Urban road pit slot hidden danger risk quantification method, electronic equipment and storage medium

Abstract

The invention discloses an urban road pit slot hidden danger risk quantification method, electronic equipment and a storage medium, and belongs to the technical field of road maintenance. In order to better perform quantitative analysis on an actual municipal road maintenance process by using a BIM technology, the method comprises the steps of obtaining road high-precision BIM model data, unmanned aerial vehicle identification road surface pit slot information data, road weighing information data and traffic vehicle information data, and obtaining analysis basic data; simulating and restoring the driving condition that the vehicle passes through the road pit slot through BIM modeling software, and analyzing the influence of the road pit slot on the vehicle speed and the influence of the road pit slot on the turning condition of the vehicle; a three-dimensional engine is used for simulating and restoring the driving condition that the vehicle passes through the road pit slot, then the scene that the vehicle passes through the road pit slot under different safety redundancy conditions for the road pit slot is analyzed, and a hidden danger risk quantification result of the road pit slot is obtained; and finally, providing an efficient maintenance method for the urban road pit slot based on the analysis result.

Description

Technical Field

[0001] The present invention belongs to the technical field of road maintenance and conservation, and particularly relates to a method for quantifying the risk of pothole hazards on urban roads, an electronic device, and a storage medium. Background Art

[0002] Building Information Modeling (BIM) is a technology based on a three-dimensional digital model. By integrating various data such as geometric information, time information, cost information, and environmental information of a project, a comprehensive and detailed virtual model of the project is formed. This model not only contains the geometric information of the building but also includes the physical and functional characteristics of the building. With the development of BIM technology, its application in the maintenance of municipal roads also has significant effects. For example:

[0003] 1. Improvement of construction quality and safety: In terms of construction simulation and optimization: Before construction, using a BIM model for construction simulation helps to identify potential problems and optimize the construction plan, reducing errors and rework in on-site construction. This not only improves construction quality but also shortens the construction time. In terms of safety management and maintenance: With the help of BIM technology to plan safety measures at the construction site, the dangerous areas and safety channels during the construction process can be intuitively displayed through a three-dimensional BIM model, thereby enhancing the safety awareness of workers and the on-site safety management level.

[0004] 2. Cost control and resource conservation: In terms of accurate budgeting and cost control: The accurate data provided by BIM technology makes the budget more accurate, reducing the problem of cost overruns caused by inaccurate data. In addition, by optimizing the construction plan to reduce rework, resources are further conserved and costs are reduced. In terms of optimal allocation of resources: Using a BIM model for refined management can better allocate human, material, and financial resources, avoid waste of resources, and improve the utilization rate of resources.

[0005] 3. Improvement of maintenance management efficiency: In terms of data integration and sharing, BIM technology can integrate various road data, including geometric data, material data, historical maintenance records, etc., to form a unified data platform. This integrated and shared data platform greatly improves the transparency and accuracy of information, making the collaborative work between departments more efficient.

[0006] Secondly, regarding the formulation of maintenance plans, with the help of the accurate data analysis characteristics of the BIM model, the municipal department can formulate road maintenance plans more scientifically. By predicting the wear and aging of roads, maintenance work can be arranged in advance to avoid excessive damage to the roads and thus generate high repair costs.

[0007] 4. Extend the service life of roads: In terms of preventive maintenance: Through the analysis and monitoring of the BIM model, potential problems of the road can be detected in a timely manner, and preventive maintenance can be carried out to avoid small problems from evolving into big problems, thereby extending the service life of the road. In terms of full life cycle management: BIM technology supports the full life cycle management of roads, and data from all stages, including design, construction, operation, and maintenance, can be managed and analyzed on the same data platform to ensure that the road is always in the best state throughout its life cycle.

[0008] However, how to use BIM technology to better quantitatively analyze and provide practical guidance for the actual municipal road maintenance process is a major problem encountered in the industry at present. Summary of the Invention

[0009] The problem to be solved by the present invention is to use BIM technology to better quantitatively analyze the actual municipal road maintenance process, and propose a method, an electronic device, and a storage medium for quantifying the hidden danger risk of potholes on urban roads.

[0010] To achieve the above object, the present invention is realized through the following technical solutions:

[0011] A method for quantifying the hidden danger risk of potholes on urban roads includes the following steps:

[0012] S1. Integrate drone equipment, high-definition shooting equipment, and road weighing equipment to collect data, obtain high-precision BIM model data of the road, drone-identified road pothole information data, road weighing information data, and traffic vehicle information data, and obtain the analysis basic data;

[0013] S2. Based on the analysis basic data obtained in step S1, establish the main body of the scene object through BIM modeling software, and at the same time use a three-dimensional engine to simulate and restore the driving condition of the vehicle passing through the road pothole, and analyze the influence of the road pothole on the vehicle speed and the influence of the road pothole on the vehicle's direction change;

[0014] S3. Based on the analysis basic data obtained in step S1 and the analysis result of the driving condition of the vehicle passing through the road pothole obtained in step S2, use a three-dimensional engine to simulate and restore the driving condition of the vehicle passing through the road pothole, and then set the scenes of the vehicle passing through the road pothole under different road pothole safety redundancy conditions, and simulate to obtain the quantified results of the hidden danger risks of different road potholes;

[0015] S4. Based on the quantified results of the hidden danger risks of the road potholes obtained in step S3, propose a method for safe maintenance of road potholes on urban roads.

