Digital Twin City Internet of Things Data Integration and Processing System

Abstract

The digital twin field of the present invention particularly relates to a digital twin city Internet of Things data integration and processing system, which includes: an average speed determination module, which determines the actual average speed of traffic according to the traffic conditions of the first waiting group within the initial green light duration; a temporary duration determination module, which determines the temporary green light duration of the waiting group according to the actual average speed and the proportion of people and vehicles in the second waiting group; a statistical module, which counts the actual number of people in the waiting group within the temporary green light duration and determines the actual speed of the waiting group; a target duration determination module, which determines the target green light duration of the third waiting group based on the position of the traffic target in the third waiting group and the actual speed; a data integration module, which integrates the target green light duration into unified data, connects to the background through the Internet of Things technology, and determines the position of the signal light through positioning when the target green light duration is abnormal. Enhance the security of the traffic management system.

Description

Technical Field

[0001] The present invention relates to the field of digital twins, and in particular to a digital twin city Internet of Things data integration and processing system. Background Art

[0002] By deploying sensors and monitoring equipment in every corner of the city, real-time perception and monitoring of the city’s operating status can be achieved, and the integration and application of technologies can be realized, so that the digital twin city can achieve more refined and intelligent management, improve urban operation efficiency, and promote the sustainable development of the city.

[0003] The patent document with Chinese patent application number CN118428121 B discloses a city-level digital twin traffic system, including: a data planning and push unit, configured to obtain data from a third-party road test monitoring device, the data from the third-party road test monitoring device includes road data and vehicle data in real space; a vehicle model module, configured to produce model data of a vehicle in a virtual three-dimensional scene; a model scene space coordinate matching module, configured to obtain data from the data planning and push unit and the vehicle model module, calibrate the spatial position of the road model, and match the road data in real space with the road model coordinates; a data fusion module, configured to divide the road into multiple sub-sections, obtain data from the third-party road test monitoring device in each sub-section, perform interpolation calculations on the obtained data, display it in the form of animation, execute the animation, and realize the display of virtual-real mapping of traffic vehicles.

[0004] The prior art does not analyze traffic lights and roads, and thus does not improve the traffic efficiency of the urban traffic management system. Summary of the invention

[0005] To this end, the present invention provides a digital twin city Internet of Things data integration and processing system, which can analyze traffic lights and the traffic conditions of people and vehicles at intersections and then adjust the green light display duration to solve the problem of inefficient operation of urban traffic.

[0006] In order to achieve the above object, the present invention provides a digital twin city Internet of Things data integration and processing system, the system comprising:

[0007] An average speed determination module is used to determine the actual average speed of traffic according to the traffic conditions of the first waiting group of people and vehicles within the initial green light duration;

[0008] A temporary duration determination module, connected to the average speed determination module, for determining the temporary green light duration of the second waiting group according to the actual average speed and the proportion of people and vehicles in the second waiting group;

[0009] a statistical module connected to the temporary duration determination module, for counting the actual number of the second waiting group passing within the temporary green light duration, and determining the actual passing speed of the second waiting group according to the actual number of passing;

[0010] a target duration determination module, connected to the statistical module, for determining a target green light duration of the third group to be passed based on the location of the passing target in the third group to be passed;

[0011] A data integration module is connected to the target duration determination module to integrate the target green light duration into a unified data. The data integration module is connected to the backend through the Internet of Things technology to determine the specific location of the traffic light through positioning when an abnormality occurs in the target green light duration.

[0012] Furthermore, the average speed determination module includes:

[0013] The data collection unit includes a plurality of video monitors respectively arranged on corresponding traffic lights, for respectively collecting the time required for the first group of people and vehicles to pass through the corresponding intersection within the initial green light duration of each traffic light;

[0014] The average speed determination unit is connected to the data acquisition unit and determines the actual average speed based on the duration and the travel distance of people and vehicles.

[0015] Further, the temporary duration determination module includes:

[0016] A number counting unit, used for counting the number of people and vehicles in the second waiting group according to data collected by the infrared sensor on the traffic light;

[0017] A traffic ratio setting unit, connected to the number counting unit, sets the traffic ratio according to the number of people and vehicles, and determines a preset traffic number of people and vehicles in the group based on the traffic ratio;

[0018] The green light duration determining unit is connected to the traffic ratio setting unit and determines the temporary green light duration according to the preset target traffic distance in the preset traffic quantity and the actual traffic average speed.

