Digital twin system

Abstract

The invention provides a digital twinning system which comprises an acquisition module, a digital twinning simulation module and a road condition monitoring module, the acquisition module is used for acquiring attribute information of a traffic network and road traffic information and sending the attribute information and the road traffic information to the digital twinning simulation module, and the digital twinning simulation module performs digital twinning simulation; and the road condition monitoring module is used for performing digital twinborn real-time display on the digital twinborn simulation scene corresponding to the attribute information of the traffic network and the acquired road traffic information, thereby realizing real-time monitoring on the digital twinborn simulation scene. Attribute information of a traffic network and road traffic information are acquired through the acquisition module, digital twinning simulation is performed through the digital twinning simulation module, and finally digital twinning real-time display is performed on a digital twinning simulation scene through the road condition monitoring module, so that a virtual scene of the intelligent networked automobile is closer to a real scene, and the real-time display of the intelligent networked automobile is realized. And the test efficiency, the verification sufficiency and the validity are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of digital twin intelligent transportation technology, and in particular to a digital twin system. Background Art

[0002] With the advancement of science and technology, the technology of intelligent connected vehicles is rapidly iterating, the business model is constantly innovating, and the application barriers are constantly breaking through, showing a new development trend. However, the testing methods and systems for intelligent connected vehicles are not yet mature. At present, the safety and functional testing technology for intelligent connected vehicles only relies on traditional physical sites or simulation methods, which has low testing efficiency, insufficient verification, high investment costs, imperfect indicators, and high safety risks, which restricts the development of intelligent connected vehicles. Summary of the invention

[0003] Based on this, the main purpose of the present invention is to provide a digital twin system to solve at least one of the above problems.

[0004] To achieve the above object, the present invention provides a digital twin system, comprising: A collection module is used to collect the attribute information of the traffic network and collect road traffic information; A digital twin simulation module is communicatively connected to the acquisition module, the digital twin simulation module is used to receive the attribute information of the traffic network and the road traffic information acquired by the acquisition module, and perform digital twin simulation according to the attribute information of the traffic network and the road traffic information, the digital twin simulation includes infrastructure twin and dynamic object twin; A road condition monitoring module is communicatively connected to the digital twin simulation module, and the road condition monitoring module is used to display the digital twin simulation scene corresponding to the attribute information of the traffic network and the collected road traffic information in real time as a digital twin.

[0005] Preferably, the infrastructure twin includes digital modeling of road network structure, digital modeling of road network rules and digital modeling of new transportation infrastructure.

[0006] Preferably, the digital modeling of the road network structure includes road section modeling, intersection modeling, sub-road section modeling and lane connector modeling.

[0007] Preferably, the digital modeling of road network rules includes road section speed limit modeling, sign marking modeling, prohibition sign modeling, and priority and priority rule modeling.

[0008] Preferably, the digital modeling of new transportation infrastructure includes signal machine modeling, checkpoint equipment modeling, high-definition video equipment modeling and radar equipment modeling.

[0009] Preferably, the dynamic object twin includes vehicle feature modeling, driving behavior modeling, space-time fusion calculation, vehicle insertion modeling and vehicle control modeling.

[0010] Preferably, the digital twin display includes three-dimensional visualization and operation index display, the three-dimensional visualization includes infrastructure visualization and dynamic object visualization, and the operation index display includes dynamic and static information display in the entire road network area and status data display of vehicle operation.

[0011] Preferably, the acquisition module includes an attribute information acquisition unit and a road traffic information acquisition unit. The attribute information acquisition unit is used to acquire attribute information of road intersections and attribute information of road sections. The road traffic information acquisition unit is used to scan the detection area by transmitting microwaves through radar to acquire road traffic information.

[0012] Preferably, the digital twin simulation module includes a working condition generation unit and a resistance correction unit that are communicatively connected. The working condition generation unit is communicatively connected to the acquisition module. The working condition generation unit is used to receive the attribute information of the traffic network and the road traffic information collected by the acquisition module and perform digital twin simulation to obtain the working condition of the actual travel scenario. The resistance correction unit is used to correct the sliding resistance of the sliding test under the reference state according to the weather information of the actual travel scenario to obtain the road resistance of the actual travel scenario.

