High-speed tunnel emergency response system supporting risk grading early warning

Abstract

This invention discloses a high-speed tunnel emergency response system supporting risk-level early warning, specifically in the field of high-speed tunnel early warning and emergency response. It addresses the problems of existing tunnel early warning and response systems being crude and lacking differentiation in vehicle risk. This is achieved through the collaborative construction of modules for emergency monitoring, impact prediction, location identification, risk quantification, graded early warning, and emergency control. The system collects traffic flow data within the tunnel and identifies emergency types, determining the scope of the emergency by combining location and intensity quantification values. It filters risky vehicle sets, extracting vehicle attributes and relative positions. Independent risk indicators are generated through semantic association metrics combined with distance and direction corrections. At the category level, risk indicators and average speed parameters are integrated to form a comprehensive risk value, and early warning levels are assigned. Finally, differentiated control instructions are sent to upstream checkpoints based on the level, implementing interception, diversion, or closure measures to achieve precise graded early warning and emergency response.

Description

Technical Field

[0001] This invention relates to the field of high-speed tunnel early warning and emergency response technology, and more specifically, to a high-speed tunnel emergency response system that supports risk-level early warning. Background Technology

[0002] In high-speed tunnel traffic scenarios, due to the enclosed space, complex environment, and dense traffic flow, any sudden situation can quickly escalate and create a chain reaction.

[0003] Compared to ordinary roads, traffic conditions inside highway tunnels are more vulnerable. Limited environmental conditions such as ventilation, lighting, and drainage make the impact of emergencies on vehicles and personnel more uncertain. For example, when fires, flooding, chemical leaks, or multi-vehicle collisions occur in tunnels, the risks faced by hazardous materials transport vehicles, passenger vehicles, and ordinary small vehicles differ significantly. Hazardous materials vehicles may explode or release toxic gases due to their load characteristics; passenger vehicles pose a high risk of mass casualties due to their high passenger density; and while ordinary vehicles may suffer less damage, their aggregation effect can still cause traffic flow collapse. However, existing tunnel emergency systems often rely on single sensor triggers and uniform alarm responses, often failing to reflect the differentiated risk relationships between different vehicle categories and emergency types. This results in emergency measures that are either too lenient, failing to provide timely risk response, or too stringent, causing unnecessary traffic closures.

[0004] How to achieve multi-source information fusion, dynamically identify the affected vehicle set when an emergency occurs, and generate differentiated risk classification warnings for different vehicles by combining the sensitivity relationship between vehicle attributes and emergency types, and then link with upstream road checkpoints to implement refined traffic flow control, has become an urgent need to improve the level of tunnel traffic safety. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a high-speed tunnel emergency response system that supports risk classification and early warning to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A high-speed tunnel emergency response system supporting risk classification and early warning includes: The emergency monitoring module is used to acquire data on identified emergencies within the high-speed tunnel. The data includes an emergency type identifier, the location of the emergency, and a quantitative value of the emergency intensity. The impact prediction module is used to dynamically calculate the impact range of a sudden event based on the event type identifier and the location of the event, and to identify the set of at-risk vehicles within that range. The location recognition module is used to extract the attributes of each vehicle in the risk vehicle set, as well as the relative positional relationship between the vehicle and the center point of the emergency. The risk quantification module is used to build a risk calculation framework based on semantic association measurement. By analyzing the semantic association strength between risk vehicle attributes and emergency situation types, it generates independent risk quantification indicators for risk vehicles. The graded early warning module is used to integrate the independent risk quantification indicators of all risky vehicles with real-time traffic flow characteristic parameters to generate a comprehensive risk warning level for risky vehicles corresponding to different assessment categories. The emergency management module is used to generate differentiated entry traffic flow response and management measures based on the warning level.

[0008] In a preferred embodiment, the emergency monitoring module acquires identified emergency data within the high-speed tunnel. This data includes an emergency type identifier, the location of the emergency, and a quantitative value representing the intensity of the emergency. Specifically, this includes: Real-time capture of traffic flow status in high-speed tunnels, identification of types of emergencies occurring in tunnels and generation of emergency type identifiers; Using the tunnel entrance as the reference origin, the location of the emergency is determined by calculating the straight-line distance along the tunnel's longitudinal axis from the reference origin. The physical signal characteristics generated by the emergency are obtained, the main frequency components and intensity characteristics of the signal are extracted, and the signal intensity is compared with the preset safety benchmark to obtain the dimensionless emergency intensity quantization value. The sudden situation type identifier, sudden situation location, and sudden situation intensity quantification value are combined into sudden situation data and transmitted to the impact prediction module.

