An interactive design and simulation system combining traffic engineering BIM and VR technologies

Abstract

The present invention relates to an interactive design and simulation system for traffic engineering that combines BIM and VR technology. The system relates to the field of traffic engineering and includes: an interactive configuration module for selecting simulated configurations and obtaining user feedback information; a route mining module for mining network frequency and obtaining a set of mobile route use cases; a virtual driving module for starting simulated driving and obtaining a three-dimensional dynamic timing diagram of a vehicle and user heart rate monitoring timing information; a safety scoring module for receiving a user's driving safety score through a user terminal; a risk assessment module for obtaining a set of driving risk evaluation values; and a safety identification module for performing safety identification and sending it to a traffic engineering design terminal. By combining BIM and VR technology, the system can achieve virtual simulation acquisition and analysis of user driving behavior, thereby enabling targeted evaluation of the driving risks of design solutions, guiding the optimized design of traffic engineering projects, and thus improving the safety of traffic engineering designs.

Description

Technical Field

[0001] The present invention relates to the field of traffic engineering, and in particular to an interactive design and simulation system combining traffic engineering BIM and VR technologies. Background Art

[0002] With the increasing construction of urban traffic engineering, in order to ensure traffic safety, traffic engineering design needs to fully consider various factors such as road environment, traffic flow, as well as the derived user driving behavior patterns and potential driving risks. However, in the prior art, traffic engineering design generally lacks general means for analyzing user driving behavior and evaluating the risks of design schemes. Designers are difficult to comprehensively obtain user driving behavior data and cannot accurately evaluate the safety performance of design schemes, resulting in insufficient safety of the final traffic engineering.

[0003] For example, in complex sections such as road intersections, multi-lane lane changes, toll stations, and ramps, due to differences in user driving behavior and defects in the design scheme itself, traffic accidents and potential safety hazards are likely to occur. Another example is that traffic engineering in mountain roads, sections adjacent to water or cliffs, and under harsh weather conditions has a high accident rate due to the lack of targeted driving risk assessment and safety design. Therefore, how to analyze user driving behavior and evaluate the risks of design schemes in traffic engineering design, and on this basis, evaluate the safety performance of traffic engineering design schemes, and then guide the optimization design of traffic engineering, so as to effectively improve traffic operation safety, is an urgent problem to be solved in this field. Summary of the Invention

[0004] In view of the technical problem that traffic engineering design in the prior art lacks general means for analyzing user driving behavior and evaluating the risks of design schemes, resulting in insufficient safety of traffic engineering design, the present invention provides an interactive design and simulation system combining traffic engineering BIM and VR technologies to solve this problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides an interactive design and simulation system combining traffic engineering BIM and VR technologies, including: an interaction configuration module, which is used to display a traffic engineering BIM model and a simulation configuration menu on the user side and prompt the user to select a simulation configuration, and obtain user feedback information, where the user feedback information includes the three-dimensional area of the BIM model and the type of simulated vehicle; a route mining module, which is used to mine the networking frequency of the three-dimensional area of the BIM model according to the type of simulated vehicle to obtain a set of use cases for mobile routes; a virtual driving module, which is used to send the set of use cases for mobile routes and the three-dimensional area of the BIM model to the associated VR driving simulation cabin of the type of simulated vehicle, and remind the user to enter the associated VR driving simulation cabin to start simulated driving, and obtain a three-dimensional dynamic time series diagram of the vehicle and user heart rate monitoring time series information; a safety scoring module, which is used to receive the user's driving safety score through the user side; a risk assessment module, which is used to perform safety assessments based on the three-dimensional dynamic time series diagram of the vehicle, the user heart rate monitoring time series information, and the user's driving safety score through a driving risk assessment network to obtain a set of driving risk evaluation values; a safety marking module, which is used to perform safety marking on the three-dimensional area of the BIM model and send it to the traffic engineering design end when all the driving risk evaluation values in the set of driving risk evaluation values are less than the driving risk evaluation threshold.

[0007] The beneficial effects of the present invention are:

[0008] Through the interactive configuration module, when the user terminal displays the traffic engineering BIM model and the simulation configuration menu and prompts the user to select the simulation configuration, user feedback information is obtained, including the three-dimensional area of the BIM model and the type of simulated vehicle. The interaction between the user and the system is realized. The user can select the road area to be evaluated and the simulated vehicle, and the system obtains the configuration information of the user. Through the route mining module, the network frequency of the three-dimensional area of the BIM model is mined according to the type of simulated vehicle, and a set of mobile route use cases is obtained. A set of mobile route use cases covering common driving scenarios is mined, providing a data basis for subsequent simulated driving analysis. Through the virtual driving module, the set of mobile route use cases and the three-dimensional area of the BIM model are sent to the associated VR driving simulation cabin of the simulated vehicle type, and the user is reminded to enter the associated VR driving simulation cabin to start the simulated driving, obtaining the three-dimensional dynamic time series diagram of the vehicle and the user heart rate monitoring time series information, and obtaining comprehensive driving behavior data. Through the safety scoring module, the user's driving safety score is received by the user terminal and used as reference data for driving risk assessment. Through the risk assessment module, safety assessment is carried out based on the driving risk assessment network, the three-dimensional dynamic time series diagram of the vehicle, the user heart rate monitoring time series information and the user's driving safety score, and a set of driving risk evaluation values is obtained, and the driving safety risk level of the road section design scheme is quantitatively evaluated. Through the safety identification module, when the set of driving risk evaluation values is less than the driving risk evaluation threshold, the three-dimensional area of the BIM model is safety-identified and then sent to the traffic engineering design terminal, so as to judge whether the safety of the road section design meets the standard, and feedback the identification of the design scheme with good safety performance to guide the optimization design of traffic engineering.

[0009] Through the combined application of BIM and VR technologies, a virtual driving environment close to reality is constructed, realizing the comprehensive collection and accurate analysis of user driving behaviors. On this basis, the safety performance of traffic engineering design schemes is comprehensively evaluated, providing a quantitative basis and improvement direction for design optimization, and effectively improving the quality of traffic engineering design. Brief Description of the Drawings

[0010] Figure 1 It is a schematic structural diagram of an interactive design and simulation system combining traffic engineering BIM and VR technologies provided by the present invention;

[0011] Figure 2 It is a schematic structural diagram of the electronic device provided by the present invention;

[0012] Figure 3 It is a schematic structural diagram of a computer-readable storage medium provided by the present invention.

