A risk assessment method and system for tunnel abnormal events

Abstract

The application discloses a kind of tunnel abnormal event-oriented risk assessment method and system, it is related to tunnel traffic safety management technical field.The method includes: collecting tunnel operating state data;Generate abnormal event basic data;According to the available sensing distance and the required braking distance of abnormal event occurrence section and its associated section, generate structure sensitive coefficient, and section correction is carried out to abnormal event basic risk value;With abnormal event occurrence section as risk source section, in combination with structure sensitive correction data, propagation direction, distance attenuation and lane disturbance state calculate the propagation risk potential field value of each tunnel section;Determine abnormal event risk assessment result.The application can convert tunnel abnormal event into section-level risk assessment result from single-point alarm, improve the accuracy of tunnel abnormal event risk influence range identification and emergency disposal decision.

Description

Technical Field

[0001] This invention relates to the field of tunnel traffic safety management technology, specifically to a risk assessment method and system for tunnel abnormal events. Background Technology

[0002] As a relatively enclosed road traffic scenario, the internal space of a tunnel is greatly affected by factors such as sight distance, slope, curvature, lane layout, and the location of emergency facilities. When abnormal events such as parking, congestion, rear-end collisions, debris blocking the road, or pedestrians entering the tunnel occur, these abnormal events usually do not only affect the location of the event, but may also spread to adjacent sections along the direction of vehicle travel or traffic flow disturbance, thus forming a continuous risk impact area.

[0003] Current methods for managing tunnel anomalies typically rely on alarm information output from video surveillance, radar detection, traffic flow detection, or electromechanical monitoring systems. Monitoring personnel then make judgments based on the alarm type and location. While these methods can achieve initial detection of anomalies, their risk assessment results often remain at the level of event location or alarm level, failing to fully reflect the differences in visibility, gradient, curves, entrance lighting, vehicle speed, and lane occupancy status across different tunnel sections.

[0004] Furthermore, in the existing process of assessing the scope of risk impact, common methods often rely on fixed distances, pre-set buffer zones, or manual experience to delineate affected sections, failing to effectively differentiate the different impacts of abnormal events on upstream and downstream sections. Taking a parking incident in the main lane as an example, vehicles approaching from upstream are more likely to be affected by deceleration, avoidance, queuing, and rear-end collision risks, while the risk propagation in downstream sections is usually weaker. If risk assessment is still conducted based on symmetrical distance ranges, it can easily lead to inaccurate identification of key risk sections, affecting speed limits, lane control, guidance dissemination, and emergency response decisions.

[0005] Therefore, there is an urgent need for a risk assessment scheme that can combine basic information on abnormal events, tunnel section structural conditions, traffic disturbance status, and risk propagation characteristics to achieve section-level risk identification, propagation direction judgment, and comprehensive risk level assessment of tunnel abnormal events. Summary of the Invention

[0006] In view of the above situation and to overcome the shortcomings of the prior art, the technical solution adopted by the present invention is as follows: The present invention provides a risk assessment method for tunnel anomaly events, the method comprising the following steps:

[0007] Step S1: Tunnel operation status data acquisition;

[0008] Step S2: Generate basic data for abnormal events;

[0009] Step S3: Structurally sensitive correction;

[0010] Step S4: Propagation risk potential field analysis;

[0011] Step S5: Risk assessment of abnormal events.

[0012] Furthermore, in step S1, the tunnel operation status data acquisition is used to obtain the traffic operation status, section sensing conditions, and section structure status within the tunnel. Specifically, it involves collecting vehicle traffic information, section sensing condition information, and section structure information within the tunnel through existing tunnel sensing equipment and infrastructure management data to obtain tunnel operation status data.

[0013] Further, in step S2, the abnormal event basic data generation is used to convert tunnel operation status data or existing abnormal alarm data into event source data that characterizes the risk source attributes of abnormal events. Specifically, based on the tunnel operation status data or existing abnormal alarm data, the abnormal event type, the section where the event occurred, the affected lane, the duration of the event, and the basic risk value are determined to obtain the abnormal event basic data.

[0014] Further, in step S3, the structural sensitivity correction is used to perform segmented correction of the basic risk value based on the structural conditions of the section where the abnormal event occurs and its adjacent affected sections. Specifically, it adopts a tunnel structural sensitivity correction method that combines sight distance and braking margin. Based on the structural safety margin between the available sensing distance and the required braking distance, the structural sensitivity coefficient of the abnormal event-related section is determined, and the structural sensitivity coefficient is matched with the basic risk value in the basic data of the abnormal event to obtain structural sensitivity correction data.

[0015] The abnormal events are one or more traffic disruption events such as parking, congestion, rear-end collision, wrong-way driving, littering obstructing the road, or pedestrian entry. A tunnel structure sensitivity correction method combining sight distance and braking margin is used to generate a structure sensitivity coefficient.

[0016] The tunnel structure sensitive correction method combining line-of-sight and braking margin includes the following steps:

[0017] Step S31: Determine the associated section, which is used to determine the range of tunnel sections that need to be structurally sensitively corrected after an abnormal event occurs. Specifically, based on the event occurrence section in the abnormal event basic data, a preset number of continuous sections are selected along the upstream side of the vehicle travel direction, and the event occurrence section is also used as the associated section to obtain the associated section set.

[0018] Step S32: Calculate the available sensing distance to determine the effective detection distance of vehicles behind the abnormal event source within each associated section. Specifically, based on the section structure information in the tunnel operation status data, determine the geometric distance, curvature limitation distance, tunnel entrance illumination influence distance, and equipment observation distance between each associated section and the event occurrence section, and determine the available sensing distance corresponding to each associated section according to the shortest constraint principle.

[0019] Step S33: Calculate the required braking distance, which is used to determine the distance required for vehicles in each associated section to decelerate or stop after detecting an abnormal event. Specifically, it calculates the required braking distance for each associated section based on the average vehicle speed, driver reaction time, road surface adhesion coefficient, and slope correction term of each associated section.

[0020] Step S34: Structural safety margin calculation, used to determine whether each associated section has sufficient space to respond to abnormal events under the current structural conditions. Specifically, the difference between the available sensing distance and the required braking distance is calculated to obtain the structural safety margin of each associated section relative to the event occurrence section.

[0021] Step S35: Structural sensitivity coefficient generation, used to convert the structural safety margin of each associated section into a segmented correction coefficient of the basic risk value. Specifically, based on the proportional relationship between the structural safety margin and the required braking distance, a segmented margin compression function is used to generate the structural sensitivity coefficient corresponding to each associated section.

