Intersection traffic digital twin method and device based on video and coordinate fusion and storage medium

Abstract

The application provides an intersection traffic digital twin method and device based on video and coordinate fusion and a storage medium. The method comprises the following steps: accessing a monitoring video stream of a target intersection and a real-time running light state of a signal machine; converting pixel coordinates of a traffic object recognized in a video picture to coordinates in a bird's-eye view coordinate system by using a projection conversion coefficient calculated in advance according to corresponding points of a video picture feature point and a geographic coordinate map; calculating a projection distance of the traffic object along a lane direction to a stop line based on the bird's-eye view coordinates and a preset vector representing a lane direction; and finally, combining the real-time running light state, the traffic object type and the projection distance, and performing fusion display in a digital twin intersection scene constructed based on the geographic coordinate map. The application solves the problems of disordered direction and information fragmentation existing in the traditional multi-path video monitoring mode, realizes unified, accurate and visualized management and control of the intersection traffic situation, and significantly improves the efficiency and intelligent level of traffic command and control.

Description

Technical Field

[0001] This application relates to the field of traffic control technology, specifically to a method for creating a digital twin of intersection traffic based on video and coordinate fusion, an electronic device, and a computer-readable storage medium. Background Technology

[0002] In the daily command and control of traffic signalized intersections in cities, traffic management personnel typically need to observe multiple surveillance video feeds from different angles simultaneously and make comprehensive judgments based on the real-time operating status (light status) of the traffic signals. This working mode has obvious drawbacks: First, multiple video feeds are scattered across different screens, resulting in a chaotic sense of location and making it difficult to quickly construct a spatial pattern of the overall traffic flow at the intersection in one's mind; second, the traffic objects (vehicles, pedestrians, etc.) in the video feeds are separate from the traffic light status and intersection channelization maps, making it impossible to intuitively and quantitatively perceive information such as vehicle queue length and position, thus affecting the accuracy and efficiency of control decisions.

[0003] Existing technologies lack an effective means to fuse, compute, and intuitively display multi-source heterogeneous information (video, lighting status, geographic information) under a unified spatiotemporal reference. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a digital twin method for intersection traffic based on video and coordinate fusion, an electronic device, and a computer-readable storage medium. This method accurately maps dynamic traffic objects in video footage onto a standard geographic coordinate map (bird's-eye view) and associates them with traffic light status, achieving an integrated, quantitative, and visual display of the intersection's operational status within a digital twin scenario. This significantly enhances the intuitiveness and scientific rigor of traffic command and control.

[0005] This application provides a method for creating a digital twin of intersection traffic based on video and coordinate fusion, characterized by the following steps: S1. Access the surveillance video stream and real-time operation status of traffic lights at the target intersection; S2. By pre-calculating the projection transformation coefficients based on the feature points of the video image and the corresponding points on the geographic coordinate map, the pixel coordinates of the traffic objects identified in the video image are transformed to the coordinates under the bird's-eye view coordinate system; S3. Based on the coordinates of the bird's-eye view and the preset vector representing the direction of the lane, calculate the projected distance of the traffic object from the lane direction to the stop line; S4. Combining the real-time operating light status, the type of traffic object, and the projection distance, a fusion display is performed in a digital twin intersection scene constructed based on a geographic coordinate map.

[0006] Furthermore, the projection transformation coefficients are obtained as follows: at least four sets of corresponding feature points are selected from the video frame and the corresponding geographic coordinate map, and their pixel coordinates image_pts and geodetic coordinates real_pts are obtained respectively. The homography matrix is ​​calculated using a function such as cv2.findHomography(image_pts, real_pts) in OpenCV as the projection transformation coefficients H.

[0007] Furthermore, the pixel coordinates of the traffic object are preferably taken from a point near the bottom center of its recognition frame, for example, at 3 / 4 of the coordinates of the lower left and upper right corners of the recognition frame, so as to make it closer to the contact point between the vehicle and the ground and improve the accuracy of mapping into the lane.

