A method and system for evaluating a construction fence position of a highway construction zone

Abstract

The application discloses a kind of expressway construction area construction fence position's evaluation method and system, it is related to safety evaluation technical field, it includes obtaining the real-time traffic flow data corresponding to expressway construction area, construction area three-dimensional point cloud data and fence structure parameter, according to construction area three-dimensional point cloud data, fence structure stability analysis model is constructed, and fence structure stress distribution data is obtained, based on real-time traffic flow data, traffic flow disturbance model is constructed, and vehicle running track disturbance coefficient is obtained;Fence structure stress distribution data and vehicle running track disturbance coefficient are carried out multidimensional coupling analysis, and fence collision risk probability data is obtained;According to fence collision risk probability data, safety evaluation index system is constructed;Safety evaluation index system is input into deep learning evaluation model, and fence position comprehensive evaluation grade is output, when comprehensive evaluation grade exceeds preset threshold, trigger early warning signal.The application has the effect of improving evaluation efficiency.

Description

Technical Field

[0001] This application relates to the field of safety evaluation technology, and in particular to a method and system for evaluating the location of construction enclosures in highway construction areas. Background Technology

[0002] Construction site fencing is a common traffic safety facility in road construction areas. The proper placement of these fencing sections plays a crucial role in ensuring traffic safety and conserving land resources within the construction zone. With my country's sustained and rapid economic development, many early-built highways, primarily four-lane dual carriageways, are no longer adequate to meet the ever-increasing traffic volume. my country is entering a peak period of highway reconstruction and expansion. The sudden changes in the traffic environment caused by road construction significantly impact traffic flow and safety, even leading to frequent traffic accidents. Considering the existing traffic flow on existing roads, construction typically involves closing some lanes or occupying road shoulders. Setting up construction fencing along the roadside in the construction area creates a relatively isolated space, which is of great significance in reducing traffic accidents.

[0003] In related technologies, the current safety evaluation of highway construction area enclosures mainly relies on manual inspection and static mechanical model analysis. Safety assessment is carried out by regularly checking the apparent damage of the enclosure structure and measuring foundation settlement. However, these technologies are mostly limited to single physical field analysis and fail to effectively integrate the spatiotemporal coupling effect of structural mechanical response and dynamic traffic flow. They also lack a risk evolution mechanism under the synergistic effect of multiple factors, which effectively reduces the evaluation efficiency and has room for improvement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a method and system for evaluating the location of construction enclosures in highway construction zones.

[0005] Firstly, this application provides a method for evaluating the location of construction enclosures in a highway construction zone, comprising the following steps:

[0006] Step S1: Obtain real-time traffic flow data, 3D point cloud data of the construction area, and fence structure parameters corresponding to the highway construction area. The real-time traffic flow data includes vehicle trajectory data, vehicle speed data, and vehicle type distribution data. The fence structure parameters include fence material strength, shear strength of connectors, and foundation anchorage depth.

[0007] Step S2: Construct a stability analysis model of the enclosure structure based on the three-dimensional point cloud data of the construction area, perform finite element stress field simulation based on the enclosure structure parameters to obtain the stress distribution data of the enclosure structure, and construct a traffic flow disturbance model based on real-time traffic flow data to obtain the vehicle trajectory disturbance coefficient.

[0008] Step S3: Perform multi-dimensional coupled analysis on the stress distribution data of the enclosure structure and the disturbance coefficient of the vehicle running trajectory to obtain the collision risk probability data of the enclosure;

[0009] Step S4: Construct a safety evaluation index system based on the collision risk probability data of the construction site fence. The safety evaluation index system includes a structural safety index, a traffic disturbance index, and an emergency response index.

[0010] Step S5: Input the structural safety index, traffic disturbance index and emergency response index into the preset deep learning evaluation model, output the comprehensive evaluation level of the fence location, and when the comprehensive evaluation level exceeds the preset threshold, generate fence location optimization suggestions and trigger an early warning signal;

[0011] Step S6: Push the comprehensive evaluation level and early warning signal to the construction management terminal and vehicle navigation system in real time, and update the electronic fence warning range simultaneously.

[0012] Preferably, step 1 includes the following steps:

[0013] Step S11: Collect real-time traffic flow data corresponding to the highway construction area through roadside millimeter-wave radar array, and use multi-target tracking algorithm to extract vehicle trajectory data, vehicle speed data and vehicle type distribution data corresponding to the lane;

[0014] Step S12: Use a 3D laser scanner mounted on a drone to acquire point cloud data of the construction area, and generate 3D point cloud data of the construction area through a point cloud registration algorithm;

[0015] Step S13: Use a stress wave detector and IoT sensors to perform real-time detection on the fence structure, thereby obtaining the fence material strength, shear strength of connectors and foundation anchorage depth corresponding to the fence structure.

[0016] Preferably, step S2, which involves constructing a stability analysis model of the enclosure structure based on the 3D point cloud data of the construction area, specifically includes the following steps:

[0017] Step S21: Convert the 3D point cloud data of the construction area into a finite element mesh model, and set the material constitutive relation and boundary conditions;

[0018] Step S22: Apply the multi-physics coupling effect of wind load, vehicle aerodynamic load and temperature load;

[0019] Step S23: Calculate the stress-strain distribution cloud map of the enclosure structure based on the material constitutive relation, boundary conditions, and multiphysics coupling effects, using a nonlinear solver;

[0020] Step S24: Extract the maximum principal stress value and critical buckling coefficient as structural stability indicators based on the stress-strain distribution cloud map of the enclosure structure, and construct a structural stability analysis model for the enclosure structure based on the structural stability indicators.

