A highway whole-chain risk identification and early warning method and system

Abstract

The application discloses a highway full-chain risk identification early warning method and system, relates to the technical field of highway risk identification, and has the technical scheme as follows: three-dimensional terrain point cloud data, wind-sand movement trajectory data, traffic flow data, highway section information and road domain structure constraint data of a to-be-monitored highway are acquired; wind-sand traffic risk correlation regions are obtained based on the wind-sand movement trajectory data, the traffic flow data and the highway section information, wherein each wind-sand traffic risk correlation region is correlated with a wind-sand influence time range and a terrain type; risk identification constraint conditions are generated according to the road domain structure constraint data and regional risk weight values; and a risk factor identification early warning scheme containing an early warning facility layout scheme, a monitoring strategy and hierarchical early warning threshold values is generated according to the risk identification constraint conditions, so that the feasibility and pertinence of the early warning scheme are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of highway risk identification technology, and more specifically, to a method and system for identifying and warning of risks across the entire highway chain. Background Technology

[0002] Wind and sand disasters have become a key factor affecting highway traffic safety and operational efficiency. Traditional methods often rely solely on single meteorological or traffic flow data, lacking the integrated use of multi-source data such as three-dimensional terrain, wind and sand movement trajectories, and road area structural constraints. This makes it difficult to comprehensively capture the entire chain of wind and sand disasters from sand source to road area and their disruption of traffic operations, resulting in blind spots in risk identification and failing to reflect the actual patterns of interaction between wind and sand and traffic. Traditional methods do not bind key elements such as the time range of wind and sand impact and terrain type to specific road sections. This fails to reflect the differences in wind and sand effects at different times, leading to the generalization of risk areas. Furthermore, the lack of quantitative analysis of the sequential interaction characteristics between wind and sand effects and traffic operation makes it impossible to objectively reflect the risk level of each area or quantitatively differentiate the risk priorities of different areas, resulting in uneven allocation of prevention and control resources. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for identifying and warning of risks across the entire highway chain.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for identifying and warning of risks across the entire highway supply chain includes the following steps:

[0006] Acquire three-dimensional topographic point cloud data, wind and sand movement trajectory data, traffic flow data, highway segment information, and road area structure constraint data of the highway to be monitored;

[0007] Based on wind and sand movement trajectory data, traffic flow data, and highway section information, wind and sand traffic risk associated areas are obtained. Each wind and sand traffic risk associated area is associated with the time range of wind and sand impact and the terrain type.

[0008] Extract the risk time series characteristics of wind and sand traffic risk associated areas, obtain the comprehensive risk evaluation value of each wind and sand traffic risk associated area based on the risk time series characteristics, and determine the corresponding regional risk weight value based on the comprehensive risk evaluation value.

[0009] Risk identification constraints are generated based on road structure constraint data and regional risk weight values; risk factor identification and early warning schemes, including early warning facility deployment plans, monitoring strategies, and graded early warning thresholds, are generated based on risk identification constraints.

[0010] Preferably, it further includes:

[0011] Three-dimensional terrain point cloud data is generated based on the three-dimensional geometric structure of the highway to be monitored and the elevation of the terrain along the route.

[0012] Preferably, the wind and sand traffic risk associated areas are obtained based on wind and sand movement trajectory data, traffic flow data, and highway section information, specifically including the following steps:

[0013] Based on the wind and sand movement trajectory data, the wind and sand propagation paths acting on the highway area are determined, and a set of wind and sand action paths is obtained;

[0014] Based on traffic flow data, the traffic operation characteristics of different road segments are distinguished, and based on the traffic operation characteristics, road segments that are susceptible to external interference are identified to obtain a set of traffic-sensitive road segments;

[0015] By associating the set of wind and sand action paths with the set of traffic-sensitive road sections, the target traffic-sensitive road sections that the wind and sand action paths can cover are determined.

[0016] Based on the road segment structure characteristics and the degree of wind and sand influence of highway segment information, the target traffic-sensitive road segment is divided into several independent road segment units.

[0017] Determine the time period during which independent road sections are affected by wind and sand, as well as the topographic features of the road area;

[0018] By binding independent road sections, periods of wind and sand impact, and road terrain features, a wind and sand traffic risk association area is formed.

[0019] Preferably, the risk time series characteristics of the wind and sand traffic risk associated area are extracted, specifically including the following steps:

[0020] Based on the time range of wind and sand impact and the terrain type, each wind and sand traffic risk associated area is divided into several feature extraction units;

[0021] The wind and sand action status and impact of each feature extraction unit are captured to obtain wind and sand action time series information; the time series correspondence between traffic operation status and wind and sand action changes is captured to obtain traffic operation time series information.

[0022] By correlating the temporal information of wind and sand action with the temporal information of traffic operation, key temporal nodes of the mutual influence between wind and sand action and traffic operation are obtained.

[0023] The correspondence between the wind and sand action state and the traffic operation state at key time nodes is clarified, and the wind, sand and traffic correlation time series information of each feature extraction unit is obtained;

[0024] All wind and sand traffic-related time-series information is integrated to form the risk time-series characteristics of the corresponding wind and sand traffic risk-related areas.

[0025] Preferably, the comprehensive risk assessment value of each wind and sand traffic risk-related area is obtained based on the risk time series characteristics, specifically including the following steps:

[0026] Based on the risk time series characteristics, we can distinguish between wind and sand related time series characteristics and traffic related time series characteristics; among them, wind and sand related time series characteristics are the time series change characteristics of the associated areas corresponding to wind and sand movement trajectory data, while traffic related time series characteristics are the time series change characteristics of the associated areas corresponding to traffic flow data.

[0027] By processing and analyzing the temporal characteristics related to wind and sand and traffic, the degree of wind and sand impact and the results of traffic operation disturbance are obtained.

[0028] The comprehensive risk assessment value of each wind and sand traffic risk-related area is determined based on the degree of wind and sand impact and the results of traffic operation interference.