[0019] Furthermore, the green light duration determination unit includes:

[0020] A monitoring subunit, which monitors the preset number of passes through the infrared sensor, and uses big data to analyze the preset target passing distance of the preset number of passes according to the monitoring result;

[0021] The duration determination subunit is connected to the monitoring subunit and calculates the temporary green light duration based on the preset target travel distance and the actual average travel speed.

[0022] Furthermore, the traffic ratio is:

[0023] The traffic ratio includes a vehicle traffic ratio and a pedestrian traffic ratio. The vehicle traffic ratio is the traffic ratio allocated to vehicles during the period from the start to the end of the green light. The pedestrian traffic ratio is the traffic ratio allocated to pedestrians during the period from the start to the end of the green light. The sum of the vehicle traffic ratio and the pedestrian traffic ratio is 1.

[0024] Furthermore, the statistical module includes:

[0025] An actual number counting unit, which captures the actual number of people waiting to pass within the temporary green light duration through the video monitoring;

[0026] The passing speed determining unit is connected to the actual number statistics, and is used to determine the actual passing speed according to the actual target passing distance in the actual passing number and the temporary green light duration.

[0027] Furthermore, the traffic speed determination unit includes:

[0028] The actual travel speed is calculated by dividing the actual target travel distance by the temporary green light duration.

[0029] Furthermore, the target duration determination module includes:

[0030] A positioning marking unit, which identifies the positions of the passing targets in the third group to be passed according to visual technology, and tracks the movement trajectories of the passing targets based on the positions of the passing targets;

[0031] The target duration determination unit is connected to the positioning mark unit and determines the target green light duration of the third group to be passed based on the movement trajectory of the passing target and the actual passing speed.

[0032] Furthermore, the moving trajectory of the passing target is:

[0033] The moving trajectory of the passing target is the trajectory from the initial position of the passing target at the roadside until it completely crosses the road and reaches the other side.

[0034] Furthermore, the data integration module includes:

[0035] An IoT device deployment unit installs sensors on traffic lights in the city to collect the target green light duration;

[0036] A data integration unit is connected to the IoT device deployment unit and integrates the target green light duration of all traffic signals in the city into unified data using data fusion technology;

[0037] The abnormal positioning unit is connected to the data integration unit and detects the unified data. If the target green light duration is abnormal, the specific signal light position is determined by positioning.

[0038] Compared with the prior art, the beneficial effect of the present invention lies in that, through the traffic conditions of the first waiting group within the initial green light duration, the actual average speed is calculated as the basis for data collection, which provides key parameters for subsequent analysis and regulation, and is used to dynamically adjust the green light duration to a temporary green light duration according to the actual average speed and the proportion of people and vehicles in the second waiting group. The flexible adjustment mechanism can ensure that while the basic traffic needs of the first group are guaranteed, the second group is allowed to pass as much as possible, thereby optimizing the overall traffic flow. Within the temporary green light duration, the system counts the actual number of people in the waiting group and calculates the actual speed based on this, which further verifies the adjustment effect and provides feedback for subsequent optimization. Based on the actual speed and the location of the traffic target in the third waiting group, the system can accurately determine the target green light duration, which helps to avoid intersection congestion and improve traffic fluency. The Internet of Things technology is used to integrate and unify all target green light duration data, which can realize real-time monitoring and sharing of global traffic information and provide comprehensive data support for urban traffic management.

[0039] In particular, the actual average speed of traffic is determined by recording the time required for the first group of people and vehicles to pass within the initial green light duration and the travel distance of the people and vehicles, which provides data support for subsequent analysis.

[0040] In particular, the distribution of traffic flow is optimized by setting the ratio of the number of people and vehicles passing through. The temporary green light duration is determined based on the preset number of people and vehicles in the group and the preset target passing distance and the actual average passing speed, thereby alleviating traffic pressure in a specific direction and laying the foundation for improving traffic efficiency.

[0041] In particular, by monitoring the preset number of passes, traffic flow can be managed more accurately, which helps to evaluate and set sufficient green light time. By calculating the temporary green light duration based on the preset target passing distance and the actual average passing speed, the backlog of vehicles and pedestrians can be reduced, thereby reducing traffic congestion.