[0022] Step S36: Generate structural sensitivity correction data, which is used to match the structural sensitivity coefficient of each associated segment with the basic risk value in the abnormal event basic data. Specifically, based on the basic risk value in the abnormal event basic data and the structural sensitivity coefficient corresponding to each associated segment, calculate the structural correction risk value corresponding to each associated segment to obtain structural sensitivity correction data.

[0023] Further, in step S4, the propagation risk potential field analysis is used to determine the risk propagation impact of the abnormal event from the event occurrence section to other tunnel sections. Specifically, it adopts a propagation risk potential field analysis method based on section direction attenuation, takes the event occurrence section determined by the abnormal event basic data as the risk source section, and calculates the propagation risk potential field value of each tunnel section relative to the risk source section in combination with the structural sensitivity correction data to obtain the propagation risk potential field data.

[0024] The propagation risk potential field analysis method based on segment directional attenuation includes the following steps:

[0025] Step S41: Determine the propagation analysis section, which is used to determine the calculation range of the risk propagation of abnormal events. Specifically, based on the event occurrence section in the basic data of abnormal events, a preset number of continuous sections are selected on the upstream and downstream sides along the tunnel travel direction to form a propagation analysis section set.

[0026] Simultaneously, before calculating the propagation risk potential field value, a baseline risk value for each propagation analysis segment in the propagation analysis segment set is determined. When the propagation analysis segment belongs to the associated segment corresponding to the structurally sensitive correction data, its structural correction risk value is used as the baseline risk value of the segment. When the propagation analysis segment does not belong to the associated segment corresponding to the structurally sensitive correction data, the basic risk value of the abnormal event is multiplied by a preset far-end baseline attenuation coefficient and used as the baseline risk value of the segment to obtain the baseline risk data of the propagation analysis segment.

[0027] Step S42: Determine the propagation direction attribute of the segment. This is used to distinguish the propagation direction relationship between different segments and the risk source segment. Specifically, based on the positional relationship between the propagation analysis segment and the event occurrence segment, each propagation analysis segment is marked as an upstream segment, an event source segment, or a downstream segment, and different propagation direction coefficients are assigned to different directions.

[0028] Among them, the propagation direction coefficient of the upstream section is greater than that of the downstream section;

[0029] Step S43: Segment distance attenuation processing, used to reflect the propagation characteristic that the risk of abnormal events gradually weakens with increasing spatial distance. Specifically, it determines the corresponding distance attenuation degree based on the segment distance between each propagation analysis segment and the event occurrence segment.

[0030] Step S44: Lane disturbance status determination, which reflects the disturbance impact of abnormal events on adjacent lanes or same-direction traffic flow. Specifically, based on the affected lanes in the abnormal event basic data and the lane occupancy status in the tunnel operation status data, the lane disturbance coefficient of each propagation analysis section is determined.

[0031] Step S45: Calculate the propagation risk potential field value, which is used to generate the propagation risk intensity corresponding to each propagation analysis segment. Specifically, it calculates the propagation risk potential field value of each propagation analysis segment based on the baseline risk value, propagation direction coefficient, distance attenuation degree, and lane disturbance coefficient of the propagation analysis segment.

[0032] Step S46: Generate propagation risk potential field data, which is used to organize the calculation results of each segment into the data format required for subsequent risk assessment. Specifically, it involves associating and storing the abnormal event number, the event occurrence segment, the propagation analysis segment, the propagation risk potential field value of each segment, the segment with the largest potential field, and the main direction of propagation to obtain the propagation risk potential field data.

[0033] Further, in step S5, the abnormal event risk assessment is used to form a section-level risk assessment result of the tunnel abnormal event based on the propagation risk potential field data. Specifically, it performs level mapping and section correlation on the propagation risk potential field value of each tunnel section to determine the comprehensive risk level of the abnormal event, the high-risk affected section and the risk diffusion direction, and obtain the abnormal event risk assessment result.

[0034] The overall risk level of the abnormal event is determined by the maximum propagation risk level in the propagation analysis segment and the number of consecutive segments that reach the preset concern level. When the maximum propagation risk level reaches the high risk level and the number of consecutive high risk segments is not less than the preset consecutive segment threshold, the overall risk level of the abnormal event is determined to be high risk or severe risk.

[0035] This invention provides a risk assessment system for tunnel anomaly events, comprising a tunnel operation status data acquisition module, an anomaly event basic data generation module, a structural sensitivity correction module, a propagation risk potential field analysis module, and an anomaly event risk assessment module;

[0036] The tunnel operation status data acquisition module is used to acquire tunnel operation status data. Through the acquisition of tunnel operation status data, tunnel operation status data is obtained, and the tunnel operation status data is sent to the abnormal event basic data generation module and the structure sensitivity correction module.

[0037] The abnormal event basic data generation module is used to generate abnormal event basic data. Through the abnormal event basic data generation, the abnormal event basic data is obtained, and the abnormal event basic data is sent to the structure sensitivity correction module and the propagation risk potential field analysis module.

[0038] The structure-sensitive correction module is used for structure-sensitive correction, obtaining structure-sensitive correction data through structure-sensitive correction, and sending the structure-sensitive correction data to the propagation risk potential field analysis module;

[0039] The propagation risk potential field analysis module is used for propagation risk potential field analysis, obtaining propagation risk potential field data through propagation risk potential field analysis, and sending the propagation risk potential field data to the abnormal event risk assessment module.

[0040] The abnormal event risk assessment module is used for abnormal event risk assessment, obtaining abnormal event risk assessment results through abnormal event risk assessment, and outputting the abnormal event risk assessment results.

[0041] The beneficial effects achieved by the present invention using the above solution are as follows:

[0042] (1) In response to the technical problem that in the existing tunnel abnormal event risk assessment process, there is a disconnect between abnormal alarms and the section operation status and structural conditions, which leads to the risk assessment remaining at a single point alarm and making it difficult to form a section-level handling basis, this solution creatively adopts a processing method that combines abnormal event basic data generation, structural sensitivity correction, propagation risk potential field analysis and risk level mapping; thereby, abnormal events can be transformed from single alarm records into section-level assessment results with spatial impact range, risk diffusion direction and comprehensive risk level, thereby improving the location and continuity of tunnel abnormal event handling;

[0043] (2) In response to the technical problem that the risk intensity is determined solely by the event type or alarm level in the existing tunnel abnormal event risk correction process, which makes it difficult to reflect the differences in structural response conditions in different sections, this solution creatively adopts a tunnel structure sensitive correction method that combines sight distance and braking margin. As a result, the basic risk value can be amplified, maintained, or mitigated in sections based on the difference between the available sensing distance and the required braking distance, so that structurally unfavorable sections are identified first and sections with sufficient structural margin are avoided from being misjudged as high-risk.

