Intelligent traffic signal control method and system based on vehicle network data integration

Abstract

The present invention discloses an intelligent traffic signal control method and system based on vehicle network data integration, and relates to the field of intelligent traffic signal control technology. It includes: position acquisition: acquiring the position information of the target intersection, obtaining the target position item, and establishing an edge computing network based on the target position item to obtain the target edge network item; range setting: setting the coverage range, obtaining the coverage range item, taking the target position item as the center, obtaining the number of vehicles in the coverage range item, and obtaining the vehicle quantity set, which includes at least one vehicle item. The present invention can match the corresponding travel time to the greatest extent according to the driving habits of each driver to achieve the best traffic volume, so that when congestion occurs, the traffic signal can be controlled according to the different congestion conditions and the time required for different vehicles to pass through the intersection, so as to achieve a more precise control effect.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic signal control, and in particular to an intelligent traffic signal control method and system based on vehicle networking data integration. Background Art

[0002] The Internet of Vehicles mainly refers to the on-board equipment on the vehicle using wireless communication technology to effectively utilize all vehicle dynamic information on the information network platform and provide different functional services during vehicle operation. The Internet of Vehicles can provide protection for the distance between vehicles and reduce the probability of vehicle collision accidents. The Internet of Vehicles can also help car owners navigate in real time and improve the efficiency of traffic operation through communication with other vehicles and network systems. Intelligent traffic signal control refers to the use of advanced information technology, communication technology and automatic control technology to dynamically manage and optimize the working status of traffic lights in order to increase traffic flow, reduce congestion and improve road safety. Real-time traffic data, including vehicle speed, flow, congestion and traffic accident information, is collected through the Internet of Vehicles technology, and the status of traffic lights is adjusted in real time through traffic signal control equipment.

[0003] Patent publication number CN11607192A is a method and system for predicting intersection signal status based on the perception of intelligent connected vehicle groups. Based on the perception ability of intelligent connected vehicle groups, it accurately collects the signal status of traffic lights to avoid collection errors caused by obstructions of vehicles and roadside facilities. For the independent control mode of a single intersection, a data-driven method is used to capture the changing rules of the signal control scheme of the intersection, and infer and predict the subsequent short-term signal status based on real-time perception data. For the collaborative control mode of multiple intersections, based on the historical signal control schemes of each intersection, a collaborative control scheme is formed by clustering and identifying the intersections of collaborative control. The subsequent signal status of the collaborative intersection is jointly predicted based on the subsequent signal status inference logic of a single intersection and the collaborative control signal scheme, providing a reliable short-term traffic signal data foundation for the planning and decision-making of intelligent connected vehicles.

[0004] During the use of the above and similar technical solutions, due to the different driving habits of each person, when the traffic light changes from red to green, the time it takes for the driver to pass through the intersection will also be different. In the event of congestion, when the travel time is a fixed value, the traffic differences caused by the driving behavior of each driver will be infinitely magnified, resulting in more serious congestion in some areas under the same travel time, and the traffic signal cannot be adjusted accordingly according to the driving habits of different drivers. Summary of the invention

[0005] The object of the present invention is to provide an intelligent traffic signal control method and system based on vehicle network data integration to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solution: an intelligent traffic signal control method and system based on vehicle network data integration, comprising:

[0007] Location acquisition: Get the location information of the target intersection, get the target location item, and establish an edge computing network based on the target location item to get the target edge network item;

[0008] Range setting: setting the coverage range, obtaining the coverage range item, taking the target location item as the center, obtaining the vehicle number information within the coverage range item, obtaining the vehicle number set, the vehicle number set including at least one vehicle item, setting the division threshold, dividing the vehicle number set into at least two levels, and obtaining at least two congestion level items;

[0009] Data acquisition: Based on the vehicle quantity set, the time required for the vehicle items in the vehicle quantity set to completely pass through the intersection is obtained through an acquisition method to obtain the vehicle time item. Based on the real-time acquisition of the number of vehicles within the coverage item, the vehicle quantity set is classified into levels to obtain the target congestion level item;

[0010] Time adjustment: Based on the target congestion level item, the traffic signal is controlled through the adjustment method. When congestion occurs, the traffic signal is controlled according to the different congestion conditions and the time required for different vehicles to pass through the intersection, which can achieve a more precise control effect;

[0011] The adjustment method comprises:

[0012] Step 1: Determine the number of traffic, set the initial number of traffic, set the increase value based on the congestion level item, obtain at least two increase items, the increase items are matched with the congestion level items respectively, and then obtain the number of traffic matching different congestion level items, and obtain at least two traffic number items;

[0013] Step 2: Target selection: based on the target congestion level item, select the traffic quantity item that matches the target congestion level item to obtain the target traffic quantity item;

[0014] Step 3: Data transmission, the vehicle time item and the target traffic quantity item are transmitted to the target edge network item, and the traffic signal is adjusted based on the vehicle time item and the target traffic quantity item. In the event of congestion, the corresponding travel time can be matched to the greatest extent according to the driving habits of each driver to achieve the best traffic volume.

