Internet of vehicles safety guardrail monitoring operation and management system and method

Abstract

The present application relates to the technical field of Internet of Vehicles, and discloses an Internet of Vehicles safety guardrail monitoring operation and management system and method, which analyzes the dangerous protection capability of the guardrail based on structural damage and material deterioration of the guardrail, analyzes driving abnormalities of the vehicle based on driving data of the vehicle, analyzes the severity of the environment when the vehicle is driving based on environmental data, analyzes road geometric characteristics and road surface conditions based on road data, analyzes the dangerous coefficient of the road based on the road geometric characteristics and the road surface conditions, predicts the risk of the vehicle hitting the guardrail based on the driving abnormalities of the vehicle, the severity of the environment when the vehicle is driving, and the dangerous coefficient of the road, simulates the collision process of the vehicle and the guardrail based on the vehicle information and the dangerous protection capability of the guardrail, and judges whether the guardrail can safely protect, so that the present application analyzes whether the guardrail can withstand the vehicle with the highest possibility of hitting, thereby being conducive to improving the protection capability of the guardrail to the greatest extent and ensuring road safety.

Description

Technical Field

[0001] This invention relates to the field of vehicle networking technology, and in particular to a vehicle networking safety barrier monitoring and maintenance management system and method. Background Technology

[0002] Guardrail maintenance is a systematic project of "monitoring-evaluation-maintenance-optimization", with the core objective of ensuring the structural safety and protective capabilities of the guardrail.

[0003] Current technologies for guardrail maintenance mostly rely on deep learning models to automatically analyze massive amounts of guardrail images, identify defect types, and classify their severity. Based on historical inspection data, traffic flow, and environmental data, they predict guardrail performance degradation trends and perform maintenance accordingly. However, they lack technical solutions that combine vehicle data, road data, and environmental data to predict the risk of vehicles colliding with guardrails, and then analyze the guardrail's protective capabilities to predict whether the guardrail can provide safe protection.

[0004] Therefore, the present invention provides a vehicle-to-everything (V2X) safety guardrail monitoring, operation and maintenance management system and method. Summary of the Invention

[0005] In view of this, the present invention provides a vehicle-to-everything (V2X) safety guardrail monitoring, operation and maintenance management system and method. The present invention combines driving data, environmental data and road data to predict vehicle collision situations, analyzes the guardrail protection capability through guardrail images, analyzes whether the guardrail can withstand the vehicle with the highest probability of impact, and repairs guardrails with insufficient protection, thereby maximizing the protection capability of guardrails and ensuring road safety.

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

[0007] In a first aspect, the present invention provides a method for monitoring, operating, and managing vehicle-to-everything (V2X) safety guardrails, comprising the following specific steps:

[0008] S1. Collect images of the guardrail, analyze the structural damage and material deterioration of the guardrail based on the images, and analyze the guardrail's ability to protect against hazards based on the structural damage and material deterioration.

[0009] S2. Retrieve the real-time navigation route of the vehicle, filter the vehicles that pass through the guardrail within the set time period, collect the driving data of the passing vehicles, and analyze the driving anomalies of the vehicles based on the driving data of the passing vehicles.

[0010] S3. Collect environmental data for a set time period and analyze the environmental severity of the environment when the vehicle is driving based on the environmental data;

[0011] S4. Collect road data within the guardrail setting range, analyze road geometric features and road surface conditions based on the road data, and analyze the road's risk factor based on the road geometric features and road surface conditions;

[0012] S5. Based on vehicle driving anomalies, the severity of the environment during vehicle driving, and the road hazard coefficient, predict the risk of a vehicle colliding with a guardrail, screen the vehicle with the highest risk of colliding with a guardrail, and collect the vehicle information of that vehicle.

[0013] S6. Based on vehicle information and the guardrail's hazard protection capabilities, simulate the collision process between the vehicle and the guardrail to determine whether the guardrail can provide safe protection.

[0014] Preferably, step S1 includes the following specific steps:

[0015] Collect images of guardrails;

[0016] Based on the analysis of guardrail images, structural damage and material degradation of guardrails are identified. The structural damage includes guardrail wear, deformation, and loosening, and the material degradation includes guardrail cracking. Guardrail wear is obtained by dividing the number of pixels in the peeling area of ​​the guardrail coating by the total number of pixels in the guardrail coating. Guardrail deformation is obtained by the vertical displacement of the guardrail midpoint. Guardrail loosening is obtained by dividing the number of loose bolts by the total number of bolts. Guardrail cracking is obtained by dividing the total number of pixels in the cracked area by the total number of pixels in the guardrail area.

[0017] Construct a prediction model for the remaining life of guardrails, input guardrail wear, deformation, loosening and cracking, and output the remaining life of guardrails;

[0018] The remaining protective capacity of the guardrail is obtained by dividing the remaining lifespan of the guardrail by the design lifespan of the guardrail. The danger protection capacity coefficient of the guardrail is obtained by dividing the remaining protective capacity of the guardrail by the preset guardrail protection capacity threshold.

