Intelligent traffic management system based on 5G positioning and video analysis

Abstract

The invention relates to the technical field of intelligent traffic management, and particularly discloses an intelligent traffic management system based on 5G positioning and video analysis, and the system comprises a driving congestion degree analysis module, a variable guide lane confirmation module, a steering signal lamp regulation and control module, a database, and a traffic regulation and control effect evaluation module. According to the invention, by combining the driving congestion degree of each lane and the turning lane corresponding to each lane in the target crossroad in the current passing time period, the lane indication direction of the variable guide lane in the next passing time period is determined, and the green light duration regulation type of the turning signal lamp corresponding to each lane in the next passing time period is determined; and the green light duration of the turn signal lamp is adjusted according to the green light duration adjustment and control type, so that the green light duration adjustment and control reasonability of the turn signal lamp is improved, the waiting time of vehicles at the crossroad is reduced, the congestion risk of the crossroad is reduced, and the traffic accident risk caused by congestion is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic management, and in particular to an intelligent traffic management system based on 5G positioning and video analysis. Background Art

[0002] With the acceleration of urbanization, traffic congestion, traffic disorder and other problems are becoming increasingly serious, seriously affecting people's travel experience. Traditional traffic management systems mainly rely on traffic lights, road monitoring and other means, but it is difficult to achieve accurate and real-time traffic management. In recent years, with the development of 5G technology and video analysis technology, new technical means have been provided for intelligent traffic management. However, the existing traffic management systems based on 5G or video analysis still have problems such as insufficient positioning accuracy and low data processing efficiency.

[0003] For example, the Chinese patent publication number CN118633115A discloses intelligent traffic management, including: a vehicle may transmit a lane use request message associated with the lane of the vehicle to a network entity. The network entity may identify lane information associated with the lane of the vehicle. The network entity may identify vehicle information associated with the vehicle. The network entity may transmit a lane use grant message based on the identified lane information or at least one of the identified vehicle information to the vehicle, and the vehicle may receive the lane use grant message from the network entity. The lane use grant message may indicate the vehicle's authority to use the lane. In the absence of this authority, the vehicle may not be allowed to use the lane. The lane may correspond to a flexible directional lane, an emergency lane, a road shoulder, an HOV lane, or an overtaking lane.

[0004] The above existing technologies still have the following problems: 1. The lane indication direction confirmation of the variable guide lane currently only depends on the congestion of the lane, and the indication direction confirmation is not combined with the driving congestion of the turning lane corresponding to the lane, which reduces the efficiency of road traffic and cannot ensure the smoothness, safety and efficiency of road traffic.

[0005] 2. When adjusting the green light duration of the turn signal lights at the intersection, the traffic congestion conditions of both the lanes and the turning lanes corresponding to the lanes were not comprehensively considered for a classified discussion on the adjustment of the green light duration, which reduced the rationality of the green light duration adjustment of the turn signal lights, increased the waiting time of vehicles at the intersection, and increased the risk of congestion at the intersection, thereby increasing the risk of traffic accidents caused by congestion. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, an intelligent traffic management system based on 5G positioning and video analysis is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an intelligent traffic management system based on 5G positioning and video analysis, including: a driving congestion analysis module, which is used to locate the target intersection through 5G positioning technology, collect road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period, and calculate the driving congestion of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period.

[0008] The variable guide lane confirmation module is used to confirm the lane indication direction of the variable guide lane in each combined lane in the target intersection within the next traffic time period.

[0009] The turn signal light control module is used to extract the green light duration of each turn signal light in the current traffic time period of the target intersection, confirm the green light duration control type of the turn signal light corresponding to each lane in the target intersection in the next traffic time period, and adjust the green light duration of the turn signal light corresponding to each lane according to the green light duration control type.

[0010] The database is used to store the floating green light duration corresponding to the reference of the unit driving congestion deviation.

[0011] The traffic control effect evaluation module is used to collect road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the next traffic time period, evaluate the traffic control effect index of the target intersection during the next traffic time period, and provide feedback.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention determines the lane indication direction of the variable guide lanes in each combined lane in the target intersection in the next traffic time period by combining the traffic congestion of each lane and the turning lane corresponding to each lane in the target intersection in the current traffic time period, thereby improving the efficiency of road traffic and ensuring smooth, safe and efficient road traffic.