[0016] Further, the specific implementation method of step S1 includes the following steps:

[0017] S1.1. Obtain a high-precision BIM model of the road section;

[0018] S1.2. Identify the pothole information on the road surface using a drone;

[0019] S1.2.1. First, set a fixed flight route for the drone and collect the image information of the road surface at a fixed time interval t0;

[0020] S1.2.2. Use the open-source object recognition algorithm MaskR-CNN to segment and recognize the image information of the road surface collected in step S1.2.1, and identify the location (LON i , LAT i ) of the pothole, as well as the size contour and area S i of the pothole i, where LON i represents the longitude of the pothole i, and LAT i represents the latitude of the pothole i;

[0021] S1.2.3. Obtain the depth distribution of the pothole area through the radar acquisition device carried by the drone;

[0022] S1.3. Set a weighing coil device at the entrance of each road section to obtain the weight information of the vehicle;

[0023] Obtain the vehicle weight distribution of road section L1 within time T as where W n (T) represents the vehicle weight distribution of the nth lane of road section L1 at time T, and n is the total number of lanes of road section L1; then calculate the average vehicle weight distribution of road section L1 within time T

[0024] S1.4. Install a high-definition shooting device at the road entrance to obtain the vehicle arrival time distribution of road section L1 within time T where t n (T) represents the arrival time distribution of all vehicles on the nth lane of road section L1;

[0025] Then use the drone to track and record the entire driving process of the vehicle, and record the driving speed of all vehicles on road section L1 in real time to obtain the driving speed distribution of all vehicles on road section L1 within time T where represents the speed sequence of the Q n th vehicle on the nth lane of road section L1 within time T, and Q n represents the total number of vehicles on the nth lane.

[0026] Further, the specific implementation method of step S2 includes the following steps:

[0027] S2.1. Use BIM modeling software to establish the main body of the scene object;

[0028] Select a straight road section. Set point O as the origin position of the UAV nest, and point O1 as the position where the UAV flies to collect and recognize the image of the pothole position P0, and obtain the flight length of the UAV as The straight-line distance between the UAV and the pothole position P0 is Set The angles with the vertical plane yoz and the horizontal plane xoy in the three-dimensional space are θ1 and θ2 respectively. If O is the origin, set the road vehicle driving direction as the horizontal axis, and the vertical and perpendicular axes perpendicular to the horizontal axis are set, then the pothole position can be obtained The coordinates of are:

[0029]

[0030] Among them, is the horizontal axis coordinate of the pothole position P0, is the vertical axis coordinate of the pothole position P0, is the vertical axis coordinate of the pothole position P0, is used to record the flight height of the UAV when the pothole position P0 is collected;

[0031] Change point O to the starting reference point of the model through BIM modeling software. After each time the UAV collects and converts the pothole position, locate the pothole position in the model, and then use the BIM model to restore the depth distribution of the pothole to complete the establishment of the pavement pothole information in the BIM modeling software;

[0032] S2.2. Based on the pavement potholes established in the BIM modeling software in step S2.1, further use a 3D engine to restore the driving conditions of the vehicle driving over the road potholes;

[0033] Suppose a vehicle with a weight of m and a limit depth of h', a driving speed of v, drives over a pothole with a horizontal curve length of l0, considering that the diameter of the pothole is greater than 20 cm and the depth of the pothole is h0;

[0034] When the depth h0 of the wheel falling into the pothole is greater than the limit depth h', the vehicle will not be able to get out of the pothole and will be stuck in the pothole; when the depth h0 of the vehicle falling into the pothole is less than the limit depth h', the vehicle speed decreases. Set the vehicle speed decrease value Δv to be proportional to the falling depth h0, then there is:

[0035]

[0036] Set the calculation formula for the acceleration a of the vehicle decelerated by the pothole as:

[0037]

[0038] Among them, Δt is the duration during which the vehicle speed decreases under the influence of potholes;

[0039] Since the vehicle is traveling at a high speed, the actual time for the vehicle to pass through the pothole is basically the same as the speed change period, so there is:

[0040]

[0041] According to Newton's laws of motion, the impact force F is:

[0042]

[0043] The stress σ on the vehicle tire is:

[0044]

[0045] Among them, S0 is the contact area between the tire and the pothole;

[0046] Compare the stress σ on the tire with the ultimate strength σ′ of the tire itself. When the stress σ is greater than the ultimate strength σ′, the tire will burst; if the stress σ is less than or equal to the ultimate strength, the vehicle will not have a flat tire. At the same time, after passing through the pothole, the vehicle speed is v1, and the calculation formula is:

[0047] v1 = v - Δv;

[0048] S2.3. Based on the vehicle speed obtained in step S2.2, analyze the possibility of collision between the vehicle and the following vehicle. Since it is difficult for the leading vehicle to take into account the situation of the following vehicle after encountering a pothole, within the driver's ultimate reaction time, assume that the leading vehicle moves at a constant speed v1 during this ultimate time, and the following vehicle brakes urgently. When the speed of the following vehicle is equal to that of the leading vehicle, if the value obtained by subtracting the traveling speed of the leading vehicle from the traveling distance of the following vehicle is greater than or equal to the initial distance between the two vehicles, the two vehicles will collide; otherwise, they will not collide;

[0049] S2.4. Analyze the vehicle's direction change situation. Assume that the structural inclination angle in the pothole is θ3, and analyze the magnitude relationship between the static friction force on the inclined plane and the component of the gravity in the vehicle's traveling direction - the downward sliding force. When the static friction force is less than the downward sliding force, the vehicle will change direction, and the direction change angle is θ3.

[0050] Furthermore, the ultimate reaction time in step S2 is 3 s.