[0019] Preferably, step S2 includes the following specific steps:

[0020] Retrieve real-time navigation routes for vehicles and filter vehicles that pass through the guardrail within a set time period;

[0021] Collect driving data of passing vehicles, including average vehicle speed, brake wear, tire pressure and continuous driving time;

[0022] The abnormal speed value is obtained by dividing the average vehicle speed by the lane speed limit; the abnormal fatigue driving value is obtained by dividing the continuous driving time by the single continuous driving time limit; and the driving risk value is obtained by multiplying the abnormal speed value and the abnormal fatigue driving value.

[0023] Brake wear anomaly value is obtained by dividing the brake pad thickness by the initial brake pad thickness. Tire pressure anomaly value is obtained by multiplying the coaxial tire pressure anomaly value and the tire pressure difference anomaly value. Specifically, the coaxial tire pressure anomaly value is obtained by calculating the tire pressure difference between the two sides of the same axle of the vehicle and dividing the tire pressure difference value by the median standard tire pressure. The tire pressure difference anomaly value is obtained by summing the absolute values ​​of the differences between the tire pressure of each tire of the vehicle and the median standard tire pressure, and then dividing by the median standard tire pressure. The vehicle condition risk value is obtained by multiplying the brake wear anomaly value and the tire pressure anomaly value.

[0024] The driving risk impact value is obtained by multiplying the driving risk value by the driving risk weight; the vehicle state risk impact value is obtained by multiplying the vehicle state risk value by the vehicle state risk weight; and the vehicle driving risk anomaly value is obtained by summing the driving risk impact value and the vehicle state risk impact value.

[0025] Preferably, step S3 includes the following specific steps:

[0026] Collect environmental data for a set time period, including rainfall and visibility;

[0027] The abnormal impact value of rainfall is obtained by dividing the rainfall amount by the rainfall threshold, the abnormal impact value of visibility is obtained by dividing the visibility threshold by the visibility, and the environmental severity impact value when the vehicle is driving is obtained by multiplying the abnormal impact value of rainfall and the abnormal impact value of visibility.

[0028] Preferably, step S4 includes the following specific steps:

[0029] Collect road data within the designated area of ​​the guardrail, including curve radius, slope, road width, road surface smoothness, and road surface friction.

[0030] The curve radius outlier is obtained by dividing the minimum radius corresponding to the road design speed by the curve radius; the slope outlier is obtained by dividing the slope by the maximum longitudinal slope corresponding to the road design speed; the road width outlier is obtained by dividing the lane width corresponding to the road design speed by the road width; and the road characteristic hazard value is obtained by multiplying the curve radius outlier, slope outlier, and road width outlier.

[0031] The abnormal values ​​of road surface smoothness are obtained by dividing the road surface smoothness by the designed road surface smoothness, the abnormal values ​​of road surface friction are obtained by dividing the designed road surface friction by the road surface friction, and the road surface hazard values ​​are obtained by multiplying the abnormal values ​​of road surface smoothness and road surface friction.

[0032] The road characteristic hazard impact value is obtained by multiplying the road characteristic hazard value and the road characteristic hazard weight; the road surface hazard impact value is obtained by multiplying the road surface hazard value and the road surface hazard weight; and the road hazard value is obtained by summing the road characteristic hazard impact value and the road surface hazard impact value.

[0033] Preferably, step S5 includes the following specific steps:

[0034] The driving risk impact value is obtained by multiplying the abnormal value of vehicle driving risk by the weight of abnormal driving risk; the environmental risk impact value is obtained by multiplying the environmental severity impact value of the vehicle driving by the weight of environmental severity; the road risk impact value is obtained by multiplying the road hazard value by the weight of road hazard; and the risk value of vehicle collision with guardrail is obtained by summing the driving risk impact value, environmental risk impact value and road risk impact value.

[0035] The vehicle with the highest risk of colliding with the guardrail is selected, and its vehicle information, including vehicle model and weight, is collected.

[0036] Preferably, step S6 includes the following specific steps:

[0037] The vehicle collision kinetic energy is obtained by multiplying the vehicle mass by the square of the average speed and then dividing by two. The guardrail's kinetic energy is obtained by multiplying the guardrail's hazard protection capability coefficient by the guardrail's maximum designed tolerance kinetic energy.

[0038] The kinetic energy of the vehicle collision is compared with the kinetic energy that the guardrail can withstand. If the kinetic energy of the vehicle collision is less than or equal to the kinetic energy that the guardrail can withstand, the guardrail is judged to be able to provide safe protection. If the kinetic energy of the vehicle collision is greater than the kinetic energy that the guardrail can withstand, the guardrail is judged to be unable to provide safe protection.