[0013] (2) The present invention improves the rationality of the green light duration control of the turn signal lights, reduces the waiting time of vehicles at the intersection, reduces the risk of congestion at the intersection, and thus reduces the risk of traffic accidents caused by congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] 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.

[0015] Figure 1 It is a schematic diagram of the system module structure connection of the present invention.

[0016] Figure 2 It is a schematic diagram of the intersection of the present invention.

[0017] Description of the accompanying drawings: 1. Combined lane, 2. Variable guide lane. DETAILED DESCRIPTION

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

[0019] See also Figure 1 As shown, the present invention provides an intelligent traffic management system based on 5G positioning and video analysis, including: a driving congestion analysis module, a variable guide lane confirmation module, a turn signal light control module, a database and a traffic control effect evaluation module.

[0020] The driving congestion analysis module is connected to the variable guide lane confirmation module, the driving congestion analysis module is connected to the turn signal light control module, the turn signal light control module is connected to the database, and the driving congestion analysis module is connected to the traffic control effect evaluation module.

[0021] The traffic congestion analysis module is used to locate the target intersection through 5G positioning technology, collect road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period, and calculate the traffic congestion of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period.

[0022] In a specific embodiment of the present invention, the road driving information includes the number of vehicles entering and the number of vehicles leaving, the license plate number and the time of entry of each vehicle entering, and the license plate number and the time of exit of each vehicle leaving.

[0023] It should be noted that the number of vehicles entering and leaving each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period is collected in the following manner: video images of vehicles entering are collected by video surveillance cameras respectively installed at the entrances of each lane and the turning lanes corresponding to each lane, and video images of vehicles leaving are collected by video surveillance cameras respectively installed at the exits of each lane and the turning lanes corresponding to each lane, the video images of vehicles entering and the video images of vehicles leaving are imported into video analysis software, and vehicles are identified and counted by video analysis technology.

[0024] It should also be noted that the license plate number and entry time of each incoming vehicle and the license plate number and exit time of each outgoing vehicle are collected by video surveillance equipment installed at the entrance and exit respectively.

[0025] In a specific embodiment of the present invention, the specific process of respectively calculating the traffic congestion of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period is as follows: A1. Extracting the number of entering vehicles and the number of exiting vehicles, as well as the license plate number and the entry time point of each entering vehicle, and the license plate number and the exit time point of each exiting vehicle from the road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period.

[0026] A2. Calculate the traffic congestion CD of each lane in the target intersection in the current traffic time period based on the number of vehicles entering and exiting each lane in the target intersection in the current traffic time period, the license plate number and entry time of each vehicle entering, and the license plate number and exit time of each vehicle exiting. i , where i represents the lane number, i=1,2,...,n.

[0027] In a specific embodiment of the present invention, the specific process of calculating the driving congestion of each lane in the target intersection during the current traffic time period is as follows: B1. Subtract the number of vehicles entering and exiting each lane in the target intersection during the current traffic time period to obtain the number of congested vehicles in each lane during the current traffic time period, denoted as ε i .

[0028] B2. Compare the license plate numbers of each entering vehicle and each leaving vehicle in each lane of the target intersection during the current traffic time period, and record vehicles with the same license plate number as target vehicles.

[0029] B3. According to the entry time of each entering vehicle and the exit time of each exiting vehicle, the entry time and exit time of each target vehicle are obtained, and the entry time and exit time are compared to obtain the travel time of each target vehicle in each lane in the current travel time period, which is recorded as Tij , where j represents the number of the target vehicle, j=1,2,...,m.

[0030] B4. Calculate the traffic congestion CD of each lane in the target intersection during the current traffic time period i , Among them, ε′ and T′ represent the number of congested vehicles and the travel time of the reference respectively, and m represents the target number of vehicles.

[0031] A3. Calculate the driving congestion ZX of the turning lanes corresponding to each lane in the target intersection in the current traffic time period in the same way as the calculation method of the driving congestion of each lane in the target intersection in the current traffic time period. i .

[0032] See also Figure 2 As shown, the variable directional lane confirmation module is used to confirm the lane indication direction of the variable directional lane in each combined lane in the target intersection within the next traffic time period.

[0033] In a specific embodiment of the present invention, the specific method for confirming the lane indication direction of the variable guide lane in each combined lane in the target intersection in the next traffic time period is: C1. Extracting the driving congestion of each lane in each combined lane and the turning lane corresponding to each lane in the current traffic time period from the driving congestion of each lane and the turning lane corresponding to each lane in the target intersection in the current traffic time period.