[0029] Preferably, the analysis of wind and sand-related temporal characteristics and traffic-related temporal characteristics yields the wind and sand impact level and traffic disruption results, specifically including the following steps:

[0030] The degree of wind and sand impact in each wind and sand traffic risk associated area is determined based on the relevant temporal characteristics of wind and sand. The degree of wind and sand impact is classified into levels according to the time range of wind and sand impact and the terrain type.

[0031] The degree of traffic disruption in each wind and sand traffic risk associated area is determined based on traffic-related temporal characteristics. The duration and scope of the traffic disruption are judged based on highway section information to obtain the traffic disruption results.

[0032] Preferably, the corresponding regional risk weight value is determined based on the comprehensive risk assessment value, specifically including the following steps:

[0033] The differences and common characteristics are determined based on the comprehensive risk assessment value;

[0034] Based on the differences and common characteristics, the comprehensive risk assessment values ​​of each wind and sand traffic risk-related area are divided into different levels of assessment values, and the degree of risk impact corresponding to the different levels of assessment values ​​is clarified.

[0035] Based on the degree of risk impact and the hierarchical classification of the comprehensive risk evaluation value, corresponding regional risk weight values ​​are assigned to each wind and sand traffic risk-related area.

[0036] Preferably, risk identification constraints are generated based on road structure constraint data and regional risk weight values, specifically including the following steps:

[0037] By processing the road structure constraint data with the regional risk weight values, we obtain the protection capacity constraint, terrain constraint, and range boundary constraint.

[0038] Integrate protection capability constraints, terrain constraints, and range boundary constraints to form risk identification constraints.

[0039] Preferably, the road structure constraint data and regional risk weight values ​​are processed to obtain protection capacity constraints, terrain constraints, and range boundary constraints, specifically including the following steps:

[0040] Extract the distribution and structural strength information of protective facilities from the road area structural constraint data, and determine the corresponding protective capacity constraints of each wind and sand traffic risk associated area based on the distribution of protective facilities, structural strength information and regional risk weight values;

[0041] Based on the roadside terrain boundary and geological stability information of the road area structure constraint data, the terrain type of each wind and sand traffic risk associated area is associated, and terrain constraints are set for the risk associated area of ​​terrain type.

[0042] Based on the distribution range of surrounding facilities, protection requirements, and regional risk weight values ​​of road area structure constraint data, the risk identification range boundaries of each wind and sand traffic risk associated area are delineated.

[0043] A highway full-chain risk identification and early warning system includes:

[0044] Acquisition module: Acquires 3D terrain point cloud data, wind and sand movement trajectory data, traffic flow data, highway segment information, and road area structure constraint data of the highway to be monitored;

[0045] The first processing module: Based on wind and sand movement trajectory data, traffic flow data, and highway section information, wind and sand traffic risk associated areas are obtained. Each wind and sand traffic risk associated area is associated with the time range of wind and sand impact and the terrain type.

[0046] The second processing module extracts the risk time series characteristics of the wind and sand traffic risk associated areas, obtains the comprehensive risk evaluation value of each wind and sand traffic risk associated area based on the risk time series characteristics, and determines the corresponding regional risk weight value based on the comprehensive risk evaluation value.

[0047] The generation module generates risk identification constraints based on road structure constraint data and regional risk weight values; and generates a risk factor identification and early warning scheme based on the risk identification constraints, which includes early warning facility deployment plan, monitoring strategy and graded early warning threshold.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] This invention integrates 3D terrain point cloud data, wind and sand movement trajectory data, traffic flow data, highway segment information, and road area structure constraint data to construct a comprehensive data support system covering both the natural environment and traffic operations. This system can comprehensively capture the interaction patterns between wind and sand disasters and traffic operations, avoiding misjudgments of risks due to missing data. Based on wind and sand movement trajectory data, traffic flow data, and road segment information, wind and sand traffic risk-related areas are delineated. The time range of wind and sand impact and terrain type are bound to specific road segment units, avoiding resource waste caused by generalized risk area analysis. By extracting risk temporal characteristics and calculating comprehensive risk evaluation values, the invention achieves a shift from qualitative description to quantitative assessment, objectively reflecting the risk level of each related area. Furthermore, based on the evaluation values, regional risk weights are assigned to quantify the risk priority of different areas, improving the accuracy and efficiency of risk management. By combining road area structure constraint data with regional risk weights, risk identification constraints are generated, thereby generating an early warning scheme that includes early warning facility deployment plan, monitoring strategy and graded early warning thresholds. This achieves a closed-loop connection between risk identification and prevention and control measures, which fully considers the actual constraints of road area protection capabilities, terrain features and surrounding facilities, and can match the corresponding early warning strategy and facility layout according to the risk level, effectively improving the feasibility and pertinence of the early warning scheme. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a method for identifying and warning of risks across the entire highway supply chain, as provided in an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of a highway full-chain risk identification and early warning system provided in an embodiment of the present invention. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0055] Reference Figures 1-2 As shown.

[0056] The embodiments further illustrate the method and system for identifying and warning of risks across the entire highway chain proposed in this invention.

[0057] A method for identifying and warning of risks across the entire highway supply chain includes the following steps:

[0058] Acquire three-dimensional topographic point cloud data, wind and sand movement trajectory data, traffic flow data, highway segment information, and road area structure constraint data of the highway to be monitored;

[0059] Based on wind and sand movement trajectory data, traffic flow data, and highway section information, wind and sand traffic risk associated areas are obtained. Each wind and sand traffic risk associated area is associated with the time range of wind and sand impact and the terrain type.

[0060] Extract the risk time series characteristics of wind and sand traffic risk associated areas, obtain the comprehensive risk evaluation value of each wind and sand traffic risk associated area based on the risk time series characteristics, and determine the corresponding regional risk weight value based on the comprehensive risk evaluation value.

[0061] Risk identification constraints are generated based on road structure constraint data and regional risk weight values; risk factor identification and early warning schemes, including early warning facility deployment plans, monitoring strategies, and graded early warning thresholds, are generated based on risk identification constraints.

[0062] Also includes:

[0063] Three-dimensional terrain point cloud data is generated based on the three-dimensional geometric structure of the highway to be monitored and the elevation of the terrain along the route.