[0021] Preferably, step S3 includes the following steps:

[0022] Step S31: Establish a vehicle deviation probability distribution model based on vehicle trajectory data, and calculate the probability density function of vehicles deviating from the construction area in each lane;

[0023] Step S32: Establish a kinetic energy impact model based on vehicle model distribution data and vehicle speed data, and calculate the impact kinetic energy of different types of vehicles at different speeds;

[0024] Step S33: Spatiotemporally match the stress distribution data of the enclosure structure with the impact kinetic energy of different types of vehicles at different speeds to obtain the dynamic bearing capacity margin of each section of the enclosure.

[0025] Step S34: Based on the dynamic bearing capacity margin of each section of the enclosure and the probability density function of vehicles deviating from the construction area in each lane, a Monte Carlo simulation is performed to obtain the collision risk probability data of the enclosure.

[0026] Preferably, the Monte Carlo simulation in step S34 specifically includes:

[0027] A two-dimensional probability distribution function is established for vehicle deviation events from the construction area in each lane and the load-bearing capacity of the enclosure. Multiple random samplings are set up, and each random sampling simultaneously generates vehicle deviation position and impact kinetic energy parameters. When the impact kinetic energy parameter corresponding to the random sampling exceeds the load-bearing capacity of the enclosure, it is recorded as a failure event. The occurrence frequency of the failure event is then calculated, and the occurrence frequency is set as the enclosure collision risk probability data.

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

[0029] Step S41: Normalize the stress distribution data of the enclosure structure, and determine the structural safety index based on the normalized stress distribution data of the enclosure structure.

[0030] Step S42: Based on the vehicle trajectory disturbance coefficient and combined with the dynamic weight allocation of peak and off-peak periods, the traffic disturbance index is determined;

[0031] Step S43: Determine the emergency response index based on the emergency access route setting parameters and rescue response time in the construction area.

[0032] Preferably, updating the electronic fence warning range in step S6 specifically includes:

[0033] The dynamic warning radius is calculated based on the comprehensive evaluation level, and the real-time warning range is displayed using a variable message sign based on the dynamic warning radius. The graded warning signal is then sent to vehicles approaching the construction area enclosure structure via V2X communication.

[0034] Preferably, the graded early warning signal includes:

[0035] When the comprehensive evaluation level is between the first preset threshold and the second preset threshold, a first-level warning signal is output, the warning light system of the crash barriers around the construction area is activated, the warning light is displayed in yellow, the volume of the vehicle navigation system is reduced and a prompt message is displayed;

[0036] When the comprehensive evaluation level is between the second and third preset thresholds, a level 2 warning signal is output, the warning light system of the crash barriers around the construction area is activated, the warning light is displayed in orange, and the vehicle-mounted HUD head-up display warning sign is activated.

[0037] When the comprehensive evaluation level exceeds the third preset threshold, a level three warning signal is output, activating the warning light system of the crash barriers around the construction area, displaying the warning light in red, and triggering the vehicle's active braking system and linking with the road management department.

[0038] Secondly, this application provides an evaluation system for the location of construction enclosures in a highway construction zone, comprising:

[0039] The data acquisition module is used to acquire real-time traffic flow data, three-dimensional point cloud data of the construction area, and fence structure parameters corresponding to the highway construction area. The real-time traffic flow data includes vehicle trajectory data, vehicle speed data, and vehicle type distribution data. The fence structure parameters include fence material strength, shear strength of connectors, and foundation anchorage depth.

[0040] The data analysis module is used to construct a stability analysis model of the enclosure structure based on the three-dimensional point cloud data of the construction area, perform finite element stress field simulation based on the enclosure structure parameters, obtain the stress distribution data of the enclosure structure, and construct a traffic flow disturbance model based on real-time traffic flow data to obtain the vehicle trajectory disturbance coefficient.

[0041] The fence collision risk probability confirmation module is used to perform multi-dimensional coupled analysis of fence structure stress distribution data and vehicle trajectory disturbance coefficient to obtain fence collision risk probability data.

[0042] The safety evaluation index system construction module is used to construct a safety evaluation index system based on the collision risk probability data of the construction site fence. The safety evaluation index system includes a structural safety index, a traffic disturbance index, and an emergency response index.

[0043] The comprehensive evaluation level generation module is used to input the structural safety index, traffic disturbance index and emergency response index into the preset deep learning evaluation model, output the comprehensive evaluation level of the fence location, and generate optimization suggestions for the fence location and trigger an early warning signal when the comprehensive evaluation level exceeds the preset threshold.

[0044] The early warning module is used to push the comprehensive evaluation level and early warning signal to the construction management terminal and vehicle navigation system in real time, and update the electronic fence warning range simultaneously.

[0045] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the evaluation method for the location of construction enclosures in a highway construction area as described in any of the above claims.