[0009] In a preferred embodiment, the impact prediction module dynamically calculates the impact range of a sudden event based on the event type identifier and the location of the event, and identifies the set of at-risk vehicles within that range, specifically including: Upon receiving emergency data, the system extracts the pre-defined basic influence radius coefficient and diffusion attenuation factor based on the type of emergency. Using the spatial coordinates of the location of the emergency as the center point, the basic influence radius coefficient is multiplied by the quantitative value of the emergency intensity to obtain the preliminary influence radius. The initial impact radius is corrected using a diffusion attenuation factor, and the corrected result is used as the predicted impact radius of the emergency. The system acquires the location coordinates of all vehicles within the predicted impact radius in real time, calculates the Euclidean distance between each vehicle's location coordinates and the center point of the emergency, and filters out all vehicles whose distance is less than the predicted impact radius of the emergency. The selected vehicle identifiers are grouped into a risk vehicle set, and the time and initial location coordinates of each vehicle entering the affected area are recorded.

[0010] In a preferred embodiment, the location identification module extracts the attributes of each vehicle in the risk vehicle set, as well as the relative positional relationship between the vehicle and the center point of the emergency situation, specifically including: Extract the identification information of each vehicle in the risk vehicle set and query the corresponding attributes, including vehicle type, drive type and hazardous chemical identification type; Establish a spatial rectangular coordinate system with the center point of the emergency as the origin, and calculate the spatial coordinate components of each vehicle through coordinate transformation; The radial distance parameter is calculated based on the straight-line distance between the risk vehicle and the origin of the emergency situation, and the azimuth parameter is calculated based on the angle between the risk vehicle's position vector and the tunnel axis direction. The radial distance and azimuth are combined to form the relative positional relationship.

[0011] In a preferred embodiment, the risk quantification module constructs a risk calculation framework based on semantic association measurement. By analyzing the semantic association strength between risky vehicle attributes and emergency situation types, it generates independent risk quantification indicators for risky vehicles, specifically including: Establish a semantic feature space for vehicle attributes in the risk vehicle set, including vehicle type, drive type, and descriptive text of hazardous chemical identification; Establish a semantic feature library for emergency types, containing standardized text descriptions of various emergency types and their physical characteristic keyword sets; Natural language processing is used to map vehicle attributes and emergency situation types to the same semantic vector space. The semantic association metric is obtained by calculating the cosine similarity between the vehicle attribute vector and the emergency situation type vector, which serves as the initial risk indicator. By combining the relative positional relationship with the center point of the emergency, the initial risk indicators of the at-risk vehicles are dynamically corrected to generate independent risk quantification indicators for each vehicle.

[0012] In a preferred embodiment, the step of dynamically correcting the initial risk index of the at-risk vehicle based on its relative position to the center point of the emergency situation, and generating an independent risk quantification index for each vehicle, specifically includes: Based on the exponential decay function of radial distance and the cosine function of azimuth angle, a distance decay correction model is constructed to calculate the position correction factor of the at-risk vehicle. The initial risk indicator is multiplied by the location correction factor, and then normalized to generate an independent risk quantification indicator.

[0013] In a preferred embodiment, the graded early warning module integrates the independent risk quantification indicators of all risky vehicles with real-time traffic flow characteristic parameters to generate a comprehensive risk warning level for risky vehicles corresponding to different assessment categories. Specifically, this includes: The risk vehicles are classified according to their vehicle attributes, and risk vehicles with the same vehicle type, drive type and hazardous chemical label type are grouped into the same assessment category. Within each assessment category, the arithmetic mean of the independent risk quantification indicators for all risky vehicles is calculated and used as the risk characteristic parameter for that assessment category. Calculate the arithmetic mean of the speeds of all risky vehicles within each assessment category, and use it as a traffic flow characteristic parameter for that assessment category; The risk characteristic parameters and traffic flow characteristic parameters are weighted and fused to calculate the comprehensive risk value for each assessment category. Based on the numerical distribution range of the comprehensive risk value, the assessment categories of the corresponding risk vehicles are divided into different warning levels, which include three levels: low, medium, and high. A warning signal is generated based on the warning level and sent to the on-board terminals of all vehicles at risk within the corresponding category.

[0014] In a preferred embodiment, the emergency control module generates differentiated entry traffic response control measures based on the warning level, specifically including: Early warning signals of different assessment categories are simultaneously sent to the upstream road checkpoint control center connected to the high-speed tunnel; When the warning level reaches the set medium risk warning level, the upstream road checkpoints will intercept vehicles that belong to the vehicle attributes corresponding to the current assessment category and guide the intercepted vehicles to alternative routes. When the warning level reaches the set high-risk warning level, the upstream road checkpoints will close the access passage to the highway tunnel.

