model is trained based on computational fluid dynamics simulation data of various hydrogen leakage scenarios. By combining historical accident statistics, the annual frequency of hydrogen leakage in the current accident scenario is calculated using the leakage frequency quantification sub-model. The risk calculation unit spatially integrates the annual frequency of hydrogen leakage with the quantification of consequences to obtain the individual risk value at each location within the tunnel. Based on the spatial distribution of the individual risk value and combined with the personnel distribution information within the tunnel, the social risk value is calculated.

[0007] Furthermore, the calculation of the annual frequency of hydrogen leakage in the current accident scenario by combining historical accident statistics and using the leakage frequency quantification sub-model includes the following steps: Assess the severity of the accidents based on the traffic accident data. Based on the vehicle type identification results, determine whether the vehicle involved in the accident was a hydrogen fuel cell vehicle; Based on historical statistics of hydrogen fuel cell vehicle accidents, the conditional probability of hydrogen storage system damage under the corresponding accident severity is obtained, as well as the conditional probability of hydrogen leakage after the hydrogen storage system is damaged. Using the leakage frequency quantification sub-model, the annual frequency of traffic accidents, the conditional probability of damage to the hydrogen storage system, and the conditional probability of hydrogen leakage are multiplied to obtain the annual frequency of hydrogen leakage in the current accident scenario.

[0008] Furthermore, the emergency response strategy specifically includes: determining the boundary of the high-risk area based on the spatial distribution of the individual risk value, generating a recommended escape route based on the boundary of the high-risk area, personnel location information and tunnel structural characteristics, and outputting an evacuation instruction containing the boundary of the high-risk area and the recommended escape route; Based on the preset range of the social risk value, the urgency level of the evacuation order is determined, and at least one of the following operations is performed in conjunction: broadcast alarm, tunnel entrance closure control, and external rescue system notification.

[0009] Furthermore, the method also includes: A risk contour map is generated based on the individual risk value, and the boundaries of high-risk areas are extracted based on the risk contour map. Based on the individual risk value, social risk value, and accident location, the traffic light status inside the tunnel is dynamically controlled, and differentiated content is displayed in segments on multiple variable message signs; the differentiated content includes the boundary of the high-risk area, recommended escape routes, and the urgency level of the evacuation instructions.

[0010] Furthermore, the tunnel ventilation control strategy aims to reduce the highest personal risk value in the tunnel to below the acceptable personal risk threshold, including: automatically adjusting the start-stop status, operating frequency and wind direction of the fans in the tunnel, controlling the wind speed and air volume distribution of the longitudinal ventilation system, and linking the opening or closing of the smoke exhaust valve and make-up air device near the accident area.

[0011] On the other hand, this application provides a tunnel fire prevention system for hydrogen fuel cell vehicles, configured to perform the aforementioned tunnel fire prevention method for hydrogen fuel cell vehicles, the system comprising: The multi-source sensing module is used to collect fire prevention and control data in the tunnel, including an accident sensing unit, a visual confirmation unit, and a hydrogen monitoring unit; the fire prevention and control data includes traffic accident data, vehicle type identification results, and multi-point real-time hydrogen concentration data. The quantitative risk assessment module is used to calculate individual risk values ​​and social risk values ​​based on the fire prevention and control data and pre-stored tunnel environmental parameters through a quantitative risk assessment model. The individual risk value represents the probability of death for an individual at any location within the tunnel within a unit of time, and the social risk value represents the cumulative frequency of multiple casualties resulting from a single accident. The tunnel environmental parameters include, but are not limited to, tunnel geometry, longitudinal ventilation speed, wind direction, ambient temperature, and humidity. The dynamic prevention and control execution module is used to activate and execute tunnel ventilation control strategy and emergency response strategy when the personal risk value exceeds the preset personal risk intervention threshold, and dynamically adjust the urgency level of the emergency response strategy according to the social risk value until the personal risk value drops below the preset acceptable personal risk threshold. The dynamic prevention and control execution module includes an intelligent ventilation control unit and a collaborative early warning and evacuation unit; The intelligent ventilation control unit is used to execute tunnel ventilation control strategies; The collaborative early warning and evacuation unit is used to dynamically control the status of traffic lights in the tunnel based on the individual risk value, social risk value, and accident location, and to display differentiated content in segments on multiple variable message signs; the differentiated content includes the boundary of high-risk areas, recommended escape routes, and the urgency level of evacuation instructions.

[0012] On the other hand, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for preventing tunnel fires in hydrogen fuel cell vehicles.

[0013] On the other hand, this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the aforementioned method for preventing tunnel fires in hydrogen fuel cell vehicles.

[0014] The beneficial effects of this application are as follows: This application provides a method for preventing and controlling fires in tunnels involving hydrogen fuel cell vehicles. This method collects traffic accident data, vehicle type identification results, and real-time hydrogen concentration data from multiple points within the tunnel. Combined with pre-stored environmental parameters such as tunnel geometry, longitudinal ventilation speed, wind direction, ambient temperature, and humidity, it quantitatively calculates an individual risk value representing the probability of death per unit time, and a social risk value reflecting the cumulative frequency of multiple casualties resulting from a single accident. When the individual risk value exceeds a preset individual risk intervention threshold, the system automatically activates tunnel ventilation control and emergency response strategies, and dynamically adjusts the urgency level of the emergency response based on the social risk value, continuing operation until the individual risk value drops to an acceptable level. This method achieves early quantitative assessment and proactive, precise prevention and control of fire risks for hydrogen fuel cell vehicles in tunnels, effectively improving the scientific nature of emergency decision-making and the timeliness of response measures, and significantly reducing the severity of casualties and accident consequences. This application also provides corresponding systems, media, and equipment. The beneficial effects of the systems, media, and equipment are similar to those of the method and will not be elaborated further here.

[0015] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 This is a flowchart of the method for preventing and controlling tunnel fires for hydrogen fuel cell vehicles provided in this application; Figure 2 This is a structural diagram of the hydrogen fuel cell vehicle tunnel fire prevention system provided in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0020] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] Hydrogen fuel cell vehicles are rapidly gaining popularity in the transportation sector due to their zero emissions, high efficiency, and rapid refueling capabilities. Their applications have expanded from early urban public transport demonstrations to various sub-sectors including commuter buses, urban delivery, cold chain transportation, long-haul logistics, municipal sanitation, and shared mobility. However, hydrogen, as an energy carrier with extremely unique physicochemical properties, has small molecular size and high permeability, making it highly susceptible to leakage through valves, joints, and other connection points. Furthermore, hydrogen's flammability in air ranges from 4% to 75%, with a minimum ignition energy of only 0.02 millijoules, far lower than common flammable gases. This means that even a tiny static electricity or electric spark can ignite leaked hydrogen, creating a high-speed jet flame. If the leak is large and the confined space is poorly ventilated, it can rapidly develop into a deflagration or even an explosion, causing serious safety accidents.