[0044] (3) In the existing process of judging the impact range of abnormal tunnel events, there is a technical problem that the impact section is determined by relying on fixed distance or manual experience, which makes it difficult to reflect the asymmetric propagation characteristics of risk along the direction of traffic. This solution creatively adopts the propagation risk potential field analysis method based on the attenuation of the section direction. As a result, the propagation risk potential field value can be calculated by combining the section direction, spatial distance and lane disturbance status, so that the determination of high-risk impact section and risk diffusion direction is more in line with tunnel traffic operation. Attached Figure Description

[0045] Figure 1 A flowchart illustrating a risk assessment method for tunnel anomaly events provided by the present invention;

[0046] Figure 2 This is a schematic diagram of a risk assessment system for tunnel anomaly events provided by the present invention;

[0047] Figure 3 A flowchart illustrating the structure-sensitive correction process in step S3;

[0048] Figure 4 This is a flowchart illustrating the process of propagating the risk potential field analysis in step S4.

[0049] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0050] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0051] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0052] Example 1, see Figure 1 The present invention provides a risk assessment method for tunnel anomaly events, the method comprising the following steps:

[0053] Step S1: Tunnel operation status data acquisition;

[0054] Step S2: Generate basic data for abnormal events;

[0055] Step S3: Structurally sensitive correction;

[0056] Step S4: Propagation risk potential field analysis;

[0057] Step S5: Risk assessment of abnormal events.

[0058] By performing the above operations, this solution addresses the technical problem in existing tunnel anomaly risk assessment processes where anomaly alarms are disconnected from the section's operational status and structural conditions, resulting in risk assessments remaining at a single-point alarm level and failing to form a basis for section-level handling. This solution creatively adopts a processing method that combines anomaly event basic data generation, structural sensitivity correction, propagation risk potential field analysis, and risk level mapping. As a result, anomalies can be transformed from single alarm records into section-level assessment results with spatial impact range, risk diffusion direction, and comprehensive risk level, improving the location and continuity of tunnel anomaly event handling.

[0059] For example, when a main lane parking incident occurs in a tunnel section, the system not only determines the incident source section and the basic risk value, but also identifies whether the incident source section and its adjacent sections constitute a continuous high-risk impact section by combining the sight distance, gradient, vehicle speed and lane occupancy status of the adjacent upstream sections. This provides clearer section-level basis for speed limits, lane control and emergency response.

[0060] Example 2, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S1, the tunnel operation status data collection is used to obtain the traffic operation status, section perception conditions and section structure status in the tunnel. Specifically, it is to collect vehicle traffic information, section perception condition information and section structure information in the tunnel through the existing tunnel sensing equipment and infrastructure management data to obtain tunnel operation status data.

[0061] In one embodiment, the existing tunnel sensing equipment includes one or more of video surveillance equipment, radar detection equipment, traffic flow detection equipment, and visibility detection equipment; the infrastructure management data includes one or more of tunnel station number, lane layout, slope, curvature, tunnel entrance location, cross passage location, and emergency parking lane location.

[0062] During the data collection process, the tunnel is divided into several continuous sections along its length, and each section is assigned a section number. The collected vehicle traffic information, section perception condition information, and section structure information are correlated according to the section number and sampling time to form section-based operational status data. Among them, the vehicle traffic information includes vehicle speed, traffic flow, vehicle density, and lane occupancy status; the section perception condition information includes visibility, tunnel entrance light / dark adaptation distance, and existing equipment observation distance; the section structure information includes section station range, slope, curvature, number of lanes, tunnel entrance distance, and emergency stopping lane distance.

[0063] For example, for a tunnel with a length of 3000m, it can be divided into sections of 100m each, forming a sequence of sections Z1 to Z30; at a certain sampling time, the average vehicle speed corresponding to section Z13 is 18km / h, the lane occupancy rate is 0.71, the visibility is 160m, the gradient is -1.8%, and the distance to the nearest emergency stopping lane is 95m. These data are then recorded as the operating status of section Z13 at that sampling time.

[0064] The above method yields tunnel operation status data organized according to sampling time, section number, and operation status field, providing a data foundation for subsequent abnormal event data generation, structural sensitivity correction, and propagation risk potential field analysis.

[0065] Example 3, see Figure 1 , Figure 2 This embodiment is based on the above embodiment. In step S2, the abnormal event basic data generation is used to convert tunnel operation status data or existing abnormal alarm data into event source data that characterizes the risk source attributes of abnormal events. Specifically, based on tunnel operation status data or existing abnormal alarm data, the abnormal event type, event occurrence section, affected lane, event duration and basic risk value are determined to obtain the abnormal event basic data.

[0066] In one implementation, when the tunnel monitoring platform, traffic incident detection system, or electromechanical monitoring system has already output abnormal alarm records, the abnormal alarm records are directly used as the basis for generating basic data for abnormal events. Specifically, the alarm type, alarm time, alarm location, associated lane, and alarm level are extracted from the abnormal alarm records, and the event occurrence section is determined based on the correspondence between the alarm location and the tunnel section number. The duration of the event is determined based on the persistence status of the same alarm within a continuous time period. The basic risk value is determined based on the alarm type and alarm level. Thus, the alarm records output by the existing system, such as parking, congestion, rear-end collision, wrong-way driving, littering obstructing the lane, or pedestrian entry, are converted into basic data for abnormal events in a unified format.

[0067] For example, at 10:15:20, the existing tunnel monitoring platform outputs a mainline parking alarm at location K1+280, with the second lane as the associated lane, and the alarm level as level two. Here, K1+280 indicates that the distance of the alarm location from the starting point of the tunnel section division is 1280m. If the tunnel is divided into sections of 100m each, and section Z13 corresponds to the range of 1200m to 1300m, then the alarm location is mapped to section Z13. If the alarm continues until 10:16:10 when it is cleared, then the abnormal event type is determined to be main lane parking, the event occurrence section is Z13, the affected lane is the second lane, the event duration is 50s, and the basic risk value is determined according to the preset risk mapping rules corresponding to level two alarms.

[0068] In another implementation, when existing abnormal alarm records are not directly available, abnormal events are generated based on tunnel operation status data. Specifically, traffic operation status within a continuous sampling period is determined by rules. When the average vehicle speed of a section is lower than a preset speed threshold and the lane occupancy rate is higher than a preset occupancy threshold, a congestion-type abnormal event is identified. When a vehicle is detected to be stationary for a long time in the same section and its position change is less than a preset displacement threshold, a parking-type abnormal event is identified. When a vehicle's driving direction is detected to be inconsistent with the preset driving direction, a reverse driving-type abnormal event is identified. When an obstacle is detected to be located in the main traffic lane and remains there for more than a preset time, a debris-occupying-the-lane-area abnormal event is identified. When a pedestrian is detected to have entered the main traffic area or the tunnel's restricted area and remains there for more than a preset time, a pedestrian entry-type abnormal event is identified.