[0015] The technical effects and advantages of the high-speed tunnel emergency response system supporting risk classification and early warning according to the present invention are as follows: By utilizing semantic association measurement methods, the sensitivity relationship between vehicle attributes and emergency types is quantified into initial risk indicators. These indicators are then corrected by considering the relative position of the vehicle to the emergency center point, thereby generating more realistic individual vehicle risk quantification indicators. This overcomes the problem that traditional methods, which rely solely on thresholds or historical data, cannot accurately reflect differentiated risks. Secondly, by classifying the independent risk indicators and setting differentiated threshold standards for hazardous materials transport vehicles, passenger vehicles, and ordinary vehicles, comprehensive risk classification and early warning based on vehicle category can be achieved, improving the targeting and effectiveness of the early warning. Finally, the system links the classification and early warning results with emergency control measures. When the risk is at different levels, upstream road checkpoints can implement multi-level response strategies such as alerts, interception, diversion, or complete closure.

[0016] By organically combining modules such as emergency monitoring, impact prediction, location identification, risk quantification, tiered early warning, and emergency control, this invention enables dynamic monitoring and intelligent response throughout the entire tunnel operation process. It effectively avoids a "one-size-fits-all" management approach, balancing safety and traffic efficiency. Overall, this invention boasts advantages such as high early warning accuracy, rich response levels, and strong adaptability, significantly enhancing traffic safety control capabilities in the event of emergencies in high-speed tunnels. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of a high-speed tunnel emergency response system that supports risk classification and early warning according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 Figure 1 This invention discloses a high-speed tunnel emergency response system supporting risk classification and early warning, comprising: The emergency monitoring module is used to acquire data on identified emergencies within the high-speed tunnel. The data includes an emergency type identifier, the location of the emergency, and a quantitative value of the emergency intensity. The impact prediction module is used to dynamically calculate the impact range of a sudden event based on the event type identifier and the location of the event, and to identify the set of at-risk vehicles within that range. The location recognition module is used to extract the attributes of each vehicle in the risk vehicle set, as well as the relative positional relationship between the vehicle and the center point of the emergency. The risk quantification module is used to build a risk calculation framework based on semantic association measurement. By analyzing the semantic association strength between risk vehicle attributes and emergency situation types, it generates independent risk quantification indicators for risk vehicles. The graded early warning module is used to integrate the independent risk quantification indicators of all risky vehicles with real-time traffic flow characteristic parameters to generate a comprehensive risk warning level for risky vehicles corresponding to different assessment categories. The emergency management module is used to generate differentiated entry traffic flow response and management measures based on the warning level.

[0020] The emergency monitoring module acquires data on identified emergencies within the high-speed tunnel. This data includes an emergency type identifier, the location of the emergency, and a quantitative value for the intensity of the emergency.

[0021] The system continuously captures traffic flow within high-speed tunnels, deploying multiple observation points longitudinally along the tunnel's interior. Each point continuously collects video footage, sound changes, chemical gas emissions, and optical data. Before analysis, the collected data is formatted and timestamped to ensure comparability across all observation points within the same timeframe. In the processing phase, the video data undergoes target detection and motion trajectory extraction. Features such as vehicle outline shape, color changes, and sudden speed drops are used to determine the presence of chain-reaction collisions or rollovers. The sound signals are subjected to spectral decomposition to identify high-decibel explosions or continuous friction sounds to determine if there is a hazardous material leak leading to deflagration or a large-scale collision. Finally, grayscale value abrupt changes in optical features are analyzed to determine the presence of rapidly spreading flames or dense smoke.

[0022] If an abnormal signal is detected in any dimension, a multimodal cross-validation mechanism is initiated, comparing video, audio, and optical data. If at least two of the three signal types exhibit consistent signs of a sudden event, the event is considered valid. Once valid, a sudden event type identifier is immediately generated, such as multi-vehicle rear-end collision, vehicle rollover, hazardous material leak, engine fire, or illegal parking. This identifier uses a unified enumeration coding method to ensure consistent identification and retrieval of sudden events during subsequent processing. After generating the sudden event type identifier, its precise location is determined by setting the tunnel entrance as a fixed reference origin and establishing a one-dimensional linear coordinate system along the tunnel's longitudinal direction. Each observation point has its distance from the tunnel entrance recorded during installation, and these distance values ​​are written into a unified distance database. By quickly retrieving the distance data of the observation point from the signal source at the time of the sudden event, the longitudinal location of the sudden event is preliminarily determined. A multi-point comparison method is used: when multiple adjacent observation points simultaneously detect anomalies, the precise location of the sudden event is narrowed down to within tens of meters by comparing the signal strength distribution trends of different points and using interpolation. If only a single observation point detects an emergency, the location is determined directly by the straight-line distance between that point and the tunnel entrance. Simultaneously, the lane number where the emergency occurred is recorded; this lane number is obtained by matching lane markings with vehicle trajectories in the video footage. The final emergency location data consists of "longitudinal distance + lane number," ensuring uniqueness.