[0023] Furthermore, most current mainstream hydrogen fuel cell vehicles use high-pressure gaseous hydrogen storage, with the nominal operating pressure of the hydrogen storage cylinders typically reaching 70 MPa. In extreme conditions such as traffic accidents, if the vehicle catches fire and the hydrogen storage system fails to release internal pressure in time through safety relief devices, the cylinder may rupture catastrophically due to thermal runaway, instantly releasing a large amount of high-pressure hydrogen gas accompanied by a violent explosion, generating a high-temperature fireball, a strong shock wave, and high-speed flying debris, posing a significant threat to surrounding personnel, facilities, and the environment. This risk is particularly prominent in typical semi-enclosed spaces such as tunnels. Tunnels have limited internal space, complex ventilation conditions, and limited evacuation routes. Once a collision or leak involving a hydrogen fuel cell vehicle occurs, hydrogen gas can easily accumulate in the ceiling area, forming a flammable cloud. Upon encountering an ignition source, this cloud could potentially trigger a chain explosion with unimaginable consequences. Therefore, there is widespread public concern about the safety of hydrogen fuel cell vehicles operating in sensitive areas such as tunnels, necessitating the establishment of a scientific, efficient, and proactive fire prevention system.

[0024] Currently, the technology for preventing and controlling fire risks from hydrogen fuel cell vehicles in tunnels is still in its early stages, mainly relying on traditional fire protection concepts and general monitoring methods, which have significant shortcomings. First, existing systems mostly adopt a passive response mechanism, typically triggering alarms and fire suppression measures only after smoke detectors, heat-sensing cables, or flame detectors confirm open flames or high temperatures. By this time, the hydrogen cloud has often already formed and may be ignited, missing the golden window for suppressing the evolution of the risk. Second, monitoring methods are singular and lack specificity. Most tunnels still use sensor networks suitable for traditional gasoline vehicle fires, deploying only a few fixed hydrogen detectors or no dedicated hydrogen monitoring equipment. This fails to achieve full coverage, multi-point linkage, and early identification of hydrogen leaks, making it difficult to support a comprehensive assessment of the risk situation. Third, existing technologies generally lack quantitative risk assessment capabilities, unable to dynamically calculate the probability of individual death and the frequency of mass casualties based on real-time leak data, vehicle status, and tunnel environmental parameters. This leads to emergency decision-making heavily relying on human experience or preset rules, which are neither accurate nor flexible, and difficult to adapt to complex and ever-changing accident scenarios.

[0025] Furthermore, even in some advanced tunnels that have introduced ventilation control systems, their control strategies are mostly based on fixed logic or simple threshold triggers, failing to establish a closed-loop linkage between risk levels and emergency measures such as ventilation intensity, evacuation orders, and traffic control. Especially when facing accident scenarios with high social risks and the potential for mass casualties, existing systems cannot dynamically adjust response levels according to the severity of the risk, easily leading to resource misallocation or delayed responses. In summary, the current technological system has significant shortcomings in terms of the comprehensiveness of risk perception, the scientific nature of assessment, the proactivity of response, and the adaptability of strategies, making it difficult to meet the high standards of inherent tunnel safety requirements in the context of large-scale application of hydrogen fuel cell vehicles. Therefore, there is an urgent need for a new fire prevention and control method that integrates multi-source perception, quantitative risk modeling, and intelligent decision control to achieve early detection, accurate assessment, rapid response, and optimal handling of risks associated with hydrogen fuel cell vehicles operating in tunnels.

[0026] To address the aforementioned issues, this application provides a method, system, medium, and equipment for tunnel fire prevention and control of hydrogen fuel cell vehicles. It constructs an active fire prevention and control system for hydrogen fuel cell vehicles in tunnel scenarios. By simultaneously collecting traffic accident data, vehicle type identification results, and real-time hydrogen concentration data from multiple points, and integrating environmental parameters such as tunnel geometry, ventilation speed, wind direction, temperature, and humidity, it achieves dynamic quantitative calculation of individual and social risk values. The individual risk value reflects the probability of death for an individual at any location within the tunnel per unit time, while the social risk value represents the cumulative frequency of multiple injuries or deaths caused by a single accident. The system uses a risk threshold as a trigger condition. Once the individual risk value exceeds a preset intervention threshold, ventilation control and emergency response strategies are immediately activated. The urgency level of emergency measures is dynamically adjusted based on the social risk value, forming an integrated mechanism of risk perception, quantitative assessment, graded response, and closed-loop control. This allows for precise intervention in the early stages of hydrogen leakage, effectively preventing the occurrence or escalation of fires or explosions, and significantly improving the safety level of tunnel operations and the scientific rigor and timeliness of emergency response.

[0027] First, the tunnel fire prevention method for hydrogen fuel cell vehicles provided in this application will be described in detail below with reference to the accompanying drawings.

[0028] Reference Figure 1 The implementation process of the tunnel fire prevention method for hydrogen fuel cell vehicles provided in this application embodiment includes, but is not limited to, the following steps.

[0029] Step S110: Collect fire prevention and control data inside the tunnel.

[0030] The fire prevention and control data includes traffic accident data, vehicle type identification results, and real-time hydrogen concentration data at multiple points.

[0031] In step S110, the necessary basic information to support subsequent risk assessment and emergency decision-making is acquired. This step uses various sensing devices deployed within the tunnel to collect three types of key data in real time: traffic accident event data to identify whether a collision, rollover, or other abnormal event that may cause hydrogen leakage has occurred; vehicle type identification results to determine whether the involved or traveling vehicles are hydrogen fuel cell vehicles, thereby determining whether there is a hydrogen source risk; and multi-point real-time hydrogen concentration data to reflect whether hydrogen is leaking and its diffusion trend from a spatial distribution perspective. These three types of data together constitute the preliminary perception layer of the potential fire risk status within the tunnel, providing real, dynamic, and multi-dimensional input for subsequent quantitative analysis.

[0032] Step S120: Based on fire prevention and control data and pre-stored tunnel environmental parameters, calculate the individual risk value and social risk value using a quantitative risk assessment model.

[0033] The individual risk value represents the probability of death for an individual at any location within the tunnel per unit of time, while the social risk value represents the cumulative frequency of multiple injuries or deaths resulting from a single accident. Tunnel environmental parameters include, but are not limited to, tunnel geometry, longitudinal ventilation velocity, wind direction, ambient temperature, and humidity.

[0034] In step S120, the collected fire prevention data is fused with the inherent environmental characteristics of the tunnel to create a model, enabling the scientific quantification of safety risks. This step utilizes pre-stored environmental parameters such as tunnel geometry, longitudinal ventilation speed, wind direction, ambient temperature, and humidity, combined with real-time input information on accidents, vehicle types, and hydrogen concentration, to output individual and social risk values ​​through a quantitative risk assessment model. The individual risk value precisely characterizes the probability of death due to a hydrogen-related accident at any location within the tunnel within a unit of time, reflecting the degree of exposure of a local individual to danger. The social risk value, from a group perspective, characterizes the cumulative frequency of multiple casualties that a single accident may cause, reflecting the widespread and severe consequences of the accident. These two indicators together constitute the core output of the risk assessment, providing an objective and comparable numerical basis for subsequent tiered response.