[0013] Preferably, the digital twin simulation module further includes a comparison optimization unit and a simulation unit that are communicatively connected, wherein the comparison optimization unit is used to compare the wheel end energy corresponding to the road resistance of the actual travel scene with the wheel end energy corresponding to the test condition of the actual travel, and continuously optimize the road resistance of the actual travel scene according to the comparison result; The simulation unit is communicatively connected to the road condition monitoring module, and the simulation unit digitally processes the optimized working conditions and road resistance of the actual travel scenario to form a digital twin simulation scenario.

[0014] The advantages of the technical solution of the present invention are as follows: the acquisition module is used to collect the attribute information of the traffic network and the road traffic information, and then the digital twin simulation module performs digital twin simulation based on the attribute information of the traffic network and the road traffic information, wherein the digital twin simulation includes infrastructure twins and dynamic object twins, and finally the road condition monitoring module is used to perform real-time digital twin display of the digital twin simulation scene, thereby realizing real-time monitoring of the digital twin simulation scene, and further being able to predict future traffic conditions, and analyze congestion conditions, required travel time, etc. through the prediction results, thereby making the virtual scene of the intelligent connected vehicle closer to the real scene, effectively improving the test efficiency, verification adequacy and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the devices shown in these drawings without paying creative work.

[0016] Figure 1 is a schematic diagram of a digital twin system according to an embodiment; Figure 2 is a schematic diagram of a collection module of an embodiment; Figure 3 Schematic diagram of a digital twin simulation module according to an embodiment; Among them, 100, acquisition module; 110, attribute information acquisition unit; 120, road traffic information acquisition unit; 200, digital twin simulation module; 210, working condition generation unit; 220, resistance correction unit; 230, comparison optimization unit; 240, simulation unit; 300, road condition monitoring module.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly. In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes A technical solution, B technical solution, and A and B technical solutions that meet both requirements. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0020] like Figure 1 and Figure 2 As shown, a digital twin system includes an acquisition module 100, a digital twin simulation module 200 and a road condition monitoring module 300. The acquisition module 100 is used to collect attribute information of the traffic network and collect road traffic information; the digital twin simulation module 200 is communicated with the acquisition module 100, and the digital twin simulation module 200 is used to receive the attribute information of the traffic network and the road traffic information collected by the acquisition module 100, and perform digital twin simulation according to the attribute information of the traffic network and the road traffic information. The digital twin simulation includes infrastructure twins and dynamic object twins; the road condition monitoring module 300 is communicated with the digital twin simulation module 200, and the road condition monitoring module 300 is used to display the digital twin simulation scene corresponding to the attribute information of the traffic network and the collected road traffic information in real time, thereby realizing real-time monitoring of the digital twin simulation scene.

[0021] The acquisition module 100 collects the attribute information of the traffic network and the road traffic information, and then the digital twin simulation module 200 performs digital twin simulation based on the attribute information of the traffic network and the road traffic information, wherein the digital twin simulation includes infrastructure twins and dynamic object twins. Finally, the road condition monitoring module 300 performs real-time digital twin display of the digital twin simulation scene, thereby realizing real-time monitoring of the digital twin simulation scene, and further predicting future traffic conditions. The prediction results can be used to analyze the congestion situation, the required travel time, etc., thereby making the virtual scene of the intelligent connected vehicle closer to the real scene, effectively improving the test efficiency, verification adequacy and effectiveness.