[0015] Furthermore, the acquisition method includes:

[0016] S1: Preliminary time acquisition: Based on the Internet of Vehicles data, the time when the target vehicle item is in the first position and passes through the intersection is obtained to obtain the preliminary time item;

[0017] S2: Comprehensive time acquisition: Based on the Internet of Vehicles data, the time it takes for the target vehicle item to pass the intersection when it is not in the first place is obtained to obtain the comprehensive time item;

[0018] S3: Position judgment, based on the Internet of Vehicles data, obtain the location of the vehicle item, obtain the time corresponding to different vehicle items, and then obtain the vehicle time item.

[0019] Furthermore, the acquisition method further includes:

[0020] S4: setting the number of times, setting the number of acquisitions, and based on the Internet of Vehicles data, taking the number of acquisitions as a benchmark, respectively acquiring the preliminary time set and the comprehensive time set of the vehicle item in the S1 and S2 states;

[0021] S5: average value acquisition, based on the preliminary time set and the comprehensive time set, respectively obtain the average time data of the vehicle item in the S1 and S2 states, obtain the preliminary average item and the comprehensive average item, and based on the preliminary average item and the comprehensive average item, obtain the vehicle time item in S3.

[0022] Furthermore, the acquisition method further includes:

[0023] S6: Time sorting: In the process of S4, the preliminary time set and the comprehensive time set are sorted to obtain a preliminary sorted time set and a comprehensive sorted time set;

[0024] S7: Determine the elimination items. Based on the preliminary sorting time set and the comprehensive sorting time set, set the elimination items, eliminate the preliminary time items and the comprehensive time items in the preliminary sorting time set and the comprehensive sorting time set, and obtain the screened preliminary time set and the screened comprehensive time set. Repeat step S5 to obtain the vehicle time items.

[0025] Furthermore, the method for obtaining the vehicle quantity set includes:

[0026] M1: Device layout, select a high-definition camera as the target device, and set at least two target devices based on the coverage items;

[0027] M2: Image acquisition, based on the target device, real-time acquisition of vehicle information images within the coverage area to obtain range image items, and based on the range image items, acquisition of vehicle number data to obtain a vehicle quantity set.

[0028] Furthermore, the method for obtaining the vehicle quantity set also includes: data sharing, based on the target edge network item and the Internet of Vehicles data, obtaining the number of devices connected to the target edge network item, obtaining the device quantity set, and then obtaining the vehicle quantity set.

[0029] Furthermore, the method for setting the division threshold includes:

[0030] N1: Capacity acquisition, based on the coverage item, obtain the capacity information of the target area and obtain the capacity data item;

[0031] N2: first ratio setting: based on the capacity data item, at least one first ratio is set, and the capacity data item is divided into at least two parts according to the first ratio, thereby obtaining a division threshold.

[0032] Furthermore, the method for setting the division threshold also includes:

[0033] N3: Quantity calculation, based on the capacity data item, obtain the quantity that can be accommodated in the target area and obtain the maximum quantity item;

[0034] N4: Setting a second ratio: based on the maximum number of items, setting at least one second ratio; dividing the maximum number of items into at least two parts according to the second ratio, and then obtaining a division threshold.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The intelligent traffic signal control method and system based on vehicle network data integration establishes an edge network system based on the location of the target intersection, sets a coverage range, and obtains vehicle information within the coverage range, so as to effectively judge the congestion situation and obtain the approximate time required for vehicles within the coverage range to pass through the intersection. The traffic signal is adjusted according to the time required for different vehicles to pass through the intersection and the specific congestion situation, so as to match the corresponding travel time to the greatest extent according to the driving habits of each driver to achieve the best traffic volume. When congestion occurs, the traffic signal can be controlled according to the different congestion situations and the time required for different vehicles to pass through the intersection, so as to achieve a more precise control effect.