[0046] In summary, this application includes the following beneficial technical effects:

[0047] This application provides a method for evaluating the location of construction enclosures in highway construction areas. It acquires real-time traffic flow data, 3D point cloud data of the construction area, and enclosure structural parameters. Based on the 3D point cloud data, a stability analysis model of the enclosure structure is constructed. Finite element stress field simulation is performed using the enclosure structural parameters to obtain stress distribution data. A traffic flow disturbance model is then constructed based on real-time traffic flow data to obtain vehicle trajectory disturbance coefficients. Multi-dimensional coupling analysis is performed on the enclosure structure stress distribution data and vehicle trajectory disturbance coefficients to obtain enclosure collision risk probability data. This effectively integrates the spatiotemporal coupling effect of structural mechanical response and dynamic traffic flow, increasing the risk evolution mechanism under the synergistic effect of multiple factors. A safety evaluation index system is constructed based on the enclosure collision risk probability data to output a comprehensive evaluation level for the enclosure location. When the comprehensive evaluation level exceeds a preset threshold, optimization suggestions for the enclosure location are generated and an early warning signal is triggered. This effectively evaluates the location of construction enclosures in highway construction areas, thereby significantly improving evaluation efficiency. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of 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.

[0049] Figure 1 This is a flowchart of the evaluation method for the location of construction enclosures in highway construction areas, as described in this application.

[0050] Figure 2This is a schematic diagram of an evaluation system for the location of construction enclosures in a highway construction area, as described in an embodiment of this application. Detailed Implementation

[0051] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0052] Example 1

[0053] This application discloses a method for evaluating the location of construction enclosures in highway construction areas.

[0054] Reference Figure 1 A method for evaluating the location of construction enclosures in a highway construction area includes the following steps:

[0055] Step S1: Obtain real-time traffic flow data, 3D point cloud data of the construction area, and fence structure parameters corresponding to the highway construction area. The real-time traffic flow data includes vehicle trajectory data, vehicle speed data, and vehicle type distribution data. The fence structure parameters include fence material strength, shear strength of connectors, and foundation anchorage depth.

[0056] Step S2: Construct a stability analysis model of the enclosure structure based on the three-dimensional point cloud data of the construction area, perform finite element stress field simulation based on the enclosure structure parameters to obtain the stress distribution data of the enclosure structure, and construct a traffic flow disturbance model based on real-time traffic flow data to obtain the vehicle trajectory disturbance coefficient.

[0057] Step S3: Perform multi-dimensional coupled analysis on the stress distribution data of the enclosure structure and the disturbance coefficient of the vehicle running trajectory to obtain the collision risk probability data of the enclosure;

[0058] Step S4: Construct a safety evaluation index system based on the collision risk probability data of the construction site fence. The safety evaluation index system includes a structural safety index, a traffic disturbance index, and an emergency response index.

[0059] Step S5: Input the structural safety index, traffic disturbance index and emergency response index into the preset deep learning evaluation model, output the comprehensive evaluation level of the fence location, and when the comprehensive evaluation level exceeds the preset threshold, generate fence location optimization suggestions and trigger an early warning signal;

[0060] Step S6: Push the comprehensive evaluation level and early warning signal to the construction management terminal and vehicle navigation system in real time, and update the electronic fence warning range simultaneously.

[0061] It should be noted that step 1 includes the following steps:

[0062] Step S11: Collect real-time traffic flow data corresponding to the highway construction area through roadside millimeter-wave radar array, and use multi-target tracking algorithm to extract vehicle trajectory data, vehicle speed data and vehicle type distribution data corresponding to the lane;

[0063] Step S12: Use a 3D laser scanner mounted on a drone to acquire point cloud data of the construction area, and generate 3D point cloud data of the construction area through a point cloud registration algorithm;

[0064] Step S13: Use a stress wave detector and IoT sensors to perform real-time detection on the fence structure, thereby obtaining the fence material strength, shear strength of connectors and foundation anchorage depth corresponding to the fence structure.

[0065] Specifically, step S11 involves collecting real-time traffic flow data corresponding to the highway construction area using a roadside millimeter-wave radar array. Millimeter-wave radar utilizes electromagnetic waves in the millimeter-wave frequency band to detect targets, offering high precision and resolution. A millimeter-wave radar array is installed on the roadside of the highway construction area, continuously emitting millimeter-wave signals and receiving signals reflected back from vehicles and other targets. Through analysis and processing of the echo signals, real-time traffic flow data of the construction area can be acquired. A multi-target tracking algorithm is then used to simultaneously track multiple vehicle targets, extracting vehicle trajectory data, speed data, and vehicle type distribution data corresponding to each lane from the complex radar echo data. For example, the multi-target tracking algorithm can calculate the driving trajectory based on the vehicle's position information at different times; obtain vehicle speed data by measuring changes in vehicle position at adjacent times; and determine vehicle type distribution data based on vehicle size, shape, and other characteristics, such as distinguishing between different types of vehicles like cars and trucks.

[0066] Step S12 utilizes a 3D laser scanner mounted on a drone to acquire point cloud data of the construction area. The drone, with its flexible flight capabilities, can fly along a predetermined route above the construction area. The 3D laser scanner calculates the distance from the target point to the scanner by emitting a laser beam and measuring the time it takes for the laser to travel from emission to reflection, thereby acquiring the 3D coordinate information of the target point. During flight, the 3D laser scanner continuously scans the construction area, collecting a large amount of point cloud data. Point cloud data is a collection of discrete 3D coordinate points, representing the shape and position information of the object's surface within the construction area. A point cloud registration algorithm aligns and fuses the point cloud data collected from different perspectives to generate 3D point cloud data of the construction area, thus constructing a 3D model of the construction area and providing intuitive spatial information for subsequent analysis and decision-making.

