Smart city construction traffic intelligent monitoring management and control system based on 5G

Abstract

The invention relates to the field of traffic monitoring and management and control, and particularly discloses a 5G-based smart city construction traffic intelligent monitoring and management and control system, which constructs a recognition model of various types of illegal behaviors of vehicles and pedestrians based on historical data, and judges whether the vehicles and pedestrians on a road have illegal behaviors or not. Further obtaining violation behavior types, violation time and violation places of violation vehicles and pedestrians; according to the traffic video data of the road, whether the road is congested or not is judged, and the congested road section, the congested time period and the congested degree are further obtained; constructing a recognition model of various types of traffic accidents based on historical data, judging whether the traffic accidents exist on the road, and further obtaining the types, time, places and severity of the traffic accidents; according to the invention, automation and intelligentization of traffic monitoring management and control are realized, manual intervention is reduced, the efficiency and precision of traffic management are improved, and a traffic management department can master traffic conditions more quickly and accurately.

Description

Technical Field

[0001] The present invention relates to the field of traffic monitoring and control, and more particularly to a 5G-based smart city traffic intelligent monitoring and control system. Background Art

[0002] With the acceleration of urbanization, urban traffic is facing more and more challenges, such as traffic congestion, traffic accidents, environmental pollution, etc. Traditional means of traffic monitoring and control can no longer meet the needs of urban development. The emergence of 5G technology provides new opportunities for intelligent traffic monitoring and control in smart city construction. 5G has the characteristics of high speed, low latency, and large capacity. It can realize the rapid transmission and processing of massive traffic data, and can also support the connection and collaborative work of various intelligent traffic equipment, such as vehicles, road facilities, sensors, etc. Through 5G technology, a more efficient, intelligent, and safe traffic monitoring and control system can be built.

[0003] However, the existing 5G-based urban traffic monitoring and control system still has some limitations and shortcomings in practical applications.

[0004] For example, the existing Chinese patent with publication number CN118470991A discloses a 5G-based intelligent traffic control system and method for smart cities. The invention obtains various data of the target city, builds a city digital twin model, and arranges video and environmental data acquisition terminals on the target roads. These terminals upload the video and environmental data to the edge computing node through 5G. The latter uses object detection, semantic segmentation and other technologies to identify traffic and environmental objects from the video, and predicts the risk index of each road section in real time through deep learning. If the risk index exceeds the limit, the edge computing node sends the control instruction to the traffic light through 5G to adjust the traffic flow. At the same time, the edge computing node uploads the congestion warning results to the encrypted blockchain and sends it to the vehicle-mounted equipment. For emergency events, the edge computing node will find the optimal troubleshooting solution through blockchain and federated learning and continuously optimize it. This invention scheme uses edge computing, blockchain and multidisciplinary technology integration to improve the analysis efficiency and service security of traffic management.

[0005] The above patent mainly monitors and controls road traffic from the dimensions of road congestion risk, personnel safety risk, and vehicle risk, but has the following shortcomings: First, the sensors used to collect video data or image data are not detected and calibrated. If the sensor operates abnormally, it will affect the quality of the video image data, and thus affect the normal implementation of road traffic control based on video image data and the making of correct decision-making analysis, which is prone to misjudgment.

[0006] Secondly, when monitoring the risks of road congestion, personal safety and vehicle risks, the monitoring only touches upon the surface and no further in-depth analysis is performed on the detailed information such as type, time, location and degree of the risks of road congestion, personal safety and vehicle risks, which is not conducive to providing better decision-making support for the traffic management department. Summary of the invention

[0007] In response to the above problems, the present invention proposes a 5G-based smart city construction traffic intelligent monitoring and control system to realize the function of traffic monitoring and control.

[0008] The technical solution adopted by the present invention to solve its technical problems is: the present invention provides a 5G-based smart city construction traffic intelligent monitoring and control system, including: a road video collector operation detection processing module, a road traffic video data acquisition module, a traffic violation identification and recording module, a traffic congestion warning processing module, a traffic accident identification and processing module, and a database.