[0013] In the drawings, the components represented by the respective reference numerals are as follows:

[0014] Interaction configuration module 11, route mining module 12, virtual driving module 13, safety scoring module 14, risk assessment module 15, safety identification module 16, electronic device 200, memory 210, processor 220, first computer program 211, computer-readable storage medium 300, second computer program 311. Detailed implementation manners

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0016] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0017] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or more advantageous than other embodiments. The following description is given to enable any person skilled in the art to make and use the present invention. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0018] Embodiment 1:

[0019] As Figure 1 shown, the embodiment of the present invention provides an interactive design and simulation system combining traffic engineering BIM and VR technologies, including:

[0020] An interaction configuration module 11, configured to obtain user feedback information when the user terminal displays a traffic engineering BIM model and a simulation configuration menu and prompts the user to select a simulation configuration, where the user feedback information includes a BIM model three-dimensional area and a simulation vehicle type.

[0021] Specifically, the interaction configuration module 11 is mainly responsible for interacting with users and receiving the configuration selections of users for traffic simulation parameters. Specifically, first, through computer graphics rendering technology, a three-dimensional BIM model of the entire traffic project is presented on the user's display device (such as a monitor or touch screen). The user can use human-computer interaction devices such as a mouse or a stylus to observe and understand the spatial layout and detailed features of the BIM model in all directions through operations such as rotation, translation, and zooming. While the BIM model is being displayed, the interaction configuration module 11 also provides a simulation configuration menu on the interface. The simulation configuration menu lists in the form of a list, a drop-down box, radio buttons, etc. the various parameters that require user decisions during the simulation process, such as traffic flow, vehicle speed distribution, signal timing plan, etc. The user can select and modify the configuration items on the menu according to actual needs and analysis objectives to customize a personalized simulation plan.

[0022] There are two configuration items on the simulation configuration menu. One is the simulation area selection, and the other is the vehicle type. For the simulation area selection, the user needs to select a three-dimensional area on the displayed BIM model, usually a certain section of the road network, an intersection, a square, etc., to determine the spatial scope of the subsequent simulation. The user can use the method of dragging the mouse or touching with a finger to select a three-dimensional area boundary on the three-dimensional BIM model. The interaction configuration module 11 will automatically intercept the digital description information of the corresponding area in the three-dimensional BIM model for subsequent simulation calculations. For the vehicle type, the user needs to specify one or several vehicles from the list of alternative vehicle models (such as including cars, buses, trucks, etc.) as the objects of the simulation. Since the kinematic characteristics of different vehicle models and the requirements for road conditions are not the same, they need to be treated separately in the simulation. After completing the necessary parameter settings, the user clicks the OK button to complete the configuration process. The interaction configuration module 11 will summarize the user's settings to form user feedback information, including the three-dimensional area of the selected BIM model and the vehicle types for simulation.

[0023] Through the interaction configuration module 11, the decision-making intention of the user and the simulation ability of the system can be effectively obtained. The user can follow simple and intuitive operation steps to flexibly configure parameters such as the three-dimensional BIM model area and the target vehicle type, so as to obtain a simulation effect that better meets the actual needs. This interactive configuration method reduces the user's usage threshold, improves the usability and practicality of the system, and lays a foundation for subsequent refined traffic simulation analysis.

[0024] The route mining module 12 is used to perform frequent connection mining on the three-dimensional area of the BIM model according to the vehicle types for simulation to obtain a set of mobile route use cases.

[0025] Specifically, the main function of the route mining module 12 is to discover and extract typical driving routes that conform to a specific vehicle type from the BIM model data. Specifically, the route mining module 12 receives the user-selected three-dimensional area and vehicle type transmitted by the interaction configuration module 11, and performs pattern matching and frequency mining based on this.

[0026] First, the route mining module 12 retrieves road network information related to vehicles of this type in the BIM model database according to the user-specified simulated vehicle type. Among them, the road network information includes attribute parameters such as the geometric shape, slope, and curvature of the road, as well as the spatial positions and descriptive information of road ancillary facilities such as traffic signs, signal lights, and lane lines. By analyzing this data, the route mining module 12 can infer the road sections suitable for vehicles of this type to travel and the traffic rules. Next, the route mining module 12 focuses on the target area boxed by the user on the three-dimensional BIM model, that is, the three-dimensional area of the BIM model. The route mining module 12 extracts the road network topology structure within the three-dimensional area of the BIM model and constructs a directed graph model composed of nodes (such as intersections) and edges (such as road sections). On this basis, the route mining module 12 applies frequent pattern mining algorithms (such as Apriori, FP-growth, etc.) to discover frequently occurring path patterns from a large amount of historical traffic trajectory data. These frequent patterns represent the typical driving routes of vehicles in this area and reflect the habitual choices of drivers in terms of time and space.

[0027] During the frequent pattern mining process, the route mining module 12 needs to measure and compare the similarities of different routes. Among them, the route mining module 12 uses sequence similarity measurement methods such as dynamic time warping and longest common subsequence to measure the similarity of different routes in space and time, so as to summarize representative frequent route patterns. After that, the route mining module 12 outputs a series of typical routes that conform to a specific vehicle type and regional environment, forming a set of mobile route use cases. Each route not only contains the spatial trajectory coordinates of vehicle movement, but also is attached with driving state parameters such as speed, acceleration, and steering. These real route use case data can be used to support subsequent traffic simulation and virtual scene construction, enabling users to simulate and experience the vehicle driving process under different working conditions in a realistic virtual environment.

[0028] Through the route mining module 12, general laws and typical patterns of vehicle driving are refined and summarized from a large amount of complex BIM model and traffic trajectory data by using data mining. This automated and intelligent route mining method can greatly improve the pertinence and accuracy of traffic simulation, providing data support for subsequent applications such as virtual driving training and traffic planning and design.

[0029] The virtual driving module 13 is used to send the mobile route use case set and the BIM model three-dimensional area to the associated VR driving simulation cabin that simulates the vehicle type, and remind the user to enter the associated VR driving simulation cabin to start the simulated driving, so as to obtain the three-dimensional dynamic time series diagram of the vehicle and the user heart rate monitoring time series information.

[0030] Specifically, the virtual driving module 13 is responsible for constructing an immersive virtual driving environment and collecting user driving behavior data. Its inputs include the mobile route use case set provided by the route mining module 12 and the user-selected BIM model three-dimensional area provided by the interaction configuration module 11.

[0031] After receiving the above input data, the virtual driving module 13 first constructs a virtual three-dimensional driving scene based on the mobile route use case set and the BIM model three-dimensional area. This scene needs to reproduce as realistically as possible the topographical features, road networks, traffic facilities and other elements of the target area. At the same time, according to the path shape, vehicle speed and other parameters in the mobile route use case set, moving vehicle objects are generated in the virtual scene. Among them, professional 3D modeling software and VR engines, such as Unity3D, Unreal Engine, etc., are used in the construction process of the entire virtual scene.