[0039] Secondly, this invention provides a vehicle-to-everything (V2X) safety barrier monitoring and maintenance management system, including:

[0040] The guardrail protection capability analysis module is used to collect guardrail images, analyze the structural damage and material deterioration of the guardrail based on the images, and analyze the guardrail's dangerous protection capability based on the structural damage and material deterioration.

[0041] The vehicle driving anomaly analysis module is used to retrieve the real-time navigation route of the vehicle, filter the vehicles that pass through the guardrail within a set time period, collect the driving data of the passing vehicles, and analyze the driving anomalies of the vehicle based on the driving data of the passing vehicles.

[0042] The environmental severity analysis module is used to collect environmental data over a set time period and analyze the environmental severity of the environment when the vehicle is driving based on the environmental data.

[0043] The road hazard coefficient analysis module is used to collect road data within the set range of the guardrail, analyze the road geometric features and road surface conditions based on the road data, and analyze the road hazard coefficient based on the road geometric features and road surface conditions.

[0044] The collision risk screening module is used to predict the risk of a vehicle colliding with a guardrail based on the vehicle's abnormal driving, the severity of the environment during the vehicle's driving, and the road's hazard coefficient. It then filters out the vehicle with the highest risk of colliding with the guardrail and collects the vehicle's information.

[0045] The safety protection judgment module is used to simulate the collision process between a vehicle and a guardrail based on vehicle information and the guardrail's hazard protection capabilities, and to determine whether the guardrail can provide safe protection.

[0046] Thirdly, the present invention provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, causing the processor to perform the steps of the vehicle network safety guardrail monitoring and maintenance management method as described in the first aspect.

[0047] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the steps of the vehicle network safety guardrail monitoring and maintenance management method as described in the first aspect.

[0048] As can be seen from the above technical solution, this invention collects guardrail images, analyzes structural damage and material degradation based on the images, analyzes the guardrail's hazard protection capability based on the structural damage and material degradation, retrieves real-time vehicle navigation routes, filters vehicles passing through the guardrail within a set time period, collects vehicle driving data, analyzes vehicle driving anomalies based on the driving data, collects environmental data for a set time period, analyzes the severity of the environment during vehicle driving based on the environmental data, collects road data within a set range of the guardrail, analyzes road geometric features and road surface conditions based on the road data, and analyzes... The road hazard coefficient is predicted based on vehicle driving anomalies, the severity of the environment during vehicle driving, and the road's hazard coefficient. The system identifies vehicles with the highest risk of impact, collects their information, and simulates the collision process between the vehicle and the guardrail based on this information and the guardrail's hazard protection capabilities. This determines whether the guardrail can provide adequate protection. The invention combines driving data, environmental data, and road data to predict vehicle collision scenarios. It analyzes guardrail images to assess their protective capabilities, determining whether the guardrail can withstand the most likely impact. Inadequate guardrail protection is addressed through repairs, thereby maximizing guardrail protection and ensuring road safety. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the vehicle-to-everything (V2X) safety barrier monitoring, operation and maintenance management method of the present invention;

[0051] Figure 2 This is a schematic diagram of the S1 process of the vehicle-to-everything (V2X) safety guardrail monitoring and maintenance management method of the present invention;

[0052] Figure 3 This is a schematic diagram of the S2 process of the vehicle-to-everything (V2X) safety guardrail monitoring and maintenance management method of the present invention;

[0053] Figure 4 This is a schematic diagram of the vehicle network safety guardrail monitoring and maintenance management system of the present invention. Detailed Implementation

[0054] 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 are within the scope of protection of the present invention.

[0055] Please see Figure 1 This invention provides a method for monitoring, operating, and managing vehicle-to-everything (V2X) safety guardrails, including the following specific steps:

[0056] S1. Collect images of the guardrail, analyze the structural damage and material deterioration of the guardrail based on the images, and analyze the guardrail's ability to protect against hazards based on the structural damage and material deterioration.

[0057] Please see Figure 2 In this embodiment, S1 includes the following specific steps:

[0058] The location of the guardrail to be detected is determined, images of the guardrail are acquired from multiple angles, a calibration board is placed in the scene, the conversion relationship between pixels and physical units is established, and information such as the location, time, and road segment number of each image is recorded. The images are then preprocessed in batches, including lens distortion correction, color equalization, and size normalization.