[0009] The road video collector operation detection processing module is connected to the road traffic video data acquisition module, the road traffic video data acquisition module is respectively connected to the traffic violation identification and recording module, the traffic congestion warning processing module, and the traffic accident identification and processing module, and the database is respectively connected to the traffic violation identification and recording module and the traffic accident identification and processing module.

[0010] Road video collector operation detection and processing module: used to obtain video data collected by video collectors installed on designated roads in the target city area, and obtain video data synchronously collected by video collectors carried by drones, compare them with each other, analyze the image quality coefficient, data transmission stability coefficient, and data transmission timeliness coefficient of the video data collected by video collectors installed on designated roads in the target city area, determine whether the operation of the video collectors installed on the designated roads is abnormal, and perform processing.

[0011] Road traffic video data acquisition module: used to collect traffic video data of designated roads in the target urban area, record it as traffic video data of designated roads, further extract the video frames to obtain the traffic image set of the designated roads, and perform preprocessing.

[0012] Traffic violation identification and recording module: used to obtain image data of various types of violations of vehicles and pedestrians on designated roads, build identification models of various types of violations of vehicles and pedestrians on designated roads, and judge whether each vehicle and pedestrian on the designated road has violated the regulations based on the traffic image set of the designated road. If there is a violation, obtain the violation type, violation time, and violation location of each violating vehicle and each violating pedestrian, and record them.

[0013] Traffic congestion warning processing module: used to determine whether there is congestion on the designated road based on the traffic video data of the designated road. If there is congestion, obtain the congested sub-sections of the designated road and the congestion time period and congestion degree of each congested sub-section, and issue warnings and feedback.

[0014] Traffic accident identification and processing module: used to obtain image data of various types of traffic accidents in the history of designated roads, build identification models of various types of traffic accidents on designated roads, and determine whether there is a traffic accident on the designated road based on the traffic image set of the designated road. If there is a traffic accident, obtain the type, time, location and severity of the traffic accident and provide feedback.

[0015] Database: used to store historical traffic video data of designated roads.

[0016] Compared with the prior art, the 5G-based smart city construction traffic intelligent monitoring and control system described in the present invention has the following beneficial effects: 1. The present invention compares the video data collected by the road-mounted video collector with the video data synchronously collected by the video collector carried by the drone, evaluates the image quality, transmission stability, and transmission timeliness of the road-mounted video collector, determines whether the operation of the road-mounted video collector is abnormal, and processes it, thereby avoiding poor video image data quality caused by abnormal sensor operation, thereby ensuring the normal progress of road traffic control and making correct decision analysis.

[0017] 2. The present invention constructs recognition models for various types of violations of vehicles and pedestrians based on historical traffic video data, and judges whether road vehicles and pedestrians have any violations according to the traffic video data of the road, and further obtains information such as the violation type, violation time, and violation location of the violating vehicles and pedestrians, and records them, thereby realizing automatic recognition and recording of traffic violations and providing a basis for law enforcement for traffic management departments.

[0018] 3. The present invention determines whether there is congestion on the road based on the traffic video data of the road, further obtains information such as the congested road section, congested time period and congestion degree, and conducts early warning and feedback, thereby providing decision support for the traffic management department and effectively alleviating traffic congestion.

[0019] 4. The present invention constructs recognition models for various types of traffic accidents based on historical traffic video data, determines whether there is a traffic accident on the road based on the traffic video data of the road, further obtains information such as the type, time, location and severity of the traffic accident, and provides feedback, thereby enabling the traffic management department to promptly discover traffic accidents and potential safety hazards and take prompt measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 This is a system module connection diagram of the present invention.

[0022] Figure 2 The road video collector of the present invention runs a detection structure block diagram.

[0023] Figure 3 It is a structural block diagram of the road traffic monitoring of the present invention. DETAILED DESCRIPTION

[0024] 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.

[0025] See also Figure 1 , Figure 2 and Figure 3 As shown, the present invention provides a 5G-based smart city construction traffic intelligent monitoring and control system, including a road video collector operation detection processing module, a road traffic video data acquisition module, a traffic violation identification and recording module, a traffic congestion warning processing module, a traffic accident identification and processing module, and a database.