[0022] Specifically, the attribute information of the traffic road network includes the attribute information of the road intersection and the attribute information of the road section, wherein the attribute information of the road intersection includes at least one of the following: the name of the city to which the road intersection belongs, the identification code of the city to which it belongs, the name of the entrance section, the name of the exit section, the name of the road intersection, the attributes of the road intersection, the road node identification corresponding to the electronic map, the road node map sheet number, the map sheet number to which the entrance section belongs, the section identification to which the entrance section belongs, the map sheet number to which the exit section belongs, the section identification to which the exit section belongs, the road direction to which the entrance section belongs, the road direction to which the exit section belongs, the entry angle of the entrance section, the exit angle of the exit section, and the geographical area to which it belongs; the attribute information of the road section includes at least one of the following: the name of the city to which the road section belongs, the identification code of the city to which it belongs, the road section name, the road node identification corresponding to the electronic map, the road node map sheet number, the map sheet number, the section identification, the road direction to which the entrance section belongs, the road direction to which the exit section belongs, the entry angle of the entrance section, the exit angle of the exit section, and the geographical area to which it belongs.

[0023] Specifically, road traffic information includes at least one of the following: traffic light status information, traffic sign information, traffic marking information, traffic flow information, electronic checkpoint information, video surveillance information, etc., among which traffic light status information includes information on whether the traffic light is currently red, green or yellow, and the time when the light changes; traffic sign information includes warning signs, prohibition signs, instruction signs, guide signs, tourist area signs and road construction safety signs; traffic marking information includes road red lines, road center lines, lane markings, ground markings, sidewalks, etc.

[0024] refer to Figure 2 The acquisition module 100 includes an attribute information acquisition unit 110 and a road traffic information acquisition unit 120. The attribute information acquisition unit 110 is used to acquire the attribute information of the road intersection and the attribute information of the road section. The road traffic information acquisition unit 120 is used to scan the detection area by transmitting microwaves through radar to acquire road traffic information.

[0025] Specifically, the attribute information acquisition unit 110 includes a camera, which collects images of the road ahead and takes 7-10 photos per second. The attribute information acquisition unit 110 also includes a 360-degree panoramic image composed of three SLR cameras + a 120-degree fisheye lens to cooperate with the acquisition system to collect the attribute information of the road intersection and the attribute information of the road section. The radar of the road traffic information acquisition unit 120 is a 32-line laser radar, which is responsible for collecting point cloud data. The laser radar is placed at a certain angle on the roof in order to collect as much road information as possible rather than sky information. In addition, the acquisition module 100 also includes a GPS, which is responsible for recording the vehicle GPS trajectory.

[0026] Infrastructure twins include digital modeling of road network structure, digital modeling of road network rules, and digital modeling of new transportation infrastructure.

[0027] The digital modeling of the road network structure includes road segment modeling, intersection modeling, sub-segment modeling and lane connector modeling. Specifically, the roads in the road network are divided into sections by intersections, and the intersections between roads are broken into sections. The road segment model mainly includes longitude and latitude coordinates, section type, section length, number of lanes, lane width, slope and cross-section form. The road segment is directional, and the road segment model can describe the road network topology designed for vehicle driving; the intersection is the intersection of two road segments. Based on the centerline of the road, an intersection model including longitude and latitude, intersection type, length, width, etc. is established, and the connection relationship between intersections and sections is calculated by expressing the traffic organization of intersections, sections and lanes; sections need to be interrupted at the place where the traffic organization changes (the intersection of the road and the flat intersection, the location where the lane ends in the road, the location where the number of lanes changes, and the location where the lane connectivity changes) to form sub-segments. The traffic organization in the same sub-segment remains unchanged, and a lane connector model needs to be set between sub-segments. The sub-section model needs to include lane widening forms, widening gradient length, etc. The lane connector describes the connection relationship between lanes, including the lane flow direction of widening or merging at the section connection and the turning relationship of the lane at the intersection. Based on the connection relationship between adjacent lanes in the section and the connection relationship between lanes at the entrance and exit of the intersection, the allowed turns of all lanes in the intersection are clarified, and the connection relationship at the lane level is established.