[0023] The physical signal characteristics corresponding to the location of the emergency are obtained. These physical signals originate from three dimensions: vibration, acoustic, and temperature. Vibration signals are collected by sensors on the tunnel walls or ground, and their spectral distribution is obtained through Fast Fourier Transform (FFT) processing. The dominant frequency component at the time of the emergency is then extracted. Typically, multi-vehicle rear-end collisions produce short-duration, strong impact signals with dominant frequencies ranging from 150 to 300 Hz; engine fires are accompanied by low-frequency deflagration signals, with dominant frequencies concentrated in the 50 to 120 Hz range. Acoustic signals are also processed through spectral analysis to obtain dominant frequency and signal intensity characteristics. For example, the high-pressure gas jet sound accompanying a hazardous material leak often manifests as a high-frequency signal above 1000 Hz. Temperature signals are provided by thermal detectors, which collect the rate of temperature rise around the emergency location as a thermal characteristic indicator. All collected physical signals undergo a standardized format conversion, retaining the dominant frequency and intensity values ​​to form two types of basic characteristic parameters, used to describe the physical manifestation of the emergency. After extracting the main frequency components and signal strength, these strength data are compared with pre-set safety benchmarks to obtain dimensionless quantized values ​​of the emergency situation intensity. The safety benchmarks are set using a combination of measured data and standards. For example, for fire-related emergencies, the signal from a thermal detector below 50 degrees Celsius is selected as the benchmark, and any intensity exceeding the benchmark is proportionally mapped to a quantized value. For collision-related emergencies, the acoustic signal generated by vehicle braking or minor collisions during normal operation is selected as the benchmark, and any intensity exceeding this benchmark is linearly amplified. For hazardous material leak-related emergencies, the jet sound intensity generated during an unloaded gas leak test or the corresponding safe concentration of the hazardous gas is selected as the benchmark, and the ratio of the actual intensity to the benchmark is used as the quantized value. To ensure consistent results, all ratios are normalized.

[0024] In the impact prediction module, the impact range of the emergency is dynamically calculated based on the emergency type identifier and the location of the emergency, and the set of risky vehicles located within the range is identified.

[0025] Upon receiving emergency data, the basic spatial impact range of the emergency is determined. A parameter database is established to store the basic impact radius coefficient and diffusion attenuation factor corresponding to different types of emergencies. These parameters are obtained by combining historical emergency data with actual experimental measurements. For example, for fire emergencies, statistical analysis of the smoke spread rate and temperature diffusion range of past tunnel fire cases shows that the smoke coverage area typically reaches 5% to 8% of the tunnel length within 30 seconds, thus setting the basic impact radius coefficient to 0.08. For hazardous material leak emergencies, experiments simulate the diffusion process of gas in the tunnel space, measuring that with ventilation closed, the diffusion range of leaked gas within 1 minute is approximately 3% to 4% of the tunnel length, thus setting the basic impact radius coefficient to 0.04. The diffusion attenuation factor is used to correct for the decreasing trend of concentration or energy with distance during diffusion, and is also obtained based on experimental curve fitting. For example, in a fire experiment, the temperature decreases by about 10% for every 50 meters increase, so the diffusion attenuation factor is set to 0.9; in a gas leak experiment, the concentration decreases exponentially with distance, and it is measured that the concentration attenuates to 30% of the original point at a distance of 100 meters, so the diffusion attenuation factor is set to 0.3.

[0026] Using the spatial coordinates of the emergency location as the center point, the basic influence radius coefficient is multiplied by the quantified value of the emergency intensity to obtain the preliminary influence radius. This preliminary radius is then corrected using a diffusion attenuation factor. The correction process is adjusted based on the experimental fitting function; for example, linear attenuation correction is used for fires, and exponential attenuation correction is used for hazardous material leaks. The precise position coordinates of all vehicles within this range are obtained, consisting of longitudinal distance and lateral lane number. When a vehicle enters the predicted influence radius, i.e., when its longitudinal distance from the emergency center point is less than the corrected radius value, its position coordinates are recorded. After obtaining the position coordinates of all vehicles, the spatial distance between them and the emergency center point is calculated to determine whether they belong to the risk vehicle set. The calculation method is based on the geometric relationship between longitudinal distance and lane lateral difference. For example, if the emergency center point is at 2350 meters in lane 2, and a vehicle is at 2500 meters in lane 3, the longitudinal difference is 150 meters, and the lateral difference is calculated as 3.5 meters based on a lane width of 3.5 meters, for a total distance of approximately 150.04 meters. If the value is less than the predicted influence radius, the vehicle is identified as a risk vehicle. The selected vehicles are assigned unique identifiers and grouped into a risk vehicle set. For each vehicle in the set, the time it enters the influence area is recorded.