[0064] Based on wind and sand movement trajectory data, traffic flow data, and highway section information, the wind and sand traffic risk associated areas are identified, specifically including the following steps:

[0065] Based on the wind and sand movement trajectory data, the wind and sand propagation paths acting on the highway area are determined, and a set of wind and sand action paths is obtained;

[0066] Based on traffic flow data, the traffic operation characteristics of different road segments are distinguished, and based on the traffic operation characteristics, road segments that are susceptible to external interference are identified to obtain a set of traffic-sensitive road segments;

[0067] By associating the set of wind and sand action paths with the set of traffic-sensitive road sections, the target traffic-sensitive road sections that the wind and sand action paths can cover are determined.

[0068] Based on the road segment structure characteristics and the degree of wind and sand influence of highway segment information, the target traffic-sensitive road segment is divided into several independent road segment units.

[0069] Determine the time period during which independent road sections are affected by wind and sand, as well as the topographic features of the road area;

[0070] By binding independent road sections, periods of wind and sand impact, and road terrain features, a wind and sand traffic risk association area is formed.

[0071] This study analyzes the effects of wind and sand on highways based on wind and sand movement trajectory data. Wind and sand movement trajectory data were collected along highways at different times and under different meteorological conditions. This data was used to determine the specific paths of wind and sand propagation from the sand source to the highway, including key information such as the starting location, direction of propagation, speed of movement, and coverage area. All wind and sand propagation paths that affect highways were identified, thus forming a complete set of wind and sand action paths.

[0072] By deploying wind and sand monitoring equipment (including wind speed and direction sensors, sand particle movement acquisition devices, and lidar) at different locations in the sand source area and along the highway, data on the movement location, movement time, movement speed, movement direction, and sand particle concentration of sand particles are collected, forming a discrete sequence of wind and sand movement trajectory points. These trajectory points are then preprocessed to remove abnormal data caused by equipment failure or extreme weather interference.

[0073] The preprocessed trajectory points are sorted chronologically, starting from the sand source area. Trajectory points with adjacent timestamps are spatially connected to reconstruct the movement of a single sand grain or a single sandstorm flow. For example, if a trajectory point with coordinates X1, Y1 is collected at the sand source area at a certain moment, and a trajectory point with coordinates X2, Y2 is collected 500 meters away from the sand source area at the next moment, and their movement directions and velocity parameters match, then these two points are connected to form a segment of the sandstorm movement trajectory. By stitching these segments together, a single, complete sandstorm movement trajectory is obtained, starting from the sand source area and gradually extending towards the highway area.

[0074] Since actual sandstorm movement is a collection of multiple sandstorm flows, cluster analysis of multiple individual sandstorm trajectories is necessary. Trajectories with similar movement directions, propagation ranges, and speed characteristics are grouped together to form different sandstorm clusters. For areas with insufficient trajectory point coverage, spatial interpolation methods are used to calculate the movement trend of sandstorms in unmonitored areas based on the movement parameters of known trajectory points, thus completing the path details of sandstorm propagation. If multiple trajectories all show the characteristic of extending southeastward from the sand source area and gradually approaching the highway area, then this direction is determined to be the main path of sandstorm propagation; if a small number of trajectories deviate northeastward, they are identified as secondary branch paths.

[0075] The trajectory of windblown sand is influenced by topographic relief, vegetation cover, and local weather conditions. Therefore, it is necessary to integrate the preprocessed trajectory data with topographic data, vegetation data, and real-time weather data of the highway area. If a section of the trajectory passes through a hilly area with a large elevation difference, the trajectory direction needs to be corrected based on the terrain slope to avoid misjudging that windblown sand has overcome terrain obstacles. If strong local wind shear is detected at a certain time, the direction and speed parameters of the trajectory for that time period need to be adjusted to make the propagation path more consistent with the actual natural conditions. This eliminates interference from non-natural factors and yields the windblown sand propagation path.

[0076] All revised main and branch paths of wind and sand propagation are summarized to form a set of wind and sand action paths. Each path includes a clear starting sand source, direction of propagation, area traversed, coverage area, and time point of arrival at the highway area, fully presenting the specific process of wind and sand propagation from the sand source to the highway area.

[0077] Traffic-sensitive road sections are identified through differentiated screening based on traffic flow data. This involves acquiring traffic flow data, including traffic volume, vehicle speed, vehicle type ratio, and congestion index, for each section of the highway. This data differentiates the traffic conditions of different road sections, including those with smooth traffic, slow traffic, and high traffic density. Based on these traffic characteristics, road sections susceptible to external disturbances are determined, such as sections with high traffic density and low efficiency, or long downhill sections. If these sections are affected by wind and sand, they are highly likely to cause traffic congestion and accidents. These sections are then aggregated into a set of traffic-sensitive road sections. For example, the section from K10 to K15 of a certain highway, with high traffic density and significant speed fluctuations, is considered a traffic-sensitive road section susceptible to disturbance and is included in this set.

[0078] Spatial correlation matching between wind and sand propagation paths and traffic-sensitive road sections is conducted to identify target road sections directly affected by wind and sand. The set of wind and sand propagation paths is spatially correlated with the set of traffic-sensitive road sections. By overlaying geospatial data, it is determined which wind and sand propagation paths cover the range of traffic-sensitive road sections, thus identifying the target traffic-sensitive road sections directly affected by wind and sand propagation paths. If the coverage area of ​​a certain wind and sand propagation path includes the traffic-sensitive road section from K10 to K15, then that road section is considered a target traffic-sensitive road section covered by the wind and sand propagation path.

[0079] By combining highway segment information with the degree of wind and sand impact, target traffic-sensitive road segments are segmented to form independent road segment units. Based on the road segment structural characteristics of the highway segment information, including road width, number of lanes, and type of roadside protection facilities, and considering the degree of wind and sand impact on different road segment structures, target traffic-sensitive road segments are segmented. For example, long, straight road segments with high wind and sand impact are segmented in 2-kilometer units; curved road segments with weaker wind and sand impact are segmented in 1-kilometer units, ultimately resulting in several independent road segment units with lengths and structural characteristics adapted to the wind and sand impact patterns.