[0008] Furthermore, when calculating the projected distance, the coordinate points of the bird's-eye view are projected onto the vector representing the lane centerline, and the directed Euclidean distance from the projected point to the starting point of the vector (usually the stop line position) is calculated. A positive distance indicates that the vehicle is in front of the stop line, while a negative distance indicates that the vehicle has crossed the stop line.

[0009] Furthermore, in the integrated display of digital twin scenarios, different icons can be used to distinguish object types such as cars, buses, and pedestrians; the status of objects can be highlighted with different colors (such as blue for normal and red for abnormal) according to the traffic light status (red / green) and whether the vehicle crosses the line when the light is red; at the same time, the "right of way" status of each lane corresponding to each phase can be rendered in real time on the background of the channelization map with different background colors.

[0010] This application also provides an electronic device, including: a memory and a processor, wherein the memory stores computer program instructions for execution on the processor, and when the processor executes the computer program instructions, it implements the intersection traffic digital twin method based on video and coordinate fusion as described above.

[0011] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the intersection traffic digital twin method based on video and coordinate fusion as described above.

[0012] As described above, the beneficial effects of this invention are as follows: by unifying scattered video monitoring data into a precise geographic coordinate system through projection transformation, it achieves deep fusion and accurate mapping of traffic dynamic and static information in the digital twin space. Management personnel can intuitively grasp the real-time location of vehicles, queue length, traffic light status, and abnormal events at all intersections on a single map, realizing the transformation from "viewing multiple videos" to "viewing a single twin map," significantly improving traffic situational awareness, control decision-making efficiency, and intelligence level. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating a digital twin method for intersection traffic based on video and coordinate fusion, provided as an embodiment of the present invention.

[0014] Figure 2 This is a schematic diagram of the configuration interface for video screen annotation in an embodiment of the present invention.

[0015] Figure 3 This is a schematic diagram illustrating the correspondence between the CAD geographic coordinate map and the marked points in the video footage in an embodiment of the present invention.

[0016] Figure 4 This is a schematic diagram of the interface for configuring lane direction vectors in an embodiment of the present invention.

[0017] Figure 5 This is a schematic diagram of the interface for configuring a virtual detection coil in a video frame according to an embodiment of the present invention.

[0018] Figure 6 This is a schematic diagram of the configuration interface for setting the properties of the detection coil in an embodiment of the present invention.

[0019] Figure 7 This is an example of the main interface of the digital twin system of the present invention, showing a channelization diagram that integrates traffic objects and traffic light status, as well as a display area for historical timing schemes.

[0020] Figure 8 This is another example of the main interface of the digital twin system of the present invention, showing a channelization diagram that integrates traffic objects and traffic light status, as well as a real-time traffic evaluation index display area. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings.

[0022] This invention addresses the problem of inconsistent positioning and incomplete traffic control when simultaneously viewing multiple surveillance videos and traffic signal statuses at urban traffic light-controlled intersections. It proposes a digital twin method and system that integrates surveillance video and traffic signal status. This system achieves integrated display of traffic objects, traffic signal status, real-time scenario evaluation, and abnormal event monitoring and reporting through steps such as accessing video and traffic signal status, annotating video footage, reconstructing aerial photographs at a 1:1 scale in CAD and extracting coordinates from selected points, and configuring vectors and detection areas in the video footage. This significantly facilitates traffic command and control at urban traffic light-controlled intersections and improves urban traffic management.

[0023] Surveillance equipment includes electronic police cameras, checkpoints, traffic cameras, and radar-guided cameras, which are responsible for collecting real-time video streams from all directions of the intersection as a data source for traffic object identification.

[0024] Level 2 signal control system: refers to the signal controller at the intersection, which is responsible for providing real-time signal light status information and executing control commands.

[0025] Intelligent signal control optimization system: As a regional management platform, it is responsible for managing signal control intelligent agents and forwarding light status data and control commands.