[0042] In particular, setting a reasonable ratio of pedestrians to vehicles can help balance traffic flow, reduce vehicle congestion, and improve the smoothness of overall traffic flow.

[0043] In particular, by recording the actual travel time, we ensure that traffic lights are optimized based on real data. By accurately calculating the actual travel speed, we can set the green light duration more reasonably to ensure smooth traffic and reduce unnecessary waiting and congestion.

[0044] In particular, the actual travel speed is calculated by dividing the actual target travel distance by the temporary green light duration, so as to calibrate the precise actual travel speed and provide accuracy for the calculation of subsequent data.

[0045] In particular, by being able to identify the moving path of the passing target, it helps to manage traffic flow more accurately, and accurately determine the target green light duration of the third group to be passed according to the movement trajectory of the passing target and the actual passing speed, thereby improving traffic smoothness.

[0046] In particular, accurate trajectory prediction provides data support for calculating the precise target green light duration.

[0047] In particular, by real-time detection of the target green light duration, it can ensure that the traffic light system operates according to the predetermined program, enhance the reliability of the entire traffic management system, reduce traffic accidents caused by traffic light failures, and improve road safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of the structure of a digital twin city Internet of Things data integration and processing system provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of the structure of an average speed determination module of a digital twin city Internet of Things data integration and processing system provided by an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of the structure of a temporary duration determination module of a digital twin city Internet of Things data integration and processing system provided by an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of the statistical module structure of the digital twin city Internet of Things data integration and processing system provided by an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of the target duration determination module structure of the digital twin city Internet of Things data integration and processing system provided by an embodiment of the present invention;

[0053] Figure 6 A schematic diagram of the data integration module structure of the digital twin city Internet of Things data integration and processing system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0056] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be understood as a limitation on the present invention.

[0057] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] See also Figure 1 As shown, the present invention provides a digital twin city Internet of Things data integration and processing system, including:

[0059] The average speed determination module 10 is used to determine the actual average speed of traffic according to the traffic conditions of the first waiting group of people and vehicles within the initial green light duration;

[0060] A temporary duration determination module 20, connected to the average speed determination module, is used to determine the temporary green light duration of the second waiting group according to the actual average speed and the proportion of people and vehicles in the second waiting group;

[0061] A counting module 30, connected to the temporary duration determination module, is used to count the actual number of the second waiting group passing within the temporary green light duration, and determine the actual passing speed of the second waiting group according to the actual number of passing;

[0062] A target duration determination module 40, connected to the statistical module, is used to determine the target green light duration of the third group to be passed based on the location of the passing target in the third group to be passed;

[0063] The data integration module 50 is connected to the target duration determination module to integrate the target green light duration into unified data. The data integration module is connected to the backend through the Internet of Things technology to determine the specific traffic light position by positioning when an abnormality occurs in the target green light duration.

[0064] Specifically, under the traffic light at the same intersection where vehicles and pedestrians interact, the actual average speed is determined based on the traffic conditions of the first group of vehicles to be passed in the direction of the interaction within the initial green light duration, a preset traffic number is set based on the proportion of vehicles and pedestrians in the second group to be passed in the direction of the interaction, a temporary green light duration for the group to be passed is determined based on a preset target traffic distance in the preset traffic number and the actual average speed, the actual traffic number of the group to be passed within the temporary green light duration is counted, and the actual target traffic distance is determined based on the actual traffic number. Determine the actual passing speed based on the actual target passing distance and the temporary green light duration, identify the position of the passing target in the third group to be passed, monitor the passing target's movement trajectory based on the target position, determine the actual target passing distance based on the passing target's movement trajectory, determine the target green light duration for the third group to be passed according to the actual target passing distance and the actual passing speed, integrate the target green light duration into unified data, and connect to the backend through the Internet of Things technology; when the target green light duration is abnormal, determine the specific traffic light position through positioning.