[0035] Step S130: When the individual risk value exceeds the preset individual risk intervention threshold, the tunnel ventilation control strategy and emergency response strategy are activated and executed, and the urgency level of the emergency response strategy is dynamically adjusted according to the social risk value until the individual risk value drops below the preset acceptable individual risk threshold.

[0036] In step S130, a closed-loop proactive intervention is implemented based on the quantified risk results. When the calculated individual risk value exceeds a preset individual risk intervention threshold, the system automatically triggers tunnel ventilation control strategies and emergency response strategies. The former aims to accelerate hydrogen dilution and discharge by adjusting the operation of the fans, while the latter includes measures such as alarms, traffic control, and personnel evacuation. During this process, the social risk value is used as a dynamic adjustment factor to determine the degree of group impact that the current accident may cause, thereby determining the urgency level of the emergency response strategy. For example, high social risk corresponds to a higher level of alert and faster evacuation orders. The entire process continues until the individual risk value falls back to an acceptable threshold.

[0037] Generally, setting an individual risk intervention threshold higher than an individual risk acceptable threshold forms the logical basis for risk response and termination.

[0038] The individual risk intervention threshold serves as a trigger for initiating proactive prevention and control measures. When the individual risk value calculated by the system (i.e., the probability of death for an individual at a certain location within the tunnel per unit time) exceeds the preset threshold, it indicates that the current accident scenario has developed to the point where it may pose a significant threat to personnel safety, and immediate intervention is necessary. At this time, the system automatically activates tunnel ventilation control strategies (such as adjusting fan operating parameters) and emergency response strategies (such as issuing alarms and traffic control). Therefore, the individual risk intervention threshold is essentially used to ensure intervention and control in the early stages of risk evolution to prevent the situation from escalating.

[0039] The acceptable individual risk threshold serves as one of the exit criteria for terminating or downgrading an emergency response. This threshold represents the level of mortality risk an individual faces in the current scenario, which has been reduced to below a generally accepted safety level in social or engineering practice. Only when the individual risk value continues to decline and remains stably below this threshold can it be said that the local individual safety threat has been essentially eliminated.

[0040] In summary, the individual risk intervention threshold, used to determine "when to begin intervention," reflects the initiative and timeliness of risk prevention and control; while the individual risk acceptable threshold, used to determine "when to stop intervention," reflects the adequacy and safety of risk management. These two thresholds complement each other, forming a closed-loop control logic that ensures prevention and control measures are neither prematurely interrupted nor unnecessarily prolonged, thereby achieving scientific, precise, and efficient safety management.

[0041] In some embodiments of this application, step S110 involves collecting fire prevention data within the tunnel, including the following steps.

[0042] Step S210: Detect vehicle collision, emergency stop, or abnormal parking events through the accident perception unit to obtain traffic accident event data.

[0043] In step S210, the operational status of vehicles traveling within the tunnel is monitored in real time by the accident sensing unit to identify initial events that may trigger hydrogen leaks or fires. This step focuses on detecting typical traffic accident characteristics such as collisions, sudden stops, or abnormal parking. Once such events are identified, corresponding traffic accident event data is generated. This data serves as one of the triggering criteria for the risk assessment process, used to determine whether potential safety threat scenarios exist within the tunnel, and provides an event basis for determining whether subsequent vehicle type identification and gas monitoring need to be initiated.

[0044] Step S220: The visual confirmation unit performs image recognition on the accident vehicle to confirm that it is a hydrogen fuel cell vehicle, thereby obtaining the vehicle type identification result.

[0045] In step S220, a visual verification unit is used to acquire and intelligently analyze images of vehicles involved in or suspected of being involved in accidents to accurately determine their power type. This step uses image recognition of vehicle appearance features, markings, or structures to confirm whether the vehicle in question is a hydrogen fuel cell vehicle. If it is confirmed to be a hydrogen fuel cell vehicle, a corresponding vehicle type identification result is generated. This information is crucial because it directly determines whether it is necessary to initiate specific monitoring and risk assessment for hydrogen leaks, avoiding misjudgment of non-hydrogen fuel cell vehicles that could lead to resource waste or response delays.

[0046] Step S230: Obtain real-time hydrogen concentration data at multiple points through the hydrogen monitoring unit.

[0047] The hydrogen monitoring unit includes hydrogen sensors distributed along the longitudinal and transverse directions of the tunnel.

[0048] In step S230, a hydrogen sensor network distributed longitudinally and laterally within the tunnel is used to acquire real-time hydrogen concentration values ​​at multiple points in space. This step emphasizes the coverage of the sensor deployment and the real-time nature of data acquisition, aiming to comprehensively capture the distribution and diffusion trends of hydrogen in the three-dimensional space of the tunnel. The obtained multi-point real-time hydrogen concentration data can reflect whether a leak has occurred, the approximate location of the leak source, and the evolution of the hydrogen cloud, serving as an indispensable key input parameter for quantifying individual and social risks.

[0049] In some embodiments of this application, a quantitative risk assessment model is used to quantify the potential fire risk within a tunnel, including a consequence quantification sub-model, a leakage frequency quantification sub-model, and a risk calculation unit. Based on real-time collected traffic accident data, hydrogen concentration data, and tunnel environmental data, this module calculates the individual and social risk values ​​at specified locations within the tunnel using a built-in risk quantification model. This module achieves full-process quantification from accident events to risk values, providing a scientific basis for the formulation of subsequent emergency response strategies.

[0050] In some embodiments of this application, in step S120, based on fire prevention and control data and pre-stored tunnel environmental parameters, a quantitative risk assessment model is used to calculate the individual risk value and the social risk value, including the following steps.

[0051] Step S310: Based on the vehicle type identification results and traffic accident event data, call the pre-stored hydrogen leak scenario library to determine the hydrogen leak rate and hydrogen leak location corresponding to the current accident.

[0052] In step S310, based on the acquired vehicle type identification results and traffic accident event data, the hydrogen leakage characteristic parameters corresponding to the current accident are matched and determined from the pre-stored hydrogen leakage scenario library. The core of this step is to map the actual accident type and vehicle attributes to a specific leakage model, thereby clarifying the hydrogen leakage rate and leakage location required for this risk assessment. These two parameters are the basic inputs for subsequent simulations of hydrogen diffusion behavior and assessment of accident consequences, ensuring that risk calculations can be customized for specific accident scenarios, rather than using general or hypothetical leakage conditions.

[0053] Step S320: Based on the hydrogen leakage rate, hydrogen leakage location, multi-point real-time hydrogen concentration data and tunnel environmental parameters, use the consequence quantification sub-model to predict at least one of the hydrogen diffusion concentration field, potential explosion overpressure field or thermal radiation field in the tunnel, as the consequence quantification result.