[0032] After the virtual driving scene is constructed, the virtual driving module 13 associates it with a physical VR driving simulation cabin. A VR driving simulation cabin is an immersive driving simulator equipped with a multi-degree-of-freedom motion platform, a steering wheel, accelerator and brake pedals, etc. The associated VR driving simulation cabin that simulates the vehicle type refers to a simulator whose hardware configuration and motion freedom match the actual driving experience of the target vehicle model. The virtual driving module 13 connects the virtual driving scene with the display system, motion control system, etc. of the VR driving simulation cabin through software interfaces, so that the operations of the user in the simulation cabin can be reflected in real time in the motion state of the vehicle in the virtual scene.

[0033] When everything is ready, the virtual driving module 13 sends a prompt message to the user to guide them into the VR driving simulation cabin to start the simulated driving. The user controls the virtual vehicle to drive on the virtual route through hardware devices such as the steering wheel and pedals in the simulation cabin, and observes the changes in the virtual scene through the display screen of the simulation cabin. During the driving process, the virtual driving module 13 will continuously record the following two types of data: one is the state parameters of the virtual vehicle, such as the motion trajectory, speed, acceleration, steering angle, etc., to form a three-dimensional dynamic time series diagram of the vehicle; the other is the physiological response data of the user, especially the change curve of the heart rate, to form the user heart rate monitoring time series information. Both types of data are sampled and stored in the form of time series for subsequent driving behavior analysis and risk assessment.

[0034] Through the virtual driving module 13, using VR virtual reality technology, the BIM model of traffic engineering is combined with typical route use cases to build an immersive virtual driving environment. Through the linkage with the physical driving simulator, users can experience the road traffic conditions in the target area as if they were on the spot. At the same time, the sequential driving data and physiological response data collected by the virtual driving module 13 provide detailed basis for evaluating the safety of traffic planning and design. This method of combining virtual driving with real-time data collection greatly improves the authenticity and data richness of traffic simulation.

[0035] The safety scoring module 14 is used to receive the user's driving safety score through the user terminal.

[0036] Specifically, the safety scoring module 14 is used to collect the user's subjective evaluation of their driving experience in the virtual driving environment. This module directly faces the end users and obtains the driving safety score data feedback by the users through the man-machine interaction interface.

[0037] After the user completes the virtual driving task, the safety scoring module 14 will automatically pop up a scoring interface to guide the user to evaluate the safety of the just-completed driving process. This interface can adopt common forms such as five-star scoring or percentage scoring, allowing users to intuitively express their subjective feelings about the safety and comfort of the virtual road environment and traffic conditions. Considering that the user's safety assessment may be affected by various factors, several common evaluation dimensions, such as road alignment design, traffic flow, and visibility, can also be listed on the scoring interface to help users score comprehensively, so as to obtain the user's driving safety score from the subjective perspective of the user.

[0038] Through the safety scoring module 14, guiding users to score and give feedback on virtual driving, obtaining the user's driving safety score, so as to evaluate the safety performance of road traffic planning and design from the subjective perspective of users, and improving the pertinence and effectiveness of road design and traffic management.

[0039] The risk assessment module 15 is used to conduct safety assessments based on the three-dimensional dynamic sequential diagram of the vehicle, the sequential information of the user's heart rate monitoring, and the user's driving safety score through the driving risk assessment network, and obtain a set of driving risk evaluation values.

[0040] Specifically, the risk assessment module 15 receives three types of data: one is the three-dimensional dynamic sequential diagram of the vehicle collected by the virtual driving module 13, the second is the sequential information of the user's heart rate monitoring synchronously recorded during the virtual driving process, and the third is the user's subjective driving safety score collected by the safety scoring module 14. The goal of the risk assessment module 15 is to comprehensively utilize these three types of data to objectively evaluate the safety risk level of the virtual driving scenario.

[0041] To achieve the fusion analysis of multi-source heterogeneous data, a driving risk assessment network is designed in the risk assessment module 15. This network uses deep learning techniques and, through models such as convolutional neural networks and long short-term memory networks, extracts features and fuses information from different types of time-series data and scoring data. For example, the three-dimensional dynamic time-series graph of the vehicle is first processed by a convolutional neural network to extract feature sequences such as speed, acceleration, and steering during the vehicle's movement; the time-series information of the user's heart rate monitoring is modeled by a long short-term memory network to explore the correlation pattern between the user's physiological response and driving risk; the user's driving safety score, as an important subjective judgment basis, is input into a fully connected network together with the above two types of objective data, and a comprehensive driving risk assessment result is obtained through weighted fusion.

[0042] The training of the driving risk assessment network requires a large amount of sample data. For this reason, the risk assessment module 15 needs to dock with the virtual driving module 13 and the safety scoring module 14 to regularly obtain the latest driving behavior data and user scoring data from these two modules. At the same time, to improve the diversity and coverage of the samples, the risk assessment module 15 can also access external traffic accident databases, driver physiological response data sets, etc. as supplements to the training data. In the specific training process, the driving risk assessment network adopts an end-to-end supervised learning paradigm, with the known traffic accident risk levels in the real world as the training objectives, and updates the weight parameters of the network through the backpropagation algorithm to continuously improve the accuracy of risk assessment. After the training of the driving risk assessment network is completed, it can be used to predict the safety risks of new virtual driving data. The risk assessment module 15 inputs the three types of data collected during the virtual driving process into the trained driving risk assessment network and automatically obtains a driving risk evaluation value. This evaluation value is a real number between 0 and 1, and the larger the value, the higher the driving risk. By batch-evaluating the driving data in different road sections and different time periods, the risk assessment module 15 finally outputs a set of driving risk evaluation values to comprehensively depict the safety risk distribution of the road traffic within the three-dimensional area of the BIM model.

[0043] Through the risk assessment module 15, using the driving risk assessment network, the safety risk level of road traffic is automatically evaluated, greatly improving the accuracy and practicality of driving risk assessment and providing support for enhancing the safety level of road traffic.

[0044] The safety identification module 16 is used to send the BIM model three-dimensional area to the traffic engineering design end after performing safety identification when the set of driving risk evaluation values is less than the driving risk evaluation threshold.