[0051] Furthermore, the specific implementation method of step S3 includes the following steps:

[0052] S3.1. Based on the analysis basic data obtained in step S1, use a 3D engine to restore the vehicle object, vehicle lane, vehicle weight, and vehicle speed;

[0053] Based on the interval time of vehicles arriving at a section as a parameter, use the normal distribution model to calculate and analyze different lanes independently, and calculate the average value μ(t n ) and standard deviation σ(t n ) of the interval time of vehicles arriving at the nth lane within a historical day, obtaining the normal distribution N[μ(t n ), σ(t n )], and only taking the part on the right side of the y-axis in combination with the actual situation; then, based on the normal distribution N[μ(t n ), σ(t n )], randomly generate the time interval values for arriving at the section to generate vehicle objects;

[0054] Based on drones, independently collect each lane to generate vehicle lanes;

[0055] Randomly generate the vehicle weight by means of weight distribution, and then assign the weight attribute to the vehicle object. The weight distribution means that the probability of generating a weight value is proportional to the ratio of the weight value in the set of all weight values. The expression is:

[0056]

[0057] Among them, represents the probability size that the weight value of is randomly generated on the nth lane, N(W n ) represents the number of different weight values on the nth lane, represents the total number of weight values of on the nth lane in the entire numerical set; then, generate the vehicle weight attribute for the vehicle object according to the above weight distribution principle;

[0058] Randomly assign the collected speed sequence to each vehicle according to the principle of equal probability to generate vehicle speeds;

[0059] S3.2. Restore the actual road traffic flow, construct the out-of-control judgment condition that the tire ultimate strength is less than the tire stress strength, and then use the 3D engine to monitor the occurrence of accidents such as tire blowouts, front and rear impact risks, and left and right impact risks of vehicles, and calculate the sum of the weights of all vehicles directly or indirectly affected by the accident W total , as a risk quantification index of the pothole for passing vehicles;

[0060] S3.3. Set the scenario of vehicles passing through road potholes with different road pothole safety redundancies. By introducing vehicle object elements into the truncated normal distribution, realize different densities of vehicle distributions and restore the traffic operation scenario during the traffic peak period;

[0061] Take the right part of the y-axis of the rectangular coordinate system for the normal distribution, set four levels of safety redundancy, and truncate the three quartiles of the normal distribution, namely Q1, Q2, and Q3, corresponding to 25%, 50%, and 75% respectively. The truncated normal distribution has the form N[μ(t n ), σ(t n ), 0 ≤ t < b, where μ(t n ) and σ(t n ) are respectively the average value and the standard deviation of the vehicle arrival interval time on the nth lane within the past day, and b is the upper limit of the independent variable value for truncating the normal distribution;

[0062] For the first quartile Q1, there is:

[0063]

[0064] where Φ is the distribution function of the standard normal distribution;

[0065] Obtain the quartile values by looking up tables and solving. Then, the vehicle arrival interval time in the range [0, Q1) corresponds to the highest safety redundancy level, the vehicle arrival interval time in the range [Q1, Q2) corresponds to the second-highest safety redundancy level, the vehicle arrival interval time in the range [Q2, Q3) corresponds to the third safety redundancy level, and the vehicle arrival interval time in the range [Q3, b] corresponds to the fourth safety redundancy level;

[0066] Then, based on the value results within different safety redundancy levels, simulate different pothole risk indicators W total results.

[0067] Furthermore, the specific implementation method of step S4 includes the following steps:

[0068] Set the risk indicators for the first pothole K1 and the second pothole K2 as W total (K1) and W total (K2) respectively. Then, the distances of the first pothole K1 and the second pothole K2 from the maintenance unit are D K1 and D K2 , and the traffic flows on the lanes where the first pothole K1 and the second pothole K2 are located in the same time period are U1 and U2 respectively;

[0069] For the first pothole K1, considering the comprehensive pothole risk value, the impact of the maintenance operation on traffic congestion, and the difficulty of the maintenance operation, and then according to the normalization requirements, finally calculate the pothole maintenance implementation V K1 for the first pothole K1 to sort the priority order of pothole maintenance implementation. The calculation formula is:

[0070]

[0071] Among them, and are the average values of the pothole risk index sequence, the lane traffic flow sequence, and the sequence of the distance values between the potholes and the maintenance units respectively;

[0072] The greater the implementation of pothole maintenance, the more this pothole needs to be preferentially maintained and repaired, providing practical and feasible maintenance suggestions for the maintenance units.

[0073] An electronic device, characterized in that it includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the described methods for quantifying the hidden danger risks of urban road potholes are implemented.

[0074] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the described method for quantifying the hidden danger risks of urban road potholes is implemented.

[0075] Advantages of the present invention:

[0076] For the method for quantifying the hidden danger risks of urban road potholes described in the present invention, effective information of the road surface potholes is obtained through algorithms such as instance segmentation, and real-time modeling is carried out with the help of BIM technology, more truly restoring the actual three-dimensional shape of the potholes, providing a real data basis for subsequent vehicle collision tests, and making the analysis results closer to the actual situation.

[0077] For the method for quantifying the hidden danger risks of urban road potholes described in the present invention, various information of vehicles on the actual road section is obtained through drones, and the traffic flow conditions of the road section are truly restored through statistical analysis methods and BIM modeling technology. On the one hand, the risk of vehicle tire blowout caused by potholes can be quantified and calculated; on the other hand, the risk of secondary accidents of other vehicles caused by vehicle instability caused by potholes can be quantified. The risks of two types of typical accidents fully reflect the actual risks of the potholes.

[0078] For the method for quantifying the hidden danger risks of urban road potholes described in the present invention, according to the actual division of the importance level of the road itself, by controlling the distribution of the statistical model, the vehicle operation risk values under different degree requirements are calculated, and a risk quantification system of different levels is constructed to better meet the road maintenance requirements.