[0026] The road video collector operation detection processing module is connected to the road traffic video data acquisition module, the road traffic video data acquisition module is respectively connected to the traffic violation identification and recording module, the traffic congestion warning processing module, and the traffic accident identification and processing module, and the database is respectively connected to the traffic violation identification and recording module and the traffic accident identification and processing module.

[0027] The road video collector operation detection and processing module is used to obtain video data collected by video collectors installed on designated roads in the target urban area, and obtain video data synchronously collected by video collectors carried by drones, compare them with each other, analyze the image quality coefficient, data transmission stability coefficient, and data transmission timeliness coefficient of the video data collected by the video collectors installed on designated roads in the target urban area, determine whether the operation of the video collectors installed on the designated roads is abnormal, and perform processing.

[0028] Furthermore, the specific working process of the road video collector operation detection processing module includes: obtaining the resolution, frame rate, color reproduction, and low-light performance coefficient of the video data collected by the video collector installed on the designated road in the target urban area and the video collector carried by the drone, and recording them as and .

[0029] As a preferred solution, the resolution, frame rate, color reproduction and low-illumination performance coefficient of video data collected by a video collector installed on designated roads within a target city area are obtained. The specific method is: obtain the video data collected by the video collector installed on designated roads within the target city area, further obtain the resolution, frame rate, color reproduction and sensitivity of the video data collected by the video collector installed on designated roads within the target city area, substitute the sensitivity of the video data collected by the video collector installed on designated roads within the target city area into the relationship function between the preset sensitivity and the low-illumination performance coefficient, and obtain the low-illumination performance coefficient of the video data collected by the video collector installed on designated roads within the target city area.

[0030] As a preferred solution, the method for obtaining the resolution, frame rate, color reproduction and low-illumination performance coefficient of video data collected by the video collector carried by the drone is based on the same principle as the method for obtaining the resolution, frame rate, color reproduction and low-illumination performance coefficient of video data collected by the video collector installed on designated roads in the target urban area.

[0031] By analyzing the formula Get the image quality coefficient of the video data collected by the video collector installed on the designated road in the target city area ,in They represent the weight factors of the preset resolution, frame rate, color reproduction, and low-light performance coefficient, respectively. .

[0032] In a specific embodiment, whether the operation of the video collector installed on the designated road is abnormal is evaluated by using the video data of a designated road section collected by the video collector installed on the designated road and the video collector carried by the drone within a set time period.

[0033] As a preferred solution, a video collector is a device for acquiring video signals, which is generally composed of a camera, an image sensor, a signal processing circuit, a storage device, and a communication interface. In the field of road traffic, a video collector is usually installed at a specific location on the road, such as an intersection, a highway, a bridge, etc., for real-time monitoring of traffic conditions. It can capture images of objects such as vehicles, pedestrians, traffic signs, and signal lights, and transmit these images to a monitoring center or other processing equipment for analysis and processing.

[0034] As a preferred solution, there may be multiple video collectors deployed on designated roads in the target urban area.

[0035] As a preferred solution, video collectors installed on designated roads in the target urban area and video collectors carried by drones are interconnected with the traffic control center through the 5G network, and can quickly transmit the collected video data to the traffic control center to achieve real-time monitoring.

[0036] As a preferred solution, the models and parameter settings of the video collectors deployed on designated roads in the target urban area and the video collectors carried by the drone are completely consistent.

[0037] As a preferred solution, drones carry video collectors to conduct regular inspections of road traffic.

[0038] As a preferred option, high resolution can provide clearer images and help identify detailed information such as vehicle license plates and pedestrian features.

[0039] As a preferred solution, the frame rate determines the smoothness of the video. For road traffic monitoring, a higher frame rate can more accurately capture the dynamics of fast-moving vehicles and pedestrians.

[0040] As a preferred solution, accurate color reproduction can make the monitoring images closer to the real scene and help judge vehicle color, traffic signs and other information.

[0041] As a preferred solution, the low-light performance of the video collector is crucial at night or in a dimly lit environment. Good low-light performance can ensure that clear images can be obtained under low-light conditions.