[0059] Based on image analysis, structural damage and material degradation of guardrails are identified. Structural damage includes wear, deformation, and loosening, while material degradation includes cracking. The process involves converting guardrail images to grayscale, applying Gaussian filtering to eliminate noise, and using the Otsu algorithm to automatically segment normal coating areas from peeling areas. A binary mask is generated, and the pixel-to-actual size ratio is calculated using a calibration plate. The number of pixels in the peeling areas is counted. Guardrail wear is obtained by dividing the number of pixels in the peeling areas by the total number of pixels in the guardrail coating. The top or bottom straight edges of the guardrail are extracted, and an ideal horizontal line is set as a reference (e.g., 0°). The angle between the actual edge line and the reference line is measured, which is the bending angle. Guardrail deformation is obtained by the vertical displacement of the guardrail's midpoint, which is obtained by multiplying the midpoint pixel offset by the calibration ratio. All bolt center points are marked using template matching, and the actual bolt positions are compared with... The design position is calculated, and the bolt offset is determined. If the bolt offset is greater than the offset threshold (e.g., 2mm), it is considered a loose bolt. The looseness of the guardrail is obtained by dividing the number of loose bolts by the total number of bolts. The Otsu algorithm is used to automatically segment the crack area (dark area) and the background (light area) to generate a binary image. Canny edge detection is applied to the preprocessed image to enhance the crack edge contour. The edge detection results are fused with the threshold segmentation results to improve the crack continuity. A connected component labeling algorithm is used to identify independent crack areas in the binary image. Each connected component represents an independent crack area. A crack area threshold (e.g., 50 pixels) is set to filter out small pseudo crack areas caused by noise. All connected components are traversed, and the number of pixels in each crack area is accumulated to obtain the total number of pixels in the crack area. The crack in the guardrail is obtained by dividing the total number of pixels in the crack area by the total number of pixels in the guardrail area.

[0060] Construct a prediction model for the remaining life of guardrails, input guardrail wear, deformation, loosening and cracking, and output the remaining life of guardrails;

[0061] This embodiment requires specific explanation of the following steps for constructing a guardrail remaining life prediction model: Collect historical guardrail inspection data; define input features, including guardrail wear, deformation, loosening, and cracking; define the target variable, i.e., remaining life; check for missing values ​​in each feature; randomly divide the dataset into training and testing sets, with the training set accounting for approximately 80% for model training and the testing set accounting for approximately 20% for final model performance evaluation; import a random forest regressor; set initial parameters, including the number of decision trees, the maximum tree depth, the minimum number of samples required for internal node splitting, and a random seed; fit the model using the features from the training set and the target variable; evaluate and validate the model; predict the remaining life using the testing set; calculate performance indicators, such as a smaller mean square error and a coefficient of determination closer to 1 indicating a better fit; plot a scatter plot of the actual remaining life and the predicted remaining life; if the points are concentrated near the 45° diagonal, the model prediction is accurate; extract the feature importance score output by the model; adjust parameters using grid search or random search; and select the parameter combination that minimizes the root mean square error or maximizes the coefficient of determination.

[0062] The remaining protective capacity of the guardrail is obtained by dividing the remaining lifespan of the guardrail by the design lifespan of the guardrail. The danger protection capacity coefficient of the guardrail is obtained by dividing the remaining protective capacity of the guardrail by the preset guardrail protection capacity threshold.

[0063] It is important to note in this embodiment that traditional guardrail monitoring relies on manual labor and often results in judgments such as "good appearance" or "slight corrosion," which can easily lead to misjudgments due to differences in observer experience or cognition. In contrast, residual life monitoring quantifies damage parameters and combines them with mathematical models to output objective data, ensuring the objectivity of the assessment results. It can accurately pinpoint the weakest and highest-risk sections of the entire road, which is conducive to proactive risk prevention and control. It ensures that all guardrails meet design standards in terms of energy absorption, guidance, and interception functions in the event of a collision, thus guaranteeing road safety.

[0064] S2. Retrieve the real-time navigation route of the vehicle, filter the vehicles that pass through the guardrail within the set time period, collect the driving data of the passing vehicles, and analyze the driving anomalies of the vehicles based on the driving data of the passing vehicles.

[0065] Please see Figure 3 In this embodiment, S2 includes the following specific steps:

[0066] The vehicle terminal uploads status data and navigation requests to the vehicle network cloud platform via 4G / 5G cellular network or DSRC / LTE-V (short-range communication), retrieves the vehicle's real-time navigation route, and filters vehicles that pass through the guardrail within a set time period;

[0067] Collect driving data of passing vehicles, including average vehicle speed, brake wear, tire pressure and continuous driving time;

[0068] In this embodiment, it is necessary to specifically explain that real-time vehicle speed data can be directly read through the vehicle speed sensor or CAN bus. If the vehicle is equipped with a brake pad thickness sensor, the brake pad thickness can be directly obtained to reflect brake wear. If the vehicle does not have a dedicated sensor, the wear degree can be indirectly inferred by reading the brake system fault code (such as "brake pad too thin" alarm) through the OBD-II interface. The tire pressure monitoring system's wireless sensor collects tire pressure data in real time. The cloud platform calculates the continuous driving time for each vehicle based on the continuously uploaded ignition status and position changes.