[0067] Step S13 utilizes a stress wave detector and IoT sensors to perform real-time monitoring of the enclosure structure. The stress wave detector emits stress waves into the enclosure structure and receives the reflected stress wave signals. Based on the characteristics of the signals, it analyzes the internal condition of the enclosure structure to obtain the strength of the enclosure material. The IoT sensors can monitor parameters such as the shear strength of the enclosure structure's connectors and the foundation anchorage depth in real time. For example, pressure sensors installed at the enclosure's connectors measure the shear force borne by the connectors in real time, thus obtaining the shear strength data of the connectors; displacement sensors installed at the foundation monitor changes in the foundation anchorage depth. Through real-time monitoring, potential safety hazards in the enclosure structure can be detected promptly, providing strong data support for safety management of the construction area.

[0068] It should be noted that step S2, which involves constructing a stability analysis model of the enclosure structure based on the 3D point cloud data of the construction area, specifically includes the following steps:

[0069] Step S21: Convert the 3D point cloud data of the construction area into a finite element mesh model, and set the material constitutive relation and boundary conditions;

[0070] Step S22: Apply the multi-physics coupling effect of wind load, vehicle aerodynamic load and temperature load;

[0071] Step S23: Calculate the stress-strain distribution cloud map of the enclosure structure based on the material constitutive relation, boundary conditions, and multiphysics coupling effects, using a nonlinear solver;

[0072] Step S24: Extract the maximum principal stress value and critical buckling coefficient as structural stability indicators based on the stress-strain distribution cloud map of the enclosure structure, and construct a structural stability analysis model for the enclosure structure based on the structural stability indicators.

[0073] Specifically, step S21 converts the 3D point cloud data of the construction area into a finite element mesh model and sets the material constitutive relation and boundary conditions. The 3D point cloud data of the construction area is a set of discrete points collected by a 3D laser scanner mounted on a UAV. A specific software algorithm converts the point cloud data into a finite element mesh model. The finite element mesh model divides the fence structure into many small elements, facilitating mechanical analysis. Next, the material constitutive relation is set, which describes the stress-strain relationship of the fence material under stress. For example, for a steel fence, its material constitutive relation may follow Hooke's law. Simultaneously, boundary conditions are set according to the actual installation of the fence. For example, the connection between the bottom of the fence and the foundation can be set as a fixed constraint to simulate the situation where the bottom of the fence cannot move or rotate.

[0074] Step S22 applies the multiphysics coupling effect of wind load, vehicle aerodynamic load, and temperature load. Wind load refers to the pressure generated by wind acting on the enclosure. Based on local meteorological data and wind speed models, the magnitude of the wind load under different working conditions is calculated and applied to the finite element model. Vehicle aerodynamic load is the aerodynamic influence of vehicles on the enclosure during movement. Its magnitude and direction are determined using fluid dynamics principles and vehicle driving parameters. Temperature load considers the impact of ambient temperature changes on the enclosure structure; for example, material expansion and thermal stress occur when the temperature rises. These three loads are simultaneously applied to the finite element model to simulate the multiphysics coupling effect experienced by the enclosure in the actual working environment.

[0075] Step S23 calculates the stress-strain distribution cloud map of the enclosure structure based on the material constitutive relation, boundary conditions, and multiphysics coupling effects using a nonlinear solver. The nonlinear solver is a computational tool specifically designed for handling nonlinear problems. Considering the nonlinear characteristics of the material (such as plastic deformation) and multiphysics coupling effects, it solves the finite element model. Through iterative calculation, it obtains the stress and strain distribution of the enclosure structure under various loads and displays it intuitively in the form of a cloud map. For example, in the stress-strain distribution cloud map, darker areas indicate larger stress or strain, which may be weak points in the enclosure structure.

[0076] Step S24 extracts the maximum principal stress and critical buckling coefficient as structural stability indicators from the stress-strain distribution cloud map of the enclosure structure. Based on these indicators, a stability analysis model for the enclosure structure is constructed. The maximum principal stress value, reflecting the maximum stress the enclosure structure withstands under load, is identified from the stress-strain distribution cloud map. The critical buckling coefficient is used to assess whether the enclosure structure will experience buckling instability under compression. By analyzing these two indicators, the stability of the enclosure structure is determined. Based on these structural stability indicators, a mathematical model is constructed to predict and evaluate the stability of the enclosure structure under different working conditions, providing a scientific basis for the design, maintenance, and safety assessment of the enclosure structure.

[0077] Finite element stress field simulation was performed using the structural parameters of the enclosure to obtain stress distribution data for the enclosure structure. The process for obtaining the stress distribution data for the enclosure structure is as follows:

[0078] The stability analysis model of the enclosure structure, constructed based on 3D point cloud data of the construction area, is imported into finite element analysis software. Material constitutive relations and boundary conditions are set in the software, taking into account the enclosure structure parameters. For example, if the bottom of the enclosure is rigidly connected to the foundation, it can be set as a fixed constraint. Based on actual working conditions, multi-physics coupling effects such as wind load (determined based on local meteorological data to determine wind speed and direction, and then calculated), vehicle aerodynamic load (calculated using parameters such as vehicle speed and model), and temperature load (considering the range of ambient temperature variations) are applied. The nonlinear solver in the software is used to solve the model. Through iterative calculations, the software generates the stress-strain distribution of the enclosure structure, presented in the form of a cloud map. From this, stress distribution data of the enclosure structure can be extracted, providing a direct understanding of the stress magnitude in various parts of the enclosure.