[0080] Key risk-related factors for each independent road segment unit are identified, and the periods during which wind and sand affect the independent road segment unit and the topographic features of the road area are clarified. By combining the time information of wind and sand movement trajectory data and local meteorological statistics, the effects of wind and sand on the independent road segment unit in different seasons and at different times are obtained. For example, the wind and sand impact on a certain road segment unit is most significant from 10:00 to 16:00 every day from March to May, and the specific time range of wind and sand impact is clarified. The topographic elevation, slope, and topographic relief type characteristics of the area where the road segment unit is located are extracted through field surveys or three-dimensional topographic data. For example, if the road segment unit is located in a plain area, hilly area, or low-lying area, different topographic features directly affect the deposition and diffusion patterns of wind and sand.

[0081] By binding independent road segment units, sandstorm impact periods, and road area topographic features, sandstorm traffic risk association areas are formed. Segmented independent road segment units are associated with their corresponding sandstorm impact time ranges and road area topographic features to construct sandstorm traffic risk association areas that possess spatial, temporal, and topographic attributes. For example, binding the independent road segment unit from K10 to K12 with the sandstorm impact period from 10:00 to 16:00 daily from March to May in spring, as well as the plain topographic features, forms a sandstorm traffic risk association area; similarly, binding elements to other independent road segment units results in multiple sandstorm traffic risk association areas covering the entire target road segment.

[0082] Extracting the temporal characteristics of risk associated with wind and sand traffic risks specifically includes the following steps:

[0083] Based on the time range of wind and sand impact and the terrain type, each wind and sand traffic risk associated area is divided into several feature extraction units;

[0084] The wind and sand action status and impact of each feature extraction unit are captured to obtain wind and sand action time series information; the time series correspondence between traffic operation status and wind and sand action changes is captured to obtain traffic operation time series information.

[0085] By correlating the temporal information of wind and sand action with the temporal information of traffic operation, key temporal nodes of the mutual influence between wind and sand action and traffic operation are obtained.

[0086] The correspondence between the wind and sand action state and the traffic operation state at key time nodes is clarified, and the wind, sand and traffic correlation time series information of each feature extraction unit is obtained;

[0087] All wind and sand traffic-related time-series information is integrated to form the risk time-series characteristics of the corresponding wind and sand traffic risk-related areas.

[0088] The system separately captures the temporal information of wind and sand action and traffic flow for each feature extraction unit. Wind and sand monitoring equipment collects the wind and sand action status (including wind speed, sand concentration, and direction of sand movement) and its impact (including road surface sand thickness and visibility changes) at different times within the unit. This data is arranged chronologically to form a continuous time series, for example, recording data every 10 minutes to obtain wind and sand action temporal information including changes in wind and sand intensity over time. Traffic flow data (including traffic volume, average vehicle speed, vehicle spacing, and congestion duration) for the corresponding time period within the unit is collected to determine the correlation between traffic flow status and wind and sand action, such as the time lag in vehicle speed decrease when wind and sand intensity increases, thus forming traffic flow information.

[0089] By placing the time-series information of wind and sand effects and traffic operation time-series information on the same timeline, and using correlation analysis or abrupt change detection algorithms, synchronous or lagging nodes between changes in wind and sand effects and changes in traffic operation can be identified. For example, if vehicle speed drops significantly after 5 minutes when wind and sand concentration exceeds a certain threshold, the moment of abrupt change in wind and sand concentration and the moment of vehicle speed decrease are key time-series nodes; similarly, the starting moment of the gradual recovery of traffic flow after wind and sand weakens is also a key time-series node.

[0090] The correspondence between the wind and sand effects and traffic operation status at key time nodes is clarified, and the wind and sand traffic correlation time series information of each feature extraction unit is obtained. For each key time node, the corresponding wind and sand effects parameters and traffic operation parameters are extracted. For example, at a key node, the wind and sand concentration is 0.3 grams per cubic meter, the visibility is 500 meters, the corresponding vehicle speed is 40 kilometers per hour, and the traffic flow is 30% lower than the baseline value. These parameters are organized in chronological order to form the correlation time series between wind and sand effects and traffic operation, which fully presents the linkage law between changes in wind and sand intensity and fluctuations in traffic operation status.

[0091] The temporal information of wind and sand traffic associations from all feature extraction units is integrated to form the risk temporal features of the corresponding wind and sand traffic risk association areas. The association temporal information of each feature extraction unit within the same association area is spliced ​​together in chronological order, and normalization processing is performed to take into account the topographic differences of different units to eliminate the bias in wind and sand impact intensity caused by different terrains. The integrated risk temporal features include not only the temporal distribution and intensity changes of wind and sand effects, but also the response patterns and interference levels of traffic operations, which can comprehensively reflect the risk evolution process of the area throughout the entire wind and sand impact cycle.

[0092] The comprehensive risk assessment value of each wind and sand traffic risk-related area is obtained based on the risk time series characteristics, specifically including the following steps:

[0093] Based on the risk time series characteristics, we can distinguish between wind and sand related time series characteristics and traffic related time series characteristics; among them, wind and sand related time series characteristics are the time series change characteristics of the associated areas corresponding to wind and sand movement trajectory data, while traffic related time series characteristics are the time series change characteristics of the associated areas corresponding to traffic flow data.

[0094] The analysis of wind and sand-related temporal characteristics and traffic-related temporal characteristics yields the wind and sand impact level and traffic disruption results, specifically including the following steps:

[0095] The degree of wind and sand impact in each wind and sand traffic risk associated area is determined based on the relevant temporal characteristics of wind and sand. The degree of wind and sand impact is classified into levels according to the time range of wind and sand impact and the terrain type.

[0096] The degree of traffic disruption in each wind and sand traffic risk associated area is determined based on traffic-related temporal characteristics, and the duration and scope of traffic disruption are judged based on highway section information to obtain the traffic disruption results.