[0034] C2. Compare the driving congestion of the turning lane corresponding to each lane in each combination of lanes in the current traffic time period with the set reference driving congestion. If the driving congestion of the turning lane corresponding to a lane in the current traffic time period is less than the set reference driving congestion, the turning lane corresponding to the lane is recorded as the target turning lane, and the lane corresponding to the target turning lane is recorded as the target lane, thereby obtaining each target lane in each combination of lanes.

[0035] C3. Extract the maximum value from the traffic congestion of each target lane in each combination lane in the current traffic time period, and use the lane indication direction of the target lane corresponding to the maximum traffic congestion as the lane indication direction of the variable guide lane in each combination lane in the target intersection in the next traffic time period.

[0036] The embodiment of the present invention combines the traffic congestion of each lane and the turning lane corresponding to each lane in the target intersection in the current traffic time period, and confirms the lane indication direction of the variable guide lane in each combined lane in the target intersection in the next traffic time period, thereby improving the efficiency of road traffic and ensuring smooth, safe and efficient road traffic.

[0037] The turn signal light control module is used to extract the green light duration of each turn signal light at the target intersection in the current traffic time period, confirm the green light duration control type of the turn signal light corresponding to each lane in the target intersection in the next traffic time period, and adjust the green light duration of the turn signal light corresponding to each lane according to the green light duration control type.

[0038] It should be noted that the green light duration of each turn signal light at the target intersection during the current traffic time period is extracted from the signal light control system of the target intersection.

[0039] In a specific embodiment of the present invention, the specific method of confirming the green light duration control type of the turn signal lights corresponding to each lane in the target intersection in the next traffic time period is: establishing a green light duration control type diagnosis model of the turn signal lights corresponding to each lane in the next traffic time period, and outputting a green light duration control type diagnosis result, wherein the green light duration control type diagnosis model is expressed as: Among them, P1, P2 and P3 represent the evaluation conditions of the diagnosis results of each green light duration control type, P1 represents [(CD i >XS)∩(ZX i >XS)]∪[(CD i <XS)∩(ZX i <XS)]∪[(CD i =XS)∩(ZX i =XS)], P2 represents (CD i >XS)∩(ZX i <XS), P3 means (CD i <XS)∩(ZX i >XS), where XS represents the travel congestion degree of the set reference.

[0040] It should be noted that when a lane is crowded but the corresponding turning lane is not crowded, it means that the corresponding turning lane can still carry more vehicles, so the green light duration can be extended to relieve the traffic pressure on the lane. When a lane is not crowded but the corresponding turning lane is crowded, it means that the corresponding turning lane cannot carry more vehicles, so the green light duration needs to be shortened to relieve the traffic pressure on the corresponding turning lane. When the lane is crowded and the corresponding turning lane is also crowded, the green light duration remains unchanged. When the lane is not crowded and the corresponding turning lane is not crowded, it means that there is no need to relieve the traffic pressure, so the green light duration remains unchanged.

[0041] In a specific embodiment of the present invention, the specific method of adjusting the green light duration of the turn signal lights corresponding to each lane according to the green light duration control type is: D1. If the green light duration control type of the turn signal light corresponding to a lane in the next traffic time period is that the green light duration remains unchanged, then the green light duration of the turn signal light corresponding to the lane remains unchanged.

[0042] D2. If the green light duration control type of the turn signal light corresponding to a lane in the next traffic time period is extended green light duration, the turn signal light corresponding to the lane is recorded as an extended turn signal light, and the green light duration of each extended turn signal light in the next traffic time period is confirmed. Wherein, g represents the number of the extended turn signal light, g=1,2,...,r.

[0043] In a specific embodiment of the present invention, the specific process of confirming the green light duration of each extended turn signal light in the next traffic time period is as follows: E1, extracting the green light duration of each extended turn signal light from the green light duration of each turn signal light in the current traffic time period of the target intersection, and recording it as

[0044] E2. Extract the driving congestion of the turning lanes corresponding to each extended turn signal light in the current traffic time period from the driving congestion of the turning lanes corresponding to each lane in the target intersection in the current traffic time period, and record it as

[0045] E3. Extract the floating green light duration corresponding to the unit driving congestion deviation from the database and record it as T0.