[0042] Furthermore, the specific working process of the road video collector operation detection processing module also includes: obtaining the number of freezes, cumulative freeze duration and frame loss rate of the video data collected by the video collector installed on the designated road in the target urban area and the video collector carried by the drone, and recording them as and .

[0043] By analyzing the formula Get the data transmission stability coefficient of the video data collected by the video collector installed on the designated road in the target city area ,in represents the correction factor of the preset data transmission stability coefficient, represents a natural constant, They respectively represent the impact factors corresponding to the preset unit freeze times, unit freeze duration, and unit frame loss rate.

[0044] Furthermore, the specific working process of the road video collector operation detection processing module also includes: obtaining the time taken by the video collector installed on the designated road in the target urban area and the video collector carried by the drone to send the collected video data to the traffic control center, and recording them as and .

[0045] By analyzing the formula Get the data transmission timeliness coefficient of the video data collected by the video collector installed on the designated roads in the target city area .

[0046] The image quality coefficient, data transmission stability coefficient and data transmission timeliness coefficient of the video data collected by the video collectors installed on the designated roads in the target urban area are calculated by weighted average, so as to obtain the operation status evaluation index of the video collectors installed on the designated roads in the target urban area.

[0047] As a preferred solution, the weights of the image quality coefficient, the data transmission stability coefficient, and the data transmission timeliness coefficient are set values, and the cumulative sum is 1.

[0048] The operating status evaluation index of the video collector installed on the designated roads in the target urban area is compared with the preset operating status evaluation index threshold. If the operating status evaluation index of the video collector installed on the designated road is less than the preset operating status evaluation index threshold, the video collector installed on the designated road is operating abnormally and the operation is fed back to the traffic control center.

[0049] It should be noted that the present invention compares the video data collected by the road-mounted video collector with the video data collected synchronously by the video collector carried by the drone, evaluates the image quality, transmission stability, and transmission timeliness of the road-mounted video collector, determines whether the operation of the road-mounted video collector is abnormal, and performs processing, thereby avoiding poor video image data quality caused by abnormal sensor operation, thereby ensuring the normal implementation of road traffic control and making correct decision-making analysis.

[0050] The road traffic video data acquisition module is used to collect traffic video data of designated roads in the target urban area, record it as traffic video data of the designated road, further obtain the traffic image set of the designated road through video frame extraction, and perform preprocessing.

[0051] As a preferred solution, the specific working process of the road traffic video data acquisition module is: obtaining the traffic video data of the designated road in the target urban area through a video collector, recording it as the traffic video data of the designated road, further obtaining the traffic image set of the designated road through video frame extraction, and performing preprocessing, wherein the preprocessing includes image denoising, image enhancement, geometric transformation, normalization processing, and color space conversion.

[0052] As a preferred solution, preprocessing the traffic image set of a designated road is an existing relatively mature technology and will not be described in detail here.

[0053] The traffic violation identification and recording module is used to obtain image data of various types of historical violations of vehicles and pedestrians on a designated road, build an identification model for various types of violations of vehicles and pedestrians on the designated road, and determine whether each vehicle and pedestrian on the designated road has committed a violation based on the traffic image set of the designated road. If a violation exists, the violation type, violation time, and violation location of each violating vehicle and each violating pedestrian are obtained and recorded.

[0054] Furthermore, the specific working process of the traffic violation identification and recording module includes: extracting historical traffic video data of a designated road stored in a database, and intercepting image data of various types of violations of historical vehicles and pedestrians on the designated road.

[0055] According to the image data of various types of violations of historical vehicles on designated roads, an image set of various types of violations of historical vehicles on designated roads is obtained, a feature set of various types of violations of historical vehicles on designated roads is identified, and a recognition model of various types of violations of historical vehicles on designated roads is constructed using a machine learning algorithm.

[0056] Similarly, a recognition model for various types of violations of pedestrians on designated roads is constructed.

[0057] As a preferred solution, the vehicle's illegal behaviors include but are not limited to: speeding, running a red light, illegal lane changing, driving in the wrong direction, etc.

[0058] As a preferred solution, pedestrian violations include but are not limited to: running red lights, crossing the road, climbing over guardrails, etc.