[0097] The comprehensive risk assessment value of each wind and sand traffic risk-related area is determined based on the degree of wind and sand impact and the results of traffic operation interference.

[0098] Using the temporal characteristics of wind-sand traffic risk-related areas as basic data, and dividing them according to data source and impact attributes, we obtain wind-sand-related temporal characteristics and traffic-related temporal characteristics. The wind-sand-related temporal characteristics originate from wind-sand movement trajectory data, specifically reflecting the changes in wind speed, sand concentration, wind-sand propagation path offset, and roadside sand accumulation over time during the wind-sand impact period. These directly reflect the intensity and variation patterns of the natural effects of wind-sand on the associated areas. The traffic-related temporal characteristics come from traffic flow data, including the fluctuation characteristics of traffic volume, average vehicle speed, vehicle density, congestion index, and accident rate over time within the associated areas. These are used to characterize the response changes in traffic operation status caused by wind-sand effects. The complex risk temporal characteristics are decomposed into two independently analyzable indicator systems. For example, the change in wind-sand concentration from 0.1 g / m³ to 0.8 g / m³ in the risk temporal characteristics of a wind-sand traffic risk-related area belongs to the wind-sand-related temporal characteristics, while the fluctuation of vehicle speed from 80 km / h to 30 km / h during the corresponding period belongs to the traffic-related temporal characteristics.

[0099] In the assessment of the degree of wind and sand impact, the degree of wind and sand impact is quantified and classified based on the relevant temporal characteristics of wind and sand, combined with the constraints of the time range of wind and sand impact and terrain type. The comprehensive value of wind and sand impact is calculated based on the core indicators in the relevant temporal characteristics: Comprehensive value of wind and sand impact = Σ(wind speed temporal weight × wind speed temporal value) + Σ(sand grain concentration temporal weight × sand grain concentration temporal value) + Σ(sand accumulation temporal weight × sand accumulation temporal value). The weights of each indicator are adjusted according to the terrain type: for plains, the wind speed weight is set to 0.4, the sand grain concentration weight to 0.35, and the sand accumulation weight to 0.25; for hilly terrain, the wind speed weight is adjusted to 0.35, the sand grain concentration weight to 0.4, and the sand accumulation weight to 0.25, ensuring that the calculation results conform to the actual patterns of wind and sand impact under different terrains. The comprehensive value of wind and sand impact is divided into different level intervals based on the time range of wind and sand impact, successively determined as mild impact, moderate impact, severe impact, and extremely severe impact. For example, if the comprehensive value of wind and sand impact in a certain prototype area is 65, which is within the range of 60 to 80, it is judged as a moderate impact level; if the comprehensive value reaches 88, it is classified as an extremely severe impact level.

[0100] Traffic disturbance values ​​are calculated using traffic-related time-series characteristics: Traffic disturbance value = Σ(vehicle speed time-series weight × vehicle speed time-series deviation) + Σ(traffic flow time-series weight × traffic flow time-series deviation) + Σ(congestion index time-series weight × congestion index time-series value). Where vehicle speed time-series deviation = actual vehicle speed - reference vehicle speed, and traffic flow time-series deviation = actual traffic flow - reference traffic flow. The reference vehicle speed and reference traffic flow are taken as the historical averages of the road segment when there is no wind or sand interference. Combining this with information on road segment type, number of lanes, and baseline traffic flow from the highway segment information, the duration and coverage of the disturbance value are obtained, ultimately determining the traffic disturbance results, including minor, moderate, severe, and critical disturbances. For example, if the base speed of a certain highway section is 80 km / h, the average actual speed during the period of wind and sand is 55 km / h, the speed time sequence deviation is -25 km / h, and the interference lasts for more than 2 hours and covers the entire road section, it is judged as a severe interference result; if the interference lasts for less than 30 minutes and only affects a single lane, it is judged as a slight interference result.

[0101] The comprehensive risk assessment value for each wind and sand traffic risk-related area was determined by integrating the wind and sand impact severity level and traffic operation disturbance results. The wind and sand impact severity level and traffic operation disturbance results were converted into quantitative scores. Specifically, the wind and sand impact severity levels of extremely severe, severe, moderate, and mild corresponded to scores of 90-100, 70-89, 50-69, and 0-49, respectively; and the traffic operation disturbance results of severe, moderate, moderate, and minor disturbance corresponded to scores of 95-100, 75-94, 50-74, and 0-49, respectively. Combining the weighting coefficients of both, a weighted summation formula was used to calculate the comprehensive risk assessment value: Comprehensive Risk Assessment Value = Wind and Sand Impact Score × Wind and Sand Impact Weight + Traffic Operation Disturbance Score × Traffic Operation Disturbance Weight. The weights for wind and sand impact and traffic operation disturbance were set according to the importance of the road segment. For general road segments, both weights were 0.5; for important traffic hub road segments, the weight for wind and sand impact was set to 0.6, and the weight for traffic operation disturbance was set to 0.4. The comprehensive risk assessment value calculated using this formula reflects both the intensity of the natural effects of wind and sand and the actual degree of disruption to traffic operations. It comprehensively and objectively reflects the overall risk level of each wind-sand-traffic risk-related area. For example, if the wind and sand impact score for a certain related area is 75 (moderate impact) and the traffic disruption score is 80 (severe disruption), and both have a weight of 0.5, then the comprehensive risk assessment value = 75 × 0.5 + 80 × 0.5 = 77.5. This value is the comprehensive risk assessment value for that area.

[0102] The corresponding regional risk weight value is determined based on the comprehensive risk assessment value, specifically including the following steps:

[0103] The differences and common characteristics are determined based on the comprehensive risk assessment value;

[0104] Based on the differences and common characteristics, the comprehensive risk assessment values ​​of each wind and sand traffic risk-related area are divided into different levels of assessment values, and the degree of risk impact corresponding to the different levels of assessment values ​​is clarified.

[0105] Based on the degree of risk impact and the hierarchical classification of the comprehensive risk evaluation value, corresponding regional risk weight values ​​are assigned to each wind and sand traffic risk-related area.