[0037] At the same time, according to the different degrees of congestion obtained, the congestion situation is divided into levels according to the vehicle information within the coverage area, and different passage numbers are allocated to different congestion situations, so that when different degrees of congestion occur, the congestion situation can be alleviated more quickly and effectively. According to the different times for different vehicles to pass through the intersection, the original passage time is replaced with the passage number, and the passage time is reversely determined based on the passage number, making the passage more targeted. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the relationship between position acquisition, range setting, data acquisition and time adjustment of the present invention;

[0039] Figure 2 It is a schematic flow chart of the adjustment method of the present invention;

[0040] Figure 3 It is a schematic diagram of the relationship between the coverage and target location items of the present invention;

[0041] Figure 4 It is a schematic diagram of the distribution of vehicle items of the present invention;

[0042] Figure 5 The present invention is a schematic diagram of the vehicle time item acquisition process. DETAILED DESCRIPTION

[0043] 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.

[0044] When a traffic jam occurs, in order to maximize the traffic efficiency, it is necessary to adjust the traffic light timing of the traffic light according to the real-time traffic data to adapt to different traffic flows. In the specific adjustment process, due to the driving habits of each driver, some drivers are careful and will pass through the intersection at a slower speed, resulting in the same traffic light timing. The congestion of some roads cannot be effectively improved, which leads to poor control of traffic signals and insufficient intelligence. During use, the technical solution provided by the present application establishes an edge network system based on the location of the target intersection, sets a coverage range, and obtains vehicle information within the coverage range, so that the congestion situation can be effectively judged, and the congestion situation is divided into levels according to the vehicle information within the coverage range, different travel times are allocated for different congestion situations, and the approximate time required for vehicles within the coverage range to pass the intersection is obtained. According to the time required for different vehicles to pass the intersection and the specific congestion situation, the traffic signal is adjusted, so that the corresponding travel time can be matched to the greatest extent according to the driving habits of each driver to achieve the best traffic volume.

[0045] like Figure 1-Figure 4As shown, the present invention provides a technical solution: an intelligent traffic signal control method and system based on vehicle network data integration, including: position acquisition: acquiring the position information of the target intersection to obtain the target position item, and establishing an edge computing network based on the target position item to obtain the target edge network item; range setting: setting the coverage range to obtain the coverage range item, taking the target position item as the center, acquiring the vehicle number information within the coverage range item to obtain a vehicle number set, the vehicle number set including at least one vehicle item, setting a division threshold, dividing the vehicle number set into at least two levels, and obtaining at least two congestion level items; data acquisition: based on the vehicle number set, acquiring the time required for the vehicle items in the vehicle number set to completely pass through the intersection through an acquisition method to obtain a vehicle time item, based on real-time acquisition of the vehicle number information within the coverage range item, classifying the vehicle number set into levels to obtain a target congestion level item; time adjustment: based on the target congestion level item, adjusting the traffic The signal is controlled. When congestion occurs, the traffic signal is controlled according to the different congestion conditions and the time required for different vehicles to pass through the intersection, so as to achieve a more precise control effect. The adjustment method includes: step 1: determining the number of passes, setting the initial number of passes, setting the increase value based on the congestion level item, and obtaining at least two increase items, the increase items are matched with the congestion level items respectively, and then the number of passes matching the different congestion level items are obtained, and at least two number of passes items are obtained; step 2: target selection, based on the target congestion level item, the number of passes item matching the target congestion level item is selected to obtain the target number of passes item; step 3: data transmission, the vehicle time item and the target number of passes item are transmitted to the target edge network item, and the traffic signal is adjusted based on the vehicle time item and the target number of passes. In the event of congestion, the corresponding pass time can be matched to the greatest extent according to the driving habits of each driver to achieve the best traffic volume.

[0046] It should be noted that the location information of the target intersection is first obtained, and the edge computing network is set at the target intersection, and the coverage range is set at the same time. The coverage range is the range extending outward from the target intersection location as the center, and the range is set to a road area with a radius of 500m, where there are at least three road areas. Within the coverage range, the Internet of Vehicles data generated by the vehicle during the startup and driving process will converge in the edge computing network, so that the edge computing network can better control the traffic signal of the target intersection according to the specific Internet of Vehicles data, obtain the number of vehicles in the coverage range, and divide the congestion into three levels by division, namely, no congestion, slight congestion and severe congestion, and classify the real-time congestion according to the number of vehicles in the coverage range obtained in real time, obtain the target congestion level item, and control the traffic signal according to the adjustment method, so that when congestion occurs, the traffic signal can be controlled according to the different congestion conditions and the time required for different vehicles to pass through the intersection, so as to achieve more precise control effect.