[0079] For the method for quantifying the hidden danger risks of urban road potholes described in the present invention, by comparing the pothole information of different road sections, according to different road maintenance levels, combined with the actual distribution and availability of maintenance resources, the importance degree and maintenance priority order of pothole maintenance on different roads are quantified, providing targeted suggestions that can effectively guide actual maintenance operations for the maintenance units and improving the efficiency of road maintenance by the maintenance units. Description of the Drawings

[0080] Figure 1 Flow chart of a method for quantifying the risk of pothole hazards on urban roads according to the present invention;

[0081] Figure 2 Schematic diagram of the BIM section of the present invention;

[0082] Figure 3 Flow chart of calculating the sum of vehicle weights using a 3D engine according to the present invention. Specific embodiments

[0083] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only a part of the embodiments of the present invention, rather than all of the specific embodiments. Usually, the components of the specific embodiments of the present invention described and shown in the accompanying drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0084] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents the selected specific embodiments of the present invention. All other specific embodiments obtained by those skilled in the art based on the specific embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0085] To further understand the content, features and effects of the present invention, the following specific embodiments are exemplified and combined with the attached Figure 1 - Attached Figure 3 The details are as follows:

[0086] Example 1:

[0087] A method for quantifying the risk of pothole hazards on urban roads includes the following steps:

[0088] S1. Integrate drone equipment, high-definition shooting equipment, and road surface weighing equipment to collect data, obtain high-precision BIM model data of the road, drone-identified road surface pothole information data, road weighing information data, and traffic vehicle information data, and obtain analysis basic data;

[0089] Furthermore, the specific implementation method of step S1 includes the following steps:

[0090] S1.1. Obtain a high-precision BIM model of the road section;

[0091] S1.2. Use a drone to identify road surface pothole information;

[0092] S1.2.1. First, set a fixed flight route for the drone and collect image information of the road surface at fixed time intervals t0;

[0093] Furthermore, the network transmission module carried by drones of the same specification transmits the image information back to the backend server in real time;

[0094] S1.2.2. Use the open-source object recognition algorithm MaskR-CNN to segment and recognize the image information of the road surface collected in step S1.2.1, and identify the position (LON i , LAT i ) of the pothole, as well as the size contour and area S i of the i-th pothole, where LON i represents the longitude of the i-th pothole, and LAT i represents the latitude of the i-th pothole;

[0095] Furthermore, train the neural network model by preparing a large number of pre-labeled road surface pothole images in advance;

[0096] S1.2.3. Obtain the depth distribution of the pothole area through the radar acquisition device carried by the drone;

[0097] S1.3. Set a weighing coil device at the entrance of each section of the road to obtain the weight information of the vehicle;

[0098] The vehicle weight distribution for section L1 within time T is obtained as where W n (T) represents the vehicle weight distribution of the n-th lane of section L1 at time T, and n is the total number of lanes in section L1; then calculate the average vehicle weight distribution of section L1 within time T

[0099] S1.4. Install a high-definition shooting device at the road entrance to obtain the vehicle arrival time distribution of section L1 within time T where t n (T) represents the arrival time distribution of all vehicles on the n-th lane of section L1;

[0100] Then use the drone to track and record the entire driving process of the vehicle, and record the driving speeds of all vehicles on section L1 in real time to obtain the driving speed distribution of all vehicles on section L1 within time T where represents the speed sequence of the Q n -th vehicle on the n-th lane of section L1 within time T, and Q n represents the total number of vehicles on the n-th lane.

[0101] S2. Based on the analysis basic data obtained in step S1, establish the main object elements through BIM modeling software, restore the driving condition of the vehicle passing through the road pothole, and analyze the influence of the road pothole on the vehicle speed and the influence of the road pothole on the vehicle steering situation;

[0102] Restore the driving condition of the vehicle on the section where the pothole is located through BIM modeling and 3D engine, and at the same time quantify the risk value of the pothole to the passing vehicle. Among them, restoring the vehicle driving condition is to utilize the blueprint events in the 3D engine, listen to and monitor various information in the events, and preset mechanical related parameters of the same specification, so as to simulate the vehicle driving condition; the risk value of the vehicle will be obtained through a quantitative method, so as to obtain the size of the risk value of the actual pothole. For drones, the time consumed for the network to transmit images and identify pothole information can be controlled within 1 s. For BIM modeling, first, it is necessary to restore the BIM model of the pothole on the road section, including the pothole position, contour, and depth, etc. By introducing the method of setting up a special coordinate system, the restoration process of the pothole is simplified. Since in urban municipal roads, the maintenance of expressways is of great importance, and most sections of expressways are straight sections, therefore, the BIM model of pothole information can be quickly restored by simplifying the positioning.

[0103] Furthermore, the specific implementation method of step S2 includes the following steps:

[0104] S2.1. Use BIM modeling software to establish the main body of the scene object, mainly the information related to the road pothole;

[0105] Select a straight section of the road, set point O as the origin position of the drone nest, and point O1 as the position where the drone flies to collect and identify the image of the pothole position P0, and obtain the flight length of the drone as The straight-line distance from the drone to the pothole position P0 is Set The angles with the vertical plane yoz and the horizontal plane xoy in the three-dimensional space are θ1 and θ2 respectively. If O is the origin, set the driving direction of the road vehicle as the horizontal axis, and the vertical axis and the vertical axis perpendicular to the horizontal axis are set, then the pothole position The coordinates of are:

[0106]

[0107] Among them, is the horizontal axis coordinate of the pothole position P0, is the vertical axis coordinate of the pothole position P0, is the vertical axis coordinate of the pothole position P0, is used to record the flight height of the drone when the pothole position P0 is collected;

[0108] Change point O to the starting reference point of the model through BIM modeling software. After each collection and conversion of the pothole position by the drone, locate the pothole position in the model, and then use the BIM model to restore the depth distribution of the pothole to complete the establishment of the road pothole information in the BIM modeling software;

[0109] Restore the behavior of the vehicle driving over the pothole, including events such as vehicle tire deformation, instability, and collision with other vehicles. The following will be analyzed in combination with the three-dimensional engine settings and basic mechanics theory. Assume a vehicle with a weight of m, a driving speed of v, driving over a pothole with a horizontal curve length of l0 (the horizontal curve length can be directly obtained from the restored BIM model). The speed reduction is caused by the impact force of the pothole on the vehicle, and at the same time, the kinetic energy decreases. Eventually, the impact force acts on the vehicle tire. When it exceeds the ultimate strength of the tire, a flat tire phenomenon will occur. Therefore, a flat tire event and the triggering conditions for the corresponding flat tire event will be set in the three-dimensional engine, that is, the ultimate strength of the tire is less than the strength exerted on the tire by the actual impact force. Analyze the impact of the pothole on the vehicle speed. In the actual process, since some tires of the vehicle hit the edge of the pothole, a reaction force is formed, which causes the vehicle to decelerate and may trigger a rear-end collision between the front and rear vehicles. If there is an inclined plane in the actual depth of the pothole, it may also cause the vehicle to lose stability and change direction, which may lead to collisions between vehicles in different lanes.