[0027] The location identification module extracts the attributes of each vehicle in the risk vehicle set, as well as the relative positional relationship between the vehicle and the center point of the emergency.

[0028] From the already screened set of risk vehicles within the impact area of ​​the emergency, unique identification information is extracted for each vehicle, and attribute queries are performed. Vehicle identification information consists of one or more combinations of license plate number, on-board unit identification code, or access card number, ensuring the uniqueness of each vehicle in the set. During execution, an established vehicle information database is invoked, with the vehicle identification used as the search condition, and the corresponding attribute information is returned. Vehicle attributes include three core fields: vehicle type, drive type, and hazardous materials identification type. The vehicle type field includes categories such as passenger cars, buses, light trucks, heavy trucks, and semi-trailer tractors, each with a unique code. The drive type field covers categories such as gasoline-powered, diesel-powered, pure electric, and hydrogen-powered. The hazardous materials identification type field corresponds to the national dangerous goods classification standards, such as flammable liquids, compressed gases, and toxic gases, each with a unique number. After the attribute query is completed, the search results are bound to the vehicle identification to form a complete attribute data record.

[0029] After obtaining the vehicle attribute information, a unified spatial reference frame is established inside the tunnel to accurately represent the spatial relationship between each vehicle and the emergency. This frame selects the point where the emergency occurs as the origin of the spatial rectangular coordinate system. The tunnel's longitudinal direction is defined as the positive X-axis, the tunnel's lateral direction as the Y-axis, and the tunnel's vertical direction as the Z-axis. The Z-axis is set to 0 by default and is not included in the calculation. Under this coordinate system, the coordinates of the emergency point are fixed at (0,0,0). Then, the position coordinates of each at-risk vehicle in the tunnel are transformed, converting its longitudinal distance, lane number, and height information in the tunnel's global positioning coordinate system into spatial rectangular coordinate components. For example, regarding longitudinal distance, if a vehicle is 200 meters in front of the emergency point, the X-axis component is +200; if the vehicle is 150 meters behind, the X-axis component is -150. Regarding lane lateral position, assuming the tunnel's single lane width is 3.5 meters, and the vehicle is in lane 2 with lanes numbered sequentially from the tunnel centerline to the right, the Y-axis component is +3.5; if the vehicle is in lane 1, the Y-axis component is 0. Each vehicle can be precisely represented in three-dimensional components within this coordinate system. For example, if a vehicle is located 120 meters behind the point of an emergency, laterally in the third lane, and vertically at 0 meters, its spatial coordinate components are (-120, 7, 0). This method standardizes the description of at-risk vehicles within the emergency situation coordinate system. After calculating the spatial coordinate components, the radial distance and azimuth angle between the vehicle and the center point of the emergency are calculated. The radial distance is calculated by taking the square root of the sum of the squares of the vehicle's X, Y, and Z components to obtain the straight-line distance between the vehicle and the emergency point. The azimuth angle is calculated by measuring the angle between the vehicle's position vector and the X-axis, using the tunnel's longitudinal X-axis as a reference axis. The angle calculation is based on the ratio of the vehicle's X and Y components. To avoid calculation ambiguity, the azimuth angle ranges from -90 degrees to +90 degrees, with negative angles for vehicles on the left and positive angles for vehicles on the right.

[0030] In the risk quantification module, a risk calculation framework based on semantic association measurement is constructed. By analyzing the semantic association strength between risk vehicle attributes and emergency situation types, an independent risk quantification index for risk vehicles is generated.

[0031] In the quantification of risks in emergencies, vehicle attributes in the risk vehicle set are semantically represented. Vehicle attributes include not only vehicle type, but also drive type and hazardous materials label type. To ensure these attributes can be accurately represented in the semantic vector space, a vehicle attribute semantic feature space is established. Specifically, vehicle attributes are first uniformly converted into text descriptions. For example, the vehicle type "heavy-duty truck" is described as "heavy-duty truck, high load capacity, high inertia." Regarding drive type, gasoline vehicles are described as "gasoline-driven, flammable, common power source," diesel vehicles as "diesel-driven, high fuel flash point, high output power," electric vehicles as "pure electric drive, contains a power battery, with a risk of thermal runaway," and hydrogen fuel cell vehicles as "hydrogen fuel-driven, contains a high-pressure hydrogen storage tank, with a risk of leakage and explosion." Hazardous materials label types are defined according to national dangerous goods transportation standards, with each type of goods described as a corresponding risk label, such as "flammable liquid, rapidly evaporating, prone to explosive combustion upon contact with fire," "compressed gas, high-pressure storage, easily diffused upon leakage," and "toxic gas, even a small leak can cause injury." These descriptive texts are standardized and stored in the vehicle attribute feature database. Ultimately, each vehicle, upon entering the risk analysis phase, can be mapped into a semantic feature unit composed of text descriptions, ensuring parsing and comparability in subsequent processing.