[0054] The consequence quantification sub-model was trained based on computational fluid dynamics simulation data from various hydrogen leakage scenarios.

[0055] In step S320, the consequence quantification sub-model is used to predict the potential physical hazards of the accident based on the determined hydrogen leakage rate, leakage location, real-time hydrogen concentration data at multiple points, and tunnel environmental parameters. The consequence quantification results output by this step include the concentration field formed by hydrogen diffusion within the tunnel, and at least one of the potential explosive overpressure field or thermal radiation field that may arise from this. These field quantities describe the spatial distribution intensity of the hazardous effects, directly reflecting the degree of explosion or combustion threat faced by different areas. The consequence quantification sub-model itself is built upon a large amount of computational fluid dynamics simulation data of hydrogen leakage scenarios, possessing high physical realism and predictive reliability.

[0056] Step S330: Combining historical accident statistics, calculate the annual frequency of hydrogen leakage in the current accident scenario using the leakage frequency quantification sub-model.

[0057] In step S330, the probability of hydrogen leakage in the current accident scenario is characterized by frequency representation using a leakage frequency quantification sub-model combined with historical accident statistics. The output of this step is the annual hydrogen leakage frequency, which is the expected number of hydrogen leakage events per unit year under similar accident conditions. This frequency indicator reflects the probability of an accident scenario occurring and serves as a crucial bridge connecting the likelihood of an accident with the severity of its consequences, providing the necessary statistical basis for subsequently multiplying the probability by the consequences to generate a risk value.

[0058] Step S340: The annual frequency of hydrogen leakage and the quantification results of consequences are spatially fused by the risk calculation unit to obtain the personal risk value at each location in the tunnel. Based on the spatial distribution of the personal risk value and combined with the personnel distribution information in the tunnel, the social risk value is calculated.

[0059] In step S340, the annual frequency of hydrogen leaks obtained in step S330 is multiplied point-by-point in the spatial dimension by the consequence quantification result output in step S320, thereby calculating the individual risk value corresponding to each location within the tunnel. This step achieves probability-consequence coupling of risk, and the result is the probability of death of an individual at each spatial point due to a hydrogen-related accident per unit time. This value not only has a clear physical meaning but can also be directly compared with a preset safety threshold to support subsequent decisions on whether to initiate intervention measures.

[0060] Furthermore, based on the spatial distribution of the calculated individual risk values ​​and incorporating information on the distribution of people within the tunnel, a comprehensive social risk value is derived. This step focuses on the safety impact at the group level. By integrating individual risks at different locations and the corresponding number or density of people in those areas, the cumulative frequency of multiple injuries or deaths that a single accident may result in is quantified. The social risk value reflects the breadth and systemic nature of the accident's consequences, providing a crucial basis for classifying the urgency level of emergency response strategies and ensuring that prevention and control measures can address both individual protection and the overall needs of public safety.

[0061] In some embodiments of this application, step S330 involves calculating the annual frequency of hydrogen leakage in the current accident scenario using a leakage frequency quantification sub-model, incorporating historical accident statistics. This includes the following steps.

[0062] Step S410: Assess the severity of the accident based on traffic accident data.

[0063] In step S410, the severity of the current accident is quantitatively assessed based on the collected traffic accident data. This step focuses on extracting key features from the information obtained from the accident sensing unit, such as collision intensity, vehicle deceleration, and stopping status, to determine whether the accident is classified as a minor scrape, moderate collision, or severe impact. The severity of the accident is a key factor affecting whether the hydrogen storage system is damaged; therefore, this assessment result provides the necessary input for subsequently determining the failure probability of the hydrogen storage system.

[0064] Step S420: Based on the vehicle type identification result, determine whether the accident vehicle is a hydrogen fuel cell vehicle.

[0065] In step S420, based on the vehicle type identification results, it is determined whether the vehicle involved is a hydrogen fuel cell vehicle. This step only focuses on confirming the vehicle's power type. If the identification result indicates that the vehicle involved in the accident is not a hydrogen fuel cell vehicle, there is no need to continue with frequency calculations related to hydrogen leakage. If it is confirmed to be a hydrogen fuel cell vehicle, it indicates a potential hydrogen source risk, thereby triggering subsequent analysis processes for the probability of damage and leakage to the hydrogen storage system. This step ensures that risk assessment is initiated only in scenarios where there is a genuine possibility of hydrogen leakage, improving calculation efficiency and targeting.

[0066] Step S430: Based on historical hydrogen fuel cell vehicle accident statistics, obtain the conditional probability of damage to the hydrogen storage system under the corresponding accident severity, and the conditional probability of hydrogen leakage after the hydrogen storage system is damaged.

[0067] In step S430, historical hydrogen fuel cell vehicle accident statistics are retrieved, and two types of conditional probabilities matching the severity of the current accident are extracted: one is the probability of structural damage to the hydrogen storage system in this type of accident, and the other is the probability of actual hydrogen leakage after the hydrogen storage system is damaged. These two conditional probabilities are derived from statistical data of real or simulated accidents, reflecting the causal relationship between hydrogen storage system failure and leakage under different accident intensities, and are key statistical parameters connecting accident events and leakage consequences.

[0068] Step S440: Using the leakage frequency quantization sub-model, multiply the annual frequency of traffic accidents, the conditional probability of hydrogen storage system damage, and the conditional probability of hydrogen leakage to obtain the annual frequency of hydrogen leakage in the current accident scenario.

[0069] In step S440, the leakage frequency quantification sub-model multiplies the basic annual frequency of traffic accidents with the two conditional probabilities mentioned above to obtain the annual frequency of hydrogen leakage under the current accident scenario. This step combines the macro-level accident incidence rate with the micro-level system failure probability to form a comprehensive frequency index, which characterizes the expected number of hydrogen leakage events occurring per unit year under similar accident conditions. This frequency value, as a probability factor in risk calculation, directly participates in the generation of individual risk values ​​and is an indispensable part of achieving risk quantification.

[0070] In some embodiments of this application, a consequence quantification sub-model is provided for predicting the consequences of hydrogen leak accidents. This model is based on a simplified computational fluid dynamics (CFD) model of real-time ventilation conditions or a pre-built scenario library, used to predict the hydrogen concentration distribution, explosion overpressure field, and thermal radiation field within the tunnel. Before practical application, the CFD model has undergone high-precision numerical simulations of numerous typical leak scenarios, covering different combinations of parameters such as leak location, wind speed, and leak rate. The simulation results, such as key physical field data like the hydrogen concentration field and explosion overpressure field, are stored in a database. During actual operation, the system acquires input variables such as the real-time hydrogen leak rate (inferred from multi-point hydrogen concentration data), leak location, and real-time ventilation wind speed and direction within the tunnel through sensors. It matches the closest simulation scenario from the pre-stored database and performs rapid interpolation correction based on real-time monitoring data, thereby achieving rapid prediction of the current accident consequences.