[0045] Specifically, the safety identification module 16 first sets a driving risk evaluation threshold. This threshold is empirically given by traffic experts according to the safety standard requirements of the traffic management department or by referring to the statistical distribution of a large amount of historical traffic accident data. When all the driving risk evaluation values in the set of driving risk evaluation values are less than this threshold, it means that the road traffic safety level in the three-dimensional area of the BIM model is generally high and can meet the requirements for safe passage; conversely, if there are driving risk evaluation values exceeding the threshold, it indicates that there are still certain potential safety hazards in this area and further investigation and rectification are needed. Therefore, when it is determined that the set of driving risk evaluation values is less than the driving risk evaluation threshold, safety identification is performed in the three-dimensional area of the BIM model and sent to the traffic engineering design end, indicating that the three-dimensional area of the BIM model meets the requirements.

[0046] Through the safety identification module 16, when the set of driving risk evaluation values is less than the driving risk evaluation threshold, after performing safety identification on the three-dimensional area of the BIM model, it is sent to the traffic engineering design end to provide decision support for traffic engineering design, thereby improving the safety of traffic engineering design.

[0047] Furthermore, the embodiments of the present application further include:

[0048] Taking the three-dimensional area of the BIM model as the background constraint and the simulated vehicle type as the foreground constraint, collect a set of compliant driving samples through the network, where the set of compliant driving samples includes a mobile route record data set;

[0049] Perform a frequent coefficient identification on the mobile route record data set to obtain a set of route frequent coefficients;

[0050] Sort out the set of mobile route use cases in the set of route frequent coefficients that are greater than or equal to the frequent coefficient threshold from the mobile route record data set.

[0051] In a feasible implementation, when the route mining module 12 automatically extracts frequently occurring typical driving routes from the three-dimensional area of the BIM model according to the specified simulated vehicle type and forms a set of mobile route use cases, first, the route mining module 12 massively collects real compliant driving behavior sample data on the Internet. During the collection process, two constraints are fully considered: one is to use the three-dimensional area of the BIM model as a background constraint to be highly consistent with background elements such as the traffic facility layout, road surface structure, and surrounding terrain in the real road environment, so as to ensure that the collected driving behavior data matches the virtual environment; the other is to use the simulated vehicle type as a foreground constraint, that is, the collected samples must be real driving data of the simulated vehicle type selected by the user (such as cars, trucks, etc.) to reflect the driving characteristics of different vehicle types. Based on the large-scale collection on the Internet, the route mining module 12 obtains a huge set of compliant driving samples, which contains a large amount of GPS record data of the moving routes of real vehicles, forming a moving route record data set.

[0052] Then, the route mining module 12 performs frequent pattern mining on the collected massive moving route record data. Specifically, the route mining module 12 uses frequent pattern mining algorithms such as Apriori, FP-growth, etc. to count the frequency of each moving route in the total sample set and calculate its corresponding route frequency coefficient. The route frequency coefficient is an index to measure the degree of route occurrence. The higher the coefficient, the more frequently the route appears in real traffic. Through frequent pattern mining, the route mining module 12 extracts a set of route frequency coefficients from the original massive record data to describe the frequent characteristics of different routes. After that, the route mining module 12 filters out the route records with a frequency coefficient greater than or equal to the preset frequency coefficient threshold from the obtained set of route frequency coefficients to form the final set of mobile route use cases. The setting of the frequency coefficient threshold needs to comprehensively consider the actual characteristics of road traffic and the statistical requirements of simulation experiments, and is given by traffic engineering experts based on experience and data analysis results. The obtained set of mobile route use cases after screening reflects the most frequently used driving routes of the selected simulated vehicle type under specific background constraints, has strong representativeness, and can be used for subsequent traffic virtual simulation.

[0053] Through the route mining module 12, the function of automatically mining typical routes from real driving behavior data is realized, and statistically representative driving routes are efficiently extracted from massive heterogeneous data, which not only improves the coverage and richness of route samples but also reduces the cost of manual calibration, providing high-quality data support for subsequent traffic virtual simulation.

[0054] Furthermore, the embodiment of the present application further includes:

[0055] Perform pairwise distance identification on the mobile route record data set to obtain a mobile route distance calibration data set;

[0056] Extract the first mobile route record data according to the mobile route record data set;

[0057] Based on the mobile route distance calibration data set, count the number of routes in the mobile route record data set whose mobile route distance deviation from the first mobile route record data is less than the mobile route distance deviation threshold, and count the ratio of the number of routes to the total number of routes in the mobile route record data set, which is set as the first mobile route frequency coefficient;

[0058] Add the first mobile route frequency coefficient to the route frequency coefficient set.

[0059] In a preferred embodiment, when performing frequency coefficient identification on the mobile route record data set, first, the route mining module 12 calculates and identifies the pairwise distances of the routes in the mobile route record data set. Specifically, the route mining module 12 traverses each mobile route in the mobile route record data set and calculates the distance metric value between it and other routes through methods such as Euclidean distance, DTW distance, and Fréchet distance. Through pairwise distance calculation, the route mining module 12 obtains a mobile route distance calibration data set that records the distance metric values between any two routes. Then, the route mining module 12 traverses the mobile route record data set and extracts one route from it each time, which is marked as the first mobile route record data. This record data will be used as a reference benchmark for calculating the occurrence frequency of other routes in the future.

[0060] Subsequently, the route mining module 12 uses the obtained mobile route distance calibration data set to count the number of routes that are relatively close to the first mobile route record data. Specifically, the route mining module 12 traverses the mobile route distance calibration data set and determines whether the distance between the first mobile route record data and other routes is less than the preset mobile route distance deviation threshold. If the distance metric value between two routes is less than this threshold, it means that these two routes are very close in terms of spatial shape and movement trend and can be regarded as different instances of the same typical route. Through this step, the route mining module 12 obtains a set of route records that are close to the first mobile route record data. Dividing the number of this set of route records by the total number of routes in the mobile route record data set, the first mobile route frequency coefficient can be obtained, which characterizes the relative frequency of the first mobile route record data appearing in real traffic. After that, the route mining module 12 adds the calculated first mobile route frequency coefficient to the route frequency coefficient set to provide a basis for subsequent frequent route screening.

[0061] Through the route mining module 12, the frequent coefficient identification of the mobile route record data set is realized, and the frequently occurring typical routes are automatically extracted from the original route data, and their occurrence frequencies are quantified. Compared with the traditional manual calibration method, it can process a large amount of route data, comprehensively mine the frequent patterns in real traffic, and control the clustering granularity of frequent routes through the mobile route distance deviation threshold, with strong adaptability.