[0028] The digital modeling of road network rules includes the modeling of road section speed limit, indicator line modeling, prohibition sign modeling, and priority and priority rule modeling. Specifically, the road section speed limit is an attribute of the road section, which describes the maximum speed allowed on the road section. The expected speed distribution of vehicles is based on the road section speed limit. According to the national standard requirements of the indicator line, combined with the field survey, based on the constructed road network structure, the corresponding indicator line model is established, and the editing function of the sign and marking is provided. The signs and markings involved mainly include: crossable lane dividing lines, variable lanes, tidal lanes, left turn waiting area lines, pedestrian crossing zebra crossings, and intersection guide lane lines; According to the national standard requirements of prohibition signs, combined with the field survey, based on the constructed road network structure, the corresponding prohibition sign model is established, and the editing function of the prohibition sign is provided. The signs and markings involved mainly include: no left turn signs, no right turn signs, no U-turn signs, no crossing lane dividing lines, mesh line no stopping areas, time-limited traffic signs, etc.; priority and priority rules indicate the order of traffic flow when lanes intersect. For example, when two sub-sections meet, when two directions are changing at an unsignalized intersection, when there is a conflict between going straight and turning left, and when there is a conflict between right-turning motor vehicles and pedestrians and non-motor vehicles, priority rules need to be set to ensure that traffic flows efficiently and orderly.

[0029] The digital modeling of new traffic infrastructure includes signal machine modeling, checkpoint equipment modeling, high-definition video equipment modeling and radar equipment modeling. Specifically, based on the distribution of signal control equipment on the road network, a signal machine model is established, which mainly includes the signal machine number, location, manufacturer and timing phase information. Based on the road network structure model, the topological relationship between the signal machine equipment and the road section, intersection, lane and lane connector is calculated. For example, the phase of the timing scheme is matched with the entrance road section and its turning corresponding lane according to the intersection where the signal machine is located; based on the layout of the checkpoint equipment on the road network, a checkpoint equipment model is established to represent the correspondence between the equipment and the road section, intersection, lane and lane connector. The model parameters include equipment number and statistical interval. For example, according to the location of the road section where the checkpoint equipment is located, the passing vehicle data is associated with the lane and turning relationship to provide support for lane-level traffic condition data analysis; based on the high-definition video Based on the layout of radar equipment on the road network, a radar equipment model is established to represent the correspondence between the equipment and road sections, intersections, lanes and lane connectors. For example, according to the location of the road section where the high-definition video equipment is located, the passing vehicle data is associated with the lane and the turning relationship, which provides support for the analysis of individual vehicle data. Based on the layout of radar equipment on the road network, a radar equipment model is established to represent the correspondence between the equipment and road sections, intersections, lanes and lane connectors. For example, according to the location of the road section where the radar equipment is located, the passing vehicle data is associated with the lane and the turning relationship, which provides support for the analysis of individual vehicle data.

[0030] In this embodiment, the infrastructure twin is the basis for building a traffic twin system, completing the digital expression of the physical infrastructure, including the appearance structure, physical dimensions, basic information, construction information, etc. of traditional infrastructure such as road length, number of lanes, lane functions, parking lots, installation poles, roadside cabinets, signal machines, electric police equipment, radar equipment, and new infrastructure, thereby constructing a centimeter-level precise digital space, where the overall accuracy of this application is <= 20 cm.