[0059] Furthermore, the specific working process of the traffic violation identification and recording module also includes: D1: based on the traffic image set of the designated road, the traffic image set of each vehicle on the designated road is obtained, and compared with the recognition model of each type of violation of the vehicle on the designated road, and the matching degree between the traffic image set of each vehicle on the designated road and the recognition model of each type of violation of the vehicle is obtained. If the matching degree between the traffic image of a vehicle on the designated road and the recognition model of a certain type of violation of the vehicle is greater than the set matching degree threshold, then the vehicle has a violation, and the type of violation is used as the violation type of the vehicle. The violation vehicles and the violation types of each violation vehicle are counted, and the violation time and violation location of each violation vehicle are further obtained, and the vehicle violation information of the designated road is summarized.

[0060] D2: Similarly, according to the analysis method of D1, it is determined whether the pedestrians on the designated road have any violations, and the violation type, violation time, and violation location of each violating pedestrian are further obtained to summarize the pedestrian violation information on the designated road.

[0061] D3: The vehicle and pedestrian violation information on designated roads will be recorded and transmitted to the traffic control center via the 5G communication network.

[0062] As a preferred option, the 5G network, as a key channel for data transmission, can ensure that data is transmitted to the traffic control center quickly and stably, ensuring that control instructions can be issued to traffic facilities and vehicles in a timely manner.

[0063] It should be noted that the present invention constructs an identification model for various types of violations of vehicles and pedestrians based on historical traffic video data, and judges whether road vehicles and pedestrians have any violations according to the traffic video data of the road, and further obtains information such as the violation type, violation time, and violation location of the violating vehicles and pedestrians, and records them, thereby realizing automatic identification and recording of traffic violations and providing a basis for law enforcement for traffic management departments.

[0064] The traffic congestion warning processing module is used to determine whether there is congestion on the designated road based on the traffic video data of the designated road. If there is congestion, it obtains the congested sub-sections of the designated road and the congestion time period and congestion degree of each congested sub-section, and provides warning and feedback.

[0065] Furthermore, the specific working process of the traffic congestion warning processing module includes: dividing the designated road according to a preset principle to obtain sub-sections of the designated road, obtaining the traffic flow and average speed of each sub-section of the designated road at each time based on the traffic video data of the designated road, substituting it into the preset analysis formula of the congestion coefficient of the road, and obtaining the congestion coefficient of each sub-section of the designated road at each time.

[0066] If the congestion coefficient of a sub-section of a designated road at a certain moment is greater than or equal to a preset congestion coefficient threshold, then there is congestion in the sub-section of the designated road, an early warning is issued, and this moment is recorded as the start time of congestion, and the moment after the start time of congestion and when the congestion coefficient is less than the preset congestion coefficient threshold is recorded as the end time of congestion. The time period between the start time of congestion and the end time of congestion is further obtained and recorded as the congestion time period of the sub-section of the designated road, and the congested sub-sections of the designated road and the congested time periods of the congested sub-sections are statistically obtained.

[0067] The congestion degree corresponding to each congestion coefficient range is set, and the congestion degree of each congested sub-section of the specified road is obtained by screening according to the congestion coefficient of each congested sub-section of the specified road.

[0068] The congested sub-sections of the designated road as well as the congestion time periods and congestion levels of each sub-section are transmitted to the traffic control center through the 5G communication network.

[0069] As a preferred solution, the average vehicle speed of each sub-section of the specified road is obtained. The specific method is: obtain the speed of each vehicle in each sub-section of the specified road, and calculate the average value to obtain the average vehicle speed of each sub-section of the specified road.

[0070] Furthermore, the analysis formula of the congestion coefficient of the road is: ,in represents the road congestion coefficient, where Indicates the correction factor of the congestion coefficient of the preset road, They represent the traffic volume and average speed of the road respectively. They represent the preset thresholds of traffic volume and average speed respectively. Represent the weight factors of preset traffic flow and average speed respectively, .

[0071] It should be noted that the present invention determines whether there is congestion on the road based on the traffic video data of the road, further obtains information such as the congested road section, congested time period and congestion degree, and performs early warning and feedback, thereby providing decision support for the traffic management department and effectively alleviating traffic congestion.