[0069] The above thresholds can be set according to tunnel design standards, operation and maintenance rules, or historical operation data;

[0070] For example, if the average vehicle speed in section Z18 decreases from 55 km / h to 18 km / h and the lane occupancy rate increases from 0.32 to 0.72 within six consecutive 10-second sampling periods, and the vehicle speed in the adjacent upstream section decreases synchronously, then it can be determined that there is an abnormal congestion in section Z18. The system will determine the abnormal event type as congestion, the event occurrence section as Z18, the affected lanes as determined by the lanes corresponding to the changes in occupancy rate, and the event duration as the continuous duration of the abnormal state.

[0071] In specific implementations that utilize video or radar detection results, video detection and radar detection serve only as preliminary sources of basic data for abnormal events. Specifically, existing target detection models or radar target tracking methods can be used to identify vehicles, pedestrians, or obstacles, and the detected target locations can be mapped to corresponding tunnel sections. When a detected target meets event judgment conditions such as parking, pedestrian entry, littering obstructing the road, or driving against traffic, a corresponding abnormal event is generated. The target detection model can employ publicly available models such as YOLOv5, YOLOv8, or Faster R-CNN, and the radar target tracking method can employ Kalman filtering or multi-target trajectory association methods. The aforementioned detection methods are used to generate event source information and are not considered necessary limitations for propagation risk potential field analysis.

[0072] In a preferred embodiment, the basic risk value is determined based on the type of abnormal event, its duration, and the degree of traffic disruption, and is calculated as follows:

[0073] ;

[0074] In the formula, Indicates the basic risk value. This indicates the risk coefficient for the type of abnormal event. This represents the normalized value of the event duration. This represents the traffic disturbance coefficient. , , These are preset weights for abnormal events, preset weights for time normalization, and preset weights for traffic disturbances.

[0075] In one implementation, , , The values ​​of are all in the range of 0 to 1, and For example, when the type of unusual event has a higher impact on risk, Take a value between 0.45 and 0.60. Take a value between 0.20 and 0.35. Take a value between 0.15 and 0.30;

[0076] In one alternative implementation, the risk coefficient for the abnormal event type Based on the preset abnormal event type, for example, parking abnormal events are set at 0.70 to 0.85, congestion abnormal events at 0.55 to 0.75, rear-end collision abnormal events at 0.80 to 1.00, wrong-way driving abnormal events at 0.85 to 1.00, littering and obstruction of the road abnormal events at 0.65 to 0.85, and pedestrian entry abnormal events at 0.85 to 1.00;

[0077] Normalized value of event duration Based on event duration With preset maximum normalization time Determined, that is ;

[0078] Traffic disturbance coefficient Determined based on the percentage decrease in vehicle speed and changes in lane occupancy before and after the abnormal event, for example... ,in This indicates the average speed of vehicles in the section before the abnormal event occurred. This indicates the average speed of vehicles in the section after the abnormal event occurred. This indicates the lane occupancy rate before the abnormal event occurred. This indicates the lane occupancy rate after an abnormal event occurs. and Preset weights;

[0079] The basic risk value is used to characterize the initial intensity of the anomalous event as a risk source, and is used for subsequent structural sensitivity correction and propagation risk potential field analysis.

[0080] Example 4, see Figure 1 , Figure 2 and Figure 3 This embodiment is based on the above embodiment. In step S3, the structural sensitivity correction is used to perform segmented correction of the basic risk value according to the structural conditions of the section where the abnormal event occurs and its adjacent affected sections. Specifically, it adopts a tunnel structural sensitivity correction method that combines sight distance and braking margin. Based on the structural safety margin between the available sensing distance and the required braking distance, the structural sensitivity coefficient of the abnormal event associated section is determined, and the structural sensitivity coefficient is matched with the basic risk value in the basic data of the abnormal event to obtain structural sensitivity correction data.

[0081] The abnormal events are one or more traffic disruption events such as parking, congestion, rear-end collision, wrong-way driving, littering obstructing the road, or pedestrian entry. A tunnel structure sensitivity correction method combining sight distance and braking margin is used to generate a structure sensitivity coefficient.

[0082] The tunnel structure sensitive correction method combining line-of-sight and braking margin includes the following steps:

[0083] Step S31: Determine the associated section, which is used to determine the range of tunnel sections that need to be structurally sensitively corrected after an abnormal event occurs. Specifically, based on the event occurrence section in the abnormal event basic data, a preset number of continuous sections are selected along the upstream side of the vehicle travel direction, and the event occurrence section is also used as the associated section to obtain the associated section set.

[0084] The tunnel is divided into several continuous segments along the direction of vehicle travel, and the segment sequence is represented as follows: , Let i represent the i-th tunnel segment, where i represents the segment number; let the segment where the abnormal event occurred be... s represents the sequence number of the segment where the abnormal event occurred in the segment sequence; let the number of upstream tracing segments be... If u represents the upstream side, then the set of associated segments is represented as:

[0085] ;

[0086] In the formula, This represents the set of associated segments corresponding to the abnormal event. Indicates the segment where the abnormal event occurred. Tracking upstream The farthest upstream associated segment after each segment, and when When less than 1, with As the furthest upstream related segment, Indicates the section where the abnormal event occurred. This indicates the number of upstream tracking segments; the number of upstream tracking segments can be set according to the type of abnormal event, segment length, and tunnel management accuracy.

[0087] For example, when the tunnel is divided into 100m sections, the main lane parking event occurs in section Z13, and the number of upstream tracking sections is 4, then the set of associated sections is Z9 to Z13.

[0088] Step S32: Calculate the available sensing distance to determine the effective detection distance of vehicles behind the abnormal event source within each associated section. Specifically, based on the section structure information in the tunnel operation status data, determine the geometric distance, curvature limitation distance, tunnel entrance illumination influence distance, and equipment observation distance between each associated section and the event occurrence section, and determine the available sensing distance corresponding to each associated section according to the shortest constraint principle.

[0089] Preferably, the first Each associated segment is relative to the event occurrence segment. The available sensing distance is determined according to the following formula:

[0090] ;

[0091] In the formula, Indicates the first The available perceptible distance of each associated segment relative to the segment where the event occurred. Indicates the first Geometric distance between each associated segment and the segment where the event occurred This indicates the visible distance after being limited by the curvature of the tunnel. This indicates the effective recognition distance after the light and dark adaptation at the opening of the hole. This indicates the effective observation distance determined by existing monitoring equipment or tunnel line-of-sight records;

[0092] Step S33: Calculate the required braking distance, which is used to determine the distance required for vehicles in each associated section to decelerate or stop after detecting an abnormal event. Specifically, it calculates the required braking distance for each associated section based on the average vehicle speed, driver reaction time, road surface adhesion coefficient, and slope correction term of each associated section.