[0032] After constructing the vehicle attribute semantic feature space, a semantic feature library is established for different emergency types. This library, based on the type identifiers generated during the emergency identification process, maps different emergencies to standardized text descriptions and supplements them with a set of physical characteristic keywords. For example, the physical characteristic keywords for a "fire" emergency include "high temperature, smoke, toxic, carbon monoxide." Keywords for a "hazardous materials leak" include "toxic, volatile, corrosive." Keywords for a "multi-vehicle rear-end collision" include "collision, impact, blockage." Keywords for a "heavy vehicle rollover" include "overturning, heavy load, obstacle." To ensure the accuracy of these text descriptions in the semantic vector space, all description texts are optimized based on historical accident corpora and emergency event records, forming a standardized corpus set covering different emergency categories.

[0033] After obtaining the semantic feature space of vehicle attributes and the semantic feature library of emergency situation types, it is necessary to map them to the same semantic vector space to quantitatively measure the strength of the association between them. To achieve this, natural language processing methods are used, especially a natural language model optimized based on an emergency situation event corpus. During training, this model incorporates a large amount of textual data related to traffic accidents and tunnel emergencies, including official emergency cases, accident investigation reports, and simulation experiment descriptions, thus enabling more accurate semantic representations of specialized terms such as "hazardous chemical leaks," "vehicle collisions," and "battery thermal runaway." Then, the similarity between the vehicle attribute vector and the emergency situation type vector is calculated. The similarity calculation method uses cosine similarity, which reflects the semantic closeness by calculating the angle between two vectors in a high-dimensional space. The higher the similarity value, the stronger the association between the vehicle attribute and the emergency situation type. For example, the semantic similarity between "hydrogen fuel cell vehicle" and "fire" emergencies is usually higher than 0.8, while the similarity between "small passenger car" and "fire" may only be 0.3. This final output value serves as the initial risk indicator.

[0034] A distance attenuation correction model is constructed, consisting of two parts: first, an exponential attenuation relationship based on radial distance, reflecting the principle that the greater the distance between the vehicle and the center point of the emergency, the smaller the risk impact; second, a cosine relationship based on azimuth angle, reflecting the consistency between the vehicle's direction and the tunnel's longitudinal direction, with the strongest impact when the vehicle is directly facing the emergency and the impact gradually weakening as it deviates from the direction. For each vehicle in the risk vehicle set, its radial distance and azimuth angle parameters are first extracted. The radial distance is input into the exponential attenuation relationship to obtain a distance correction value, and the azimuth angle is input into the cosine relationship to obtain a direction correction value. The two are multiplied to form a position correction factor. Subsequently, the initial risk index calculated by the semantic association between vehicle attributes and emergency type is multiplied by the position correction factor to generate a corrected risk value that includes the spatial position influence. Finally, the corrected risk values ​​of all risk vehicles are normalized to obtain an independent risk quantification index for each vehicle.

[0035] The tiered early warning module integrates the independent risk quantification indicators of all risky vehicles with real-time traffic flow characteristic parameters to generate a comprehensive risk warning level for risky vehicles corresponding to different assessment categories.

[0036] Based on the generation of the risk vehicle set and the completion of independent risk quantification index calculations, the vehicles in the set are further classified. The classification is based on vehicle attribute information, including three dimensions: vehicle type, drive type, and hazardous materials label type. Classification begins by reading the attribute data of each vehicle, using vehicle type as the first-level grouping condition. Within the same type group, further subdivision is made according to drive method. Finally, within the drive method group, the final classification is based on the hazardous materials label type, such as flammable liquid transport, compressed gas transport, and toxic gas transport. Through this three-level classification, each vehicle is ultimately assigned to a unique assessment category, and the granularity of the classification can be flexibly set according to risk management needs.

[0037] After classification, risk characteristic parameters are calculated within each assessment category. These parameters are based on the independent risk quantification indicators of all vehicles in that category, and an overall representative value is obtained through arithmetic averaging. Specifically, the independent risk indicator values ​​of all vehicles within a category are summed one by one, then divided by the number of vehicles in that category to obtain an average value. This average value serves as the overall risk characteristic parameter for that category, reflecting the overall risk level of vehicles in that category under current emergencies. Traffic flow characteristic parameters primarily take the average speed of vehicles in that category, reflecting the operational status of the vehicle group in the tunnel. The calculation method involves collecting speed data for each vehicle within the category; speed values ​​are provided by the location identification and motion monitoring system, in meters per second or kilometers per hour. The average speed is obtained by summing all vehicle speed values ​​and dividing by the number of vehicles. The final traffic flow characteristic parameters, along with the risk characteristic parameters, serve as input conditions for subsequent comprehensive risk value calculations, ensuring that the risk assessment for each category includes not only risk sensitivity but also traffic operational status.