[0079] The process of obtaining the vehicle trajectory disturbance coefficient by constructing a traffic flow disturbance model based on real-time traffic flow data is as follows:

[0080] Real-time traffic flow data in the highway construction area is continuously collected using roadside millimeter-wave radar arrays. Multi-target tracking algorithms are used to process this data, accurately extracting vehicle trajectory data, speed data, and vehicle type distribution data for each lane. Based on this extracted data, a traffic flow disturbance model is constructed. For example, it considers the different disturbances to the surrounding air caused by different vehicle types (cars, trucks, etc.) traveling at different speeds, and their varying impacts on the construction site enclosure structure. A mathematical model is established, using vehicle trajectory, speed, and vehicle type as variables to analyze the relationship between these factors and the disturbances experienced by the enclosure structure. Through simulation and calculation, the degree of disturbance to the enclosure structure caused by vehicle movement is quantified, yielding a vehicle trajectory disturbance coefficient. For instance, when a truck approaches the enclosure at a higher speed, the calculated disturbance coefficient is relatively larger, indicating that the vehicle's movement significantly interferes with the stability of the enclosure structure.

[0081] It should be noted that step S3 includes the following steps:

[0082] Step S31: Establish a vehicle deviation probability distribution model based on vehicle trajectory data, and calculate the probability density function of vehicles deviating from the construction area in each lane;

[0083] Step S32: Establish a kinetic energy impact model based on vehicle model distribution data and vehicle speed data, and calculate the impact kinetic energy of different types of vehicles at different speeds;

[0084] Step S33: Spatiotemporally match the stress distribution data of the enclosure structure with the impact kinetic energy of different types of vehicles at different speeds to obtain the dynamic bearing capacity margin of each section of the enclosure.

[0085] Step S34: Based on the dynamic bearing capacity margin of each section of the enclosure and the probability density function of vehicles deviating from the construction area in each lane, a Monte Carlo simulation is performed to obtain the collision risk probability data of the enclosure.

[0086] Specifically, in step S31, a vehicle deviation probability distribution model is established based on vehicle trajectory data. The vehicle trajectory data collected by the roadside millimeter-wave radar array includes the vehicle's driving path information in each lane. Statistical analysis methods are used to process a large number of vehicle trajectories to establish the vehicle deviation probability distribution model. For example, the frequency and magnitude of vehicles deviating from their normal trajectories when driving in different lanes are statistically analyzed to calculate the probability density function of vehicles deviating from the construction area in each lane. This probability density function describes the likelihood of a vehicle deviating from the construction area at a certain location.

[0087] Step S32 establishes a kinetic energy impact model based on vehicle model distribution data and vehicle speed data. Different types of vehicles have different masses and travel speeds, resulting in different impact kinetic energies. The mass parameters of different vehicle models are determined based on the vehicle model distribution data, and combined with the vehicle speed data, the impact kinetic energy of different types of vehicles at different speeds is calculated using the kinetic energy formula. For example, a large truck with a large mass will have a much greater impact kinetic energy than a small car if it travels at a high speed.

[0088] Step S33 involves spatiotemporally matching the stress distribution data of the enclosure structure with the impact kinetic energy of different types of vehicles at different speeds. The stress distribution data reflects the stress state of various parts of the enclosure at different times, while the vehicle impact kinetic energy reflects the potential impact of a vehicle collision. Spatiotemporally matching the two determines the relationship between the stress borne by the enclosure and the potential vehicle impact kinetic energy at a specific time and location, thereby obtaining the dynamic bearing capacity margin of each section of the enclosure. For example, at a specific location, if the stress on the enclosure is relatively low, and the potential vehicle impact kinetic energy is also relatively low, then the dynamic bearing capacity margin of that section is relatively high.

[0089] Step S34 involves performing Monte Carlo simulations based on the dynamic load-bearing capacity margin of each section of the construction site fence and the probability density function of vehicles deviating from the construction area in each lane. Monte Carlo simulation is a method that estimates results through random sampling. A large number of random samples are performed using a computer program to simulate situations where vehicles deviate from the construction area and collide with the construction site fence. The probability of a collision with the fence in each simulation is calculated by combining the dynamic load-bearing capacity margin of each section of the fence. After multiple simulations, the probability data of fence collision risk is statistically obtained.

[0090] Furthermore, the Monte Carlo simulation in step S34 specifically includes:

[0091] A two-dimensional probability distribution function is established for vehicle deviation events from the construction area in each lane and the load-bearing capacity of the enclosure. Multiple random samplings are set up, and each random sampling simultaneously generates vehicle deviation position and impact kinetic energy parameters. When the impact kinetic energy parameter corresponding to the random sampling exceeds the load-bearing capacity of the enclosure, it is recorded as a failure event. The occurrence frequency of the failure event is then calculated, and the occurrence frequency is set as the enclosure collision risk probability data.

[0092] It should be noted that step S4 includes the following steps:

[0093] Step S41: Normalize the stress distribution data of the enclosure structure, and determine the structural safety index based on the normalized stress distribution data of the enclosure structure.

[0094] Step S42: Based on the vehicle trajectory disturbance coefficient and combined with the dynamic weight allocation of peak and off-peak periods, the traffic disturbance index is determined;

[0095] Step S43: Determine the emergency response index based on the emergency access route setting parameters and rescue response time in the construction area.