[0047] It should be noted that the initial traffic quantity is determined first. In the absence of congestion, the initial traffic quantity is set to 20. At this time, according to the three levels of division, the set increase value is 10 respectively, and two increase items are obtained, namely 30 and 40. The increase items 30 and 40 match the slight congestion and severe congestion respectively, and then three traffic quantity items are obtained, namely 20, 30 and 40. According to the real-time information on the number of vehicles within the coverage area, the congestion situation is judged, and the target traffic quantity item is obtained. Based on the vehicle network data, the time required for the vehicle to pass the intersection and the target traffic quantity item are transmitted to the edge computing network, and the edge computing network adjusts the traffic signal according to the time required for the vehicle to pass the intersection and the target traffic quantity item.

[0048] In the specific implementation process, a certain intersection is a four-way intersection, one horizontal and one vertical. In the road area within the coverage range, the four-way intersection is slightly congested. At this time, the number of horizontal and vertical rotations is 30 respectively. The traffic signal is adjusted by predicting the total time required for 30 vehicles to pass through the intersection based on the time required for the vehicle to pass through the intersection, so that the adjustment result is more targeted.

[0049] like Figure 5 As shown, the acquisition method includes: S1: preliminary time acquisition, based on the Internet of Vehicles data, obtaining the time when the target vehicle item passes through the intersection when it is in the first place, and obtaining the preliminary time item; S2: comprehensive time acquisition, based on the Internet of Vehicles data, obtaining the time when the target vehicle item passes through the intersection when it is not in the first place, and obtaining the comprehensive time item; S3: position judgment, based on the Internet of Vehicles data, obtaining the position of the vehicle item, obtaining the time corresponding to different vehicle items, and then obtaining the vehicle time item.

[0050] It should be noted that since the vehicle network data will record various data such as the vehicle's speed and driving status during driving, the time required for the vehicle to pass through the intersection can be obtained. When the vehicle is in the first, second, third, etc. positions, the time it takes to pass the intersection is different. Therefore, based on the past vehicle network data, the time required for vehicles in different positions to pass the intersection in previous time periods is obtained, and based on the real-time vehicle location, the time corresponding to different vehicle items is obtained.

[0051] In the specific implementation process, it is obtained through the Internet of Vehicles data that when car A is in the first place, the time it takes to pass the intersection when the red light changes to green is 10s, when it is in the second place, the time it takes to pass the intersection is 14s, and when it is in the third place, the time it takes to pass the intersection is 17s; when car B is in the first place, the time it takes to pass the intersection when the red light changes to green is 11s, when it is in the second place, the time it takes to pass the intersection when it is in the third place, the time it takes to pass the intersection is 19s; when car C is in the first place, the time it takes to pass the intersection when the red light changes to green is 9s, when it is in the second place, the time it takes to pass the intersection when it is in the third place, and so on. The time required for different vehicles to pass the intersection when they are in different positions is obtained, and based on the real-time information obtained, it is concluded that car A is in the first place, car C is in the second place, and car B is in the third place. Therefore, the time it takes for cars ABC to pass the intersection will be based on parking space B, which is 19s.

[0052] The acquisition method also includes: S4: setting the number of times, setting the number of acquisitions, and based on the Internet of Vehicles data, taking the number of acquisitions as a benchmark, respectively acquiring a preliminary time set and a comprehensive time set of the vehicle item in the S1 and S2 states; S5: acquiring the average value, based on the preliminary time set and the comprehensive time set, respectively acquiring the average time data of the vehicle item in the S1 and S2 states, obtaining a preliminary average item and a comprehensive average item, and based on the preliminary average item and the comprehensive average item, obtaining the vehicle time item in S3.

[0053] It should be noted that the number of acquisitions is set to five times. Based on the Internet of Vehicles data, the time required for the vehicle to pass through the intersection at different positions is obtained five times respectively, and the average value is taken to obtain the time when the vehicle finally passes through the intersection. The time when the vehicle passes through the intersection obtained in this way is more accurate.

[0054] In the specific implementation process, the Internet of Vehicles data shows that when car A was in the first place five times, the time it took to pass the intersection when the red light changed to green was 10s, 11s, 11s, 9s, and 9s, so the average preliminary time of car A was 10s.