[0110] S2.2. Based on the road potholes established in the BIM modeling software in step S2.1, further use the three-dimensional engine to restore the driving condition of the vehicle driving over the road potholes;

[0111] Assume a vehicle with a weight of m and an ultimate depth of h', a driving speed of v, driving over a pothole with a horizontal curve length of l0, considering that the diameter of the pothole is greater than 20 cm and the depth of the pothole is h0;

[0112] When the depth h0 of the pothole that the wheel falls into is greater than the ultimate depth h', the vehicle will not be able to get out of the pothole and will be stuck in the pothole; when the depth h0 of the pothole that the vehicle falls into is less than the ultimate depth h', the vehicle speed decreases. Set the vehicle speed reduction value Δv to be proportional to the falling depth h0, then there is:

[0113]

[0114] Set the calculation formula for the acceleration a of the vehicle decelerated by the pothole as:

[0115]

[0116] Among them, Δt is the duration of the vehicle speed reduction affected by the pothole;

[0117] Since the vehicle driving speed is relatively high, the actual time of driving over the pothole is basically the same as the speed change period, then there is:

[0118]

[0119] According to Newton's laws of motion, the impact force F is:

[0120]

[0121] The stress σ on the vehicle tire is:

[0122]

[0123] where S0 is the contact area between the tire and the pothole;

[0124] Compare the stress σ on the tire with the ultimate strength σ′ of the tire itself. When the stress σ is greater than the ultimate strength σ′, the tire will burst; if the stress σ is less than or equal to the ultimate strength, the vehicle will not have a flat tire. At the same time, after passing through the pothole, the driving speed of the vehicle is v1, and the calculation formula is:

[0125] v1 = v - Δv;

[0126] Analyze the possibility of the vehicle colliding with the vehicle behind. Since the vehicle in front has difficulty taking into account the situation of the vehicle behind after encountering a pothole, within the driver's ultimate reaction time (about 3 s), assume that the vehicle in front moves at a constant speed v1 within this ultimate time, and the vehicle behind brakes emergently. When the speed of the vehicle behind is equal to that of the vehicle in front, if the value obtained by subtracting the driving speed of the vehicle in front from the driving distance of the vehicle behind is greater than or equal to the initial distance between the two vehicles, the two vehicles will collide; otherwise, they will not.

[0127] S2.3. Based on the vehicle speed obtained in step S2.2, analyze the possibility of the vehicle colliding with the vehicle behind. Since the vehicle in front has difficulty taking into account the situation of the vehicle behind after encountering a pothole, within the driver's ultimate reaction time, assume that the vehicle in front moves at a constant speed v1 within this extremely short time, and the vehicle behind brakes emergently. When the speed of the vehicle behind is equal to that of the vehicle in front, if the value obtained by subtracting the driving speed of the vehicle in front from the driving distance of the vehicle behind is greater than or equal to the initial distance between the two vehicles, the two vehicles will collide; otherwise, they will not collide;

[0128] S2.4. Analyze the vehicle's direction change situation. Assume that the structural inclination angle in the pothole is θ3, and analyze the magnitude relationship between the static friction force and the component of gravity in the vehicle's traveling direction - the downward sliding force on the inclined plane. When the static friction force is less than the downward sliding force, the vehicle will change direction, and the direction change angle is θ3.

[0129] Furthermore, the ultimate reaction time in step S2 is 3 s;

[0130] S3. Based on the analysis basic data obtained in step S1 and the analysis result of the driving condition of the vehicle passing through the road pothole obtained in step S2, use a 3D engine to simulate and restore the driving condition of the vehicle passing through the road pothole, and then set up scenarios where vehicles with different road pothole safety redundancies pass through the road pothole, and simulate to obtain the quantified results of the hidden danger risks of different road potholes;

[0131] Further, the specific implementation method of step S3 includes the following steps:

[0132] S3.1. Based on the analysis basic data obtained in step S1, use a 3D engine to restore the vehicle object, vehicle lane, vehicle weight, and vehicle speed;

[0133] Based on the interval time of the vehicle arriving at the section as a parameter, use the normal distribution model to independently calculate and analyze different lanes, and calculate the average value μ(t n ) and standard deviation σ(t n ) of the vehicle arrival section interval time on the nth lane within one day of history, and obtain the normal distribution N[μ(t n ), σ(t n ), and only take the part on the right side of the y-axis in combination with the actual situation; then, based on the normal distribution N[μ(t n ), σ(t n ), randomly generate the time interval values for arriving at the section to generate vehicle objects;

[0134] Generate vehicle lanes based on the independent collection of each lane by drones;

[0135] Randomly generate vehicle weights by using the method of weight allocation, and then assign the weight attribute to the vehicle object. The weight allocation means that the probability of generating a weight value is proportional to the ratio of the weight value in all weight value sets. The expression is:

[0136]

[0137] Among them, represents the probability that the weight value of is randomly generated on the nth lane, and N(W n ) represents the number of different weight values on the nth lane, represents the total number of weight values of on the nth lane in all value sets; then, generate the vehicle weight attribute for the vehicle object according to the above weight allocation principle;

[0138] Randomly assign the collected speed sequence to each vehicle according to the principle of equal probability to generate vehicle speed;