[0046] E4. Set the green light duration of each extended turn signal light in the next traffic time period

[0047]

[0048] D3. If the green light duration control type of the turn signal light corresponding to a lane in the next traffic time period is a shortened green light duration, the turn signal light corresponding to the lane is recorded as a shortened turn signal light, and the green light duration of each shortened turn signal light in the next traffic time period is confirmed. Wherein, p represents the number of the shortened turn signal light, p=1, 2, ..., q.

[0049] In a specific embodiment of the present invention, the specific process of confirming the green light duration of each shortened turn signal light in the next traffic time period is as follows: F1, extracting the green light duration of each shortened turn signal light from the green light duration of each turn signal light in the current traffic time period of the target intersection, and recording it as T p ′.

[0050] F2. Extract the driving congestion of the turning lanes corresponding to each shortened turn signal light in the current traffic time period from the driving congestion of the turning lanes corresponding to each lane in the target intersection in the current traffic time period, and record it as

[0051] F3: Set the green light duration of each turn signal light in the next traffic time period

[0052]

[0053] The embodiment of the present invention confirms the green light duration control type of the turn signal lights corresponding to each lane in the target intersection during the next traffic time period, and adjusts the green light duration of the turn signal lights corresponding to each lane according to the green light duration control type, thereby improving the rationality of the green light duration control of the turn signal lights, reducing the waiting time of vehicles at the intersection, reducing the congestion risk at the intersection, and thus reducing the risk of traffic accidents caused by congestion.

[0054] The database is used to store the floating green light duration corresponding to the unit driving congestion deviation. The data sources in the database of this embodiment are shown in Table 1.

[0055] Table 1 Data sources in the database

[0056]

[0057] The traffic control effect evaluation module is used to collect road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the next traffic time period, evaluate the traffic control effect index of the target intersection during the next traffic time period, and feed it back.

[0058] It should be noted that the method for collecting road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the next traffic time period is consistent with the method for collecting road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period mentioned above, and will not be repeated here.

[0059] In a specific embodiment of the present invention, the specific process of evaluating the traffic control effect index of the target intersection in the next traffic time period is as follows: G1, extracting the number of vehicles entering and the number of vehicles leaving, as well as the license plate number and the entry time point of each vehicle entering, and the license plate number and the exit time point of each vehicle leaving from the road driving information of each lane and the turning lane corresponding to each lane in the target intersection in the next traffic time period, and calculating the driving congestion of each lane and the turning lane corresponding to each lane in the target intersection in the next traffic time period in the same way as the calculation method of the driving congestion of each lane in the target intersection in the current traffic time period, and recording them as and

[0060] G2. Set the traffic control effect index of the target intersection in the next traffic time period Wherein, ΔXS represents the deviation of the set reference driving congestion, e represents a natural constant, and n represents the number of lanes.

[0061] It should be noted that the traffic control effect index can measure the traffic smoothness of the target intersection in the next traffic time period. The level of the index directly reflects whether the traffic control measures have effectively alleviated traffic congestion and improved road traffic efficiency. The traffic control effect index is an important basis for traffic management departments to formulate and adjust traffic management strategies. Through in-depth analysis of the index, weak links and potential problems in traffic control can be identified, so as to formulate improvement measures in a targeted manner. From a more macro perspective, evaluating the traffic control effect index will help promote the sustainable development of urban transportation and contribute to the green development of urban transportation.

[0062] It should also be noted that the traffic control effect index of the target intersection in the next traffic time period is compared with the set reference traffic control effect index. If the traffic control effect index of the target intersection in the next traffic time period is greater than or equal to the set reference traffic control effect index, it indicates that the traffic at the target intersection is smooth in the next traffic time period, and it also illustrates the effectiveness of the lane indication direction adjustment of the variable lane in the next traffic time period and the green light duration adjustment of the turn signal lights corresponding to each lane; if the traffic control effect index of the target intersection in the next traffic time period is less than the set reference traffic control effect index, it indicates that the traffic at the target intersection is not smooth in the next traffic time period, and it also illustrates the ineffectiveness of the lane indication direction adjustment of the variable lane in the next traffic time period and the green light duration adjustment of the turn signal lights corresponding to each lane. In this case, feedback needs to be provided to the traffic management department, and the traffic management department needs to formulate better traffic management strategies to alleviate the traffic pressure at the target intersection.