[0071] For hydrogen leaks, the model can predict the spatiotemporal distribution of hydrogen concentration within the tunnel over a future period. For potential deflagration or explosions, the model can calculate the maximum overpressure at various points within the tunnel, assessing the risk of structural damage. For jet fires, the model can calculate the thermal radiation flux at each point, determining the safety of personnel exposure areas. Furthermore, to improve response speed, machine learning methods can be employed to train a lightweight "surrogate model" based on extensive CFD simulation data. This model can output prediction results within seconds based on input parameters, significantly reducing computational latency and meeting the real-time requirements of tunnel fire prevention systems. This consequence quantification sub-model achieves efficient mapping from the initial state of an accident to the potential hazard field, providing crucial consequence input for subsequent quantitative assessment of individual and social risks.

[0072] In some embodiments of this application, a leakage frequency quantization sub-model is used to calculate the hydrogen leakage frequency under specific accident scenarios; the annual frequency of traffic accidents, the conditional probability of hydrogen storage system damage, and the conditional probability of hydrogen leakage are multiplied to obtain the annual frequency of hydrogen leakage in the current accident scenario. The following calculation formula can be used: ;in, This indicates the annual frequency of traffic accidents of a specific severity occurring at a specific location within the tunnel, calculated based on historical data. This represents the conditional probability that an accident will cause damage to the hydrogen storage system, obtained from collision experiments or historical data; This represents the probability of a specific aperture leak occurring under a given level of damage, and can be obtained based on component failure mode analysis.

[0073] In some embodiments of this application, a risk calculation unit is provided, which integrates the annual frequency of hydrogen leaks output by the leakage frequency quantification sub-model with the severity of accident consequences predicted by the consequence quantification sub-model to calculate the quantified individual risk value and social risk value at a specified location within the tunnel. This unit achieves a scientific assessment of individual injury probabilities by combining different types of accident consequences (such as explosion overpressure and thermal radiation) with their corresponding injury criteria. Specifically, for overpressure effects, physiological thresholds for eardrum rupture or lung injury are used as the judgment criteria; for thermal radiation, injury models are established based on injury thresholds at different exposure times. Based on this, the individual risk value at any point in each scenario is calculated by combining the leakage frequency and corresponding consequence intensity under specific scenarios.

[0074] The individual risk value is defined as the probability of death an individual at any location within the tunnel (e.g., escape route entrance, fire hydrant location, etc.) may experience per unit of time (usually per year), reflecting the individual's safety level in the hazardous environment at that location. The social risk is represented by an FN curve, where F represents the frequency of a single accident resulting in more than N deaths, describing the likelihood of a mass casualty incident. On a two-dimensional tunnel plan, individual risk contour lines can be drawn to form a risk contour map, visually displaying the distribution of high-risk areas and aiding in emergency decision-making and evacuation route planning. This risk calculation unit realizes the transformation from a physical field to a life safety indicator, providing crucial quantitative evidence for proactive intervention in fire prevention and control systems.

[0075] In some embodiments of this application, step S130 specifically includes the following emergency response strategy: determining the boundary of a high-risk area based on the spatial distribution of individual risk values, generating recommended escape routes based on the high-risk area boundary, personnel location information, and tunnel structural characteristics, and outputting an evacuation instruction containing the high-risk area boundary and the recommended escape routes, thus transforming the quantified individual risk values ​​into specific and executable personnel evacuation guidance information. Specifically, by analyzing the distribution of individual risk values ​​in the tunnel space, the system can identify areas where the risk level exceeds the safety threshold and delineate the high-risk area boundary accordingly. Based on this, combined with real-time acquired personnel location information and the tunnel's own structural characteristics, such as the location of escape passages, exit layout, and lane separation, the system generates one or more recommended escape routes that avoid high-risk areas and lead to safe exits. Finally, the high-risk area boundary and the recommended escape routes are integrated into a complete evacuation instruction for output, providing clear, dynamic, and personalized escape guidance for on-site personnel, improving evacuation efficiency and safety.

[0076] Furthermore, based on the preset range in which the social risk value falls, the urgency level of the evacuation order is determined, and at least one of the following actions is triggered: broadcasting an alarm, closing and controlling the tunnel entrance, and notifying external rescue systems. Specifically, based on the preset range in which the social risk value falls, the urgency level of the evacuation order is graded, and corresponding emergency response actions are triggered. The social risk value reflects the overall severity of the accident's potential to cause mass casualties, and its magnitude is directly related to the intensity and speed of the emergency response. The system automatically determines the appropriate urgency level for the current evacuation order, such as low, medium, high, or emergency, based on the risk range to which the value belongs. On this basis, at least one supporting measure is executed simultaneously, including broadcasting an alarm message into the tunnel to remind personnel to take immediate action, closing the tunnel entrance to prevent more vehicles from entering the danger zone, or sending a notification to external fire, medical, and other rescue systems to dispatch resources in advance. This mechanism ensures that the strength of the emergency response matches the potential social harm of the accident, maximizing the rational allocation of resources and the timeliness of the response.

[0077] In some embodiments of this application, the method further includes: Step S510: Generate a risk contour map based on the individual risk value, and extract the boundary of the high-risk area based on the risk contour map.

[0078] In step S510, the calculated individual risk values ​​are spatially represented in a visual form, and the extent of the danger zone is precisely defined accordingly. This step generates a risk contour map by interpolating the individual risk values ​​at various locations within the tunnel and drawing contour lines, visually reflecting the spatial gradient distribution of risk. Based on this, the system automatically extracts the boundary contour of high-risk areas from the contour map according to preset risk thresholds. This boundary not only provides crucial geographical constraints for subsequent escape route planning but also defines a clear scope for emergency information dissemination and traffic control, ensuring that prevention and control measures are precisely applied to truly threatened areas.

[0079] Step S520: Based on individual risk values, social risk values, and the location of the accident, dynamically control the status of traffic lights within the tunnel and display differentiated content in segments on multiple variable message signs. The differentiated content includes high-risk area boundaries, recommended escape routes, and the urgency level of evacuation instructions.

[0080] In step S520, by combining individual risk values, social risk values, and accident location information, dynamic and coordinated control is implemented on traffic lights and variable message signs within the tunnel to achieve real-time distribution of risk information and proactive guidance of traffic flow. This step adjusts the status of traffic lights according to the current risk situation, for example, setting red lights upstream of the accident to prevent vehicles from entering the high-risk area, or guiding lane changes downstream to accelerate evacuation. Simultaneously, multiple variable message signs deployed along the tunnel display segmented, location-specific differentiated content, including high-risk area boundaries, recommended escape routes for the current location, and the urgency level of evacuation instructions determined based on social risk values. This on-demand, zoned, and dynamic information dissemination method effectively avoids information overload or misleading information, improving drivers' and passengers' risk awareness and response efficiency.