[0139] S3.2. Restore the actual road traffic flow, construct the out-of-control judgment condition that the ultimate strength of the tire is less than the tire stress strength, and then use the 3D engine to monitor the occurrence of tire blowouts, front and rear impact risks, and left and right impact risk accident collision events of the tire vehicle, and calculate the sum of the weights of all vehicles directly or indirectly affected by the accident, W total , as the risk quantification index of the pothole to the passing vehicles;

[0140] Furthermore, set the scenario restoration with different safety redundancies. The above restorations are all based on the long-term data collection of the road section by drones. However, for the maintenance unit, it is not only necessary to know the risk of potholes to vehicle traffic under normal conditions, but also to know the maximum harm that potholes will cause under extreme conditions, so as to set maintenance strategies with different safety redundancies. Among the four elements restored above, the vehicle lane, vehicle weight, and vehicle speed are all relatively random and do not change much with the scenario. Therefore, focus on the vehicle object element, and by introducing the truncated normal distribution, realize the vehicle distribution with different densities and restore the traffic operation scenario during the traffic peak period. Since the vehicle arrival interval time is a value not less than 0, the normal distribution only takes the right part of the y-axis of the rectangular coordinate system.

[0141] S3.3. Set the scenario of vehicles passing through road potholes with different road pothole safety redundancies. By introducing the vehicle object element into the truncated normal distribution, realize the vehicle distribution with different densities and restore the traffic operation scenario during the traffic peak period;

[0142] The normal distribution takes the right part of the y-axis of the rectangular coordinate system, set four levels of safety redundancy, and the three quartiles of the truncated normal distribution are Q1, Q2, and Q3, corresponding to 25%, 50%, and 75% respectively. The form of the truncated normal distribution is N[μ(t n ),σ(t n ),0≤t<b, where μ(t n ) and σ(t n ) are the average value and standard deviation of the vehicle arrival interval time on the nth lane within a day in history respectively, and b is the upper limit of the independent variable value for intercepting the normal distribution;

[0143] For the first quartile Q1, there is:

[0144]

[0145] Among them, Φ is the distribution function of the standard normal distribution;

[0146] The quartile values are obtained by looking up a table and solving, and then, for the vehicle arrival interval time in the range of [0, Q1), the corresponding highest safety redundancy level is determined; for the vehicle arrival interval time in the range of [Q1, Q2), the corresponding second-highest safety redundancy level is determined; for the vehicle arrival interval time in the range of [Q2, Q3), the corresponding third safety redundancy level is determined; and for the vehicle arrival interval time in the range of [Q3, b], the corresponding fourth safety redundancy level is determined;

[0147] Then, according to the value-taking results within different safety redundancy level ranges, different pothole risk indicators W are simulated total Result.

[0148] S4. Based on the hidden danger risk quantification result of the road pothole obtained in step S3, a method for safe maintenance of urban road potholes is proposed.

[0149] Furthermore, the specific implementation method of step S4 includes the following steps:

[0150] Set the risk indicators for the first pothole K1 and the second pothole K2 to be W total (K1) and W total (K2) respectively, and then the distances of the first pothole K1 and the second pothole K2 from the maintenance unit are D K1 and D K2 , and the traffic flows of the lanes where the first pothole K1 and the second pothole K2 are located in the same time period are U1 and U2 respectively;

[0151] For the first pothole K1, considering the comprehensive pothole risk value, the impact of the maintenance operation on traffic congestion, and the difficulty of the maintenance operation, and then according to the normalization requirement, the pothole maintenance implementation V of the first pothole K1 is finally calculated K1 to sort the priority order of pothole maintenance implementation, and the calculation formula is:

[0152]

[0153] where and are the average values of the pothole risk indicator sequence, the lane traffic flow sequence, and the pothole distance from the maintenance unit sequence respectively;

[0154] The larger the pothole maintenance implementation, the more priority is needed for the pothole to be maintained and repaired, providing a true and feasible maintenance suggestion for the maintenance unit.

[0155] Example 2:

[0156] An electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of a method for quantifying the hidden danger risk of urban road potholes described in Example 1 are implemented.

[0157] The computer device of the present invention may include devices such as a processor and a memory, for example, a single-chip microcomputer including a central processing unit. Moreover, when the processor is used to execute the computer program stored in the memory, the steps of the above-mentioned method for quantifying the risk of hidden dangers in urban road potholes are implemented.

[0158] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0159] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0160] Embodiment 3:

[0161] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the method for quantifying the risk of hidden dangers in urban road potholes described in Embodiment 1 is implemented.

[0162] The computer-readable storage medium of the present invention may be any form of storage medium readable by the processor of the computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. A computer program is stored on the computer-readable storage medium. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned method for quantifying the risk of hidden dangers in urban road potholes can be implemented.

[0163] The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0164] What are the key technical points and the points to be protected in the present invention?

[0165] The present invention integrates various data acquisition tools such as unmanned aerial vehicles, weighing coils, and high-definition shooting units, and uses various data acquisition methods such as multiple target recognition algorithms and geospatial coordinate calculations to provide effective data for subsequent calculation and analysis;

[0166] The present invention uses tools of BIM modeling and 3D engine to restore real events from the virtual side. By means of event listening of the 3D engine combined with the mechanical analysis method of vehicle driving stability, a quantitative scenario construction of the risk of potholes to driving is realized, combining virtual and real, and theory with new technical means;

[0167] With the help of the powerful event simulation ability of the 3D engine, the present invention makes the accidental event of vehicle accidents caused by potholes into a repeatable restoration scenario that can be quantitatively calculated and analyzed by using targeted and practical restoration methods for different elements. This provides a new perspective for the application of new technologies such as BIM modeling, 3D engine, and even digital twin;

[0168] The present invention creatively introduces statistical methods and theories. By statistically quantifying the input end of the 3D engine based on actual data, event control is realized at the element level. Compared with the control strategies of individual attributes or individual components of the 3D engine itself, the statistical method combined with actual data provides a more effective analysis method for the 3D engine.