[0026] The intelligent agent is the core execution unit of this invention. It is deployed at the edge or in the cloud and integrates functions such as video analysis, coordinate mapping, digital twin rendering, and intelligent decision-making.

[0027] See Figure 1 This invention provides a method for creating a digital twin of intersection traffic based on video and coordinate fusion, comprising the following steps: S1: Access the surveillance video stream and real-time operation status of traffic lights at the target intersection; Here, we access multi-source data to obtain the raw data needed to build a digital twin.

[0028] The system includes: Accessing the traffic signal's operational status: The traffic control agent configures the target traffic signal's network parameters (IP, account, password) and, through the intelligent traffic control optimization system, acquires the traffic signal's real-time operational status at a high frequency (e.g., per second), including the current phase, light color, and remaining time. Accessing surveillance video streams: The traffic control agent configures the streaming parameters for each surveillance device, stably accessing real-time video streams from all directions of the intersection (such as electronic police and checkpoint videos) to provide input for subsequent target recognition.

[0029] S2: By pre-calculating the projection transformation coefficients based on the feature points of the video image and the corresponding points on the geographic coordinate map, the pixel coordinates of the traffic objects identified in the video image are transformed to the coordinates under the bird's-eye view coordinate system; This is the core of achieving video-space and geospatial alignment, encompassing initial configuration and real-time computation. Specifically, the initial configuration phase: Video screen annotation and CAD coordinate extraction: Calibration is performed for each monitored direction (entrance lane). First, select an electronic alarm / checkpoint device in the system configuration interface. Then, the operator uses the mouse to select four highly recognizable and fixed feature points (such as...) in the video screen. Figure 2 As shown), its pixel coordinates image_pts are recorded. Meanwhile, in a CAD drawing that has achieved a 1:1 reconstruction of the intersection (such as...),... Figure 3 In the table shown, find the physical location that completely corresponds to the above 4 points and extract its precise geodetic coordinates real_pts (see Table 1 example).

[0030] Table 1 Calculate the bird's-eye view transformation coefficient H: Using the four corresponding sets of image_pts and real_pts mentioned above, calculate the homography transformation matrix H using a computer vision algorithm (e.g., calling the OpenCV function cv2.findHomography(image_pts, real_pts)). This matrix H represents the mathematical relationship for projecting video pixel coordinates onto a unified bird's-eye view coordinate system.

[0031] Configure lane vectors: To quantify vehicle positions, a directional reference must be defined for each lane or lane group. For example... Figure 4 As shown, in the configuration interface, starting from the stop line, draw an arrow along the center line of the lane in a direction away from the intersection. The starting and ending coordinates of this arrow define the vector used to calculate the distance (such as the "south to north" vector).

[0032] Configure the detection area: To support refined statistics and event detection, virtual detection coils can be drawn in the video frame (e.g., ...). Figure 5 Users can define the shape and position of the coil by dragging and dropping, and can set its properties (such as...). Figure 6 ), such as coil name, associated phase, detection type (vehicle, pedestrian, count, overflow) and target vehicle model.

[0033] Further, the poster real-time processing stage (projection conversion): Target recognition: AI analysis of real-time video streams accurately identifies traffic objects such as vehicles and pedestrians, along with their bounding boxes.

[0034] Pixel coordinate transformation: Determine the pixel coordinates of the target representative point: To improve mapping accuracy, take the lower left corner of the target recognition box. and the top right corner The point at 3 / 4 of the pixel coordinates is used as the representative coordinate (x, y), that is... .

[0035] Transform to bird's-eye view coordinates: Using a pre-calculated transformation coefficient H, the pixel coordinates (x, y) are converted to coordinates (X, Y) in the bird's-eye view coordinate system. The calculation relationship is (X, Y) = H · (x, y).

[0036] S3: Based on the coordinates of the bird's-eye view and the preset vector representing the direction of the lane, calculate the projected distance of the traffic object from the lane direction to the stop line; Here, geometric calculations are performed based on the results of step S2.