[0106] Based on the comprehensive risk assessment values ​​of various wind and sand traffic risk-related areas, we extracted the differences and common characteristics. Common characteristics refer to the shared patterns in the changing trends of comprehensive risk assessment values ​​and the composition of influencing factors among the related areas. For example, the comprehensive risk assessment values ​​of multiple related areas show an upward trend during the peak wind and sand season in spring, and the contribution ratio of wind and sand impact scores to traffic operation interference scores is close to 1:1. These patterns are considered common characteristics. Difference characteristics refer to the numerical differences, fluctuation range differences, and risk source emphase differences in the comprehensive risk assessment values ​​among the related areas. For example, the comprehensive risk assessment value of one related area is 85, while that of another related area is 42, showing a significant difference. Or, the risk in one area mainly stems from wind and sand impact, while the risk in another area mainly stems from traffic operation interference. These differences are considered differential characteristics.

[0107] Based on both differential and common characteristics, the comprehensive risk assessment values ​​of each associated region are hierarchically classified, clarifying the degree of risk impact corresponding to different levels. The baseline logic for level division is determined by combining common characteristics, and the level intervals are refined based on differential characteristics, dividing the comprehensive risk assessment values ​​into multiple levels. For example, based on common characteristics, a comprehensive risk assessment value of 60 is known to be the critical value for risk impact in most areas. Combining the distribution of assessment values ​​in the differential characteristics, assessment values ​​of 0 to 49 are classified as low-risk, 50 to 69 as medium-risk, 70 to 89 as high-risk, and 90 to 100 as extremely high-risk. The degree of risk impact corresponding to each level is also clarified: low-risk level indicates minor wind and sandstorm interference with traffic, basically not affecting normal passage; medium-risk level indicates some interference, requiring enhanced monitoring; high-risk level indicates significant interference, easily causing traffic problems; and extremely high-risk level indicates severe interference, posing a major safety hazard. For example, a certain related area has a comprehensive risk assessment value of 78, which is classified as a high-risk level, corresponding to a significant level of risk impact from interference; another area has an assessment value of 35, which is classified as a low-risk level, corresponding to a level of risk impact from minor interference.

[0108] Based on the degree of risk impact and the hierarchical classification of the comprehensive risk assessment value, corresponding regional risk weight values ​​are assigned to each wind and sand traffic risk-related area. The weight value is determined using a weighted allocation method, based on the degree of risk impact corresponding to each level and the specific location of the comprehensive risk assessment value within that level. The regional risk weight value = basic weight of the level + (assessment value - lower limit of the level) ÷ (upper limit of the level - lower limit of the level) × weight fluctuation value within the level. The basic weight of the level is set according to the degree of risk impact: 0.1 for low-risk levels, 0.3 for medium-risk levels, 0.6 for high-risk levels, and 0.9 for extremely high-risk levels. The weight fluctuation value within the level is 20% of the basic weight of each level, used to reflect subtle differences in assessment values ​​within the same level. For example, if the comprehensive risk assessment value of a certain high-risk level associated area is 78, the lower limit of this level is 70, and the upper limit of this level is 89, then the regional risk weight value can be calculated as 0.6 + (78-70) ÷ (89-70) × 0.1 ≈ 0.65. If the assessment value of another area at the same level is 85, then the regional risk weight value is 0.6 + (85-70) ÷ (89-70) × 0.12 ≈ 0.69. This method ensures both the significant difference in weight values ​​between different levels and the subtle differences in risk levels within the same level, ultimately forming a regional risk weight value that accurately matches the risk level of each area.

[0109] Risk identification constraints are generated based on road structure constraint data and regional risk weight values, specifically including the following steps:

[0110] The road structure constraint data and regional risk weight values ​​are processed to obtain protection capacity constraints, terrain constraints, and range boundary constraints. The specific steps include:

[0111] Extract the distribution and structural strength information of protective facilities from the road area structural constraint data, and determine the corresponding protective capacity constraints of each wind and sand traffic risk associated area based on the distribution of protective facilities, structural strength information and regional risk weight values;

[0112] Based on the roadside terrain boundary and geological stability information of the road area structure constraint data, the terrain type of each wind and sand traffic risk associated area is associated, and terrain constraints are set for the risk associated area of ​​terrain type.

[0113] Based on the distribution range and protection requirements of surrounding facilities in the road area constrained by the road area structure data, as well as the regional risk weight values, the risk identification range boundaries of each wind and sand traffic risk associated area are delineated.

[0114] Integrate protection capability constraints, terrain constraints, and range boundary constraints to form risk identification constraints.

[0115] The distribution and structural strength information of protective facilities were extracted from the road area structure constraint data, and the protective capacity constraints of each wind and sand traffic risk-related area were clarified by combining the regional risk weight values. The types, distribution locations, coverage areas, and structural strength parameters of protective facilities were analyzed from the road area structure constraint data, including the deployment density, height, material, and wind resistance level of windbreak and sand-fixing forest belts, sand-blocking fences, sand-control grids, and roadside guardrails. This information was then integrated with the risk weight values ​​of the corresponding areas, and the protective capacity constraint threshold was obtained through quantitative calculation. The protective capacity constraint threshold = Σ(facility type weight × facility distribution density × structural strength value) × regional risk weight value. The facility type weight was set according to wind and sand prevention effectiveness, with sand-blocking fences having a weight of 0.4, sand-control grids having a weight of 0.3, and windbreak forest belts having a weight of 0.3. The structural strength value was expressed as a numerical value corresponding to the wind resistance level, such as a strength value of 10 for wind resistance of level 10. For example, if the regional risk weight of a certain associated area is 0.65, the distribution density of sand-blocking fences in the area is 2 fences per 100 meters with a structural strength value of 12, the distribution density of sand-control grids is 3 fences per 100 meters with a structural strength value of 8, and the coverage rate of windbreak forest belts is 30% with a structural strength value of 15, then by substituting these values ​​into the formula, the protection capacity constraint threshold can be calculated as (0.4×2×12+0.3×3×8+0.3×0.3×15)×0.65=11.7975. This threshold represents the protection capacity constraint of the area, indicating the maximum wind and sand risk intensity that the current protection facilities can withstand. If the actual wind and sand impact intensity exceeds this threshold, then enhanced protection measures need to be initiated.