[0069] The abnormal speed value is obtained by dividing the average vehicle speed by the lane-limited speed. The abnormal fatigue driving value is obtained by dividing the continuous driving time by the single continuous driving time limit (e.g., 4 hours). The driving risk value is obtained by multiplying the abnormal speed value and the abnormal fatigue driving value.

[0070] Brake wear anomaly value is obtained by dividing the brake pad thickness by the initial brake pad thickness. Tire pressure anomaly value is obtained by multiplying the coaxial tire pressure anomaly value and the tire pressure difference anomaly value. Specifically, the coaxial tire pressure anomaly value is obtained by calculating the tire pressure difference between the two sides of the same axle of the vehicle and dividing the tire pressure difference value by the median standard tire pressure. The tire pressure difference anomaly value is obtained by summing the absolute values ​​of the differences between the tire pressure of each tire of the vehicle and the median standard tire pressure, and then dividing by the median standard tire pressure. The vehicle condition risk value is obtained by multiplying the brake wear anomaly value and the tire pressure anomaly value.

[0071] The driving risk impact value is obtained by multiplying the driving risk value by the driving risk weight; the vehicle state risk impact value is obtained by multiplying the vehicle state risk value by the vehicle state risk weight; and the vehicle driving risk anomaly value is obtained by summing the driving risk impact value and the vehicle state risk impact value.

[0072] In this embodiment, it should be specifically noted that severe brake wear will lead to an extended braking distance, making it impossible to slow down and avoid guardrails in time. Low tire pressure will reduce tire grip (especially on curves or wet roads), making it easy to skid and crash into guardrails. Even a slight loss of control at high speeds may result in a direct collision with the guardrail. Fatigue driving will reduce reaction speed and make it impossible to correct the direction in time. By continuously monitoring the vehicle status and driving behavior, potential driving risks can be identified more accurately.

[0073] S3. Collect environmental data for a set time period and analyze the environmental severity of the environment when the vehicle is driving based on the environmental data;

[0074] In this embodiment, S3 includes the following specific steps:

[0075] Collect environmental data for a specified time period, including rainfall and visibility.

[0076] In this embodiment, it should be specifically explained that rainwater will form a water film on the road surface, reducing the coefficient of friction between the tire and the ground (for example, the coefficient of friction on dry asphalt road is about 0.8, while on wet road it drops to below 0.4), resulting in a significant increase in braking distance (for example, at a speed of 80km / h, the braking distance on wet road is 30%-50% longer than on dry road), making rear-end collisions or skidding more likely. When the water depth exceeds the tire's drainage capacity, the tire will float on the water surface and completely lose traction, causing the vehicle to skid out of control. When the set time period is at night, when the sky is dark and visibility is low due to fog, dust, or other turbid weather, the driver cannot identify the brake lights of the vehicle in front, pedestrians, or sudden road conditions in time, and cannot clearly identify the road boundaries, making it easy to deviate from the lane and crash into the guardrail.

[0077] The abnormal impact value of rainfall is obtained by dividing the rainfall amount by the rainfall threshold (e.g., rainfall of 10 mm per hour), the abnormal impact value of visibility is obtained by dividing the visibility threshold (e.g., visibility of less than 100 meters) by the visibility, and the environmental severity impact value when the vehicle is driving is obtained by multiplying the abnormal impact value of rainfall and the abnormal impact value of visibility.

[0078] S4. Collect road data within the guardrail setting range, analyze road geometric features and road surface conditions based on the road data, and analyze the road's risk factor based on the road geometric features and road surface conditions;

[0079] In this embodiment, S4 includes the following specific steps:

[0080] Collect road data within the designated area of ​​the guardrail. The road data includes curve radius, slope, road width, road surface smoothness, and road surface friction.

[0081] This embodiment requires specific explanation as follows: The road centerline trajectory is calculated based on high-precision GNSS / IMU fusion. Three consecutive points are taken on the trajectory line, and the instantaneous radius of curvature is calculated using the three-point circle method. Multiple calculations are performed on the entire curve, and the average value is taken as the curve radius of the curve. A digital elevation model (DEM) is generated based on LiDAR point cloud or high-precision trajectory data. Along the road centerline direction, the ratio of the elevation difference between two adjacent points to the horizontal distance is calculated and then converted into a percentage slope. On a cross-section perpendicular to the road centerline, the road surface edge is clearly displayed using point cloud data. The horizontal distances from the left and right edges to the centerline are calculated and summed to obtain the road width at that cross-section. Z-axis acceleration data collected by the IMU are used to perform frequency analysis and digital integration on the vertical acceleration data to calculate the smoothness index, measured in m / km. A larger smoothness index indicates a more uneven road surface. A road friction analysis model is trained, taking rainfall, road surface temperature, road surface material (e.g., asphalt, concrete), and road surface characteristics (e.g., texture, contaminants, roughness, and porosity) as input, and outputting a predicted friction coefficient value.