[0065] Specifically, this embodiment calculates the actual average speed of traffic as the basis for data collection through the traffic conditions of the first waiting group within the initial green light duration, and provides key parameters for subsequent analysis and regulation. It is used to dynamically adjust the green light duration to a temporary green light duration according to the actual average speed of traffic and the proportion of people and vehicles in the second waiting group. The flexible adjustment mechanism can ensure that while guaranteeing basic traffic needs, the overall traffic operation is optimized. Within the temporary green light duration, the system counts the actual number of people in the waiting group, and calculates the actual speed of traffic based on this, further verifying the adjustment effect and providing feedback for subsequent optimization. Based on the actual speed of traffic and the location of the traffic target in the third waiting group, the system can accurately determine the target green light duration, which helps to avoid intersection congestion and improve traffic smoothness. The Internet of Things technology is used to integrate and unify all target green light duration data, which can realize real-time monitoring and sharing of global traffic information and provide comprehensive data support for urban traffic management.

[0066] See also Figure 2 As shown, the average speed determination module 10 includes:

[0067] The data collection unit 11 includes a plurality of video monitors respectively arranged on corresponding traffic lights, for respectively collecting the time required for the first group of people and vehicles to pass through the corresponding intersection within the initial green light duration of each traffic light;

[0068] The average speed determination unit 12 is connected to the data acquisition unit and determines the actual average speed based on the duration and the travel distance of people and vehicles.

[0069] Specifically, the first waiting-to-pass group is an initially monitored waiting-to-pass group, and the collecting unit is used to collect the time required for all the waiting-to-pass group to cross the road and reach the other side.

[0070] Specifically, this embodiment provides data support for subsequent analysis by recording the time required for the first group of people and vehicles to pass within the initial green light duration and the travel distance of the people and vehicles to determine the actual average speed of travel.

[0071] See also Figure 3 As shown, the temporary duration determination module 20 includes:

[0072] A number counting unit 21, used for counting the number of people and vehicles in the second waiting group according to data collected by the infrared sensor on the traffic light;

[0073] A traffic ratio setting unit 22, connected to the number counting unit, sets a traffic ratio according to the number of people and vehicles, and determines a preset traffic number based on the traffic ratio;

[0074] The green light duration determining unit 23 is connected to the traffic ratio setting unit, and determines the temporary green light duration according to the preset target traffic distance in the preset traffic quantity and the actual traffic average speed.

[0075] Specifically, the second group to be passed is the group to be passed at the next traffic light of the first group to be passed. The preset number of the second group to be passed is set according to the traffic ratio, and the temporary green light duration is determined according to the preset target traffic distance in the preset traffic number and the actual average traffic speed.

[0076] Specifically, this embodiment optimizes the distribution of traffic flow by setting the ratio of the number of people and vehicles passing through, determines the preset number of people and vehicles in the group and determines the temporary green light duration according to the preset target passing distance and the actual average passing speed, thereby alleviating traffic pressure in a specific direction and laying the foundation for improving traffic efficiency.

[0077] Specifically, the green light duration determination unit includes:

[0078] A monitoring subunit, which monitors the preset number of passes through the infrared sensor, and uses big data to analyze the preset target passing distance of the preset number of passes according to the monitoring result;

[0079] The duration determination subunit is connected to the monitoring subunit and calculates the temporary green light duration based on the preset target travel distance and the actual average travel speed.

[0080] Specifically, by monitoring the distance of the last batch of pedestrians and vehicles in the preset number of pedestrians and vehicles passing through during the period from the start to the end of the green light, starting from the initial position of the road until they completely cross the road to the other side, the distance is the preset target passing distance, and the temporary green light duration is calculated based on the preset target passing distance and the actual average passing speed.

[0081] Specifically, this embodiment can manage traffic flow more accurately by monitoring the preset number of passes, which helps to evaluate and set sufficient green light time. By calculating the temporary green light duration by the preset target passing distance and the actual average passing speed, the backlog of vehicles and pedestrians can be reduced, thereby reducing traffic congestion.

[0082] Specifically, the traffic ratio is:

[0083] Under the traffic lights where vehicles and pedestrians interact at the same intersection, the traffic ratio includes the vehicle traffic ratio and the pedestrian traffic ratio. The vehicle traffic ratio is the traffic ratio allocated to vehicles during the period from the start to the end of the green light, and the pedestrian traffic ratio is the traffic ratio allocated to pedestrians during the period from the start to the end of the green light. The sum of the vehicle traffic ratio and the pedestrian traffic ratio is 1.