[0169] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0170] Although the present application has been described above with reference to specific embodiments, various improvements can be made thereto and components thereof can be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application can be combined with each other in any way, and the exhaustive description of the combinations is not given in this specification only for the sake of saving space and resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for quantifying the risk of urban road pothole hazards, characterized in that, It includes the following steps: S1. Integrate the drone equipment, high-definition shooting equipment, and road weighing equipment to collect data, obtain the data of the high-precision BIM model of the road, the data of the drone identifying the pothole information on the road surface, the road weighing information data, and the traffic vehicle information data, and obtain the analysis basic information data; S2. Based on the analysis basic data obtained in step S1, use BIM modeling software to establish the main body of the scene object, and at the same time use a 3D engine to simulate and restore the driving condition of the vehicle passing through the road pothole, and analyze the influence of the road pothole on the vehicle speed and the influence of the road pothole on the vehicle steering situation; S3. Based on the analysis basic data obtained in step S1 and the analysis result of the driving condition of the vehicle passing through the road pothole obtained in step S2, use a 3D engine to simulate and restore the driving condition of the vehicle passing through the road pothole, and then set the scene of the vehicle passing through the road pothole under different road pothole safety redundancy conditions, and simulate to obtain the quantified result of the hidden danger risk of different road potholes; S4. Based on the quantified result of the hidden danger risk of the road pothole obtained in step S3, propose a method for safe maintenance of urban road potholes.

2. The risk quantification method for pothole hazards on urban roads according to claim 1, wherein, The specific implementation method of step S1 includes the following steps: S1.

1. Obtain the high-precision BIM model of the road section; S1.

2. Use the drone to identify the pothole information on the road surface; S1.2.

1. First, set the fixed flight route of the drone, and collect the image information of the road surface in the way of a fixed time interval t0; S1.2.

2. Use the open-source object recognition algorithm Mask R-CNN to segment and recognize the image information of the road surface collected in step S1.2.1, and identify the location (LON i , LAT i ) of the pothole, as well as the size contour and area S i of pothole i, where LON i represents the longitude of pothole i, and LAT i represents the latitude of pothole i; S1.2.

3. Obtain the depth distribution of the pothole area through the radar collection equipment carried by the drone; S1.

3. Set a weighing coil device at the entrance of each section of the road to obtain the weight information of the vehicle; Obtain the vehicle weight distribution of section L1 within time T as where W n (T) represents the vehicle weight distribution of the nth lane of section L1 at time T, and n is the total number of lanes of section L1; then calculate the average vehicle weight distribution of section L1 within time T S1.

4. Install high-definition shooting equipment at the road entrance to obtain the vehicle arrival time distribution of section L1 within time T where t n (T) represents the arrival time distribution of all vehicles on the nth lane of section L1; Then, use a drone to track and record the entire driving process of the vehicle, and record in real time the driving speeds of all vehicles on section L1, obtaining the driving speed distribution of all vehicles on section L1 within time T where represents the speed sequence of the Q n th vehicle in the nth lane of section L1 within time T, and Q n represents the total number of vehicles in the nth lane 3. The method for quantifying the risk of pothole hazards on urban roads according to claim 2, characterized in that, The specific implementation method of step S2 includes the following steps: S2.

1. Use BIM modeling software to establish the main body of the scene object; Select a straight-line section, set point O as the origin position of the UAV nest, and point O1 as the position where the UAV flies to collect and recognize the image of the pothole position P0, and obtain the flight length of the UAV as The straight-line distance between the UAV and the pothole position P0 is Set The angles with the three-dimensional space perpendicular plane yoz and the horizontal plane xoy are θ1 and θ2 respectively. If O is the origin, the driving direction of the road vehicle is set as the horizontal axis, and the vertical axis and the vertical axis perpendicular to the horizontal axis are set, then the pothole position The coordinates of are: Among them, is the horizontal axis coordinate of the pit position P0, is the vertical axis coordinate of the pit position P0, is the vertical axis coordinate of the pit position P0, is used to record the flight altitude of the UAV when the pit position P0 is collected; Change point O to the starting reference point of the model through BIM modeling software. After each time the drone collects and converts the pothole position, locate the pothole position in the model, and then use the BIM model to restore the depth distribution of the pothole to complete the establishment of the pothole information on the road surface in the BIM modeling software; S2.

2. Based on the potholes on the road surface established in BIM modeling software in step S2.1, further use a 3D engine to restore the driving condition of the vehicle passing through the road pothole; Suppose a vehicle with a weight of m, a limit depth of h', a driving speed of v, and a driving through a pothole with a horizontal curve length of l0. Consider that the diameter of the pothole is greater than 20 cm and the depth of the pothole is h0; When the depth h0 of the wheel falling into the pothole is greater than the limit depth h', the vehicle will not be able to get out of the pothole and will be stuck in the pothole; when the depth h0 of the vehicle falling into the pothole is less than the limit depth h', the vehicle speed decreases. Set the vehicle speed decrease value Δv to be proportional to the falling depth h0, then there is: Set the calculation formula for the acceleration a of the vehicle decelerated by the pothole as: Among them, Δt is the duration of the vehicle speed reduction affected by the pothole; Since the vehicle driving speed is relatively high, the actual time of driving through the pothole is basically the same as the speed change period, then there is: According to Newton's laws of motion, the impact force F is: The stress σ on the vehicle tire is: Among them, S0 is the contact area between the tire and the pothole; Compare the stress σ on the tire with the ultimate strength σ′ of the tire itself. When the stress σ is greater than the ultimate strength σ′, the tire will burst; if the stress σ is less than or equal to the ultimate strength, the vehicle will not burst. At the same time, after passing through the pothole, the driving speed of the vehicle is v1, and the calculation formula is: v1 = v - Δv; S2.