[0096] Specifically, step S41 processes the stress distribution data of the enclosure structure and determines the structural safety index by normalizing the stress distribution data. Normalization scales the data proportionally to fit within a specific range, typically [0,1]. For example, if the original range of the enclosure structure stress distribution data is from 10MPa to 100MPa, the normalization formula transforms all stress data into the [0,1] range, eliminating the influence of dimensions and facilitating subsequent analysis and comparison. After normalization, the structural safety index is confirmed based on the processed data. Certain rules can be set; for example, if most of the normalized stress data falls within a lower range (e.g., less than 0.5), the structural safety index is considered high, indicating that the enclosure structure is in a relatively safe state; conversely, if most data is close to 1, the structural safety index is low, potentially indicating a safety hazard.

[0097] Step S42 determines the traffic disturbance index based on the vehicle trajectory disturbance coefficient, while considering the dynamic weight allocation during peak and off-peak hours. The vehicle trajectory disturbance coefficient is obtained through the traffic flow disturbance model constructed earlier based on real-time traffic flow data. It reflects the degree of interference of vehicle movement on the stability of the enclosure structure. Traffic flow and vehicle movement characteristics differ between peak and off-peak hours, resulting in different impacts on the enclosure. Therefore, different weights need to be assigned to peak and off-peak hours. For example, assuming a weight of 0.7 for peak hours and 0.3 for off-peak hours. If the vehicle trajectory disturbance coefficient obtained during peak hours is large, multiplying it by 0.7 will result in a larger contribution to the traffic disturbance index; while even if the disturbance coefficient is relatively small during off-peak hours, multiplying it by 0.3 will still have a certain impact on the traffic disturbance index. By comprehensively considering the situation at different times, the traffic disturbance index is determined, more comprehensively reflecting the impact of traffic conditions on the enclosure structure.

[0098] Step S43 determines the emergency response index based on the emergency access route setup parameters and rescue response time in the construction area. The emergency access route setup parameters include the number, width, and location of the emergency access routes. For example, a greater number of routes, wider widths, and more rationally located routes are more conducive to rescue operations. Rescue response time refers to the time from the occurrence of an accident to the arrival of rescue forces and the commencement of rescue operations. An evaluation model is established to quantify the emergency access route setup parameters and rescue response time. For instance, each additional emergency access route increases the score, and a shorter rescue response time also increases the score. These scores are combined to obtain the emergency response index. A high emergency response index indicates a strong emergency response capability in the construction area when dealing with emergencies, while a low index indicates a weaker emergency response capability.

[0099] The steps of inputting structural safety index, traffic disturbance index, and emergency response index into a preset deep learning evaluation model, outputting a comprehensive evaluation level for the fence location, and generating fence location optimization suggestions and triggering an early warning signal when the comprehensive evaluation level exceeds a preset threshold specifically include:

[0100] The structural safety index, traffic disturbance index, and emergency response index are used as input data and fed into a pre-set deep learning evaluation model. This model, trained on a large amount of data, learns the complex relationship between these indices and the overall evaluation level of the fence location. For example, the model may find that when the structural safety index is high, the traffic disturbance index is low, and the emergency response index is high, the overall evaluation level of the fence location will be high, indicating that the location is relatively superior.

[0101] The deep learning evaluation model calculates and analyzes the input index data, outputting a comprehensive evaluation level for the location of the construction site hoarding. This comprehensive evaluation level is then compared to a preset threshold. If the comprehensive evaluation level exceeds the preset threshold, it indicates a problem with the hoarding location, and optimization suggestions are generated. For example, if a high traffic disturbance index leads to an unsatisfactory comprehensive evaluation level, the optimization suggestion might be to adjust the hoarding location to reduce its impact on traffic. Simultaneously, an early warning signal is triggered, alerting relevant personnel to the potential risks of the hoarding location and prompting them to take timely measures to address the issue.

[0102] It should be noted that updating the electronic fence warning range in step S6 specifically includes:

[0103] The dynamic warning radius is calculated based on the comprehensive evaluation level, and the real-time warning range is displayed using a variable message sign based on the dynamic warning radius. The graded warning signal is then sent to vehicles approaching the construction area enclosure structure via V2X communication.

[0104] Specifically, a correspondence model is established between the comprehensive evaluation level and the dynamic warning radius. For example, the comprehensive evaluation level is divided into five levels. Level one indicates extremely high safety at the fence location, with a corresponding dynamic warning radius possibly set at 10 meters; Level five indicates a significant risk at the fence location, with a dynamic warning radius set at 50 meters. As the comprehensive evaluation level increases, the dynamic warning radius increases accordingly. Once the comprehensive evaluation level of a specific fence location is obtained, the dynamic warning radius for that location is calculated based on the aforementioned correspondence model. For instance, if the comprehensive evaluation level of a fence location is level three, its dynamic warning radius is determined to be 30 meters according to the pre-set correspondence. Based on the calculated dynamic warning radius, a variable message sign (VMS) is used to display the real-time warning range. The VMS is installed in a suitable location near the construction area and obtains the dynamic warning radius information through a data connection with the system. The dynamic warning radius is then converted into a visualized warning range and displayed on the VMS. For example, variable message signs can use graphics and text to clearly mark the warning area with a radius of 30 meters centered on the construction site, reminding passing vehicles and pedestrians to pay attention to safety and make preparations in advance. Furthermore, by dynamically adjusting the warning radius and displaying the warning area in real time, the safety of the construction area and its surroundings can be more effectively guaranteed.