[0093] Preferably, the first The required braking distance for each associated section is determined according to the following formula:

[0094] ;

[0095] In the formula, Indicates the first The required braking distance for each associated section Indicates the first The average vehicle speed in each associated section, Indicates the driver's reaction time. Represents gravitational acceleration. Indicates the road surface adhesion coefficient. This represents the slope correction term; the slope correction term for uphill sections takes a positive value, and the slope correction term for downhill sections takes a negative value; when the average vehicle speed is output in km / h by the traffic detection equipment, it is converted to m / s before being substituted into the required braking distance formula;

[0096] In practice, the average vehicle speed can be obtained from the vehicle traffic information in step S1, the slope correction item can be obtained from the section structure information in step S1, and the driver reaction time and road surface adhesion coefficient can be set according to tunnel operation and maintenance rules, road safety analysis specifications or historical operation data.

[0097] Step S34: Structural safety margin calculation, used to determine whether each associated section has sufficient space to respond to abnormal events under the current structural conditions. Specifically, the difference between the available sensing distance and the required braking distance is calculated to obtain the structural safety margin of each associated section relative to the event occurrence section.

[0098] Preferably, the structural safety margin is determined according to the following formula:

[0099] ;

[0100] In the formula, Indicates the first Each associated segment is relative to the event occurrence segment. Structural safety margin Indicates the first The available perceptible distance of each associated segment relative to the segment where the event occurred. Indicates the first The required braking distance for each associated section;

[0101] when When, it indicates that the associated section has a positive reaction margin in terms of structural conditions; when When this occurs, it indicates that the available sensing distance for the associated section is insufficient to cover the required braking distance, and the risk of abnormal events needs to be structurally amplified.

[0102] Step S35: Structural sensitivity coefficient generation, used to convert the structural safety margin of each associated section into a segmented correction coefficient of the basic risk value. Specifically, based on the proportional relationship between the structural safety margin and the required braking distance, a segmented margin compression function is used to generate the structural sensitivity coefficient corresponding to each associated section.

[0103] Preferably, the structural sensitivity coefficient is determined according to the following formula:

[0104] ;

[0105] In the formula, Indicates the first Each associated segment is relative to the event occurrence segment. The structural sensitivity coefficient, Indicates structural safety margin. Indicates the required braking distance. Indicates the safety margin slow-release threshold. This represents the risk amplification factor. Indicates the risk mitigation coefficient;

[0106] When the available sensing distance is less than the required braking distance, the basic risk value is amplified using the structural sensitivity coefficient; when the available sensing distance is slightly higher than the required braking distance but does not reach the safety margin mitigation threshold, the basic risk value remains unchanged; when the available sensing distance is significantly greater than the required braking distance, the basic risk value is moderately mitigated using the structural sensitivity coefficient; to avoid over-correction of risk, the structural sensitivity coefficient can be limited to a preset range, for example, between 0.75 and 1.80.

[0107] Step S36: Generate structural sensitivity correction data, which is used to match the structural sensitivity coefficient of each associated segment with the basic risk value in the abnormal event basic data. Specifically, based on the basic risk value in the abnormal event basic data and the structural sensitivity coefficient corresponding to each associated segment, calculate the structural correction risk value corresponding to each associated segment to obtain structural sensitivity correction data.

[0108] Preferably, the structural modification risk value is determined according to the following formula:

[0109] ;

[0110] in, Indicates an abnormal event For the Structural correction risk value of each associated segment This represents the base risk value in the basic data of abnormal events. Indicates the first The structural sensitivity coefficient of each associated segment relative to the event occurrence segment;

[0111] For example, in one specific embodiment, the tunnel is divided into continuous sections of 100m each. The basic data of the abnormal event obtained in step S2 is as follows: the abnormal event type is parking in the main lane, the event occurrence section is Z13, the affected lane is the second lane, and the basic risk value is 0.72. For this parking event in the main lane, the four upstream sections of the event occurrence section and the event occurrence section itself are selected as the associated sections, that is, the set of associated sections is Z9 to Z13.

[0112] Taking section Z12 as an example, the geometric distance from section Z12 to section Z13 where the incident occurred is 100m. However, according to the section structure information, affected by the curve sight distance and local slope changes, its usable perception distance is 58m. The average vehicle speed in section Z12 is 58km / h, the driver's reaction time is taken as 1.5s, the road surface adhesion coefficient is taken as 0.35, and the slope correction term is determined according to the downhill conditions of this section. Therefore, the required braking distance is calculated to be approximately 68m.

[0113] Therefore, the structural safety margin is:

[0114] ;

[0115] This indicates that the available sensing distance for section Z12 under the current structural conditions is insufficient to cover the required braking distance, necessitating a structural amplification of the basic risk value. If the structural sensitivity coefficient calculated using the piecewise margin compression function is 1.18, then the structural correction risk value corresponding to section Z12 is:

[0116] ;

[0117] Taking section Z11 as an example, the geometric distance from section Z11 to the incident-occurring section Z13 is 200m, and the usable sensing distance after curvature limitation is 135m. If the required braking distance is calculated to be approximately 69m based on the average vehicle speed, reaction time, road surface adhesion coefficient, and slope conditions of this section, then the structural safety margin is 66m. If the safety margin mitigation threshold is 50m, then section Z11 has a sufficient structural response margin, and its structural sensitivity coefficient can be less than or close to 1, thereby moderately mitigating or keeping the basic risk value unchanged.

[0118] Therefore, the tunnel structure sensitive correction method based on line-of-sight-braking margin coupling can transform the single basic risk value in the basic data of abnormal events into a structural correction risk value for the associated section, so that the subsequent propagation risk potential field analysis can further consider the amplification or mitigation effect of the tunnel section structural conditions on the risk of abnormal events.

[0119] By performing the above operations, this solution addresses the technical problem in existing tunnel anomaly risk correction processes where risk intensity is determined solely based on event type or alarm level, making it difficult to reflect the differences in structural response conditions across different sections. It creatively employs a tunnel structure-sensitive correction method that combines sight distance and braking margin. This allows for the segmented amplification, maintenance, or mitigation of the basic risk value based on the difference between the available sensing distance and the required braking distance, prioritizing the identification of structurally unfavorable sections and preventing sections with sufficient structural margin from being misjudged as high-risk.

[0120] For example, when a certain section upstream of an abnormal event is affected by the curve's sight distance, slope, or observation conditions, causing the vehicle's available sensing distance to be less than the required braking distance, the system increases the risk value corresponding to that section through the structural sensitivity coefficient, thereby more accurately reflecting the actual risk caused by the vehicle's insufficient ability to detect abnormalities, decelerate, or stop in that section.