3. Based on the vehicle speed obtained in step S2.2, analyze the possibility of collision between the vehicle and the following vehicle. Since it is difficult for the leading vehicle to take into account the situation of the following vehicle after encountering a pothole, within the driver's ultimate reaction time, assume that the leading vehicle moves at a constant speed v1 during this ultimate time, and the following vehicle brakes emergently. When the speed of the following vehicle is equal to that of the leading vehicle, if the value obtained by subtracting the driving speed of the leading vehicle from the driving distance of the following vehicle is greater than or equal to the initial distance between the two vehicles, the two vehicles will collide; otherwise, they will not collide. S2.

4. Analyze the vehicle's direction change situation. Assume that the structural inclination angle in the pothole is θ3, and analyze the magnitude relationship between the static friction force on the inclined plane and the component of gravity in the vehicle's traveling direction - the downward sliding force. When the static friction force is less than the downward sliding force, the vehicle will change direction, and the direction change angle is θ3.

4. A method for quantifying the risk of pothole hazards on urban roads according to claim 3, characterized in that, The ultimate reaction time in step S2 is 3 s.

5. A method for quantifying the risk of pothole hazards on urban roads according to claim 4, characterized in that, The specific implementation method of step S3 includes the following steps: S3.

1. Based on the analysis basic data obtained in step S1, use a 3D engine to restore the vehicle object, vehicle lane, vehicle weight, and vehicle speed; Based on the interval time of vehicles arriving at a section as a parameter, use the normal distribution model to calculate and analyze different lanes independently, and calculate the average value μ(t n ) and the standard deviation σ(t n ) of the interval time of vehicles arriving at the nth lane within a historical day, to obtain the normal distribution N[μ(t n ), σ(t n ), and only take the part on the right side of the y-axis in combination with the actual situation; then, based on the normal distribution N[μ(t n ), σ(t n )], randomly generate the numerical values of the time intervals for arriving at the section to generate vehicle objects; Generate vehicle lanes based on the independent acquisition of each lane by the drone; Randomly generate vehicle weights in a way of weight assignment, and then assign the weight attribute to the vehicle object. The weight assignment means that the probability of generating a weight value is proportional to the ratio of the weight value in all weight value sets. The expression is: Among them, represents the probability that the weight value of the nth lane is is randomly generated. N(W n ) represents the number of different weight values in the nth lane. represents the total number of weight values of in the entire numerical set for the nth lane; then, generate the vehicle weight attribute for the vehicle object according to the above principle of weight distribution. Randomly assign the collected speed sequences to each vehicle according to the principle of equal probability to generate vehicle speeds; S3.

2. Restore the actual road traffic flow, construct the out-of-control judgment condition that the ultimate strength of the tire is less than the tire stress strength, and then use the 3D engine to monitor the occurrence of tire blowouts, front and rear impact risks, and left and right impact risk accident collision events of tire vehicles, and calculate the sum of the weights of all vehicles directly or indirectly affected by the accident W total , as a risk quantification index of the pothole on passing vehicles; S3.

3. Set the scenarios of vehicles passing through road potholes with different road pothole safety redundancies. By introducing vehicle object elements into the truncated normal distribution, different densities of vehicle distributions are realized, and the traffic operation scenarios during the traffic peak period are restored; Take the right part of the y-axis of the rectangular coordinate system for the normal distribution, set four levels of safety redundancy, and truncate the three quartiles of the normal distribution, namely Q1, Q2, and Q3, corresponding to 25%, 50%, and 75% respectively. The form of the truncated normal distribution is N[μ(t n ), σ(t n ), 0 ≤ t < b, where μ(t n ) and σ(t n ) are the average value and standard deviation of the vehicle arrival section interval time on the nth lane within the past day respectively, and b is the upper limit of the independent variable value for truncating the normal distribution; For the first quartile Q1, there is: Among them, Φ is the distribution function of the standard normal distribution; Obtain the quartile values by looking up tables and solving equations. Then, the corresponding highest safety redundancy level is for the vehicle arrival interval time in the range of [0, Q1), the corresponding second-highest safety redundancy level is for the vehicle arrival interval time in the range of [Q1, Q2), the corresponding third safety redundancy level is for the vehicle arrival interval time in the range of [Q2, Q3), and the corresponding fourth safety redundancy level is for the vehicle arrival interval time in the range of [Q3, b]; Then, according to the value results within different safety redundancy levels, different pit risk indicators W are simulated total Results 6. The risk quantification method for pothole hazards on urban roads according to claim 5, wherein The specific implementation method of step S4 includes the following steps: Set the risk indicators for the first pit K1 and the second pit K2 as W total (K1) and W total (K2), and then the distances of the first pit K1 and the second pit K2 from the maintenance unit are and During the same period, the traffic flows of the lanes where the first pit K1 and the second pit K2 are located are U1 and U2 respectively; For the comprehensive pothole risk value, the impact of maintenance operations on traffic congestion, and the difficulty of maintenance operations of the first pothole K1, and then according to the normalization requirements, the implementation of pothole maintenance for the first pothole K1 is finally calculated It is used to sort the priority order of pothole maintenance implementation, and the calculation formula is: Among them, and are the average values of the pothole risk index sequence, the lane traffic flow sequence, and the sequence of the distance values between the potholes and the maintenance units, respectively. The greater the implementation of pothole maintenance, the more this pothole needs to be preferentially maintained and repaired, providing practical and implementable maintenance suggestions for the maintenance unit.

7. An electronic device, characterized in that, It includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it realizes the steps of a method for quantifying the hidden danger risk of urban road potholes according to any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements a method for quantifying the risk of pothole hazards on urban roads according to any one of claims 1-6.

Images