[0084] Specifically, according to the traffic planning, the vehicle traffic ratio is 0.6, and the pedestrian traffic ratio is 0.4. For example, if there are 100 pedestrians at the intersection and 150 vehicles at the intersection, the preset vehicle traffic number is 150x0.6=90, and the preset pedestrian traffic number is 100x0.4=40.

[0085] Specifically, this embodiment helps to balance traffic flow, reduce vehicle congestion, and improve the smoothness of overall traffic flow by setting a reasonable ratio of pedestrians to vehicles.

[0086] See also Figure 4 As shown, the statistical module 30 includes:

[0087] An actual number counting unit 31 is configured to capture the actual number of the group waiting to pass within the temporary green light duration through the video monitoring;

[0088] The traffic speed determining unit 32 is connected to the actual number counting unit, and is used to determine the actual traffic speed according to the actual target traffic distance in the actual traffic number and the temporary green light duration.

[0089] Specifically, by monitoring the actual distance of the last batch of pedestrians and vehicles passing through the actual number of pedestrians and vehicles during the period from the start to the end of the green light, starting from the initial position of the road until they completely cross the road to the other side, the actual distance is the actual target passing distance, and the actual passing speed is calculated based on the actual target passing distance and the temporary green light duration.

[0090] Specifically, this embodiment ensures that traffic lights are optimized based on real data by recording actual travel time, and sets green light duration more reasonably by accurately calculating actual travel speed, thereby ensuring smooth traffic and reducing unnecessary waiting and congestion.

[0091] Specifically, the passing speed determination unit is:

[0092] The actual travel speed is calculated by dividing the actual target travel distance by the temporary green light duration.

[0093] Specifically, this embodiment calculates the actual passing speed by dividing the actual target passing distance by the temporary green light duration, calibrates the precise actual passing speed and provides accuracy for the calculation of subsequent data.

[0094] See also Figure 5 As shown, the target duration determination module 40 includes:

[0095] A positioning marking unit 41 is configured to identify the positions of the passing targets in the third group of passing targets according to visual technology, and to track the movement trajectories of the passing targets based on the positions of the passing targets;

[0096] The target duration determination unit 42 is connected to the positioning marking unit, and determines the target green light duration of the third group to be passed based on the moving trajectory of the passing target and the actual passing speed.

[0097] Specifically, the third group to be passed is the group to be passed at the next traffic light of the second group to be passed. The starting position of the last batch of people and vehicles in the third group to be passed is predicted based on the preset target distance of the first group to be passed and the actual target distance of the second group to be passed. The starting position is the position of the target. The motion trajectory of the passing target is monitored based on the position of the target. The target green light duration of the third group to be passed is calculated according to the motion trajectory of the passing target and the actual passing speed.

[0098] Specifically, this embodiment helps to manage traffic flow more accurately by being able to identify the moving path of the passing target, and accurately determines the target green light duration for the third group to be passed according to the movement trajectory of the passing target and the actual passing speed, thereby improving traffic fluency.

[0099] Specifically, the moving trajectory of the passing target is:

[0100] The moving trajectory of the passing target is the trajectory from the initial position of the passing target at the roadside until it completely crosses the road and reaches the other side.

[0101] Specifically, this embodiment provides data support for calculating the precise target green light duration through accurate trajectory prediction.

[0102] See also Figure 6 As shown, the data integration module 50 includes:

[0103] The IoT device deployment unit 51 installs sensors on the traffic lights in the city to collect the target green light duration;

[0104] The data integration unit 52 is connected to the IoT device deployment unit and integrates the target green light duration of all traffic signals into unified data using data fusion technology;

[0105] The abnormal positioning unit 53 is connected to the data integration unit and detects the unified data. If the target green light duration is abnormal, the specific signal light position is determined by positioning.

[0106] Specifically, this embodiment can ensure that the traffic light system operates according to a predetermined program by detecting the target green light duration in real time, thereby enhancing the reliability of the entire traffic management system, reducing traffic accidents caused by traffic light failures, and improving road safety.