[0072] The traffic accident identification and processing module is used to obtain image data of various types of traffic accidents in the history of a specified road, construct an identification model for various types of traffic accidents on the specified road, and determine whether there is a traffic accident on the specified road based on the traffic image set of the specified road. If there is a traffic accident, the type, time, location and severity of the traffic accident are obtained and feedback is provided.

[0073] Furthermore, the specific working process of the traffic accident identification and processing module includes: extracting historical traffic video data of designated roads stored in the database, intercepting image data of various types of traffic accidents in the history of designated roads, obtaining image sets of various types of traffic accidents in the history of designated roads, identifying feature sets of various types of traffic accidents in the history of designated roads, and using machine learning algorithms to construct identification models of various types of traffic accidents on designated roads.

[0074] Furthermore, the specific working process of the traffic accident identification and processing module also includes: substituting the traffic image set of the designated road into the identification model of each type of traffic accident on the designated road, determining whether there is a traffic accident on the designated road and obtaining the type of traffic accident, and further obtaining the time and place of the traffic accident.

[0075] Obtain images of the accident vehicle before and after the traffic accident, images of public facilities, and the congestion coefficient of the road section where the accident vehicle is located, and further obtain the vehicle damage coefficient, public facility damage coefficient, and road congestion coefficient of the traffic accident, which are recorded as , by analyzing the formula Get the severity of the traffic accident ,in Indicates the correction factor of the preset traffic accident severity, They represent the weights of the preset vehicle damage coefficient, public facility damage coefficient and road congestion coefficient respectively. .

[0076] The type, time, location and severity of traffic accidents are fed back to the traffic control center through the 5G communication network.

[0077] As a preferred solution, the vehicle damage coefficient, public facility damage coefficient and road congestion coefficient of the traffic accident are obtained. The specific method is: based on the images of the accident vehicle before and after the traffic accident, three-dimensional models of the accident vehicle before and after the traffic accident are constructed, and compared with each other to obtain the deformation degree of the accident vehicle, and substitute it into the preset relationship function between the deformation degree of the accident vehicle and the vehicle damage coefficient to obtain the vehicle damage coefficient of the traffic accident.

[0078] Similarly, based on the analysis method of the vehicle damage coefficient of the traffic accident, the public facility damage coefficient of the traffic accident is obtained.

[0079] Obtain the congestion coefficient of the road section where the accident vehicle is located before and after the traffic accident, subtract the congestion coefficient of the road section where the accident vehicle is located before the traffic accident from the congestion coefficient of the road section where the accident vehicle is located after the traffic accident, and obtain the congestion coefficient difference of the road section where the accident vehicle is located, and substitute it into the relationship function between the preset congestion coefficient difference and the road congestion increase coefficient to obtain the road congestion increase coefficient of the traffic accident.

[0080] It should be noted that the present invention constructs recognition models for various types of traffic accidents based on historical traffic video data, determines whether there is a traffic accident on the road according to the traffic video data of the road, further obtains information such as the type, time, location and severity of the traffic accident, and provides feedback, so that the traffic management department can promptly discover traffic accidents and potential safety hazards and take prompt measures.

[0081] The database is used to store historical traffic video data of a designated road.