[0105] Furthermore, the tiered early warning signal includes:

[0106] When the comprehensive evaluation level is between the first preset threshold and the second preset threshold, a first-level warning signal is output, the warning light system of the crash barriers around the construction area is activated, the warning light is displayed in yellow, the volume of the vehicle navigation system is reduced and a prompt message is displayed;

[0107] When the comprehensive evaluation level is between the second and third preset thresholds, a level 2 warning signal is output, the warning light system of the crash barriers around the construction area is activated, the warning light is displayed in orange, and the vehicle-mounted HUD head-up display warning sign is activated.

[0108] When the comprehensive evaluation level exceeds the third preset threshold, a level three warning signal is output, activating the warning light system of the crash barriers around the construction area, displaying the warning light in red, and triggering the vehicle's active braking system and linking with the road management department.

[0109] Example 2

[0110] This application also discloses an evaluation system for the location of construction enclosures in highway construction areas.

[0111] Reference Figure 2 An evaluation system for the location of construction enclosures in a highway construction area, comprising:

[0112] The data acquisition module is used to acquire real-time traffic flow data, three-dimensional point cloud data of the construction area, and fence structure parameters corresponding to the highway construction area. The real-time traffic flow data includes vehicle trajectory data, vehicle speed data, and vehicle type distribution data. The fence structure parameters include fence material strength, shear strength of connectors, and foundation anchorage depth.

[0113] The data analysis module is used to construct a stability analysis model of the enclosure structure based on the three-dimensional point cloud data of the construction area, perform finite element stress field simulation based on the enclosure structure parameters, obtain the stress distribution data of the enclosure structure, and construct a traffic flow disturbance model based on real-time traffic flow data to obtain the vehicle trajectory disturbance coefficient.

[0114] The fence collision risk probability confirmation module is used to perform multi-dimensional coupled analysis of fence structure stress distribution data and vehicle trajectory disturbance coefficient to obtain fence collision risk probability data.

[0115] The safety evaluation index system construction module is used to construct a safety evaluation index system based on the collision risk probability data of the construction site fence. The safety evaluation index system includes a structural safety index, a traffic disturbance index, and an emergency response index.

[0116] The comprehensive evaluation level generation module is used to input the structural safety index, traffic disturbance index and emergency response index into the preset deep learning evaluation model, output the comprehensive evaluation level of the fence location, and generate optimization suggestions for the fence location and trigger an early warning signal when the comprehensive evaluation level exceeds the preset threshold.

[0117] The early warning module is used to push the comprehensive evaluation level and early warning signal to the construction management terminal and vehicle navigation system in real time, and update the electronic fence warning range simultaneously.

[0118] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0119] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0120] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for evaluating the location of construction enclosures in a highway construction area, characterized in that, Includes the following steps: Step S1: Obtain real-time traffic flow data, 3D point cloud data of the construction area, and fence structure parameters corresponding to the highway construction area. The real-time traffic flow data includes vehicle trajectory data, vehicle speed data, and vehicle type distribution data. The fence structure parameters include fence material strength, shear strength of connectors, and foundation anchorage depth. Step S2: Construct a stability analysis model of the enclosure structure based on the three-dimensional point cloud data of the construction area, perform finite element stress field simulation based on the enclosure structure parameters to obtain the stress distribution data of the enclosure structure, and construct a traffic flow disturbance model based on real-time traffic flow data to obtain the vehicle trajectory disturbance coefficient. Step S3: Perform multi-dimensional coupled analysis on the stress distribution data of the enclosure structure and the disturbance coefficient of the vehicle running trajectory to obtain the collision risk probability data of the enclosure; Step S3 includes the following steps: Step S31: Establish a vehicle deviation probability distribution model based on vehicle trajectory data, and calculate the probability density function of vehicles deviating from the construction area in each lane; Step S32: Establish a kinetic energy impact model based on vehicle model distribution data and vehicle speed data, and calculate the impact kinetic energy of different types of vehicles at different speeds; Step S33: Spatiotemporally match the stress distribution data of the enclosure structure with the impact kinetic energy of different types of vehicles at different speeds to obtain the dynamic bearing capacity margin of each section of the enclosure. Step S34: Based on the dynamic bearing capacity margin of each section of the enclosure and the probability density function of vehicles deviating from the construction area in each lane, a Monte Carlo simulation is performed to obtain the collision risk probability data of the enclosure. The Monte Carlo simulation in step S34 specifically includes: A two-dimensional probability distribution function is established for vehicle deviation events from the construction area in each lane and the load-bearing capacity of the enclosure. Multiple random samplings are set up, and each random sampling generates vehicle deviation position and impact kinetic energy parameters simultaneously. When the impact kinetic energy parameter corresponding to the random sampling exceeds the load-bearing capacity of the enclosure, it is recorded as a failure event. The occurrence frequency of the failure event is then calculated, and the occurrence frequency is set as the enclosure collision risk probability data. Step S4: Construct a safety evaluation index system based on the collision risk probability data of the construction site fence. The safety evaluation index system includes a structural safety index, a traffic disturbance index, and an emergency response index. Step S5: Input the structural safety index, traffic disturbance index and emergency response index into the preset deep learning evaluation model, output the comprehensive evaluation level of the fence location, and when the comprehensive evaluation level exceeds the preset threshold, generate fence location optimization suggestions and trigger an early warning signal; Step S6: Push the comprehensive evaluation level and early warning signal to the construction management terminal and vehicle navigation system in real time, and update the electronic fence warning range simultaneously.