[0031] Dynamic object twins include vehicle feature modeling, driving behavior modeling, spatiotemporal fusion calculation, vehicle injection modeling, and vehicle control modeling. Specifically, vehicle feature modeling extracts vehicle feature-related parameters based on vehicle passing data, and performs vehicle feature modeling, mainly including: vehicle type (car, bus, etc.), vehicle color, license plate number (vehicle unique ID), body length, and vehicle maximum speed. Provide a variety of vehicle models such as large trucks, buses, SUVs, sedans, and police cars to build two- and three-dimensional rendering models of each vehicle type; driving behavior modeling In order to carefully depict the operating status of motor vehicles on the road network, it is necessary to establish and correct the microscopic model of vehicle driving. The microscopic model mainly refers to the vehicle following model and the lane changing model, and its parameters directly affect the interaction characteristics between individual motor vehicles. The parameters of the following model mainly include parking spacing, headway, expected acceleration and deceleration, etc. The parameters of the lane changing model depend on the model used, which can be mainly summarized as lane changing urgency and driver perfection. The established driving behavior model will be used in the simulation engine to simulate the operating status of traffic flow; the spatiotemporal fusion calculation accesses the traffic flow detection data from multiple sources such as checkpoint videos and radars with full sample volume and full time period in the area. A series of analyses such as data extraction, fusion, reduction, and association are carried out to address the differences in format and type of traffic flow parameter information obtained from different channels, as well as information missing and erroneous information from the same detection equipment, so as to obtain more accurate road conditions and richer traffic travel information (such as travel demand and travel routes), more comprehensive grasp of the traffic travel situation and travel characteristics in the region, and synchronous processing of multiple IoT sensing devices in time and space. By accessing and processing the operation data of connected vehicles, the platform can view the relevant attributes of specific vehicles in real time, including basic vehicle information such as the vehicle license plate number, vehicle type, body color, cruising range, instantaneous speed, and estimated time of arrival at the next intersection, vehicle driving direction, and other operation information, to achieve vehicle-road collaborative operation status monitoring, and better serve the dispatching management of operating vehicles and signal control optimization; vehicle spreading modeling is based on historical vehicle reconstruction trajectory or real-time vehicle passing detection data, and vehicles are spread into the road network model constructed in the three-dimensional high-precision map. The vehicle spreading model mainly includes parameters such as the road section location, lane number, departure time, and departure speed of the vehicle departure. In the constructed road network structure, the appearance of traffic participants in the twin world is consistent with the time and space of the real world through the time and space correspondence between the vehicle spreading model and the vehicle passing data; vehicle control modeling is based on the time difference between the appearance of the vehicle in the adjacent two vehicle passing data, and combined with radar tracking data, a vehicle control model is constructed at the motor vehicle operation intersection in the twin world, mainly including controlling the vehicle's running speed and lane changing behavior, accurately depicting the traffic flow operation state, and realizing vehicle operation tracking in the twin world. In real-time twinning, smooth transition of vehicle operation can be achieved by interpolating the perception fusion data of each frame, which can be applied to the vehicle control model.

[0032] The digital twin display includes three-dimensional visualization and operation indicator display. The three-dimensional visualization includes infrastructure visualization and dynamic object visualization. The operation indicator display includes dynamic and static information display in the entire road network area and vehicle operation status data display.

[0033] Specifically, infrastructure visualization is based on the high-precision map of the GIS system, and displays the basic objects such as sections, intersections and lanes of the system's road traffic network, as well as road network signs and markings, and traffic equipment in a three-dimensional manner through front-end rendering. The visualization call of the road network model includes the integrated display of two-dimensional and three-dimensional data, and provides visualization calls for static scenes, including: roads, signboards, lamp poles, electric police poles, signal lights, guardrails, buildings, etc.; dynamic object visualization loads lane-level high-precision maps according to actual conditions, which can display intersection channelization and static traffic organization. Based on the uniqueness and continuity of the time and space of traffic participating objects, the data of the front-end perception equipment is processed in time and space, and combined with the relevant data of connected vehicles, the individual-level real-time trajectory information is sprinkled into the high-precision map, including the position coordinates of each frame of the vehicle, vehicle category, vehicle color and other parameters, to achieve holographic intersection and section monitoring. At the macro level, it can present the real-time status of vehicles in the area, real-time high-definition road conditions in the entire area, and the distribution of intelligent connected vehicles in the road network. At the micro level, it can observe the microscopic operation trajectories of vehicles in intersections and monitor incidents, accidents, violations and safety behaviors at intersections, including speeding, illegal lane changes, and driving in the wrong direction.