[0082] The curve radius outlier is obtained by dividing the minimum radius corresponding to the road design speed by the curve radius; the slope outlier is obtained by dividing the slope by the maximum longitudinal slope corresponding to the road design speed; the road width outlier is obtained by dividing the lane width corresponding to the road design speed by the road width; and the road characteristic hazard value is obtained by multiplying the curve radius outlier, slope outlier, and road width outlier.

[0083] The abnormal values ​​of road surface smoothness are obtained by dividing the road surface smoothness by the designed road surface smoothness, the abnormal values ​​of road surface friction are obtained by dividing the designed road surface friction by the road surface friction, and the road surface hazard values ​​are obtained by multiplying the abnormal values ​​of road surface smoothness and road surface friction.

[0084] The road characteristic hazard impact value is obtained by multiplying the road characteristic hazard value and the road characteristic hazard weight; the road surface hazard impact value is obtained by multiplying the road surface hazard value and the road surface hazard weight; and the road hazard value is obtained by summing the road characteristic hazard impact value and the road surface hazard impact value.

[0085] This embodiment specifically explains that by analyzing the hazard value of complex high-risk road sections formed by the superposition of multiple defects, the impact of dangerous roads on vehicle driving safety is quantified.

[0086] S5. Based on vehicle driving anomalies, the severity of the environment during vehicle driving, and the road hazard coefficient, predict the risk of a vehicle colliding with a guardrail, screen the vehicle with the highest risk of colliding with a guardrail, and collect the vehicle information of that vehicle.

[0087] In this embodiment, S5 includes the following specific steps:

[0088] The driving risk impact value is obtained by multiplying the abnormal value of vehicle driving risk by the weight of abnormal driving risk; the environmental risk impact value is obtained by multiplying the environmental severity impact value of the vehicle driving by the weight of environmental severity; the road risk impact value is obtained by multiplying the road hazard value by the weight of road hazard; and the risk value of vehicle collision with guardrail is obtained by summing the driving risk impact value, environmental risk impact value and road risk impact value.

[0089] The vehicle with the highest risk of colliding with the guardrail is selected, and its vehicle information, including model and weight, is collected.

[0090] S6. Based on vehicle information and the guardrail's hazard protection capabilities, simulate the collision process between the vehicle and the guardrail to determine whether the guardrail can provide safe protection.

[0091] In this embodiment, S6 includes the following specific steps:

[0092] The vehicle collision kinetic energy is obtained by multiplying the vehicle mass by the square of the average speed and then dividing by two. The guardrail's kinetic energy is obtained by multiplying the guardrail's hazard protection capability coefficient by the guardrail's maximum designed tolerance kinetic energy.

[0093] The kinetic energy of the vehicle collision is compared with the kinetic energy that the guardrail can withstand. If the kinetic energy of the vehicle collision is less than or equal to the kinetic energy that the guardrail can withstand, the guardrail is judged to be safe. If the kinetic energy of the vehicle collision is greater than the kinetic energy that the guardrail can withstand, the guardrail is judged to be unsafe. Guardrails that cannot withstand safety are repaired.

[0094] In this embodiment, the steps for obtaining the weights and set thresholds are as follows: acquire historical guardrail data, vehicle driving data, environmental data, and road data; import the above data into the steps of this embodiment to obtain the guardrail's hazard protection capability and the risk value of a vehicle hitting the guardrail; filter out the vehicle with the highest risk value to determine whether it can provide safe protection; import the historical impact results and the judgment results of this embodiment into the trained fitting software for fitting; and output the weights and set thresholds with the highest judgment accuracy.

[0095] Please see Figure 4 This invention provides a vehicle-to-everything (V2X) safety barrier monitoring and maintenance management system, including:

[0096] The guardrail protection capability analysis module is used to collect guardrail images, analyze the structural damage and material deterioration of the guardrail based on the images, and analyze the guardrail's dangerous protection capability based on the structural damage and material deterioration.

[0097] The vehicle driving anomaly analysis module is used to retrieve the real-time navigation route of the vehicle, filter the vehicles that pass through the guardrail within a set time period, collect the driving data of the passing vehicles, and analyze the driving anomalies of the vehicle based on the driving data of the passing vehicles.

[0098] The environmental severity analysis module is used to collect environmental data over a set time period and analyze the environmental severity of the environment when the vehicle is driving based on the environmental data.

[0099] The road hazard coefficient analysis module is used to collect road data within the set range of the guardrail, analyze the road geometric features and road surface conditions based on the road data, and analyze the road hazard coefficient based on the road geometric features and road surface conditions.

[0100] The collision risk screening module is used to predict the risk of a vehicle colliding with a guardrail based on the vehicle's abnormal driving, the severity of the environment during the vehicle's driving, and the road's hazard coefficient. It then filters out the vehicle with the highest risk of colliding with the guardrail and collects the vehicle's information.