2. The method for evaluating the location of construction enclosures in a highway construction area according to claim 1, characterized in that, Step 1 includes the following steps: Step S11: Collect real-time traffic flow data corresponding to the highway construction area through roadside millimeter-wave radar array, and use multi-target tracking algorithm to extract vehicle trajectory data, vehicle speed data and vehicle type distribution data corresponding to the lane; Step S12: Use a 3D laser scanner mounted on a drone to acquire point cloud data of the construction area, and generate 3D point cloud data of the construction area through a point cloud registration algorithm; Step S13: Use a stress wave detector and IoT sensors to perform real-time detection on the fence structure, thereby obtaining the fence material strength, shear strength of connectors and foundation anchorage depth corresponding to the fence structure.

3. The method for evaluating the location of construction enclosures in a highway construction area according to claim 1, characterized in that, Step S2, which involves constructing a stability analysis model for the enclosure structure based on the 3D point cloud data of the construction area, specifically includes the following steps: Step S21: Convert the 3D point cloud data of the construction area into a finite element mesh model, and set the material constitutive relation and boundary conditions; Step S22: Apply the multi-physics coupling effect of wind load, vehicle aerodynamic load and temperature load; Step S23: Calculate the stress-strain distribution cloud map of the enclosure structure based on the material constitutive relation, boundary conditions, and multiphysics coupling effects, using a nonlinear solver; Step S24: Extract the maximum principal stress value and critical buckling coefficient as structural stability indicators based on the stress-strain distribution cloud map of the enclosure structure, and construct a structural stability analysis model for the enclosure structure based on the structural stability indicators.

4. The method for evaluating the location of construction enclosures in a highway construction area according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Normalize the stress distribution data of the enclosure structure, and determine the structural safety index based on the normalized stress distribution data of the enclosure structure. Step S42: Based on the vehicle trajectory disturbance coefficient and combined with the dynamic weight allocation of peak and off-peak periods, the traffic disturbance index is determined; Step S43: Determine the emergency response index based on the emergency access route setting parameters and rescue response time in the construction area.

5. The method for evaluating the location of construction enclosures in a highway construction area according to claim 1, characterized in that, Step S6, updating the electronic fence warning range, specifically includes: The dynamic warning radius is calculated based on the comprehensive evaluation level, and the real-time warning range is displayed using a variable message sign based on the dynamic warning radius. The graded warning signal is then sent to vehicles approaching the construction area enclosure structure via V2X communication.

6. The method for evaluating the location of construction enclosures in a highway construction area according to claim 5, characterized in that, The tiered early warning signals include: When the comprehensive evaluation level is between the first preset threshold and the second preset threshold, a first-level warning signal is output, the warning light system of the crash barriers around the construction area is activated, the warning light is displayed in yellow, the volume of the vehicle navigation system is reduced and a prompt message is displayed; When the comprehensive evaluation level is between the second and third preset thresholds, a level 2 warning signal is output, the warning light system of the crash barriers around the construction area is activated, the warning light is displayed in orange, and the vehicle-mounted HUD head-up display warning sign is activated. When the comprehensive evaluation level exceeds the third preset threshold, a level three warning signal is output, activating the warning light system of the crash barriers around the construction area, displaying the warning light in red, and triggering the vehicle's active braking system and linking with the road management department.

7. An evaluation system for the location of construction enclosures in a highway construction area, applied to the evaluation method for the location of construction enclosures in a highway construction area as described in any one of claims 1-6, characterized in that, include: The data acquisition module is used to acquire real-time traffic flow data, three-dimensional point cloud data of the construction area, and fence structure parameters corresponding to the highway construction area. The real-time traffic flow data includes vehicle trajectory data, vehicle speed data, and vehicle type distribution data. The fence structure parameters include fence material strength, shear strength of connectors, and foundation anchorage depth. The data analysis module is used to construct a stability analysis model of the enclosure structure based on the three-dimensional point cloud data of the construction area, perform finite element stress field simulation based on the enclosure structure parameters, obtain the stress distribution data of the enclosure structure, and construct a traffic flow disturbance model based on real-time traffic flow data to obtain the vehicle trajectory disturbance coefficient. The fence collision risk probability confirmation module is used to perform multi-dimensional coupled analysis of fence structure stress distribution data and vehicle trajectory disturbance coefficient to obtain fence collision risk probability data. The safety evaluation index system construction module is used to construct a safety evaluation index system based on the collision risk probability data of the construction site fence. The safety evaluation index system includes a structural safety index, a traffic disturbance index, and an emergency response index. The comprehensive evaluation level generation module is used to input the structural safety index, traffic disturbance index and emergency response index into the preset deep learning evaluation model, output the comprehensive evaluation level of the fence location, and generate optimization suggestions for the fence location and trigger an early warning signal when the comprehensive evaluation level exceeds the preset threshold. The early warning module is used to push the comprehensive evaluation level and early warning signal to the construction management terminal and vehicle navigation system in real time, and update the electronic fence warning range simultaneously.

8. A computer-readable storage medium, characterized in that: The system stores instructions that, when executed on a computer, cause the computer to perform an evaluation method for the location of construction enclosures in a highway construction area as described in any one of claims 1 to 6.

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