[0034] Specifically, the operation indicators display the traffic operation status data based on real-time twin and simulation, and present the traffic flow operation status data and its change process in the three-dimensional twin world in a macro, medium and micro multi-scale all-round visualization. The macro level displays the dynamic and static information of the entire road network area. The static information includes the total kilometers of the road network, the number of intersections in the road network, etc. The dynamic operation information includes the number of vehicles in the network, the traffic flow in and out of the area, the average travel speed and the average delay. At the meso level, the traffic capacity of the road section within the selected time and space range, the road section load, the queue length of the road section and the average operation speed of the road section can be called and displayed. At the micro level, the traffic operation indicators at the lane level, turn level and intersection level can be called and displayed at the level of a single intersection. Lane-level indicators mainly include traffic flow, saturation, average speed, time occupancy, space occupancy, headway, headway distance, delay time, queue length and number of stops by vehicle type. Turn-level indicators mainly include delay time, average speed, headway, headway, number of stops, traffic status and queue length. The intersection-level indicators mainly include parameters such as capacity, flow, saturation, average driving speed, time occupancy, as well as intersection imbalance, congestion, overflow, average delay, etc.; the individual level mainly displays the status data of vehicle operation, including individual vehicle travel information and individual vehicle trajectory data. The basic information of individual vehicles such as license plate number, body color, usage nature, license plate type, etc., as well as vehicle travel information such as departure point, destination, travel time, trip distance, etc. can be called. Vehicle trajectory data mainly includes parameters such as matching road network object identification, trajectory vehicle time, trajectory vehicle speed, geographic location, north angle, and vehicle characteristics. The interface can be called to view the travel trajectory of individual vehicles in the digital road network at the refined lane level, and dynamically display the vehicle driving status, intersection lights, and the operation of surrounding vehicles.

[0035] refer to Figure 3The digital twin simulation module 200 includes a working condition generation unit 210 and a resistance correction unit 220 which are connected in communication. The working condition generation unit 210 is connected in communication with the acquisition module 100. The working condition generation unit 210 is used to receive the attribute information of the traffic network and the road traffic information collected by the acquisition module 100 and perform digital twin simulation to obtain the working condition of the actual travel scene. The resistance correction unit 220 is used to correct the sliding resistance of the sliding test under the reference state according to the weather information of the actual travel scene to obtain the road resistance of the actual travel scene. Specifically, the specific method for optimizing and adjusting the working condition of the actual travel scene is: by comparing the working condition characteristic parameters of the working condition generated by the actual travel scene with the working condition data collected by the road test, the generated working condition is optimized to make the generated working condition close to the actual working condition; the specific method for optimizing and adjusting the road resistance of the actual travel scene is: analyzing the influence of different weather factors on the resistance, comparing the wheel end energy corresponding to the road resistance of the actual travel scene under the same working condition and the wheel end energy difference of the vehicle test, and adjusting the rolling resistance correction coefficient under different road types and weather conditions.

[0036] refer to Figure 3 The digital twin simulation module 200 also includes a comparison and optimization unit 230 and a simulation unit 240 that are communicatively connected. The comparison and optimization unit 230 is used to compare the wheel-end energy corresponding to the road resistance of the actual travel scene with the wheel-end energy corresponding to the test working condition of the actual travel, and continuously optimize the road resistance of the actual travel scene according to the comparison result; the simulation unit 240 is communicatively connected to the road condition monitoring module 300, and the simulation unit 240 digitizes the optimized working condition and road resistance of the actual travel scene to form a digital twin simulation scene.

[0037] The present invention first obtains the road conditions and weather conditions in the actual travel scenario through the navigation and weather APP, and obtains the actual travel working conditions according to the road conditions in the actual travel scenario and the driving behavior of the driver. The working condition generation takes the user's driving behavior into consideration, which reflects the user differences, that is, there are differences in the travel conditions generated by different users, and then different energies are consumed. The vehicle resistance is corrected according to the weather information, and the road, environment and other factors of the actual travel scenario are digitally converted to the model end to realize the digital twin of the road in the actual travel scenario, which can be applied to the actual road vehicle economy evaluation and path planning in the vehicle research and development process. The whole process has the technical characteristics of science, objectivity, rigor, speed, low cost, etc., which can support the energy consumption evaluation of the actual travel scenario in the vehicle research and development process, improve the vehicle experience of new energy vehicle products, and enhance the market competitiveness of products; at the same time, the method has the characteristics of high precision, low cost, fast response, good experience, etc.; this method has a wide range of application and can be applied to different cities and different routes.