[0037] Project the coordinates P(X, Y) of the traffic object in the bird's-eye view onto the vector AB (starting point A(x1, y1), ending point B(x2, y2)) corresponding to its lane, and calculate the directed distance D from the traffic object along the lane direction to the stop line (starting point A). The specific calculation process is as follows: a) Calculate vectors AB, AP, and the square of the length of AB. .

[0038] b) Calculate the projection scale .

[0039] c) Calculate the coordinates of the projection point .

[0040] d) Calculate the Euclidean distance from the projection point to the starting point A. .

[0041] e) Determine the directed distance D: The calculation principle and example results can be found in the relevant description and Table 3 in the instruction manual.

[0042] S4: Combining the real-time running light status, the type of traffic object, and the projection distance, a fusion display is performed in a digital twin intersection scene constructed based on a geographic coordinate map.

[0043] Here, all the aforementioned processing results are visualized in an integrated manner.

[0044] In the digital twin main interface built based on high-precision CAD channelization maps (such as...) Figure 7 , Figure 8 (As shown in the background): Dynamic object drawing: Based on the type of traffic object (car, bus, truck, non-motorized vehicle, pedestrian), different icons are used to draw it at its actual bird's-eye view coordinates (X, Y).

[0045] Status and light status visualization: The color of objects is dynamically changed according to the status of traffic lights and vehicle behavior rules. For example, vehicles that cross the stop line during a red light are displayed as red (abnormal), while normal vehicles are displayed as blue. At the same time, the light color of each phase is rendered on the corresponding lane area (decision area) with different background colors (such as gray for red light and light green for green light).

[0046] Comprehensive information display: Real-time display of lane-level traffic evaluation indicators (such as lane-level traffic performance indicators) on the side or panel of the interface. Figure 8 ), and historical and current timing scheme information (such as Figure 7 (Top right corner)

[0047] Based on the above high-fidelity digital twin scenarios, the system can further realize the following advanced functions: Traffic evaluation and scheme optimization: The system can automatically evaluate the current signal control scheme based on real-time data in the twin scenario (such as queue length and number of stops), providing a basis for optimization.

[0048] Intelligent control decision-making: By combining real-time traffic flow data with the current light status, the system can automatically generate or suggest sensing control strategies (such as extending green lights or turning off red lights earlier), and send them to the traffic signal controllers for execution through the platform.

[0049] Abnormal event monitoring and reporting: Monitoring: Utilizing the precise location and status of vehicles in the twin map, rules are set for automatic event detection. Examples include abnormal parking (vehicles continuously remaining in the same position during a green light), exit overflow (vehicles continuously exceeding a threshold within the exit lane loop), and traffic accidents (vehicles abnormally clustering and remaining stationary).

[0050] Reporting and dissemination: After detecting an event, the system can automatically report it to the superior management platform and issue early warning information to surrounding vehicles through vehicle-road cooperative devices (RSU / OBU).

[0051] The following is a specific example: Event monitoring: Utilize the precise location and status information provided by digital twins to set rules for automatic monitoring.

[0052] Abnormal parking: If the system detects a vehicle at the same location in the same entrance lane a times within a consecutive detection cycle during the green light period, an abnormal parking warning will be triggered.

[0053] Exit Overflow: When the number of vehicles exceeding the set threshold NOC reaches b times within a consecutive detection cycle in the "overflow coil" set at the exit lane, an exit overflow warning is triggered.

[0054] Traffic accident: If the system detects abnormal vehicle aggregation or stationary status c times at the same location within d consecutive detection cycles during the green light period, a traffic accident warning will be triggered.

[0055] Event reporting: The intelligent agent will automatically report any abnormal events it detects to the superior management platform, such as the intelligent signal control optimization system.

[0056] Event announcement: Intelligent agents can use vehicle-to-everything (V2X) technologies, such as roadside units (RSUs) and on-board units (OBUs), to release abnormal event information to surrounding vehicles, thereby improving traffic safety.