[0082] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A 5G-based smart city construction traffic intelligent monitoring and control system, characterized in that: include: Road video collector operation detection processing module: used to obtain video data collected by video collectors installed on designated roads in the target city area, and obtain video data synchronously collected by video collectors carried by drones, compare them with each other, analyze the image quality coefficient, data transmission stability coefficient, and data transmission timeliness coefficient of the video data collected by the video collectors installed on designated roads in the target city area, determine whether the operation of the video collectors installed on the designated roads is abnormal, and perform processing; Road traffic video data acquisition module: used to collect traffic video data of designated roads in the target urban area, record it as traffic video data of designated roads, further extract the video frames to obtain the traffic image set of designated roads, and perform preprocessing; Traffic violation identification and recording module: used to obtain image data of various types of violations of vehicles and pedestrians on designated roads, build identification models of various types of violations of vehicles and pedestrians on designated roads, and judge whether each vehicle and pedestrian on the designated road has violated the regulations based on the traffic image set of the designated road. If there is a violation, obtain the violation type, violation time, and violation location of each violating vehicle and each violating pedestrian, and record them; Traffic congestion warning processing module: used to determine whether the designated road is congested based on the traffic video data of the designated road. If there is congestion, obtain the congested sub-sections of the designated road and the congestion time period and congestion degree of each congested sub-section, and provide warning and feedback; Traffic accident identification and processing module: used to obtain image data of various types of traffic accidents in the history of the specified road, build identification models of various types of traffic accidents on the specified road, and determine whether there is a traffic accident on the specified road based on the traffic image set of the specified road. If there is a traffic accident, obtain the type, time, location and severity of the traffic accident and provide feedback; Database: used to store historical traffic video data of designated roads.

2. According to the 5G-based smart city construction traffic intelligent monitoring and control system according to claim 1, it is characterized by: The specific working process of the road video collector operation detection processing module includes: The resolution, frame rate, color reproduction, and low-light performance coefficient of the video data collected by the video collectors installed on the designated roads in the target urban area and the video collectors carried by the drone are obtained and recorded as and ; By analyzing the formula Get the image quality coefficient of the video data collected by the video collector installed on the designated road in the target city area ,in They represent the weight factors of the preset resolution, frame rate, color reproduction, and low-light performance coefficient, respectively. .

3. According to a 5G-based smart city construction traffic intelligent monitoring and control system according to claim 2, it is characterized by: The specific working process of the road video collector running detection processing module also includes: Obtain the number of freezes, cumulative freeze duration, and frame loss rate of video data collected by video collectors installed on designated roads in the target city area and by video collectors carried by drones, and record them as and ; By analyzing the formula Get the data transmission stability coefficient of the video data collected by the video collector installed on the designated road in the target city area ,in represents the correction factor of the preset data transmission stability coefficient, represents a natural constant, They respectively represent the impact factors corresponding to the preset unit freeze times, unit freeze duration, and unit frame loss rate.

4. According to a 5G-based smart city construction traffic intelligent monitoring and control system according to claim 3, it is characterized by: The specific working process of the road video collector running detection processing module also includes: Get the time taken by the video collector installed on the designated road in the target city area and the video collector carried by the drone to send the collected video data to the traffic control center, and record them as and ; By analyzing the formula Get the data transmission timeliness coefficient of the video data collected by the video collector installed on the designated roads in the target city area ; The image quality coefficient, data transmission stability coefficient, and data transmission timeliness coefficient of the video data collected by the video collectors installed on the designated roads in the target urban area are calculated by weighted average, so as to obtain the operation status evaluation index of the video collectors installed on the designated roads in the target urban area; The operating status evaluation index of the video collector installed on the designated roads in the target urban area is compared with the preset operating status evaluation index threshold. If the operating status evaluation index of the video collector installed on the designated road is less than the preset operating status evaluation index threshold, the video collector installed on the designated road is operating abnormally and the operation is fed back to the traffic control center.

5. According to a 5G-based smart city construction traffic intelligent monitoring and control system according to claim 1, it is characterized by: The specific working process of the traffic violation identification and recording module includes: Extract the historical traffic video data of the designated road stored in the database, and capture the image data of various types of violations of vehicles and pedestrians on the designated road; According to the image data of various types of violations of the historical vehicles on the designated roads, an image set of various types of violations of the historical vehicles on the designated roads is obtained, a feature set of various types of violations of the historical vehicles on the designated roads is identified, and a recognition model of various types of violations of the historical vehicles on the designated roads is constructed by using a machine learning algorithm; Similarly, a recognition model for various types of violations of pedestrians on designated roads is constructed.