[0062] Furthermore, the embodiments of the present application further include:

[0063] Construct a mobile route distance calibration function:

[0064]

[0065] Wherein, represents the first-dimensional coordinate of the i-th point on path P1, represents the second-dimensional coordinate of the i-th point on path P1, represents the third-dimensional coordinate of the i-th point on path P1, represents the first-dimensional coordinate of the j-th point on path P2, represents the second-dimensional coordinate of the j-th point on path P2, represents the third-dimensional coordinate of the j-th point on path P2, is the point on path P2 that is closest to in distance, represents the direction vector between the j-th point and the next point on path P2, represents the direction vector between the i-th point and the next point on path P1, θ ij represents the angular difference determined by calculating the dot product and modulus length of the two direction vectors, ΔL represents the length deviation between path P1 and path P2, d ij represents and the Euclidean distance of, M represents the total number of points on path P2, N represents the total number of points on path P1, λ represents the weight coefficient of the direction difference, used to adjust the contribution of the direction difference in the total deviation, μ represents the weight coefficient of the path length deviation, used to adjust the contribution of the path length deviation in the total deviation, w ij represents and the importance weight in the path deviation calculation of;

[0066] Perform pairwise distance identification on the mobile route record data set according to the mobile route distance calibration function to obtain a mobile route distance calibration data set.

[0067] In a preferred embodiment, in the frequency coefficient identification, the route mining module 12 calculates the pairwise distances and calibrates the routes in the mobile route record dataset. In order to accurately quantify the similarity between different routes, a mobile route distance calibration function is proposed, which is used to comprehensively consider the characteristics of routes in multiple aspects such as spatial shape, movement direction, and length difference, and construct a distance metric model between routes.

[0068] The specific form of the mobile route distance calibration function is: Construct the mobile route distance calibration function:

[0069]

[0070] where D(P1, P2) represents the overall deviation distance between path P1 and path P2, which is obtained by summing three sub-distance metrics by weight. They are: Euclidean distance d ij , angular difference |θ ij |, and length deviation ΔL. In the calculation of the overall deviation distance D(P1, P2), it is necessary to traverse all sampling points on path P1 and path P2, and for each pair of sampling points and calculate the three sub-distance metrics between them, and multiply by the corresponding weight coefficients λ, μ, and w ij , and finally sum the weighted sub-distances of all sampling point pairs. Among them, λ is used to balance the contribution of the angular difference to the total deviation, μ is used to balance the contribution of the length deviation to the total deviation, and w ij represents the importance of the i-th sampling point and the j-th sampling point in the deviation calculation. By setting these weight parameters, the influence of different features on the overall route deviation can be flexibly adjusted, and the adaptability of the distance metric can be improved.

[0071] Among the three sub-distance metrics, the Euclidean distance d ij represents the straight-line distance between two sampling points in three-dimensional space, which is used to characterize the spatial shape difference of the path. The angular difference θ ij represents the included angle between the path tangent vectors at two sampling points, which is used to characterize the movement direction difference of the path. Specifically, θ ij is obtained by calculating the dot product of two tangent vectors and , dividing by the magnitudes of and , and then taking the arccosine. The length deviation ΔL represents the difference in the total lengths of the two paths, which is used to characterize the scale difference of the paths.

[0072] Based on the above moving route distance calibration function, the route mining module 12 can calculate the deviation distance between any two routes in the moving route record dataset. In specific implementation, the route mining module 12 traverses all route pairs in the moving route record dataset, extracts features such as sampling point coordinates and tangent vectors for each pair of routes, substitutes them into the distance calibration function to solve the deviation distance D(P1, P2) between the route pair, and stores the result in the moving route distance calibration dataset. Through exhaustive calculation, a complete moving route distance calibration dataset is finally obtained to comprehensively measure the similarity between any two routes in the moving route record dataset.

[0073] By constructing the moving route distance calibration function, a measurement model that comprehensively considers the spatial shape, motion direction, and length difference is provided for measuring the similarity between routes. By designing three sub-distance metrics of Euclidean distance, angle difference, and length deviation, and introducing flexible weight parameters, the deviation characteristics between routes can be comprehensively characterized, providing a data basis for subsequent frequent route mining.

[0074] Furthermore, the embodiments of the present application further include:

[0075] Collect the first candidate compliant driving sample through networking, where the first candidate compliant driving sample includes a driving scenario record model and a vehicle record type;

[0076] When the vehicle record type is the same as the simulated vehicle type, input the BIM model three-dimensional area into the first feature extraction channel of the background comparison network to obtain the first feature extraction result, and input the driving scenario record model into the second feature extraction channel of the background comparison network to obtain the second feature extraction result, where the first feature extraction channel and the second feature extraction channel are twin convolutional neural networks;

[0077] Input the first feature extraction result and the second feature extraction result into the feature comparison channel to obtain a feature deviation value;

[0078] When the feature deviation value is less than or equal to the feature deviation threshold, add the first candidate compliant driving sample to the compliant driving sample set.

[0079] In a preferred implementation manner, in the frequent networking mining, the route mining module 12 needs to collect a large number of real driving samples that meet specific background constraints and foreground constraints on the Internet. In order to automatically screen out effective samples that meet the above two constraint conditions, a method for collecting a compliant driving sample set based on deep learning is proposed.

[0080] First, the route mining module 12 collects a candidate compliant driving sample from the Internet through technologies such as web crawlers as the first candidate compliant driving sample. The first candidate compliant driving sample includes two parts: one is the driving scenario recording model, that is, the three-dimensional modeling data of the real road environment; the other is the vehicle type record type, that is, the vehicle type that generates this driving record. Then, it is judged whether the vehicle type record type is the same as the simulated vehicle type. If they are the same, it means that the first candidate compliant driving sample meets the foreground constraint condition. At this time, the route mining module 12 uses the pre-trained background comparison network to judge whether the driving scenario of the first candidate compliant driving sample matches the three-dimensional area of the BIM model. Among them, the background comparison network adopts the structure of a siamese convolutional neural network, including two parallel feature extraction channels, namely the first feature extraction channel and the second feature extraction channel. The first feature extraction channel is responsible for extracting features from the three-dimensional area of the BIM model to obtain the first feature vector reflecting the environmental features of the three-dimensional area of the BIM model as the first feature extraction result; the second feature extraction channel is responsible for extracting features from the driving scenario recording model to obtain the second feature vector reflecting the real road environmental features as the second feature extraction result.