[0116] Based on the roadside topographic boundaries and geological stability information from the road area structure constraint data, topographic constraints are set by associating the topographic types of each wind-blown sand traffic risk-related area. Roadside topographic boundary data, including topographic elevation difference, slope, aspect, and topographic relief parameters, is extracted. Simultaneously, geological stability information, such as stratigraphic structure, soil type, and landslide or wind erosion risk levels, is obtained. This information is then matched with the topographic types of the associated areas to clarify the differences in the patterns of wind-blown sand propagation, deposition, and diffusion across different topographic types, thereby setting targeted topographic constraints. For example, in plain areas, the topographic constraint is that the wind-blown sand propagation path must be consistent with the prevailing wind direction, and the sand accumulation height must not exceed 0.2 meters above the road surface elevation. In hilly areas, the topographic constraint is that the wind-blown sand monitoring frequency on windward slopes must be increased to once every 5 minutes, and the risk of sand accumulation on leeward slopes must be closely monitored. In geologically unstable areas, additional requirements for slope wind erosion monitoring are added to prevent wind-blown sand erosion from exacerbating geological disaster risks. By transforming topographic and geological features into executable constraint rules in this way, topographic-level limitations are provided for risk identification.

[0117] Based on the distribution range, protection requirements, and regional risk weight values ​​of roadside facilities in the road area structure constraint data, the risk identification range boundaries of each wind and sand traffic risk associated area are delineated. First, the distribution of important facilities around the road area is analyzed, including service areas, toll stations, bridges, tunnels, and residential areas, clarifying the protection requirements and safety distances for each type of facility. Then, the coverage range of the identification range is adjusted based on the regional risk weight values. The risk identification range radius = basic identification radius × regional risk weight value × surrounding facility protection level coefficient. The basic identification radius is set according to the highway grade, generally 500 meters. The surrounding facility protection level coefficient is set according to the importance of the facility: 1.2 for service areas and toll stations, 1.5 for bridges and tunnels, and 1.0 for ordinary road sections. For example, if the regional risk weight of a certain associated area is 0.69, and there is a bridge nearby with a protection level coefficient of 1.5, substituting into the formula, we can get the risk identification range radius = 500 × 0.69 × 1.5 = 517.5 meters. That is, the risk identification range of this area needs to cover an area with a radius of 517.5 meters centered on the road segment, to ensure that the safety protection needs of important surrounding facilities are included in the risk identification scope, while avoiding the waste of monitoring resources due to an excessively large range.

[0118] A comprehensive risk identification constraint system is formed by integrating protective capacity constraints, terrain constraints, and boundary constraints. The spatial scope of risk identification is defined by boundary constraints; within this scope, terrain constraints define the direction of wind and sand risk; and protective capacity constraints define the trigger threshold for risk warnings, thus ultimately forming a constraint system. For example, the risk identification constraint for a certain related area is as follows: within an identification range of 517.5 meters, focusing on the windward slope of hilly terrain, the system focuses on monitoring the wind and sand propagation path and sand accumulation height. When the intensity of wind and sand impact exceeds the protective capacity constraint threshold of 11.7975, the corresponding risk warning process is initiated. These constraints ensure both the accuracy and relevance of risk identification and the efficient allocation of resources.

[0119] A highway full-chain risk identification and early warning system includes:

[0120] Acquisition module: Acquires 3D terrain point cloud data, wind and sand movement trajectory data, traffic flow data, highway segment information, and road area structure constraint data of the highway to be monitored;

[0121] The first processing module: Based on wind and sand movement trajectory data, traffic flow data, and highway section information, wind and sand traffic risk associated areas are obtained. Each wind and sand traffic risk associated area is associated with the time range of wind and sand impact and the terrain type.

[0122] The second processing module extracts the risk time series characteristics of the wind and sand traffic risk associated areas, obtains the comprehensive risk evaluation value of each wind and sand traffic risk associated area based on the risk time series characteristics, and determines the corresponding regional risk weight value based on the comprehensive risk evaluation value.

[0123] The generation module generates risk identification constraints based on road structure constraint data and regional risk weight values; and generates a risk factor identification and early warning scheme based on the risk identification constraints, which includes early warning facility deployment plan, monitoring strategy and graded early warning threshold.

[0124] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0125] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying and warning of risks across the entire highway supply chain, characterized in that, Includes the following steps: Acquire three-dimensional topographic point cloud data, wind and sand movement trajectory data, traffic flow data, highway segment information, and road area structure constraint data of the highway to be monitored; Based on wind and sand movement trajectory data, traffic flow data, and highway section information, wind and sand traffic risk associated areas are obtained. Each wind and sand traffic risk associated area is associated with the time range of wind and sand impact and the terrain type. Extract the risk time series characteristics of wind and sand traffic risk associated areas, obtain the comprehensive risk evaluation value of each wind and sand traffic risk associated area based on the risk time series characteristics, and determine the corresponding regional risk weight value based on the comprehensive risk evaluation value. Risk identification constraints are generated based on road structure constraint data and regional risk weight values; risk factor identification and early warning schemes, including early warning facility deployment plans, monitoring strategies, and graded early warning thresholds, are generated based on risk identification constraints.

2. The method for identifying and warning of risks across the entire highway chain according to claim 1, characterized in that, Also includes: Three-dimensional terrain point cloud data is generated based on the three-dimensional geometric structure of the highway to be monitored and the elevation of the terrain along the route.