[0055] The acquisition method also includes: S6: time sorting. In the process of S4, the preliminary time set and the comprehensive time set are sorted to obtain the preliminary sorted time set and the comprehensive sorted time set; S7: elimination item determination. Based on the preliminary sorted time set and the comprehensive sorted time set, elimination items are set to eliminate the preliminary time items and the comprehensive time items in the preliminary sorted time set and the comprehensive sorted time set to obtain the screened preliminary time set and the screened comprehensive time set, and repeat step S5 to obtain the vehicle time item.

[0056] It should be noted that the preliminary time items and the comprehensive time items in the preliminary time set and the comprehensive time set are eliminated by setting elimination items, wherein the set elimination items are the first and the last items ranked in the preliminary sorting time set and the comprehensive sorting time set. In this way, incidental situations that may occur when vehicles pass through intersections can be eliminated.

[0057] In the specific implementation process, it is obtained through the Internet of Vehicles data that when car A ranked first five times, the time it took to pass the intersection when the red light changed to green was 10s, 11s, 9s, 18s, and 9s. The time was sorted and the time it took to pass the intersection five times was 18s, 11s, 10s, 9s, and 9s. 18s and 9s were eliminated, and the average preliminary time of car A was 10s.

[0058] The method for obtaining a vehicle quantity set includes: M1: equipment layout, selecting a high-definition camera as the target device, and setting at least two target devices based on the coverage range item; M2: image acquisition, obtaining vehicle information images within the coverage range in real time based on the target device, obtaining range image items, and obtaining vehicle number data based on the range image items to obtain a vehicle quantity set.

[0059] It should be noted that the vehicle information image within the coverage area is obtained by a high-definition camera, and the vehicle data is obtained based on the image, wherein the number of target devices is three.

[0060] The method for obtaining the vehicle quantity set also includes: data sharing, based on the target edge network item and the Internet of Vehicles data, obtaining the number of devices connected to the target edge network item, obtaining the device quantity set, and then obtaining the vehicle quantity set.

[0061] It should be noted that since the Internet of Vehicles data generated during the vehicle startup and driving process will converge in the edge computing network, the specific number of connected devices can be obtained based on the edge computing network, and then the vehicle quantity set can be obtained.

[0062] The method for setting the partition threshold includes: N1: capacity acquisition, based on the coverage range item, acquiring the capacity information of the target area to obtain the capacity data item; N2: first ratio setting, based on the capacity data item, setting at least one first ratio, dividing the capacity data item into at least two parts according to the first ratio, and then obtaining the partition threshold.

[0063] It should be noted that the capacity of the target area is first obtained and a first ratio is set. The first ratios are 33% and 66% respectively. At this time, the capacity is divided into three parts according to the first ratio, namely 0-33%, 33%-66% and 66%-100% respectively. At this time, these three parts correspond to no congestion, slight congestion and severe congestion respectively. This division method is used to obtain the congestion situation more accurately when large vehicles and small vehicles are mixed.

[0064] The method for setting the division threshold also includes: N3: quantity calculation, based on the capacity data item, obtaining the accommodative quantity of the target area and obtaining the maximum quantity item; N4: second ratio setting, based on the maximum quantity item, setting at least one second ratio, dividing the maximum quantity item into at least two parts according to the second ratio, and then obtaining the division threshold.

[0065] It should be noted that the capacity is obtained based on the capacity data item, and two second ratios are set according to the maximum capacity. The second ratios are 33% and 66% respectively. The maximum capacity is divided into three parts according to the second ratios, namely 0-33%, 33%-66% and 66%-100% respectively. At this time, these three parts correspond to no congestion, slight congestion and severe congestion respectively. This division method can be used to quickly obtain the congestion situation when there are no large vehicles.