[0081] After obtaining the first feature extraction result and the second feature extraction result, the route mining module 12 inputs the first feature extraction result and the second feature extraction result into the feature comparison channel of the background comparison network to calculate the feature deviation value between the two features. Among them, the feature deviation calculation method of the feature comparison channel can use methods such as Euclidean distance, cosine similarity, and vector inner product. For example, when using Euclidean distance, the deviation value can be expressed as:

[0082]

[0083] where Feature1 and Feature2 represent the first feature vector and the second feature vector respectively, and Feature1 i and Feature2 i represent the i-th element of the two feature vectors Feature1 and Feature2 respectively. The smaller the value of Distance, the closer the two feature vectors are, which also means that the driving scenario recording model of the first candidate compliant driving sample is more matched with the three-dimensional area of the BIM model.

[0084] After that, the route mining module 12 compares the feature deviation value between the first feature extraction result and the second feature extraction result with a preset feature deviation value threshold. If the feature deviation value is less than or equal to the threshold, it is considered that the driving scenario recording model of the first candidate compliant driving sample is similar enough to the BIM model three-dimensional area to meet the background constraint condition. At this time, the route mining module 12 incorporates the first candidate compliant driving sample into the compliant driving sample set for subsequent frequent route mining.

[0085] By using deep learning technology to automatically screen real driving samples that meet background constraints and foreground constraints, the efficiency and accuracy of sample collection are greatly improved. Among them, the background comparison network composed of a siamese convolutional neural network can effectively capture the similarity between the BIM model and the driving scenario. The feature comparison channel can flexibly select the deviation measurement method, and the setting of the matching threshold takes into account both the sample quality and the richness of the samples, enabling accurate collection of effective samples from a large amount of heterogeneous network data and laying a data foundation for subsequent frequent route mining.

[0086] Furthermore, the route mining module 12 further includes: when the vehicle recording type is different from the simulated vehicle type, or the feature deviation value is greater than the feature deviation threshold, the first candidate compliant driving sample is updated.

[0087] In a feasible implementation manner, when the vehicle recording type of the first candidate compliant driving sample is different from the simulated vehicle type, or the feature deviation value between its driving scenario and the BIM model three-dimensional area is greater than the set feature deviation threshold, the sample is regarded as an invalid sample and will not be added to the compliant driving sample set. At this time, a candidate compliant driving sample is collected from the Internet again as the new first candidate compliant driving sample, which is used as the object for the next background constraint and foreground constraint screening to continuously enrich the compliant driving sample set.

[0088] Furthermore, the driving risk assessment network includes a balance risk assessment channel, an emotion risk assessment channel, and a fully connected channel. Therefore, through the driving risk assessment network, based on the three-dimensional dynamic time series diagram of the vehicle, the user heart rate monitoring time series information, and the user driving safety score, a safety assessment is performed to obtain a driving risk evaluation value set, including:

[0089] Process the three-dimensional dynamic time series diagram of the vehicle through the balance risk assessment channel to obtain a balance risk assessment value;

[0090] Statistically analyze the proportion of the time domain step length greater than or equal to the heart rate threshold in the user heart rate monitoring time series information through the emotion risk assessment channel, which is set as the emotion risk assessment value;

[0091] Set the first weight for the balance risk assessment value, the second weight for the emotional risk assessment value, and the third weight for the user's driving safety score, where the sum of the second weight and the third weight is less than the first weight;

[0092] Through the fully connected channel, perform weighted fitting on the normalized data of the balance risk assessment value, the emotional risk assessment value, and the user's driving safety score to obtain a driving risk assessment value, and add it to the driving risk assessment value set.

[0093] In a preferred embodiment, the driving risk assessment network consists of three processing channels, namely the balance risk assessment channel, the emotional risk assessment channel, and the fully connected channel. Among them, the balance risk assessment channel is responsible for analyzing the balance stability during the vehicle movement process, the emotional risk assessment channel is responsible for analyzing the impact of the driver's emotional fluctuations on driving safety, and the fully connected channel comprehensively considers the objective risk assessment and subjective safety score to generate the final driving risk assessment value.

[0094] Specifically, the balance risk assessment channel takes the three-dimensional dynamic time series diagram of the vehicle as input, and uses algorithms such as spatio-temporal convolutional neural networks to extract balance stability-related features such as acceleration, angular velocity, and tilt angle during the vehicle movement process, and calculates an assessment value reflecting the vehicle imbalance risk accordingly. The higher this assessment value, the greater the possibility of unstable states such as rollover and out-of-control during vehicle driving, and the higher the driving risk. The emotional risk assessment channel takes the user's heart rate monitoring time series information as input, and focuses on analyzing the driver's emotional stress level during driving. By statistically calculating the proportion of time when the heart rate exceeds the normal threshold, this channel can quantitatively evaluate the degree of the driver's emotional fluctuations. The higher the proportion of arrhythmia, the more unstable the driver's emotions, and the greater the potential threat to driving safety. At the final stage of risk assessment, the fully connected channel comprehensively combines the balance risk assessment value, the emotional risk assessment value, and the driving safety score of the user's subjective scoring, and performs weighted fusion to obtain a normalized driving risk assessment value. To balance the importance of various indicators, this channel adopts a weight distribution strategy: the balance risk assessment value is given the highest first weight, while the weights of the emotional risk assessment value and the subjective score are relatively low, which are the second weight and the third weight respectively, and the sum of the two does not exceed the first weight. This weight setting reflects the decisive influence of objective risk factors on driving safety, and also takes into account the reference value of the driver's subjective feelings. Through weighted fusion, the driving risk assessment network can output a comprehensive driving risk assessment value, ranging from 0 to 1. The closer this value is to 1, the higher the safety risk during driving, and vice versa. The risk assessment module 15 calculates the risk assessment value for each segment of driving data and summarizes the results into a complete set of driving risk assessment values, which serves as the basis for quantitatively evaluating the driving safety level of the entire area.

[0095] By comprehensively considering risk information from multiple dimensions such as vehicle movement balance, driver emotional stability, and subjective safety perception, it is possible to more accurately and comprehensively evaluate potential safety hazards during the driving process, providing an important basis for traffic road design.

[0096] Furthermore, the balance risk assessment channel includes a bump amplitude feature attention extraction layer, a bump frequency feature attention extraction layer, and a balance risk assessment layer. Therefore, by processing the three-dimensional dynamic time series diagram of the vehicle through the balance risk assessment channel, a balance risk assessment value is obtained, including:

[0097] Process the three-dimensional dynamic time series diagram of the vehicle through the bump amplitude feature attention extraction layer to extract bump amplitude time series information;

[0098] Process the three-dimensional dynamic time series diagram of the vehicle through the bump frequency feature attention extraction layer to extract bump frequency time series information;

[0099] Through the balance risk assessment layer, count the proportion of the time domain step length in which the bump amplitude in the bump amplitude time series information is greater than or equal to the bump amplitude threshold, and / or the bump frequency in the bump frequency time series information is greater than or equal to the bump frequency threshold, and set it as the balance risk assessment value.