[0057] Through the above specific implementation methods, this invention deeply integrates and accurately maps multi-source heterogeneous traffic information under a unified spatiotemporal benchmark, realizing the transformation of intersection status from "multi-screen monitoring" to "one-map overview", significantly improving the intuitiveness, accuracy and initiative of traffic management.

[0058] In one embodiment, the method for calculating the projection transformation coefficient in step S2 includes: S21: Select at least four feature points in one frame of the monitoring video stream and record their pixel coordinates image_pts in the video frame; S22: In the geographic coordinate map, find the ground coordinate points corresponding to the at least four feature points described in step S21, and record their coordinates real_pts; S23: Determine the projection transformation coefficient H based on the image_pts and real_pts using the homography matrix calculation function.

[0059] In one embodiment, in step S23, the homography matrix calculation function is H = cv2.findHomography(image_pts, real_pts).

[0060] In one embodiment, in step S2, the pixel coordinates (x, y) of the traffic object are determined as follows: Obtain the bottom left pixel coordinates of the target bounding box and the pixel coordinates of the top right corner ,calculate (x, y) are used as the pixel coordinates of the traffic object.

[0061] In one embodiment, calculating the projection distance in step S3 includes the following sub-steps: S31: Define the vector, whose starting point is A. The destination is B. The bird's-eye view coordinates of the traffic object are P(x, y); S32: Calculate the squares of the lengths of vectors AB, AP, and line segment AB. ; S33: Calculate the projection scale ; S34: Calculate the coordinates of the projection point : ; S35: Calculate the projection point Euclidean distance to starting point A ; S36: Determine the directed projection distance D: .

[0062] In one embodiment, step S4, the fusion display includes: Different graphic symbols are used to represent the traffic objects in the digital twin intersection scenario, depending on their type. Based on the real-time operating light status and whether the traffic object is in a violation state, different colors are assigned to the graphic symbol representing the object in the digital twin intersection scenario.

[0063] In one embodiment, the method further includes step S5: based on the real-time data integrated by the fusion display, performing at least one of the following operations: S51: Traffic Operation Status Evaluation; S52: Generate signal control optimization instructions; S53: Monitoring and dissemination of abnormal traffic incidents.

[0064] In one embodiment, the abnormal traffic event includes at least one of abnormal parking, exit lane overflow, or traffic accident.

[0065] This invention also provides a digital twin system for intersection traffic based on video and coordinate fusion, comprising: The data access module is used to access the surveillance video stream and real-time operation status of the traffic lights at the target intersection; The coordinate mapping and calculation module is used to convert the pixel coordinates of the identified traffic objects into bird's-eye view coordinates based on the projection transformation coefficient, and to calculate the projection distance to the stop line based on the preset vector. The digital twin engine module is used to build and render a digital twin intersection scene based on a geographic coordinate map, and integrate the real-time running light status, traffic object information and their projection distance for display.

[0066] Furthermore, it also includes at least one of the following functional modules: The configuration and management module is used to configure feature points in video images, corresponding points on geographic coordinate maps, lane vectors, and detection areas. The analysis and decision-making module is used to perform traffic evaluation, control decisions, or event monitoring based on the fused information provided by the digital twin engine module.

[0067] By integrating the functions of these modules, the constructed device not only realizes the digital twin method of intersection traffic based on video and coordinate fusion, solving the problems of location confusion and information fragmentation in traditional multi-channel video surveillance, but also achieves unified, accurate and visualized control of intersection traffic conditions, significantly improving the efficiency and intelligence level of traffic command and control.

[0068] The examples listed above are for reference only. To avoid redundancy, they will not be listed one by one here. In actual development or application, they can be flexibly combined according to actual needs. However, any combination belongs to the technical solution of this application and is covered by the protection scope of this application.

[0069] This application also provides a computer device, including a processor and a memory, wherein the memory stores computer program instructions for execution on the processor, and when the processor executes the computer program instructions, it implements the intersection traffic digital twin method based on video and coordinate fusion as described above.

[0070] This application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the intersection traffic digital twin method based on video and coordinate fusion described above.