[0081] In some embodiments of this application, in step S130, the tunnel ventilation control strategy aims to reduce the highest personal risk value in the tunnel to below the acceptable personal risk threshold, including: automatically adjusting the start-stop status, operating frequency and wind direction of the fans in the tunnel, controlling the wind speed and air volume distribution of the longitudinal ventilation system, and linking the opening or closing of the smoke exhaust valve and make-up air device near the accident area.

[0082] Specifically, the core optimization goal is to reduce the highest individual risk level within the tunnel to below a preset acceptable individual risk threshold by actively regulating the tunnel ventilation system. To achieve this goal, the system automatically adjusts the start / stop status, operating frequency, and airflow direction of the tunnel fans, thereby precisely controlling the spatial and temporal distribution of airflow velocity and volume in the longitudinal ventilation system. This regulation aims to accelerate the dilution and discharge of hydrogen, suppress the formation and accumulation of flammable clouds, and reduce the risk of explosion or fire in localized areas. Simultaneously, the system coordinates the opening or closing of smoke exhaust valves and make-up air devices near the accident area to further optimize airflow organization, guide hazardous gases to safe directions, and prevent their diffusion to densely populated areas or critical facilities. The entire ventilation regulation strategy uses risk levels as feedback, forming a closed-loop control system to ensure that the implementation of ventilation measures directly serves the risk reduction objective, enhancing the scientific and effective nature of prevention and control.

[0083] Secondly, refer to Figure 2 This application provides a tunnel fire prevention and control system for hydrogen fuel cell vehicles, configured to execute the aforementioned tunnel fire prevention and control method for hydrogen fuel cell vehicles. The system includes a multi-source sensing module, a quantitative risk assessment module, and a dynamic prevention and control execution module.

[0084] The multi-source sensing module is used to collect fire prevention data within the tunnel, including an accident detection unit, a visual confirmation unit, and a hydrogen monitoring unit. The fire prevention data includes traffic accident data, vehicle type identification results, and real-time hydrogen concentration data at multiple points. This data provides fundamental information support for subsequent risk assessment and emergency response. By integrating multiple sensors and identification technologies, this module can achieve immediate perception of accidents, accurate identification of the types of vehicles involved, and precise monitoring of hydrogen leaks, ensuring that the system can acquire critical data in the first instance to initiate appropriate prevention and control measures.

[0085] The quantitative risk assessment module calculates individual and social risk values ​​based on fire prevention data and pre-stored tunnel environmental parameters using a quantitative risk assessment model. The individual risk value represents the probability of death for an individual at any location within the tunnel within a unit of time, while the social risk value represents the cumulative frequency of multiple injuries or deaths resulting from a single accident. Tunnel environmental parameters include, but are not limited to, tunnel dimensions, longitudinal ventilation velocity, wind direction, ambient temperature, and humidity. This module utilizes environmental parameters such as tunnel dimensions, longitudinal ventilation velocity, wind direction, ambient temperature, and humidity, combined with an advanced quantitative risk assessment model, to provide scientifically quantified risk results, laying the foundation for developing effective prevention and control strategies.

[0086] The dynamic prevention and control execution module is used to activate and execute tunnel ventilation control strategies and emergency response strategies when an individual's risk value exceeds a preset individual risk intervention threshold. It dynamically adjusts the urgency level of the emergency response strategy based on the social risk value until the individual's risk value drops below a preset acceptable individual risk threshold. This module ensures a seamless transition from risk identification to rapid response, achieving effective control of potential hazards and maximizing personnel safety while minimizing property damage.

[0087] The dynamic prevention and control execution module includes an intelligent ventilation control unit and a collaborative early warning and evacuation unit.

[0088] The intelligent ventilation control unit is used to execute tunnel ventilation control strategies. As part of the dynamic prevention and control execution module, the intelligent ventilation control unit focuses on implementing tunnel ventilation control strategies. Its function is to automatically adjust the start / stop status, operating frequency, and airflow direction of the fans to optimize the wind speed and airflow distribution of the longitudinal ventilation system, and to coordinate the opening or closing of smoke exhaust valves and make-up air devices near the accident area. This series of operations aims to rapidly reduce the hydrogen concentration, prevent the formation of explosive mixtures, and thus effectively mitigate the risk of fire or explosion.

[0089] The coordinated early warning and evacuation unit dynamically controls the traffic light status within the tunnel based on individual risk levels, social risk levels, and the accident location, displaying differentiated information in segments on multiple variable message signs. This differentiated information includes high-risk area boundaries, recommended escape routes, and the urgency level of evacuation instructions. This unit, also a dynamic prevention and control execution module, is responsible for dynamically adjusting the traffic light status within the tunnel based on individual risk levels, social risk levels, and the accident location, and displaying differentiated information on multiple variable message signs. This information includes high-risk area boundaries, recommended escape routes, and the urgency level of evacuation instructions, aiming to guide the safe evacuation of personnel within the tunnel while preventing more vehicles from entering the danger zone. This refined information dissemination mechanism improves evacuation efficiency and safety, reducing the potential harm caused by accidents.

[0090] In some embodiments of this application, taking a 1.5-kilometer-long urban highway tunnel equipped with a longitudinal ventilation system as an example, this application achieves early identification, quantitative assessment, and proactive intervention of potential fire or explosion risks after an accident involving a hydrogen fuel cell vehicle in the tunnel through three core links: multi-source perception, quantitative risk assessment, and dynamic prevention and control execution.

[0091] First, during the event triggering and confirmation phase, the system collects key data through a multi-source sensing module. The accident perception unit, using microwave vehicle detectors deployed at the tunnel entrance, middle section, and exit, detects a sudden change in vehicle speed and abnormal queuing at kilometer marker K1+A, initially determining it to be a traffic accident. The visual confirmation unit then invokes high-definition video event detection cameras within approximately 100 meters before and after the accident point. Through built-in AI algorithms, it analyzes the video stream, confirming it as a two-vehicle rear-end collision and identifying one of the vehicles as a hydrogen fuel cell vehicle. Simultaneously, the distributed hydrogen monitoring units begin operation: catalytic combustion or laser spectroscopy hydrogen sensors deployed every 30 meters longitudinally at a height of 1.0 to 1.5 meters above the road surface on the tunnel sidewalls, and high-sensitivity electrochemical hydrogen sensors deployed every 50 meters at the tunnel ceiling, detect hydrogen volume concentrations near the sidewalls of 1.5% LEL and 0.8% LEL (LEL is the lower explosive limit; 4% hydrogen volume concentration corresponds to 100% LEL) within 10 seconds of the accident, with the concentrations showing an upward trend. The system synchronously reads the wind speed and direction data inside the tunnel and finds that the current wind speed is 2.0 m / s and the direction is from south to north.