[0063] 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. An intelligent traffic management system based on 5G positioning and video analysis, characterized in that: include: Traffic congestion analysis module, variable guide lane confirmation module, turn signal control module, database and traffic control effect evaluation module; The driving congestion analysis module is connected to the variable guide lane confirmation module, the driving congestion analysis module is connected to the turn signal light control module, the turn signal light control module is connected to the database, and the driving congestion analysis module is connected to the traffic control effect evaluation module; The driving congestion analysis module is used to locate the target intersection through 5G positioning technology, collect the road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period, and calculate the driving congestion of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period; A variable guide lane confirmation module is used to confirm the lane indication direction of the variable guide lane in each combined lane in the target intersection within the next traffic time period; A turn signal light control module is used to extract the green light duration of each turn signal light at the target intersection in the current traffic time period, confirm the green light duration control type of the turn signal light corresponding to each lane in the target intersection in the next traffic time period, and adjust the green light duration of the turn signal light corresponding to each lane according to the green light duration control type; A database for storing floating green light duration corresponding to the reference of unit driving congestion deviation; The traffic control effect evaluation module is used to collect road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the next traffic time period, evaluate the traffic control effect index of the target intersection during the next traffic time period, and provide feedback.

2. According to claim 1, an intelligent traffic management system based on 5G positioning and video analysis is characterized in that: The road driving information includes the number of vehicles entering and the number of vehicles leaving, the license plate number and the time of entry of each vehicle entering, and the license plate number and the time of exit of each vehicle leaving.

3. According to claim 2, an intelligent traffic management system based on 5G positioning and video analysis is characterized in that: The specific process of respectively calculating the driving congestion of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period is as follows: A1. Extracting the number of vehicles entering and exiting, the license plate number and the entry time of each vehicle entering, and the license plate number and the exit time of each vehicle exiting from the road driving information of each lane and the turning lane corresponding to each lane in the target intersection during the current traffic time period; A2. Calculate the traffic congestion CD of each lane in the target intersection in the current traffic time period based on the number of vehicles entering and exiting each lane in the target intersection in the current traffic time period, the license plate number and entry time of each vehicle entering, and the license plate number and exit time of each vehicle exiting. i , where i represents the lane number, i=1,2,...,n; A3. Calculate the driving congestion ZX of the turning lanes corresponding to each lane in the target intersection in the current traffic time period in the same way as the calculation method of the driving congestion of each lane in the target intersection in the current traffic time period. i .

4. According to claim 3, an intelligent traffic management system based on 5G positioning and video analysis is characterized in that: The specific process of calculating the traffic congestion of each lane in the target intersection during the current traffic time period is as follows: B1. Subtract the number of vehicles entering and exiting each lane in the target intersection during the current traffic time period to obtain the number of congested vehicles in each lane during the current traffic time period, denoted as ε i ; B2. Compare the license plate numbers of each vehicle entering and each vehicle leaving each lane of the target intersection during the current traffic time period, and record the vehicles with the same license plate number as target vehicles; B3. According to the entry time of each entering vehicle and the exit time of each exiting vehicle, the entry time and exit time of each target vehicle are obtained, and the entry time and exit time are compared to obtain the travel time of each target vehicle in each lane in the current travel time period, which is recorded as T ij , where j represents the number of the target vehicle, j = 1, 2, ..., m; B4. Calculate the traffic congestion CD of each lane in the target intersection during the current traffic time period i , Among them, ε′ and T′ represent the number of congested vehicles and the travel time of the reference respectively, and m represents the target number of vehicles.

5. According to claim 3, an intelligent traffic management system based on 5G positioning and video analysis is characterized in that: The specific method of confirming the lane indication direction of the variable guide lane in each combined lane at the target intersection in the next traffic time period is: C1. Extract the driving congestion of each lane and the turning lane corresponding to each lane in each combined lane in the current traffic time period from the driving congestion of each lane and the turning lane corresponding to each lane in the target intersection in the current traffic time period; C2. Compare the driving congestion of the turning lane corresponding to each lane in each combination of lanes in the current traffic time period with the set reference driving congestion. If the driving congestion of the turning lane corresponding to a lane in the current traffic time period is less than the set reference driving congestion, then the turning lane corresponding to the lane is recorded as the target turning lane, and the lane corresponding to the target turning lane is recorded as the target lane, thereby obtaining each target lane in each combination of lanes; C3. Extract the maximum value from the traffic congestion of each target lane in each combination lane in the current traffic time period, and use the lane indication direction of the target lane corresponding to the maximum traffic congestion as the lane indication direction of the variable guide lane in each combination lane in the target intersection in the next traffic time period.