[0038] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent device transformations made using the contents of the present invention's specification and drawings, or direct / indirect applications in other related technical fields, under the inventive concept of the present invention are included in the patent protection scope of the present invention.

Claims

1. A digital twin system, characterized in that: include: A collection module is used to collect the attribute information of the traffic network and collect road traffic information; A digital twin simulation module is communicatively connected to the acquisition module, the digital twin simulation module is used to receive the attribute information of the traffic network and the road traffic information acquired by the acquisition module, and perform digital twin simulation according to the attribute information of the traffic network and the road traffic information, the digital twin simulation includes infrastructure twin and dynamic object twin; A road condition monitoring module is communicatively connected to the digital twin simulation module. The road condition monitoring module is used to display the digital twin simulation scene corresponding to the attribute information of the traffic network and the collected road traffic information in real time in a digital twin, thereby realizing real-time monitoring of the digital twin simulation scene.

2. The digital twin system according to claim 1, characterized in that: The infrastructure twin includes digital modeling of road network structure, digital modeling of road network rules and digital modeling of new transportation infrastructure.

3. The digital twin system according to claim 2, characterized in that: The digital modeling of the road network structure includes road segment modeling, intersection modeling, sub-road segment modeling and lane connector modeling.

4. The digital twin system according to claim 2, characterized in that: The digital modeling of road network rules includes road speed limit modeling, sign marking modeling, prohibition sign modeling, and priority and priority rule modeling.

5. The digital twin system according to claim 2, characterized in that: The digital modeling of new transportation infrastructure includes signal machine modeling, checkpoint equipment modeling, high-definition video equipment modeling and radar equipment modeling.

6. The digital twin system according to claim 1, characterized in that: The dynamic object twin includes vehicle feature modeling, driving behavior modeling, space-time fusion calculation, vehicle insertion modeling and vehicle control modeling.

7. The digital twin system according to claim 1, characterized in that: The digital twin display includes three-dimensional visualization and operation index display. The three-dimensional visualization includes infrastructure visualization and dynamic object visualization. The operation index display includes dynamic and static information display in the entire road network area and vehicle operation status data display.

8. The digital twin system according to claim 1, characterized in that: The acquisition module includes an attribute information acquisition unit and a road traffic information acquisition unit. The attribute information acquisition unit is used to acquire the attribute information of the road intersection and the attribute information of the road section. The road traffic information acquisition unit is used to scan the detection area by transmitting microwaves through radar to acquire road traffic information.

9. The digital twin system according to claim 1, characterized in that: The digital twin simulation module includes a working condition generation unit and a resistance correction unit that are communicatively connected. The working condition generation unit is communicatively connected to the acquisition module. The working condition generation unit is used to receive the attribute information of the traffic network and the road traffic information collected by the acquisition module and perform digital twin simulation to obtain the working condition of the actual travel scenario. The resistance correction unit is used to correct the sliding resistance of the sliding test under the reference state according to the weather information of the actual travel scenario to obtain the road resistance of the actual travel scenario.

10. The digital twin system according to claim 9, characterized in that: The digital twin simulation module also includes a comparison optimization unit and a simulation unit that are communicatively connected, wherein the comparison optimization unit is used to compare the wheel end energy corresponding to the road resistance of the actual travel scene with the wheel end energy corresponding to the test condition of the actual travel, and continuously optimize the road resistance of the actual travel scene according to the comparison result; The simulation unit is communicatively connected to the road condition monitoring module, and the simulation unit digitally processes the optimized working conditions and road resistance of the actual travel scenario to form a digital twin simulation scenario.

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