[0121] Example 5, see Figure 1 , Figure 2 and Figure 4 This embodiment is based on the above embodiment. In step S4, the propagation risk potential field analysis is used to determine the risk propagation impact of the abnormal event from the event occurrence section to other tunnel sections. Specifically, it adopts the propagation risk potential field analysis method based on section direction attenuation. The event occurrence section determined by the abnormal event basic data is used as the risk source section. Combined with the structural sensitivity correction data, the propagation risk potential field value of each tunnel section relative to the risk source section is calculated to obtain the propagation risk potential field data.

[0122] The propagation risk potential field analysis method based on segment directional attenuation includes the following steps:

[0123] Step S41: Determine the propagation analysis section, which is used to determine the calculation range of the risk propagation of abnormal events. Specifically, based on the event occurrence section in the basic data of abnormal events, a preset number of continuous sections are selected on the upstream and downstream sides along the tunnel travel direction to form a propagation analysis section set.

[0124] Simultaneously, before calculating the propagation risk potential field value, a baseline risk value for each propagation analysis segment in the propagation analysis segment set is determined. When the propagation analysis segment belongs to the associated segment corresponding to the structurally sensitive correction data, its structural correction risk value is used as the baseline risk value of the segment. When the propagation analysis segment does not belong to the associated segment corresponding to the structurally sensitive correction data, the basic risk value of the abnormal event is multiplied by a preset far-end baseline attenuation coefficient and used as the baseline risk value of the segment to obtain the baseline risk data of the propagation analysis segment.

[0125] Among them, for traffic disruption-type abnormal events that mainly affect the reaction of vehicles behind, such as parking, rear-end collisions, congestion, littering, or pedestrians entering the road, the number of upstream propagation segments is greater than the number of downstream propagation segments.

[0126] For example, the tunnel is divided into 100m sections, and the abnormal event occurred in section Z13; for the main lane parking event, Z8 to Z16 can be selected as the propagation analysis section, where Z8 to Z12 is the upstream propagation section, Z13 is the risk source section, and Z14 to Z16 is the downstream propagation section;

[0127] Step S42: Determine the propagation direction attribute of the segment. This is used to distinguish the propagation direction relationship between different segments and the risk source segment. Specifically, based on the positional relationship between the propagation analysis segment and the event occurrence segment, each propagation analysis segment is marked as an upstream segment, an event source segment, or a downstream segment, and different propagation direction coefficients are assigned to different directions.

[0128] Among them, the propagation direction coefficient of the upstream section is greater than that of the downstream section. This is because abnormal events such as parking, congestion, and obstacles blocking the road usually cause vehicles behind to slow down, queue, and rear-end collisions first. Although the downstream section may be affected by traffic flow interruption, its propagation risk is usually weaker than that of the upstream section.

[0129] In practice, the propagation direction coefficient of the event source segment can be set to 1, the propagation direction coefficient of the upstream segment can be set to a value greater than 1, and the propagation direction coefficient of the downstream segment can be set to a value less than 1 or close to 1. For example, in the main lane parking event, the propagation direction coefficient of the upstream segment is 1.2, the propagation direction coefficient of the downstream segment is 0.6, and the propagation direction coefficient of the event source segment is 1.

[0130] Step S43: Segment distance attenuation processing, used to reflect the propagation characteristic that the risk of abnormal events gradually weakens with increasing spatial distance. Specifically, it determines the corresponding distance attenuation degree based on the segment distance between each propagation analysis segment and the event occurrence segment.

[0131] Among them, the risk propagation impact of abnormal events decreases exponentially with the increase of the distance between sections; the distance between sections can be determined based on the difference in section numbers and the section length, or it can be determined based on the distance between tunnel stations.

[0132] In one implementation, when the tunnel is divided into equal-length sections, the k-th propagation analysis section is related to the event occurrence section. The distance between the segments Determined based on the difference in segment numbers and the preset segment length, i.e. When the tunnel is divided into sections of non-equal length, It is determined based on the difference between the center station number of the kth propagation analysis segment and the center station number of the event occurrence segment;

[0133] For example, if there are three 100m segments between Z10 and Z13, then the distance between them relative to the risk source segment is 300m; the distance between Z12 and Z13 is 100m, and the impact of the propagation of abnormal events is usually higher than that of Z10.

[0134] Step S44: Lane disturbance status determination, which reflects the disturbance impact of abnormal events on adjacent lanes or same-direction traffic flow. Specifically, based on the affected lanes in the abnormal event basic data and the lane occupancy status in the tunnel operation status data, the lane disturbance coefficient of each propagation analysis section is determined.

[0135] Specifically, the lane disturbance coefficient increases when an abnormal event occupies the main lane, the adjacent lane has a high occupancy rate, or the vehicle needs to change lanes to avoid it; the lane disturbance coefficient can be reduced when the abnormal event is located in the emergency stopping lane or does not affect the main traffic lane.

[0136] For example, if a parking incident occurs in the second lane and the adjacent first lane is heavily occupied, there is insufficient space for vehicles to avoid the incident in the upstream section, and the lane disturbance coefficient can be taken as 1.15 to 1.30; if the abnormal vehicle is located in the emergency stopping lane and does not affect the passage of the main lane, the lane disturbance coefficient can be taken as 0.80 to 1.00.

[0137] Step S45: Calculate the propagation risk potential field value, which is used to generate the propagation risk intensity corresponding to each propagation analysis segment. Specifically, it calculates the propagation risk potential field value of each propagation analysis segment based on the baseline risk value, propagation direction coefficient, distance attenuation degree, and lane disturbance coefficient of the propagation analysis segment.

[0138] Preferably, the propagation risk potential field value is determined according to the following formula:

[0139] ;

[0140] in, Indicates an abnormal event For the The propagation risk potential field value formed by each propagation analysis segment Indicates an abnormal event For the The baseline risk value for each propagation analysis segment is calculated using the following formula: In the formula, Indicates anomalous events in structure-sensitive correction data. For the Structural correction risk value for each section This represents the far-end reference attenuation coefficient of the non-structurally sensitive correction section; Indicates the first Each segment is relative to the segment where the event occurred. The propagation direction coefficient; Indicates the first The distance between each segment and the segment where the event occurred; Indicates the distance attenuation coefficient; Indicates the first Lane disturbance coefficient for each section;

[0141] Distance attenuation coefficient Based on the section length, tunnel speed limit level, and abnormal event type preset; in the implementation method with a section length of 100m, It can be between 0.001 and 0.006;

[0142] For example, the abnormal event is a stop in the main lane, and the event occurs in section Z13. For the upstream section Z12, if the structural correction risk value obtained in step S3 is 0.8496, the distance from Z12 to Z13 is 100m, the propagation direction coefficient is 1.2, the distance attenuation coefficient is 0.003, and the lane disturbance coefficient is 1.15, then the propagation risk potential field value of Z12 can be calculated by the above formula.