3. The method for identifying and warning of risks across the entire highway chain according to claim 1, characterized in that, Based on wind and sand movement trajectory data, traffic flow data, and highway section information, the wind and sand traffic risk associated areas are identified, specifically including the following steps: Based on the wind and sand movement trajectory data, the wind and sand propagation paths acting on the highway area are determined, and a set of wind and sand action paths is obtained; Based on traffic flow data, the traffic operation characteristics of different road segments are distinguished, and based on the traffic operation characteristics, road segments that are susceptible to external interference are identified to obtain a set of traffic-sensitive road segments; By associating the set of wind and sand action paths with the set of traffic-sensitive road sections, the target traffic-sensitive road sections that the wind and sand action paths can cover are determined. Based on the road segment structure characteristics and the degree of wind and sand influence of highway segment information, the target traffic-sensitive road segment is divided into several independent road segment units. Determine the time period during which independent road sections are affected by wind and sand, as well as the topographic features of the road area; By binding independent road sections, periods of wind and sand impact, and road terrain features, a wind and sand traffic risk association area is formed.

4. The method for identifying and warning of risks across the entire highway chain according to claim 1, characterized in that, Extracting the temporal characteristics of risk associated with wind and sand traffic risks specifically includes the following steps: Based on the time range of wind and sand impact and the terrain type, each wind and sand traffic risk associated area is divided into several feature extraction units; The wind and sand action status and impact of each feature extraction unit are captured to obtain wind and sand action time series information; the time series correspondence between traffic operation status and wind and sand action changes is captured to obtain traffic operation time series information. By correlating the temporal information of wind and sand action with the temporal information of traffic operation, key temporal nodes of the mutual influence between wind and sand action and traffic operation are obtained. The correspondence between the wind and sand action state and the traffic operation state at key time nodes is clarified, and the wind, sand and traffic correlation time series information of each feature extraction unit is obtained; All wind and sand traffic-related time-series information is integrated to form the risk time-series characteristics of the corresponding wind and sand traffic risk-related areas.

5. The method for identifying and warning of risks across the entire highway chain according to claim 1, characterized in that, The comprehensive risk assessment value of each wind and sand traffic risk-related area is obtained based on the risk time series characteristics, specifically including the following steps: Based on the risk time series characteristics, we can distinguish between wind and sand related time series characteristics and traffic related time series characteristics; among them, wind and sand related time series characteristics are the time series change characteristics of the associated areas corresponding to wind and sand movement trajectory data, while traffic related time series characteristics are the time series change characteristics of the associated areas corresponding to traffic flow data. By processing and analyzing the temporal characteristics related to wind and sand and traffic, the degree of wind and sand impact and the results of traffic operation disturbance are obtained. The comprehensive risk assessment value of each wind and sand traffic risk-related area is determined based on the degree of wind and sand impact and the results of traffic operation interference.

6. The method for identifying and warning of risks across the entire highway chain according to claim 5, characterized in that, The analysis of wind and sand-related temporal characteristics and traffic-related temporal characteristics yields the wind and sand impact level and traffic disruption results, specifically including the following steps: The degree of wind and sand impact in each wind and sand traffic risk associated area is determined based on the relevant temporal characteristics of wind and sand. The degree of wind and sand impact is classified into levels according to the time range of wind and sand impact and the terrain type. The degree of traffic disruption in each wind and sand traffic risk associated area is determined based on traffic-related temporal characteristics. The duration and scope of the traffic disruption are judged based on highway section information to obtain the traffic disruption results.

7. The method for identifying and warning of risks across the entire highway chain according to claim 1, characterized in that, The corresponding regional risk weight value is determined based on the comprehensive risk assessment value, specifically including the following steps: The differences and common characteristics are determined based on the comprehensive risk assessment value; Based on the differences and common characteristics, the comprehensive risk assessment values ​​of each wind and sand traffic risk-related area are divided into different levels of assessment values, and the degree of risk impact corresponding to the different levels of assessment values ​​is clarified. Based on the degree of risk impact and the hierarchical classification of the comprehensive risk evaluation value, corresponding regional risk weight values ​​are assigned to each wind and sand traffic risk-related area.

8. The method for identifying and warning of risks across the entire highway chain according to claim 1, characterized in that, Risk identification constraints are generated based on road structure constraint data and regional risk weight values, specifically including the following steps: By processing the road structure constraint data with the regional risk weight values, we obtain the protection capacity constraint, terrain constraint, and range boundary constraint. Integrate protection capability constraints, terrain constraints, and range boundary constraints to form risk identification constraints.

9. The method for identifying and warning of risks across the entire highway chain according to claim 8, characterized in that, The road structure constraint data and regional risk weight values ​​are processed to obtain protection capacity constraints, terrain constraints, and range boundary constraints. The specific steps include: Extract the distribution and structural strength information of protective facilities from the road area structural constraint data, and determine the corresponding protective capacity constraints of each wind and sand traffic risk associated area based on the distribution of protective facilities, structural strength information and regional risk weight values; Based on the roadside terrain boundary and geological stability information of the road area structure constraint data, the terrain type of each wind and sand traffic risk associated area is associated, and terrain constraints are set for the risk associated area of ​​terrain type. Based on the distribution range of surrounding facilities, protection requirements, and regional risk weight values ​​of road area structure constraint data, the risk identification range boundaries of each wind and sand traffic risk associated area are delineated.

10. A highway full-chain risk identification and early warning system, applied to the highway full-chain risk identification and early warning method according to any one of claims 1 to 9, characterized in that, include: Acquisition module: Acquires 3D terrain point cloud data, wind and sand movement trajectory data, traffic flow data, highway segment information, and road area structure constraint data of the highway to be monitored; The first processing module: Based on wind and sand movement trajectory data, traffic flow data, and highway section information, wind and sand traffic risk associated areas are obtained. Each wind and sand traffic risk associated area is associated with the time range of wind and sand impact and the terrain type. The second processing module extracts the risk time series characteristics of the wind and sand traffic risk associated areas, obtains the comprehensive risk evaluation value of each wind and sand traffic risk associated area based on the risk time series characteristics, and determines the corresponding regional risk weight value based on the comprehensive risk evaluation value. The generation module generates risk identification constraints based on road structure constraint data and regional risk weight values; and generates a risk factor identification and early warning scheme based on the risk identification constraints, which includes early warning facility deployment plan, monitoring strategy and graded early warning threshold.

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