[0107] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital twin city Internet of Things data integration and processing system, characterized in that: include: An average speed determination module is used to determine the actual average speed of traffic according to the traffic conditions of the first waiting group of people and vehicles within the initial green light duration; A temporary duration determination module, connected to the average speed determination module, for determining the temporary green light duration of the second waiting group according to the actual average speed and the proportion of people and vehicles in the second waiting group; a statistical module connected to the temporary duration determination module, for counting the actual number of the second waiting group passing within the temporary green light duration, and determining the actual passing speed of the second waiting group according to the actual number of passing; a target duration determination module, connected to the statistical module, for determining a target green light duration of the third group to be passed based on the location of the passing target in the third group to be passed; A data integration module is connected to the target duration determination module to integrate the target green light duration into a unified data. The data integration module is connected to the backend through the Internet of Things technology to determine the specific location of the traffic light through positioning when an abnormality occurs in the target green light duration.

2. The digital twin city Internet of Things data integration and processing system according to claim 1 is characterized in that: The average speed determination module comprises: The data collection unit includes a plurality of video monitors respectively arranged on corresponding traffic lights, for respectively collecting the time required for the first group of people and vehicles to pass through the corresponding intersection within the initial green light duration of each traffic light; The average speed determination unit is connected to the data acquisition unit and determines the actual average speed based on the duration and the travel distance of people and vehicles.

3. The digital twin city Internet of Things data integration and processing system according to claim 2 is characterized in that: The temporary duration determination module comprises: A number counting unit, used for counting the number of people and vehicles in the second waiting group according to data collected by the infrared sensor on the traffic light; A traffic ratio setting unit, connected to the number counting unit, sets the traffic ratio according to the number of people and vehicles, and determines a preset traffic number of people and vehicles in the group based on the traffic ratio; The green light duration determining unit is connected to the traffic ratio setting unit and determines the temporary green light duration according to the preset target traffic distance in the preset traffic quantity and the actual traffic average speed.

4. The digital twin city Internet of Things data integration and processing system according to claim 3 is characterized in that: The green light duration determination unit comprises: A monitoring subunit, which monitors the preset number of passes through the infrared sensor, and uses big data to analyze the preset target passing distance of the preset number of passes according to the monitoring result; The duration determination subunit is connected to the monitoring subunit and calculates the temporary green light duration based on the preset target travel distance and the actual average travel speed.

5. The digital twin city Internet of Things data integration and processing system according to claim 4 is characterized in that: The traffic ratio is: The traffic ratio includes a vehicle traffic ratio and a pedestrian traffic ratio. The vehicle traffic ratio is the traffic ratio allocated to vehicles during the period from the start to the end of the green light. The pedestrian traffic ratio is the traffic ratio allocated to pedestrians during the period from the start to the end of the green light. The sum of the vehicle traffic ratio and the pedestrian traffic ratio is 1.

6. The digital twin city Internet of Things data integration and processing system according to claim 5 is characterized in that: The statistics module includes: An actual number counting unit, which captures the actual number of people waiting to pass within the temporary green light duration through the video monitoring; The traffic speed determination unit is connected to the actual number counting unit and is used to determine the actual traffic speed according to the actual target traffic distance in the actual traffic number and the temporary green light duration.

7. The digital twin city Internet of Things data integration and processing system according to claim 6 is characterized in that: The passing speed determination unit is: The actual travel speed is calculated by dividing the actual target travel distance by the temporary green light duration.

8. The digital twin city Internet of Things data integration and processing system according to claim 7 is characterized in that: The target duration determination module includes: A positioning marking unit, which identifies the positions of the passing targets in the third group to be passed according to visual technology, and tracks the movement trajectories of the passing targets based on the positions of the passing targets; The target duration determination unit is connected to the positioning mark unit and determines the target green light duration of the third group to be passed based on the movement trajectory of the passing target and the actual passing speed.

9. The digital twin city Internet of Things data integration and processing system according to claim 8 is characterized in that: The moving trajectory of the passing target is: The movement trajectory of the passing target is the trajectory from the initial position of the passing target until it completely crosses the road and reaches the opposite side.

10. The digital twin city Internet of Things data integration and processing system according to claim 9 is characterized in that: The data integration module includes: An IoT device deployment unit installs sensors on traffic lights in the city to collect the target green light duration; A data integration unit is connected to the IoT device deployment unit and integrates the target green light durations of all traffic signals into unified data using data fusion technology; The abnormal positioning unit is connected to the data integration unit and detects the unified data. If the target green light duration is abnormal, the specific signal light position is determined by positioning.

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