[0092] Subsequently, the system initiated an initial quantitative risk assessment. The quantitative risk assessment module performed calculations based on the aforementioned real-time data. The leakage frequency quantification sub-model, combining historical data and component reliability information, calculated the annual hydrogen leakage frequency for this specific accident scenario to be 1×10⁻⁶. -4 / year. The consequence quantification sub-model calls a pre-built simplified computational fluid dynamics (CFD) model, inputting parameters such as accident location K1+A, vehicle type, initial hydrogen concentration of 1.5% LEL, and wind speed of 2.0 m / s, and quickly extrapolates that: under the current ventilation conditions, a hydrogen cloud will accumulate in the L1+B area at the top of the tunnel within 60 seconds, reaching a concentration of 35% LEL; if this cloud is ignited and explodes, it may generate an overpressure of 0.03 MPa. The risk calculation unit integrates the leakage frequency with the severity of the consequences, combined with overpressure injury criteria (such as eardrum rupture and lung injury thresholds), to calculate that the instantaneous personal risk value for personnel within 50 meters upstream of the accident has reached 5×10. -4 / Year.

[0093] Since this individual risk value has exceeded the preset individual risk intervention threshold (1×10⁻⁶), -4 / year, meaning that action must be taken, the system immediately activates the dynamic prevention and control execution module. The intelligent ventilation control unit aims to reduce the highest personal risk value within the tunnel to an acceptable personal risk threshold (1×10). -6 The following optimization objectives (per year) were determined through CFD simulation: increasing the ventilation velocity from 2.0 m / s to 5.0 m / s, maintaining a south-to-north wind direction, and issuing instructions to the fan control system. The coordinated early warning and evacuation unit, based on the real-time generated risk contour map, displays "No Entry" and activates red lights on the traffic guidance screens (VMS) at the tunnel entrances upstream and downstream of the accident site; inside the tunnel, the VMS dynamically displays "Danger Ahead, Please Slow Down in the Left Lane," guiding vehicles to avoid high-risk areas. Simultaneously, the system sends a dynamic alarm to the fire brigade, including: "Alarm Level: High; Type: Hydrogen fuel cell vehicle tunnel accident, hydrogen leak; Location: XX tunnel K1+A; Current Predicted Risk: Medium probability of gas cloud explosion; Recommendation: Prioritize ventilation dilution, and upon arrival, prioritize cooling adjacent vehicles and structures." The urgency of the evacuation order is directly related to the social risk value (FN curve position).

[0094] The system continued monitoring and risk reassessment. After 60 seconds, the hydrogen sensor data showed the concentration peak had shifted to position K1+A1, and the peak value had decreased to 15% LEL, indicating the ventilation strategy was initially effective. The risk assessment module iterated again, concluding that the individual risk value had decreased to 8×10⁻⁶. -5 The annual ventilation velocity is below the individual risk intervention threshold but still above the individual risk acceptable threshold. Based on this, the system dynamically adjusts its strategy to maintain a ventilation velocity of 5.0 m / s.

[0095] Subsequently, the system updates the hydrogen concentration data every 30 seconds. Once the hydrogen concentration continues to drop below 2% LEL and video confirms there is no open flame, the risk value has decreased to an acceptable level. The system automatically deactivates the alarm, ventilation returns to normal mode, and traffic control measures are manually lifted after confirmation by on-site personnel, completing the entire dynamic prevention and control closed loop. In summary, this embodiment fully demonstrates the entire process of the invention from accident perception and risk quantification to dynamic response and risk deactivation, reflecting the technical advantages of multi-source fusion, quantitative driving, and closed-loop control.

[0096] Furthermore, embodiments of this application provide a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the aforementioned method for preventing tunnel fires in hydrogen fuel cell vehicles.

[0097] Furthermore, embodiments of this application provide an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the aforementioned method for preventing tunnel fires in hydrogen fuel cell vehicles.

[0098] In summary, the tunnel fire prevention method, system, medium, and equipment for hydrogen fuel cell vehicles provided in this application have the following technical effects.

[0099] This solution breaks through the limitations of traditional passive response firefighting models, deeply integrating multi-source real-time sensing, quantitative risk modeling, and dynamic closed-loop control for the first time. This enables early identification, precise quantification, and proactive intervention of fire risks posed by hydrogen fuel cell vehicles in tunnels. By collecting traffic accident data, vehicle type identification results, and multi-point real-time hydrogen concentration data, combined with tunnel geometry and environmental parameters, the system can scientifically calculate individual risk values ​​representing the probability of individual death and social risk values ​​reflecting the frequency of group casualties, shifting risk assessment from experience-based judgment to data-driven approaches. Based on this, the system automatically activates ventilation control and emergency response strategies using risk thresholds as triggers, and dynamically adjusts the urgency level of the response according to the level of social risk, ensuring that prevention and control measures match the severity of the accident. Simultaneously, by generating high-risk area boundaries, recommending escape routes, and issuing differentiated evacuation instructions in segments on variable message signs, the system significantly improves the targeting and efficiency of personnel evacuation. Furthermore, intelligent ventilation control optimizes wind speed and volume distribution and links with smoke extraction and ventilation systems, effectively suppressing hydrogen accumulation and accelerating risk reduction. Overall, this technical solution significantly improves the inherent safety level of hydrogen fuel cell vehicles operating in tunnel scenarios, providing reliable safety assurance for the large-scale application of hydrogen transportation in complex infrastructure environments.

[0100] It should be noted that in all specific embodiments of this application, all data processing activities related to user identity or personal characteristics, such as user information, user behavior data, historical data, and location information, will be conducted in accordance with the principles of legality, legitimacy, and necessity. All data collection, use, storage, and processing will be subject to compliance with applicable national and regional laws, regulations, and industry standards, and informed consent from users will be obtained in a clear and explicit manner before processing. For the processing of sensitive personal information, separate consent from users will be obtained through prominent means such as pop-up prompts and independent confirmation pages. If any processing conflicts with laws and regulations, the laws and regulations will prevail, and necessary data processing will only be carried out within the scope permitted by laws and regulations, ensuring that all data-based applications, analyses, and technical implementations are conducted within the scope permitted by laws and regulations.

[0101] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0102] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of ordinary skill of an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary skill. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0103] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0105] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Additionally, computer-readable media can even be paper or other suitable media on which programs can be printed, for example, by optically scanning the paper or other media, then editing, interpreting, or, if necessary, processing it in a suitable manner to obtain the program electronically, and then storing it in computer memory.

[0106] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0107] In the foregoing description of this specification, the reference to terms such as "one embodiment / implementation," "another embodiment / implementation," or "certain embodiments / implementations," etc., indicates that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in an embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0108] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0109] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for preventing tunnel fires in hydrogen fuel cell vehicles, characterized in that, Includes the following steps: Collect fire prevention and control data inside the tunnel, including traffic accident data, vehicle type identification results, and multi-point real-time hydrogen concentration data. Based on the fire prevention and control data and pre-stored tunnel environmental parameters, a quantitative risk assessment model is used to calculate individual risk values ​​and social risk values. The individual risk value represents the probability of death of an individual at any location in the tunnel within a unit of time, and the social risk value represents the cumulative frequency of multiple casualties caused by a single accident. The tunnel environmental parameters include, but are not limited to, tunnel geometry, longitudinal ventilation speed, wind direction, ambient temperature, and humidity. When the personal risk value exceeds the preset personal risk intervention threshold, the tunnel ventilation control strategy and emergency response strategy are activated and executed, and the urgency level of the emergency response strategy is dynamically adjusted according to the social risk value until the personal risk value drops below the preset acceptable personal risk threshold.