[0101] The safety protection judgment module is used to simulate the collision process between a vehicle and a guardrail based on vehicle information and the guardrail's hazard protection capabilities, and to determine whether the guardrail can provide safe protection.

[0102] This invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the vehicle network safety guardrail monitoring and maintenance management method as described in the above embodiments.

[0103] This invention provides a computer-readable storage medium storing a computer program thereon, which, when executed, implements the steps of the vehicle network safety guardrail monitoring and maintenance management method described in the above embodiments.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, electronic devices, computer program products, and computer storage media described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0105] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0106] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0107] In the several embodiments provided by this invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0108] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0110] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of the present invention through a computer device (which may be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0111] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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. Such 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 monitoring, operating, and managing vehicle-to-everything (V2X) safety guardrails, characterized in that: The specific steps include the following: S1. Collect images of the guardrail, analyze the structural damage and material deterioration of the guardrail based on the images, and analyze the guardrail's ability to protect against hazards based on the structural damage and material deterioration. S2. Retrieve the real-time navigation route of the vehicle, filter the vehicles that pass through the guardrail within the set time period, collect the driving data of the passing vehicles, and analyze the driving anomalies of the vehicles based on the driving data of the passing vehicles. S2 includes the following specific steps: Retrieve real-time navigation routes for vehicles and filter vehicles that pass through the guardrail within a set time period; Collect driving data of passing vehicles, including average vehicle speed, brake wear, tire pressure and continuous driving time; The abnormal speed value is obtained by dividing the average vehicle speed by the lane speed limit; the abnormal fatigue driving value is obtained by dividing the continuous driving time by the single continuous driving time limit; and the driving risk value is obtained by multiplying the abnormal speed value and the abnormal fatigue driving value. Brake wear anomaly value is obtained by dividing the brake pad thickness by the initial brake pad thickness. Tire pressure anomaly value is obtained by multiplying the coaxial tire pressure anomaly value and the tire pressure difference anomaly value. Specifically, the coaxial tire pressure anomaly value is obtained by calculating the tire pressure difference between the two sides of the same axle of the vehicle and dividing the tire pressure difference value by the median standard tire pressure. The tire pressure difference anomaly value is obtained by summing the absolute values ​​of the differences between the tire pressure of each tire of the vehicle and the median standard tire pressure, and then dividing by the median standard tire pressure. The vehicle condition risk value is obtained by multiplying the brake wear anomaly value and the tire pressure anomaly value. The driving risk impact value is obtained by multiplying the driving risk value by the driving risk weight; the vehicle state risk impact value is obtained by multiplying the vehicle state risk value by the vehicle state risk weight; and the vehicle driving risk anomaly value is obtained by summing the driving risk impact value and the vehicle state risk impact value. S3. Collect environmental data for a set time period and analyze the environmental severity of the environment when the vehicle is driving based on the environmental data; S4. Collect road data within the guardrail setting range, analyze road geometric features and road surface conditions based on the road data, and analyze the road's risk factor based on the road geometric features and road surface conditions; S5. Based on vehicle driving anomalies, the severity of the environment during vehicle driving, and the road hazard coefficient, predict the risk of a vehicle colliding with a guardrail, screen the vehicle with the highest risk of colliding with a guardrail, and collect the vehicle information of that vehicle. S6. Based on vehicle information and the guardrail's hazard protection capabilities, simulate the collision process between the vehicle and the guardrail to determine whether the guardrail can provide safe protection.

2. The vehicle-to-everything (V2X) safety barrier monitoring, operation, and maintenance management method according to claim 1, characterized in that, S1 includes the following specific steps: Collect images of guardrails; Based on the analysis of guardrail images, structural damage and material degradation of guardrails are identified. The structural damage includes guardrail wear, deformation, and loosening, and the material degradation includes guardrail cracking. Guardrail wear is obtained by dividing the number of pixels in the peeling area of ​​the guardrail coating by the total number of pixels in the guardrail coating. Guardrail deformation is obtained by the vertical displacement of the guardrail midpoint. Guardrail loosening is obtained by dividing the number of loose bolts by the total number of bolts. Guardrail cracking is obtained by dividing the total number of pixels in the cracked area by the total number of pixels in the guardrail area. Construct a prediction model for the remaining life of guardrails, input guardrail wear, deformation, loosening and cracking, and output the remaining life of guardrails; The remaining protective capacity of the guardrail is obtained by dividing the remaining lifespan of the guardrail by the design lifespan of the guardrail. The danger protection capacity coefficient of the guardrail is obtained by dividing the remaining protective capacity of the guardrail by the preset guardrail protection capacity threshold.