[0071] It is understood that the above scenarios are merely examples and do not constitute a limitation on the application scenarios of the technical solutions provided in the embodiments of this application. The technical solutions of this application can also be applied to other scenarios. For example, as those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0072] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0073] The steps in the method of this application embodiment can be adjusted, combined, or deleted according to actual needs.

[0074] The units in the device of this application embodiment can be merged, divided, and deleted according to actual needs.

[0075] In this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions are generally described in detail only when they appear for the first time. When they appear again, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions that are not described in detail later can be referred to their previous relevant detailed descriptions.

[0076] In this application, the descriptions of the various embodiments have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0077] The technical features of the present application can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present application.

[0078] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, controlled terminal, or network device, etc.) to execute the methods of each embodiment of this application.

[0079] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a storage medium or transmitted from one storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, storage disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for creating a digital twin of intersection traffic based on video and coordinate fusion, characterized in that, Includes the following steps: S1: Access the surveillance video stream and real-time operation status of traffic lights at the target intersection; S2: By pre-calculating the projection transformation coefficients based on the feature points of the video image and the corresponding points on the geographic coordinate map, the pixel coordinates of the traffic objects identified in the video image are transformed to the coordinates under the bird's-eye view coordinate system; S3: Based on the coordinates of the bird's-eye view and the preset vector representing the direction of the lane, calculate the projected distance of the traffic object from the lane direction to the stop line; S4: Combining the real-time running light status, the type of traffic object, and the projection distance, a fusion display is performed in a digital twin intersection scene constructed based on a geographic coordinate map.

2. The method according to claim 1, characterized in that, The method for calculating the projection transformation coefficient in step S2 includes: S21: Select at least four feature points in one frame of the monitoring video stream and record their pixel coordinates image_pts in the video frame; S22: In the geographic coordinate map, find the ground coordinate points corresponding to the at least four feature points described in step S21, and record their coordinates real_pts; S23: Determine the projection transformation coefficient H based on the image_pts and real_pts using the homography matrix calculation function.

3. The method according to claim 2, characterized in that, In step S23, the homography matrix calculation function is H = cv2.findHomography(image_pts, real_pts).

4. The method according to claim 1, characterized in that, In step S2, the pixel coordinates (x, y) of the traffic object are determined as follows: Obtain the bottom left pixel coordinates of the target bounding box and the pixel coordinates of the top right corner ,calculate , (x, y) are used as the pixel coordinates of the traffic object.

5. The method according to claim 1, characterized in that, Step S3, calculating the projection distance, includes the following sub-steps: S31: Define the vector, whose starting point is A. The destination is B. The bird's-eye view coordinates of the traffic object are P(x, y); S32: Calculate the squares of the lengths of vectors AB, AP, and line segment AB. ; S33: Calculate the projection scale ; S34: Calculate the coordinates of the projection point : ； S35: Calculate the projection point Euclidean distance to starting point A ; S36: Determine the directed projection distance D: .

6. The method according to claim 1, characterized in that, In step S4, the fusion display includes: Different graphic symbols are used to represent the traffic objects in the digital twin intersection scenario, depending on their type. Based on the real-time operating light status and whether the traffic object is in a violation state, different colors are assigned to the graphic symbol representing the object in the digital twin intersection scenario.

7. The method according to claim 1, characterized in that, It also includes step S5: Based on the real-time data integrated by the fusion display, perform at least one of the following operations: S51: Traffic Operation Status Evaluation; S52: Generate signal control optimization instructions; S53: Monitoring and dissemination of abnormal traffic incidents.

8. The method according to claim 7, characterized in that, The abnormal traffic event includes at least one of abnormal parking, exit lane overflow, or traffic accident.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer program instructions for execution on the processor, wherein when the processor executes the computer program instructions, it implements the intersection traffic digital twin method based on video and coordinate fusion as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It stores computer instructions, which, when executed by a processor, implement the intersection traffic digital twin method based on video and coordinate fusion as described in any one of claims 1-8.

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