2. The tunnel fire prevention method for hydrogen fuel cell vehicles according to claim 1, characterized in that, The process of collecting fire prevention data within the tunnel includes the following steps: The accident perception unit detects vehicle collisions, sudden stops, or abnormal parking events to obtain traffic accident event data. The visual confirmation unit performs image recognition on the accident vehicle to confirm that it is a hydrogen fuel cell vehicle, thereby obtaining the vehicle type identification result. The real-time hydrogen concentration data at multiple points is obtained through a hydrogen monitoring unit; the hydrogen monitoring unit includes hydrogen sensors distributed along the longitudinal and transverse directions of the tunnel.

3. The tunnel fire prevention method for hydrogen fuel cell vehicles according to claim 1, characterized in that, The quantitative risk assessment model includes a consequence quantification sub-model, a leakage frequency quantification sub-model, and a risk calculation unit; Based on the fire prevention and control data and pre-stored tunnel environmental parameters, the individual risk value and social risk value are calculated using a quantitative risk assessment model, including the following steps: Based on the vehicle type identification results and traffic accident event data, the pre-stored hydrogen leak scenario library is called to determine the hydrogen leak rate and hydrogen leak location corresponding to the current accident. Based on the hydrogen leakage rate, hydrogen leakage location, multi-point real-time hydrogen concentration data, and tunnel environmental parameters, the consequence quantification sub-model is used to predict at least one of the hydrogen diffusion concentration field, potential explosion overpressure field, or thermal radiation field in the tunnel, as the consequence quantification result; the consequence quantification sub-model is trained based on computational fluid dynamics simulation data of various hydrogen leakage scenarios. By combining historical accident statistics, the annual frequency of hydrogen leakage in the current accident scenario is calculated using the leakage frequency quantification sub-model. The risk calculation unit spatially integrates the annual frequency of hydrogen leakage with the quantification of consequences to obtain the individual risk value at each location within the tunnel. Based on the spatial distribution of the individual risk value and combined with the personnel distribution information within the tunnel, the social risk value is calculated.

4. The tunnel fire prevention method for hydrogen fuel cell vehicles according to claim 3, characterized in that, The calculation of the annual frequency of hydrogen leaks in the current accident scenario, by combining historical accident statistics and using the leakage frequency quantification sub-model, includes the following steps: Assess the severity of the accidents based on the traffic accident data. Based on the vehicle type identification results, determine whether the vehicle involved in the accident was a hydrogen fuel cell vehicle; Based on historical statistics of hydrogen fuel cell vehicle accidents, the conditional probability of hydrogen storage system damage under the corresponding accident severity is obtained, as well as the conditional probability of hydrogen leakage after the hydrogen storage system is damaged. Using the leakage frequency quantification sub-model, the annual frequency of traffic accidents, the conditional probability of damage to the hydrogen storage system, and the conditional probability of hydrogen leakage are multiplied to obtain the annual frequency of hydrogen leakage in the current accident scenario.

5. The tunnel fire prevention method for hydrogen fuel cell vehicles according to claim 1, characterized in that, The emergency response strategy specifically includes: determining the boundary of the high-risk area based on the spatial distribution of the individual risk value, generating a recommended escape route based on the boundary of the high-risk area, personnel location information and tunnel structural characteristics, and outputting an evacuation instruction containing the boundary of the high-risk area and the recommended escape route; Based on the preset range of the social risk value, the urgency level of the evacuation order is determined, and at least one of the following operations is performed in conjunction: broadcast alarm, tunnel entrance closure control, and external rescue system notification.

6. The tunnel fire prevention method for hydrogen fuel cell vehicles according to claim 5, characterized in that, The method further includes: A risk contour map is generated based on the individual risk value, and the boundaries of high-risk areas are extracted based on the risk contour map. Based on the individual risk value, social risk value, and accident location, the traffic light status inside the tunnel is dynamically controlled, and differentiated content is displayed in segments on multiple variable message signs; the differentiated content includes the boundary of the high-risk area, recommended escape routes, and the urgency level of the evacuation instructions.

7. The tunnel fire prevention method for hydrogen fuel cell vehicles according to claim 1, characterized in that, The tunnel ventilation control strategy aims to reduce the highest personal risk value in the tunnel to below the acceptable personal risk threshold. It includes: automatically adjusting the start-stop status, operating frequency and wind direction of the fans in the tunnel, controlling the wind speed and air volume distribution of the longitudinal ventilation system, and linking the opening or closing of the smoke exhaust valves and make-up air devices near the accident area.

8. A tunnel fire prevention system for hydrogen fuel cell vehicles, characterized in that, The system, configured to perform a tunnel fire prevention method for a hydrogen fuel cell vehicle as described in any one of claims 1 to 7, comprises: The multi-source sensing module is used to collect fire prevention and control data in the tunnel, including an accident sensing unit, a visual confirmation unit, and a hydrogen monitoring unit; the fire prevention and control data includes traffic accident data, vehicle type identification results, and multi-point real-time hydrogen concentration data. The quantitative risk assessment module is used to calculate individual risk values ​​and social risk values ​​based on the fire prevention and control data and pre-stored tunnel environmental parameters through a quantitative risk assessment model. The individual risk value represents the probability of death for an individual at any location within the tunnel within a unit of time, and the social risk value represents the cumulative frequency of multiple casualties resulting from a single accident. The tunnel environmental parameters include, but are not limited to, tunnel geometry, longitudinal ventilation speed, wind direction, ambient temperature, and humidity. The dynamic prevention and control execution module is used to activate and execute tunnel ventilation control strategy and emergency response strategy when the personal risk value exceeds the preset personal risk intervention threshold, and dynamically adjust the urgency level of the emergency response strategy according to the social risk value until the personal risk value drops below the preset acceptable personal risk threshold. The dynamic prevention and control execution module includes an intelligent ventilation control unit and a collaborative early warning and evacuation unit; The intelligent ventilation control unit is used to execute tunnel ventilation control strategies; The collaborative early warning and evacuation unit is used to dynamically control the status of traffic lights in the tunnel based on the individual risk value, social risk value, and accident location, and to display differentiated content in segments on multiple variable message signs; the differentiated content includes the boundary of high-risk areas, recommended escape routes, and the urgency level of evacuation instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the tunnel fire prevention method for hydrogen fuel cell vehicles as described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor executes the computer program, it implements the tunnel fire prevention method for hydrogen fuel cell vehicles as described in any one of claims 1 to 7.

Images