[0100] In a preferred embodiment, in the driving risk assessment network, the balance risk assessment channel is a dedicated channel for analyzing the driving stability of the vehicle. In order to more precisely characterize the impact of the vehicle's bump state on driving safety, a bump amplitude feature attention extraction layer, a bump frequency feature attention extraction layer, and a balance risk assessment layer are designed in the balance risk assessment channel. The bump amplitude feature attention extraction layer and the bump frequency feature attention extraction layer respectively extract the time series features of the vehicle's bumping process from the two perspectives of vertical displacement amplitude and frequency, and on this basis, the balance risk assessment layer comprehensively evaluates the risk level of driving balance.

[0101] Specifically, the bump amplitude feature attention extraction layer takes the three-dimensional dynamic time-series graph of the vehicle as input and focuses on analyzing the dynamic change law of the vertical displacement amplitude of the vehicle. By analyzing each frame of the three-dimensional dynamic time-series graph of the vehicle, this layer can accurately extract the bump amplitude information of the vehicle body at each moment and organize it into a complete bump amplitude time-series signal. During the extraction process, this layer introduces an attention mechanism, which adaptively adjusts the weights of bump information at different moments and different regions through learning, so that the extracted time-series features can focus more on the key bump events that have the greatest impact on the balance risk. At the same time, the bump frequency feature attention extraction layer focuses on analyzing the frequency characteristics of vehicle bumps. By performing time-frequency domain transformation and multi-scale analysis on the three-dimensional dynamic time-series graph of the vehicle, this layer can automatically identify the dominant frequency components of the vertical displacement of the vehicle and reveal the periodic law of vehicle bumps. Similarly, this layer also uses an attention mechanism to optimize the extraction quality of frequency features, so that the extraction results can more accurately reflect the frequency domain characteristics of the balance risk. After the extraction of bump amplitude and frequency features is completed, the balance risk assessment channel transmits the extraction results to the balance risk assessment layer, which comprehensively analyzes the impact of the vehicle bump state on driving safety. Specifically, the balance risk assessment layer presets two key risk determination thresholds, namely the bump amplitude threshold and the bump frequency threshold. The former reflects the dangerous limit of the vehicle body bump displacement, while the latter reflects the dangerous frequency range of vehicle bumps. The balance risk assessment layer compares the bump amplitude time-series signal and the frequency time-series signal with the thresholds and calculates the time proportion of dangerous bump events exceeding the safety limit value during the entire driving process. This proportional value is the balance risk assessment value for measuring the driving stability of the vehicle. The higher this value is, the greater the risk of the vehicle losing control or overturning on the bumpy road surface, and the worse the balance stability is.

[0102] Through two complementary feature perspectives of bump amplitude and frequency, the balance control state of the vehicle under complex road conditions can be accurately characterized, potential out-of-control risks can be detected in a timely manner, and important bases for traffic safety early warning and auxiliary decision-making can be provided. At the same time, using the attention mechanism in deep learning to optimize the extraction representation of key risk information is more intelligent, efficient, and has stronger adaptability and robustness than traditional artificial feature methods.

[0103] The interactive design and simulation system combining traffic engineering BIM and VR technology provided by the embodiments of the present invention has at least the following technical effects:

[0104] An interaction configuration module is used to, when the client displays a traffic engineering BIM model and a simulation configuration menu and prompts the user to select a simulation configuration, obtain user feedback information, where the user feedback information includes the three-dimensional area of the BIM model and the type of simulated vehicle, to realize the interaction between the user and the system, enable the user to select the road area and the type of simulated vehicle to be evaluated, and the system obtains the user's simulation configuration requirement information accordingly, providing conditions for subsequent simulation evaluation. A route mining module is used to perform frequent network mining on the three-dimensional area of the BIM model according to the type of simulated vehicle, obtain a set of mobile route use cases, and automatically mine and generate a set of mobile route use cases covering various typical driving conditions according to the type of vehicle selected by the user and the BIM road model, so as to construct a comprehensive driving behavior evaluation sample space. A virtual driving module is used to send the set of mobile route use cases and the three-dimensional area of the BIM model to the associated VR driving simulation cabin of the type of simulated vehicle, and remind the user to enter the associated VR driving simulation cabin to start simulated driving, obtain the three-dimensional dynamic time series diagram of the vehicle and the user heart rate monitoring time series information, and obtain a data set for driving behavior analysis. A safety scoring module is used to receive the user's driving safety score through the client, so as to obtain the user's subjective driving safety score for different routes, which is used as a reference for measuring the driving experience. A risk assessment module is used to perform safety assessment based on the three-dimensional dynamic time series diagram of the vehicle, the user heart rate monitoring time series information and the user's driving safety score through a driving risk assessment network, obtain a set of driving risk evaluation values, and quantitatively evaluate the driving safety risk level of the current traffic engineering design scheme. A safety identification module is used to, when the set of driving risk evaluation values is less than the driving risk evaluation threshold, perform safety identification on the three-dimensional area of the BIM model and then send it to the traffic engineering design end. If the requirements are met, safety identification is performed on the scheme and feedback is given to the designers to complete the safety verification, so as to realize the targeted assessment of the driving risk of the design scheme, guide the optimization design of traffic engineering, and improve the safety of traffic engineering design.

[0105] Embodiment 2:

[0106] Please refer to Figure 2 , Figure 2 which is a schematic diagram of an embodiment of the electronic device provided by the embodiment of the present invention. As Figure 2 shown, an electronic device 200 provided by an embodiment of the present invention includes a memory 210, a processor 220, and a first computer program 211 stored on the memory 210 and executable on the processor 220. When the processor 220 executes the first computer program 211, it implements an interactive design and simulation system combining traffic engineering BIM and VR technologies.

[0107] Embodiment 3:

[0108] Please refer to Figure 3 , Figure 3Schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. As Figure 3 shown, this embodiment provides a computer-readable storage medium 300, on which a second computer program 311 is stored. When the second computer program 311 is executed by a processor, it implements an interactive design and simulation system that combines traffic engineering BIM and VR technologies.

[0109] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0110] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0112] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the functions specified in one Figure 1One process or multiple processes and / or boxes Figure 1 Steps of functions specified in one box or multiple boxes.

[0114] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic inventive concept.