[0143] For the downstream section Z15, although its distance can also be included in the calculation, its propagation risk potential field value is lower than that of the upstream section under the same distance condition because the downstream propagation direction coefficient is lower.

[0144] Step S46: Generate propagation risk potential field data, which is used to organize the calculation results of each segment into the data format required for subsequent risk assessment. Specifically, it involves associating and storing the abnormal event number, the event occurrence segment, the propagation analysis segment, the propagation risk potential field value of each segment, the segment with the largest potential field, and the main direction of propagation to obtain the propagation risk potential field data.

[0145] The main direction of propagation is determined based on the number of propagation analysis segments that have reached a preset attention threshold distributed upstream or downstream of the event occurrence segment and the cumulative value of the propagation risk potential field.

[0146] When the cumulative potential field value on the upstream side is greater than the cumulative potential field value on the downstream side, the main propagation direction is determined to be upstream diffusion;

[0147] When the cumulative potential field value on the downstream side is greater than the cumulative potential field value on the upstream side, the main direction of propagation is determined to be downstream diffusion;

[0148] When the difference between the cumulative potential field value on the upstream side and the cumulative potential field value on the downstream side is less than the preset direction determination threshold, the main propagation direction will be determined as bidirectional diffusion or weak direction diffusion.

[0149] In practice, when the propagation risk potential field value of multiple consecutive upstream segments is higher than the preset attention threshold, the consecutive segments can be marked as the upstream propagation impact zone; when the propagation risk potential field value of a segment outside the event occurrence segment is higher than that of a segment near the event source segment, the segment can be recorded as a high-risk segment outside the event point for subsequent step S5 to determine the high-risk impact segment.

[0150] The risk potential field data is used in step S5 to determine the comprehensive risk level of the abnormal event, the high-risk impact area, and the direction of risk diffusion.

[0151] By performing the above operations, this solution addresses the technical problem that existing methods for determining the impact range of tunnel anomalies rely on fixed distances or manual experience to delineate affected sections, making it difficult to reflect the asymmetric propagation characteristics of risks along the direction of travel. This solution creatively adopts a propagation risk potential field analysis method based on section direction attenuation. Therefore, it can calculate the propagation risk potential field value by combining section direction, spatial distance, and lane disturbance status, making the determination of high-risk affected sections and risk diffusion directions more consistent with tunnel traffic operations.

[0152] For example, in the event of events such as parking, congestion, or debris blocking the main lane, upstream vehicles are usually more likely to cause deceleration, queuing, and rear-end collision risks. Therefore, the system uses a high upstream propagation direction coefficient combined with distance attenuation and lane disturbance status to identify the main high-risk impact sections and determine the main direction of risk spread.

[0153] Example 6, see Figure 1 and Figure 2This embodiment is based on the above embodiment. In step S5, the abnormal event risk assessment is used to form a section-level risk assessment result of the tunnel abnormal event based on the propagation risk potential field data. Specifically, it performs level mapping and section association on the propagation risk potential field value of each tunnel section to determine the comprehensive risk level of the abnormal event, the high-risk affected section and the risk diffusion direction, and obtains the abnormal event risk assessment result.

[0154] The direction of risk diffusion is determined based on the main direction of propagation in the propagation risk potential field data and the distribution position of high-risk impact sections relative to the event occurrence section.

[0155] The overall risk level of the abnormal event is determined by the maximum propagation risk level in the propagation analysis segment and the number of consecutive segments that reach the preset concern level. When the maximum propagation risk level reaches the high risk level and the number of consecutive high risk segments is not less than the preset consecutive segment threshold, the overall risk level of the abnormal event is determined to be high risk or severe risk.

[0156] In one implementation, the propagation risk potential field value of each propagation analysis section is mapped according to a preset risk level threshold, and each section is divided into low-risk section, medium-risk section, high-risk section or severe-risk section; wherein, the preset risk level threshold can be determined according to the tunnel's historical operation data, operation and maintenance management rules or emergency response grading standards.

[0157] For example, a transmission risk potential value less than 0.30 is defined as low risk, a value not less than 0.30 and less than 0.60 is defined as medium risk, a value not less than 0.60 and less than 0.85 is defined as high risk, and a value not less than 0.85 is defined as severe risk.

[0158] After completing the level mapping, adjacent segments with risk levels reaching the preset concern level are associated to form high-risk impact segments. For example, when the propagation risk potential field values ​​of segments Z10, Z11, and Z12 continuously exceed the high-risk threshold, Z10 to Z12 are identified as high-risk impact segments. When the high-risk segment is mainly located upstream of the event occurrence segment, the risk diffusion direction is determined to be upstream diffusion; when the high-risk segment is mainly located downstream of the event occurrence segment, the risk diffusion direction is determined to be downstream diffusion.

[0159] In one specific embodiment, the abnormal event occurs in section Z13. Step S4 obtains the propagation risk potential field values ​​of Z9 to Z16. If the propagation risk potential field values ​​corresponding to Z11, Z12 and Z13 reach the high-risk level, and the maximum propagation risk potential field value appears in Z12 except for the event source section, then the comprehensive risk level of the abnormal event is determined to be high-risk, Z11 to Z13 is determined to be the high-risk impact section, and the risk diffusion direction is determined to be from Z13 to the upstream side.

[0160] Example 7, see Figure 1 and Figure 2 Based on the above embodiments, this embodiment provides a risk assessment system for tunnel anomaly events, including a tunnel operation status data acquisition module, an anomaly event basic data generation module, a structural sensitivity correction module, a propagation risk potential field analysis module, and an anomaly event risk assessment module.

[0161] The tunnel operation status data acquisition module is used to acquire tunnel operation status data. Through the acquisition of tunnel operation status data, tunnel operation status data is obtained, and the tunnel operation status data is sent to the abnormal event basic data generation module and the structure sensitivity correction module.

[0162] The abnormal event basic data generation module is used to generate abnormal event basic data. Through the abnormal event basic data generation, the abnormal event basic data is obtained, and the abnormal event basic data is sent to the structure sensitivity correction module and the propagation risk potential field analysis module.

[0163] The structure-sensitive correction module is used for structure-sensitive correction, obtaining structure-sensitive correction data through structure-sensitive correction, and sending the structure-sensitive correction data to the propagation risk potential field analysis module;

[0164] The propagation risk potential field analysis module is used for propagation risk potential field analysis, obtaining propagation risk potential field data through propagation risk potential field analysis, and sending the propagation risk potential field data to the abnormal event risk assessment module.