6. The intelligent traffic management system based on 5G positioning and video analysis according to claim 3 is characterized in that: The specific method of confirming the green light duration control type of the turn signal lights corresponding to each lane in the target intersection in the next traffic time period is: establishing a green light duration control type diagnosis model of the turn signal lights corresponding to each lane in the next traffic time period, and outputting a green light duration control type diagnosis result, wherein the green light duration control type diagnosis model is expressed as: Among them, P1, P2 and P3 represent the evaluation conditions of the diagnosis results of each green light duration control type, P1 represents [(CD i >XS)∩(ZX i >XS)]∪[(CD i <XS)∩(ZX i <XS)]∪[(CD i =XS)∩(ZX i =XS)], P2 represents (CD i >XS)∩(ZX i <XS), P3 means (CD i <XS)∩(ZX i >XS), where XS represents the travel congestion degree of the set reference.

7. The intelligent traffic management system based on 5G positioning and video analysis according to claim 6 is characterized in that: The specific method of adjusting the green light duration of the turn signal lights corresponding to each lane according to the green light duration control type is: D1. If the green light duration control type of the turn signal light corresponding to a lane in the next traffic time period is that the green light duration remains unchanged, the green light duration of the turn signal light corresponding to the lane remains unchanged; D2. If the green light duration control type of the turn signal light corresponding to a lane in the next traffic time period is extended green light duration, the turn signal light corresponding to the lane is recorded as an extended turn signal light, and the green light duration of each extended turn signal light in the next traffic time period is confirmed. Wherein, g represents the number of the extended turn signal light, g = 1, 2, ..., r; D3. If the green light duration control type of the turn signal light corresponding to a lane in the next traffic time period is a shortened green light duration, the turn signal light corresponding to the lane is recorded as a shortened turn signal light, and the green light duration of each shortened turn signal light in the next traffic time period is confirmed. Wherein, p represents the number of the shortened turn signal light, p=1, 2, ..., q.

8. An intelligent traffic management system based on 5G positioning and video analysis according to claim 7, characterized in that: The specific process of confirming the green light duration of each extended turn signal light in the next traffic time period is as follows: E1. Extract the green light duration of each extended turn signal light from the green light duration of each turn signal light in the current traffic time period at the target intersection and record it as E2. Extract the driving congestion of the turning lanes corresponding to each extended turn signal light in the current traffic time period from the driving congestion of the turning lanes corresponding to each lane in the target intersection in the current traffic time period, and record it as E3. Extract the floating green light duration corresponding to the unit driving congestion deviation from the database and record it as T0; E4. Set the green light duration of each extended turn signal light in the next traffic time period 9. An intelligent traffic management system based on 5G positioning and video analysis according to claim 8, characterized in that: The specific process of confirming the shortened green light duration of each turn signal light in the next traffic time period is as follows: F1. Extract the green light duration of each shortened turn signal light from the green light duration of each turn signal light at the target intersection during the current traffic time period and record it as T p ′; F2. Extract the driving congestion of the turning lanes corresponding to each shortened turn signal light in the current traffic time period from the driving congestion of the turning lanes corresponding to each lane in the target intersection in the current traffic time period, and record it as F3: Set the green light duration of each turn signal light in the next traffic time period 10. The intelligent traffic management system based on 5G positioning and video analysis according to claim 4, characterized in that: The specific process of evaluating the traffic control effect index of the target intersection in the next traffic time period is as follows: G1. Extract the number of vehicles entering and exiting, the license plate number and the entry time of each vehicle entering, and the license plate number and the exit time of each vehicle exiting from the road driving information of each lane and the turning lane corresponding to each lane in the target intersection in the next traffic time period. Calculate the traffic congestion of each lane and the turning lane corresponding to each lane in the target intersection in the next traffic time period in the same way as the calculation method of the traffic congestion of each lane in the target intersection in the current traffic time period, and record them as and G2. Set the traffic control effect index of the target intersection in the next traffic time period Wherein, ΔXS represents the deviation of the set reference driving congestion, e represents a natural constant, and n represents the number of lanes.

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