[0115] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. An interactive design and simulation system combining traffic engineering BIM and VR technologies, characterized in that, include: An interactive configuration module, configured to display a traffic engineering BIM model and a simulation configuration menu on a user terminal and prompt the user to select a simulation configuration, thereby obtaining user feedback information, wherein the user feedback information includes the BIM model 3D area and the simulated vehicle type; A route mining module, configured to perform network frequency mining on the three-dimensional area of the BIM model according to the type of simulated transportation vehicle to obtain a set of mobile route use cases; a virtual driving module, configured to transmit the movement route use case set and the three-dimensional BIM model area to a VR driving simulation chamber associated with the simulated vehicle type, and to prompt a user to enter the associated VR driving simulation chamber to start simulated driving, thereby obtaining a three-dimensional dynamic timing diagram of the vehicle and user heart rate monitoring timing information; A safety scoring module, configured to receive a user's driving safety score via a user terminal; a risk assessment module configured to perform a safety assessment based on the three-dimensional dynamic time sequence diagram of the vehicle, the user's heart rate monitoring time sequence information, and the user's driving safety score through a driving risk assessment network to obtain a driving risk evaluation value set; A safety identification module, wherein when the driving risk assessment value set is less than a driving risk assessment threshold, the safety identification module is used to perform a safety identification on the three-dimensional area of the BIM model and send the identification to the traffic engineering design end; The execution steps of the route mining module include: Using the BIM model 3D area as a background constraint and the simulated vehicle type as a foreground constraint, collecting a set of compliant driving samples online, wherein the compliant driving sample set includes a movement route record dataset; Performing frequent coefficient identification on the mobile route record data set to obtain a route frequent coefficient set; sorting the set of mobile route use cases whose frequency coefficients are greater than or equal to a frequency coefficient threshold value from the mobile route record data set; The execution steps of the route mining module further include: Performing pairwise distance marking on the movement route record data set to obtain a movement route distance calibration data set; extracting first movement route record data according to the movement route record data set; Based on the movement route distance calibration dataset, counting the number of routes whose movement route distance deviations between the movement route record dataset and the first movement route record dataset are less than a movement route distance deviation threshold, and counting the ratio of the number of routes to the total number of routes in the movement route record dataset as a first movement route frequency coefficient; The first moving route frequency coefficient is added to the route frequency coefficient set.

2. The system according to claim 1, wherein The execution steps of the route mining module also include: Construct a moving route distance calibration function: in, Represents the first dimension coordinate of the i-th point on the path P1, Represents the second-dimensional coordinate of the i-th point on path P1, Represents the third-dimensional coordinate of the i-th point on path P1, Represents the first dimension coordinate of the j-th point on path P2, Represents the second-dimensional coordinate of the j-th point on path P2, Represents the third-dimensional coordinate of the j-th point on path P2, For path P2 and The nearest point, Represents the direction vector between the jth point on path P2 and the next point, Represents the direction vector between the i-th point and the next point on the path P1, θ ij Characterizes the angular difference between two direction vectors determined by calculating the dot product and modulus of the two direction vectors. ΔL represents the length deviation between path P1 and path P2. ij Characterization and The Euclidean distance, M represents the total number of points in path P2, N represents the total number of points in path P1, λ represents the weight coefficient of direction difference, which is used to adjust the contribution of direction difference in the total deviation, μ represents the weight coefficient of path length deviation, which is used to adjust the contribution of path length deviation in the total deviation, w ij Characterization and Importance weight in path deviation calculation; The moving route distance calibration function is used to perform pairwise distance marking on the moving route record data sets to obtain a moving route distance calibration data set.

3. The system according to claim 1, wherein: The execution steps of the route mining module also include: Collecting a first candidate compliant driving sample through an Internet connection, wherein the first candidate compliant driving sample includes a driving scene record model and a vehicle record type; When the vehicle record type is the same as the simulated vehicle type, the BIM model 3D area is input into a first feature extraction channel of a background comparison network to obtain a first feature extraction result, and the driving scene record model is input into a second feature extraction channel of the background comparison network to obtain a second feature extraction result, wherein the first feature extraction channel and the second feature extraction channel are twin convolutional neural networks; Inputting the first feature extraction result and the second feature extraction result into a feature comparison channel to obtain a feature deviation value; When the feature deviation value is less than or equal to a feature deviation threshold, the first to-be-selected compliant driving sample is added to the compliant driving sample set.

4. The system according to claim 3, wherein: The execution step of the route mining module further includes: updating the first to-be-selected compliant driving sample when the vehicle record type is different from the simulated vehicle type, or the feature deviation value is greater than the feature deviation threshold.

5. The system according to claim 1, wherein: The driving risk assessment network includes a balance risk assessment channel, an emotion risk assessment channel, and a fully connected channel. The execution steps of the risk assessment module include: Processing the three-dimensional dynamic time sequence diagram of the vehicle through the balance risk assessment channel to obtain a balance risk assessment value; The emotional risk assessment channel is used to count the proportion of time domain steps greater than or equal to the heart rate threshold in the user's heart rate monitoring time series information, and set it as an emotional risk assessment value; Setting a first weight for the balance risk assessment value, a second weight for the emotional risk assessment value, and a third weight for the user's driving safety score, wherein the sum of the second weight and the third weight is less than the first weight; Through the fully connected channel, weighted fitting is performed on the normalized data of the balance risk assessment value, the emotional risk assessment value, and the user's driving safety score to obtain a driving risk assessment value, which is added to the driving risk assessment value set.

6. The system according to claim 5, wherein The balance risk assessment channel includes a turbulence amplitude feature attention extraction layer, a turbulence frequency feature attention extraction layer and a balance risk assessment layer. The execution steps of the risk assessment module also include: Processing the three-dimensional dynamic time sequence diagram of the vehicle through the bump amplitude feature attention extraction layer to extract bump amplitude time sequence information; Processing the three-dimensional dynamic time sequence diagram of the vehicle through the turbulence frequency feature attention extraction layer to extract turbulence frequency time sequence information; Through the balance risk assessment layer, the proportion of time domain steps in which the turbulence amplitude in the turbulence amplitude time series information is greater than or equal to the turbulence amplitude threshold, or / and the proportion of time domain steps in which the turbulence frequency in the turbulence frequency time series information is greater than or equal to the turbulence frequency threshold are counted and set as the balance risk assessment value.

7. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby realizing an interactive design and simulation system combining BIM and VR technology for traffic engineering as described in any one of claims 1 to 6.

8. A non-transitory computer-readable storage medium, characterized in that, The computer software program is stored in the storage medium, and when the computer software program is executed by a processor, it implements an interactive design and simulation system combining traffic engineering BIM and VR technologies as described in any one of claims 1-6.

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