[0066] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. An intelligent traffic signal control method based on vehicle network data integration, characterized in that: include: Location acquisition: Get the location information of the target intersection, get the target location item, and establish an edge computing network based on the target location item to get the target edge network item; Range setting: setting the coverage range, obtaining the coverage range item, taking the target location item as the center, obtaining the vehicle number information within the coverage range item, obtaining the vehicle number set, the vehicle number set including at least one vehicle item, setting the division threshold, dividing the vehicle number set into at least two levels, and obtaining at least two congestion level items; Data acquisition: Based on the vehicle quantity set, the time required for the vehicle items in the vehicle quantity set to completely pass through the intersection is obtained through an acquisition method to obtain the vehicle time item. Based on the real-time acquisition of the number of vehicles within the coverage item, the vehicle quantity set is classified into levels to obtain the target congestion level item; Time adjustment: Based on the target congestion level item, the traffic signal is controlled through the adjustment method. When congestion occurs, the traffic signal is controlled according to the different congestion conditions and the time required for different vehicles to pass through the intersection, which can achieve a more precise control effect; The adjustment method comprises: Step 1: Determine the number of traffic, set the initial number of traffic, set the increase value based on the congestion level item, obtain at least two increase items, the increase items are matched with the congestion level items respectively, and then obtain the number of traffic matching different congestion level items, and obtain at least two traffic number items; Step 2: Target selection: based on the target congestion level item, select the traffic quantity item that matches the target congestion level item to obtain the target traffic quantity item; Step 3: Data transmission: the vehicle time item and the target traffic quantity item are transmitted to the target edge network item, and the traffic signal is adjusted based on the vehicle time item and the target traffic quantity item. In the event of congestion, the corresponding traffic time can be matched to the greatest extent according to the driving habits of each driver to achieve the best traffic volume; The acquisition method comprises: S1: Preliminary time acquisition: Based on the Internet of Vehicles data, the time when the target vehicle item is in the first position and passes through the intersection is obtained to obtain the preliminary time item; S2: Comprehensive time acquisition: Based on the Internet of Vehicles data, the time it takes for the target vehicle item to pass the intersection when it is not in the first place is obtained to obtain the comprehensive time item; S3: Position judgment: Based on the Internet of Vehicles data, the position of the vehicle item is obtained, the time corresponding to different vehicle items is obtained, and then the vehicle time item is obtained; The methods for setting the partition threshold include: N1: Capacity acquisition, based on the coverage item, obtain the capacity information of the target area and obtain the capacity data item; N2: first ratio setting: based on the capacity data item, at least one first ratio is set, and the capacity data item is divided into at least two parts according to the first ratio, thereby obtaining a division threshold.

2. The intelligent traffic signal control method based on vehicle network data integration according to claim 1 is characterized in that: The acquisition method further includes: S4: setting the number of times, setting the number of acquisitions, and based on the Internet of Vehicles data, taking the number of acquisitions as a benchmark, respectively acquiring the preliminary time set and the comprehensive time set of the vehicle item in the S1 and S2 states; S5: average value acquisition, based on the preliminary time set and the comprehensive time set, respectively obtain the average time data of the vehicle item in the S1 and S2 states, obtain the preliminary average item and the comprehensive average item, and based on the preliminary average item and the comprehensive average item, obtain the vehicle time item in S3.

3. The intelligent traffic signal control method based on vehicle network data integration according to claim 2 is characterized in that: The acquisition method further includes: S6: Time sorting: In the process of S4, the preliminary time set and the comprehensive time set are sorted to obtain a preliminary sorted time set and a comprehensive sorted time set; S7: Determine the elimination items. Based on the preliminary sorting time set and the comprehensive sorting time set, set the elimination items, eliminate the preliminary time items and the comprehensive time items in the preliminary sorting time set and the comprehensive sorting time set, and obtain the screened preliminary time set and the screened comprehensive time set. Repeat step S5 to obtain the vehicle time items.

4. The intelligent traffic signal control method based on vehicle network data integration according to claim 1 is characterized in that: The method for obtaining the vehicle quantity set includes: M1: Device layout, select a high-definition camera as the target device, and set at least two target devices based on the coverage items; M2: Image acquisition, based on the target device, real-time acquisition of vehicle information images within the coverage area to obtain range image items, and based on the range image items, acquisition of vehicle number data to obtain a vehicle quantity set.

5. The intelligent traffic signal control method based on vehicle network data integration according to claim 4 is characterized in that: The method for obtaining the vehicle quantity set also includes: data sharing, based on the target edge network item and the Internet of Vehicles data, obtaining the number of devices connected to the target edge network item, obtaining the device quantity set, and then obtaining the vehicle quantity set.

6. The intelligent traffic signal control method based on vehicle network data integration according to claim 1 is characterized by: The method for setting the division threshold also includes: N3: Quantity calculation, based on the capacity data item, obtain the quantity that can be accommodated in the target area and obtain the maximum quantity item; N4: Setting a second ratio: based on the maximum number of items, setting at least one second ratio; dividing the maximum number of items into at least two parts according to the second ratio, and then obtaining a division threshold.

7. An intelligent traffic signal control system based on vehicle networking data integration, characterized by: An intelligent traffic signal control method based on vehicle network data integration as described in any one of claims 1-6 is used.

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