3. The vehicle-to-everything (V2X) safety barrier monitoring, operation, and maintenance management method according to claim 2, characterized in that, S3 includes the following specific steps: Collect environmental data for a set time period, including rainfall and visibility; The abnormal impact value of rainfall is obtained by dividing the rainfall amount by the rainfall threshold, the abnormal impact value of visibility is obtained by dividing the visibility threshold by the visibility, and the environmental severity impact value when the vehicle is driving is obtained by multiplying the abnormal impact value of rainfall and the abnormal impact value of visibility.

4. The vehicle-to-everything (V2X) safety barrier monitoring, operation, and maintenance management method according to claim 3, characterized in that, S4 includes the following specific steps: Collect road data within the designated area of ​​the guardrail, including curve radius, slope, road width, road surface smoothness, and road surface friction. The curve radius outlier is obtained by dividing the minimum radius corresponding to the road design speed by the curve radius; the slope outlier is obtained by dividing the slope by the maximum longitudinal slope corresponding to the road design speed; the road width outlier is obtained by dividing the lane width corresponding to the road design speed by the road width; and the road characteristic hazard value is obtained by multiplying the curve radius outlier, slope outlier, and road width outlier. The abnormal values ​​of road surface smoothness are obtained by dividing the road surface smoothness by the designed road surface smoothness, the abnormal values ​​of road surface friction are obtained by dividing the designed road surface friction by the road surface friction, and the road surface hazard values ​​are obtained by multiplying the abnormal values ​​of road surface smoothness and road surface friction. The road characteristic hazard impact value is obtained by multiplying the road characteristic hazard value and the road characteristic hazard weight; the road surface hazard impact value is obtained by multiplying the road surface hazard value and the road surface hazard weight; and the road hazard value is obtained by summing the road characteristic hazard impact value and the road surface hazard impact value.

5. The vehicle-to-everything (V2X) safety barrier monitoring and maintenance management method according to claim 4, characterized in that, S5 includes the following specific steps: The driving risk impact value is obtained by multiplying the abnormal value of vehicle driving risk by the weight of abnormal driving risk; the environmental risk impact value is obtained by multiplying the environmental severity impact value of the vehicle driving by the weight of environmental severity; the road risk impact value is obtained by multiplying the road hazard value by the weight of road hazard; and the risk value of vehicle collision with guardrail is obtained by summing the driving risk impact value, environmental risk impact value and road risk impact value. The vehicle with the highest risk of colliding with the guardrail is selected, and its vehicle information, including vehicle model and weight, is collected.

6. The vehicle-to-everything (V2X) safety barrier monitoring, operation, and maintenance management method according to claim 5, characterized in that, S6 includes the following specific steps: The vehicle collision kinetic energy is obtained by multiplying the vehicle mass by the square of the average speed and then dividing by two. The guardrail's kinetic energy is obtained by multiplying the guardrail's hazard protection capability coefficient by the guardrail's maximum designed tolerance kinetic energy. The kinetic energy of the vehicle collision is compared with the kinetic energy that the guardrail can withstand. If the kinetic energy of the vehicle collision is less than or equal to the kinetic energy that the guardrail can withstand, the guardrail is judged to be able to provide safe protection. If the kinetic energy of the vehicle collision is greater than the kinetic energy that the guardrail can withstand, the guardrail is judged to be unable to provide safe protection.

7. A vehicle-to-everything (V2X) safety barrier monitoring and maintenance management system, used to implement the V2X safety barrier monitoring and maintenance management method as described in any one of claims 1-6, characterized in that, include: The guardrail protection capability analysis module is used to collect guardrail images, analyze the structural damage and material deterioration of the guardrail based on the images, and analyze the guardrail's dangerous protection capability based on the structural damage and material deterioration. The vehicle driving anomaly analysis module is used to retrieve the real-time navigation route of the vehicle, filter the vehicles that pass through the guardrail within a set time period, collect the driving data of the passing vehicles, and analyze the driving anomalies of the vehicle based on the driving data of the passing vehicles. The environmental severity analysis module is used to collect environmental data over a set time period and analyze the environmental severity of the environment when the vehicle is driving based on the environmental data. The road hazard coefficient analysis module is used to collect road data within the set range of the guardrail, analyze the road geometric features and road surface conditions based on the road data, and analyze the road hazard coefficient based on the road geometric features and road surface conditions. The collision risk screening module is used to predict the risk of a vehicle colliding with a guardrail based on the vehicle's abnormal driving, the severity of the environment during the vehicle's driving, and the road's hazard coefficient. It then filters out the vehicle with the highest risk of colliding with the guardrail and collects the vehicle's information. The safety protection judgment module is used to simulate the collision process between a vehicle and a guardrail based on vehicle information and the guardrail's hazard protection capabilities, and to determine whether the guardrail can provide safe protection.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of the vehicle network safety guardrail monitoring and maintenance management method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the steps of the vehicle network safety guardrail monitoring and maintenance management method as described in any one of claims 1-6.

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