[0165] The abnormal event risk assessment module is used for abnormal event risk assessment, obtaining abnormal event risk assessment results through abnormal event risk assessment, and outputting the abnormal event risk assessment results.

[0166] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0167] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0168] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A risk assessment method for tunnel anomaly events, characterized in that: The method includes the following steps: Step S1: Tunnel operation status data collection, collecting vehicle traffic information, environmental monitoring information and section structure information in the tunnel to obtain tunnel operation status data; Step S2: Generate basic data for abnormal events. Based on tunnel operation status data or existing abnormal alarm data, determine the type of abnormal event, the section where the event occurred, the affected lanes, the duration of the event, and the basic risk value to obtain basic data for abnormal events. Step S3: Structural sensitivity correction. A tunnel structural sensitivity correction method combining line-of-sight and braking margin is adopted. Based on the structural safety margin between the available sensing distance and the required braking distance, the structural sensitivity coefficient of the abnormal event-related section is determined, and the structural sensitivity coefficient is matched with the basic risk value in the abnormal event basic data to obtain structural sensitivity correction data. Step S4: Propagation risk potential field analysis. The propagation risk potential field analysis method based on segment direction attenuation is adopted. The event occurrence segment determined by the abnormal event basic data is taken as the risk source segment. Combined with the structural sensitivity correction data, the propagation risk potential field value of each tunnel segment relative to the risk source segment is calculated to obtain the propagation risk potential field data. Step S5: Abnormal event risk assessment. The propagation risk potential field values ​​of each tunnel section are mapped by level and correlated with the sections to determine the comprehensive risk level of abnormal events, high-risk affected sections and risk diffusion direction, and obtain the abnormal event risk assessment results.

2. The risk assessment method for tunnel anomaly events according to claim 1, characterized in that: In step S3, the tunnel structure sensitivity correction method combining line-of-sight and braking margin includes the following steps: determining the associated section, calculating the available sensing distance, calculating the required braking distance, calculating the structural safety margin, generating the structural sensitivity coefficient, and generating the structural sensitivity correction data; The associated segment determination is achieved by selecting a preset number of continuous segments upstream of the vehicle's driving direction based on the event occurrence segments in the abnormal event basic data, and including the event occurrence segments as associated segments to obtain an associated segment set.

3. The risk assessment method for tunnel anomaly events according to claim 2, characterized in that: In step S3, the available sensing distance calculation is performed by determining the geometric distance, curvature limitation distance, tunnel entrance illumination influence distance, and equipment observation distance between each associated section and the event occurrence section based on the section structure information in the tunnel operation status data, and determining the available sensing distance corresponding to each associated section according to the shortest constraint principle. The required braking distance is calculated based on the average vehicle speed, driver reaction time, road surface adhesion coefficient, and slope correction term for each associated section.

4. The risk assessment method for tunnel anomaly events according to claim 3, characterized in that: In step S3, the structural safety margin calculation involves calculating the difference between the available sensing distance and the required braking distance to obtain the structural safety margin of each associated segment relative to the event occurrence segment. The structural sensitivity coefficient is generated by using a piecewise margin compression function to generate the structural sensitivity coefficient corresponding to each associated segment, based on the proportional relationship between the structural safety margin and the required braking distance. The structural sensitivity correction data is generated by calculating the structural correction risk value corresponding to each associated segment based on the basic risk value in the abnormal event basic data and the structural sensitivity coefficient corresponding to each associated segment, thus obtaining the structural sensitivity correction data.

5. The risk assessment method for tunnel anomaly events according to claim 4, characterized in that: In step S4, the propagation risk potential field analysis method based on segment direction attenuation includes the following steps: determining the propagation analysis segment, determining the segment propagation direction attribute, processing the segment distance attenuation, determining the lane disturbance state, calculating the propagation risk potential field value, and generating the propagation risk potential field data; The propagation analysis segment determination involves selecting a preset number of continuous segments along the upstream and downstream sides of the tunnel's travel direction, based on the event occurrence segments in the abnormal event basic data, to form a propagation analysis segment set.

6. The risk assessment method for tunnel anomaly events according to claim 5, characterized in that: In step S4, the segment propagation direction attribute is determined to distinguish the propagation direction relationship between different segments relative to the risk source segment. Specifically, based on the positional relationship between the propagation analysis segment and the event occurrence segment, each propagation analysis segment is marked as an upstream segment, an event source segment, or a downstream segment, and different propagation direction coefficients are assigned to different directions; wherein, the propagation direction coefficient of the upstream segment is greater than that of the downstream segment. The segment distance attenuation process determines the corresponding degree of distance attenuation based on the segment distance between each propagation analysis segment and the event occurrence segment.

7. The risk assessment method for tunnel anomaly events according to claim 6, characterized in that: In step S4, the lane disturbance state is determined by determining the lane disturbance coefficient for each propagation analysis section based on the affected lanes in the abnormal event basic data and the lane occupancy status in the tunnel operation status data. The propagation risk potential field value is calculated based on the baseline risk value of the propagation analysis section, the propagation direction coefficient, the distance attenuation degree, and the lane disturbance coefficient. The propagation risk potential field data generation involves associating and storing the abnormal event number, the event occurrence segment, the propagation analysis segment, the propagation risk potential field value of each segment, the segment with the largest potential field, and the main propagation direction to obtain the propagation risk potential field data.

8. A risk assessment system for tunnel anomaly events, used to implement the risk assessment method for tunnel anomaly events as described in any one of claims 1-7, characterized in that: It includes a tunnel operation status data acquisition module, an abnormal event basic data generation module, a structural sensitivity correction module, a propagation risk potential field analysis module, and an abnormal event risk assessment module.

9. A risk assessment system for tunnel anomaly events according to claim 8, characterized in that: The tunnel operation status data acquisition module is used to acquire tunnel operation status data. Through the acquisition of tunnel operation status data, tunnel operation status data is obtained, and the tunnel operation status data is sent to the abnormal event basic data generation module and the structure sensitivity correction module. The abnormal event basic data generation module is used to generate abnormal event basic data. Through the abnormal event basic data generation, the abnormal event basic data is obtained, and the abnormal event basic data is sent to the structure sensitivity correction module and the propagation risk potential field analysis module. The structure-sensitive correction module is used for structure-sensitive correction, obtaining structure-sensitive correction data through structure-sensitive correction, and sending the structure-sensitive correction data to the propagation risk potential field analysis module; The propagation risk potential field analysis module is used for propagation risk potential field analysis, obtaining propagation risk potential field data through propagation risk potential field analysis, and sending the propagation risk potential field data to the abnormal event risk assessment module. The abnormal event risk assessment module is used for abnormal event risk assessment, obtaining abnormal event risk assessment results through abnormal event risk assessment, and outputting the abnormal event risk assessment results.

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