[0038] Risk characteristic parameters and traffic flow characteristic parameters are weighted and fused to obtain a more comprehensive overall risk value. The weight values ​​are derived from historical data backtesting and a comprehensive setting of traffic flow density. During calculation, the risk characteristic parameters are multiplied by their corresponding weights, and then the traffic flow characteristic parameters are multiplied by their weights to obtain the final overall risk value. After obtaining the overall risk value, a warning level needs to be assigned based on the interval in which the value falls. The warning levels are set at three levels: low risk, medium risk, and high risk. The interval division method is based on the overall risk value and the incidence of chain accidents during historical actual emergencies, ensuring that the classification results accurately reflect the degree of risk. After the division is completed, a corresponding warning signal is generated for each assessment category, and the signal is simultaneously sent to the on-board terminals of all vehicles within the category to remind the corresponding risky vehicles to take emergency evasive action.

[0039] The emergency management module generates differentiated entrance traffic response and control measures based on the warning level.

[0040] After generating the tiered early warning results, the warning signals corresponding to different assessment categories are simultaneously sent to the upstream road checkpoint control center connected to the highway tunnel. The signal content uses structured data packets, including fields such as category number, warning level, generation time, and validity period.

[0041] When the warning level reaches medium risk, upstream road checkpoints will restrict traffic for vehicles belonging to designated categories. At the checkpoint entrance, license plate recognition is used to compare the vehicle's attributes with a vehicle attribute database. If the vehicle's attributes match the warning category, an automatic isolation device will be triggered to restrict its entry into the tunnel. Restricted vehicles will be guided to pre-planned alternative routes according to the warning signal instructions. The guidance process is conducted through electronic signs and voice broadcasts, providing information including the diversion direction and the nearest safe exit. This process ensures that only vehicles in the risk category are restricted, while ordinary vehicles can still pass normally, thus balancing risk control and traffic continuity.

[0042] When the warning level reaches high risk, upstream road checkpoints will be completely closed, prohibiting all vehicles from entering the tunnel. The procedure involves immediately switching the entrance traffic lights to red, physically isolating and closing the lanes, and simultaneously triggering upstream diversion alerts. These alerts include displaying "Tunnel Closed" information on variable message signs and navigation systems simultaneously providing diversion routes. Once activated, the closure will continue until a new safety signal is generated and the high-risk warning is lifted. This process ensures that no new traffic enters the tunnel during high-risk situations, allowing sufficient time and space for internal emergency response and rescue.

[0043] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0044] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0045] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0046] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0047] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0048] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0049] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0050] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 instructions 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 described in the various embodiments of this application. 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.

[0051] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0052] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-speed tunnel emergency response system supporting risk-level early warning, characterized in that, include: The emergency monitoring module is used to acquire data on identified emergencies within the high-speed tunnel. The data includes an emergency type identifier, the location of the emergency, and a quantitative value of the emergency intensity. The impact prediction module is used to dynamically calculate the impact range of a sudden event based on the event type identifier and the location of the event, and to identify the set of at-risk vehicles within that range. The location recognition module is used to extract the attributes of each vehicle in the risk vehicle set, as well as the relative positional relationship between the vehicle and the center point of the emergency. The risk quantification module is used to build a risk calculation framework based on semantic association metrics. By analyzing the semantic association strength between risky vehicle attributes and emergency types, it generates independent risk quantification indicators for risky vehicles, specifically including: Establish a semantic feature space for vehicle attributes in the risk vehicle set, including vehicle type, drive type, and descriptive text of hazardous chemical identification; Establish a semantic feature library for emergency types, containing standardized text descriptions of various emergency types and their physical characteristic keyword sets; Natural language processing is used to map vehicle attributes and emergency situation types to the same semantic vector space. The semantic association metric is obtained by calculating the cosine similarity between the vehicle attribute vector and the emergency situation type vector, which serves as the initial risk indicator. By combining the relative positional relationship with the center point of the emergency, the initial risk indicators of the at-risk vehicles are dynamically corrected to generate independent risk quantification indicators for each vehicle. The graded early warning module is used to integrate the independent risk quantification indicators of all risky vehicles with real-time traffic flow characteristic parameters to generate a comprehensive risk warning level for risky vehicles corresponding to different assessment categories. The emergency management module is used to generate differentiated entry traffic flow response and management measures based on the warning level.