6. According to a 5G-based smart city construction traffic intelligent monitoring and control system according to claim 5, it is characterized by: The specific working process of the traffic violation identification and recording module also includes: D1: Based on the traffic image set of the designated road, the traffic image set of each vehicle on the designated road is obtained, and compared with the recognition model of each type of violation of the vehicle on the designated road, and the matching degree between the traffic image set of each vehicle on the designated road and the recognition model of each type of violation of the vehicle is obtained. If the matching degree between the traffic image of a certain vehicle on the designated road and the recognition model of a certain type of violation of the vehicle is greater than the set matching degree threshold, the vehicle has a violation, and the type of violation is used as the violation type of the vehicle. The violation vehicles and the violation types of each violation vehicle are counted, and the violation time and violation location of each violation vehicle are further obtained, and the violation information of the vehicles on the designated road is summarized; D2: Similarly, according to the analysis method of D1, it is determined whether each pedestrian on the designated road has violated the rules, and the violation type, violation time, and violation location of each violating pedestrian are further obtained, and the violation information of pedestrians on the designated road is summarized; D3: The vehicle and pedestrian violation information on designated roads will be recorded and transmitted to the traffic control center via the 5G communication network.

7. According to a 5G-based smart city construction traffic intelligent monitoring and control system according to claim 1, it is characterized by: The specific working process of the traffic congestion warning processing module includes: Divide the designated road according to the preset principle to obtain each sub-section of the designated road, obtain the traffic volume and average speed of each sub-section of the designated road at each time according to the traffic video data of the designated road, substitute it into the preset analysis formula of the congestion coefficient of the road, and obtain the congestion coefficient of each sub-section of the designated road at each time; If the congestion coefficient of a sub-section of a designated road at a certain time is greater than or equal to a preset congestion coefficient threshold, then the sub-section of the designated road is congested, an early warning is issued, and the time is recorded as the start time of congestion, and the time corresponding to the congestion coefficient less than the preset congestion coefficient threshold after the start time of congestion is recorded as the end time of congestion, and the time period between the start time of congestion and the end time of congestion is further obtained, and recorded as the congestion time period of the sub-section of the designated road, and the congested sub-sections of the designated road and the congested time periods of the congested sub-sections are statistically obtained; The congestion degree corresponding to each congestion coefficient range is set, and the congestion degree of each congested sub-section of the specified road is obtained by screening according to the congestion coefficient of each congested sub-section of the specified road; The congested sub-sections of the designated road as well as the congestion time periods and congestion levels of each sub-section are transmitted to the traffic control center through the 5G communication network.

8. According to the 5G-based smart city construction traffic intelligent monitoring and control system according to claim 7, it is characterized by: The analysis formula of the congestion coefficient of the road is: ,in represents the congestion coefficient of the road, where Indicates the correction factor of the congestion coefficient of the preset road, They represent the traffic volume and average speed of the road respectively. They represent the preset thresholds of traffic volume and average speed respectively. Represent the weight factors of preset traffic flow and average speed respectively, .

9. According to a 5G-based smart city construction traffic intelligent monitoring and control system according to claim 7, it is characterized by: The specific working process of the traffic accident identification and processing module includes: Extract the historical traffic video data of the designated road stored in the database, intercept the image data of various types of traffic accidents in the history of the designated road, obtain the image set of various types of traffic accidents in the history of the designated road, identify the feature set of various types of traffic accidents in the history of the designated road, and use the machine learning algorithm to build an identification model for various types of traffic accidents on the designated road.

10. The 5G-based smart city construction traffic intelligent monitoring and control system according to claim 9 is characterized in that: The specific working process of the traffic accident identification and processing module also includes: Substitute the traffic image set of the designated road into the recognition model of various types of traffic accidents on the designated road to determine whether there is a traffic accident on the designated road and obtain the type of traffic accident, and further obtain the time and location of the traffic accident; Obtain images of the accident vehicle before and after the traffic accident, images of public facilities, and the congestion coefficient of the road section where the accident vehicle is located, and further obtain the vehicle damage coefficient, public facility damage coefficient, and road congestion coefficient of the traffic accident, which are recorded as , by analyzing the formula Get the severity of the traffic accident ,in Indicates the correction factor of the preset traffic accident severity, They represent the weights of the preset vehicle damage coefficient, public facility damage coefficient and road congestion coefficient respectively. ; The type, time, location and severity of traffic accidents are fed back to the traffic control center through the 5G communication network.

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