2. The high-speed tunnel emergency response system supporting risk classification and early warning according to claim 1, characterized in that, The emergency monitoring module acquires identified emergency data within the high-speed tunnel. This data includes an emergency type identifier, the location of the emergency, and a quantitative value indicating the intensity of the emergency. Real-time capture of traffic flow status in high-speed tunnels, identification of the types of emergencies occurring in tunnels and generation of emergency type identifiers; Using the tunnel entrance as the reference origin, the location of the emergency is determined by calculating the straight-line distance along the tunnel's longitudinal axis from the reference origin. The physical signal characteristics generated by the emergency are obtained, the main frequency components and intensity characteristics of the signal are extracted, and the signal intensity is compared with the preset safety benchmark to obtain the dimensionless emergency intensity quantization value. The sudden situation type identifier, sudden situation location, and sudden situation intensity quantification value are combined into sudden situation data and transmitted to the impact prediction module.

3. The high-speed tunnel emergency response system supporting risk classification and early warning according to claim 1, characterized in that, The impact prediction module dynamically calculates the impact range of a sudden event based on the event type identifier and the event location, and identifies the set of at-risk vehicles within that range, specifically including: Upon receiving emergency data, the system extracts the pre-defined basic influence radius coefficient and diffusion attenuation factor based on the type of emergency. Using the spatial coordinates of the location of the emergency as the center point, the basic influence radius coefficient is multiplied by the quantitative value of the emergency intensity to obtain the preliminary influence radius. The initial impact radius is corrected using a diffusion attenuation factor, and the corrected result is used as the predicted impact radius of the emergency. The system acquires the location coordinates of all vehicles within the predicted impact radius in real time, calculates the Euclidean distance between each vehicle's location coordinates and the center point of the emergency, and filters out all vehicles whose distance is less than the predicted impact radius of the emergency. The selected vehicle identifiers are grouped into a risk vehicle set, and the time and initial location coordinates of each vehicle entering the affected area are recorded.

4. A high-speed tunnel emergency response system supporting risk classification and early warning according to claim 1, characterized in that, The location identification module extracts the attributes of each vehicle in the risk vehicle set, as well as the relative positional relationship between the vehicle and the center point of the emergency situation, specifically including: Extract the identification information of each vehicle in the risk vehicle set and query the corresponding attributes, including vehicle type, drive type and hazardous chemical identification type; Establish a spatial rectangular coordinate system with the center point of the emergency as the origin, and calculate the spatial coordinate components of each vehicle through coordinate transformation; The radial distance parameter is calculated based on the straight-line distance between the risk vehicle and the origin of the emergency situation, and the azimuth parameter is calculated based on the angle between the risk vehicle's position vector and the tunnel axis direction. The radial distance and azimuth are combined to form the relative positional relationship.

5. A high-speed tunnel emergency response system supporting risk classification and early warning according to claim 1, characterized in that, The method of dynamically correcting the initial risk indicators of at-risk vehicles by combining the relative positional relationship with the center point of the emergency situation, and generating independent risk quantification indicators for each vehicle, specifically includes: Based on the exponential decay function of radial distance and the cosine function of azimuth angle, a distance decay correction model is constructed to calculate the position correction factor of the at-risk vehicle. The initial risk indicator is multiplied by the location correction factor, and then normalized to generate an independent risk quantification indicator.

6. A high-speed tunnel emergency response system supporting risk-level early warning according to claim 1, characterized in that, The tiered early warning module integrates the independent risk quantification indicators of all at-risk vehicles with real-time traffic flow characteristic parameters to generate comprehensive risk warning levels for at-risk vehicles corresponding to different assessment categories. Specifically, this includes: The risk vehicles are classified according to their vehicle attributes, and risk vehicles with the same vehicle type, drive type and hazardous chemical label type are grouped into the same assessment category. Within each assessment category, the arithmetic mean of the independent risk quantification indicators for all risky vehicles is calculated and used as the risk characteristic parameter for that assessment category. Calculate the arithmetic mean of the speeds of all risky vehicles within each assessment category, and use it as a traffic flow characteristic parameter for that assessment category; The risk characteristic parameters and traffic flow characteristic parameters are weighted and fused to calculate the comprehensive risk value for each assessment category. Based on the numerical distribution range of the comprehensive risk value, the assessment categories of the corresponding risk vehicles are divided into different warning levels, which include three levels: low, medium, and high. A warning signal is generated based on the warning level and sent to the on-board terminals of all vehicles at risk within the corresponding category.

7. A high-speed tunnel emergency response system supporting risk classification and early warning according to claim 1, characterized in that, The emergency control module generates differentiated entry traffic response and control measures based on the warning level, specifically including: Early warning signals of different assessment categories are simultaneously sent to the upstream road checkpoint control center connected to the high-speed tunnel; When the warning level reaches the set medium risk warning level, the upstream road checkpoints will intercept vehicles that belong to the vehicle attributes corresponding to the current assessment category and guide the intercepted vehicles to alternative routes. When the warning level reaches the set high-risk warning level, the upstream road checkpoints will close the access passage to the highway tunnel.

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