Road safety monitoring method based on Internet of Things

Abstract

The invention discloses a road safety monitoring method based on the Internet of Things, and relates to the technical field of road safety. A corresponding urban area traffic data center is arranged according to the urban area, and is in wireless communication connection with a traffic navigation system; constructing an urban regional vital communication line map model based on a traffic navigation system, and obtaining road information data; setting a camera data network, obtaining road video image data, sending the road video image data to an urban area traffic data center for storage, and generating an intersection road data set; a data processing model is constructed in an urban regional traffic data center, a road vehicle passing number set corresponding to the intersection road data set is obtained, and a congestion data value range is obtained; a congestion state level is obtained, and an urban regional traffic network system is constructed; and finally, acquiring scheduling data based on an urban area traffic network system, and managing and scheduling vital communication lines corresponding to the urban area according to the scheduling data. According to the invention, real-time monitoring and scheduling of urban roads are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of road safety, in particular to a road safety monitoring method based on Internet of Things. BACKGROUND

[0002] There are many urban roads, which are concentrated in a limited area of the city, and form a network with crisscrossing, resulting in many intersections that affect the smooth flow of traffic. Therefore, various measures such as setting color light signal control, roundabout intersection, channelized traffic, and overpass intersection are needed to facilitate traffic flow. There are many types of urban transportation tools, and the speed difference is large. In order to avoid mutual obstruction and interference, separate lanes are organized, and isolation belts, isolation piers, guardrails or line methods are used for separation.

[0003] In this era of advanced technology, there are more and more transportation tools, but the urban road area is limited. During the traffic peak period, in bad weather or under road repair conditions, the urban road intersection is prone to congestion. However, in the prior art, traffic police are usually used to dredge vehicles and people, which cannot realize real-time monitoring and accurate scheduling, resulting in more serious traffic congestion problems, affecting road utilization and vehicle passing efficiency. Therefore, in order to solve the above problems, a road safety monitoring method based on Internet of Things is provided. SUMMARY

[0004] In order to solve the above technical problems, the present application provides a road safety monitoring method based on Internet of Things.

[0005] The purpose of the present application can be achieved by the following technical scheme: a road safety monitoring method based on Internet of Things, the method comprising the following steps:

[0006] Step S1: setting a corresponding urban area traffic data center according to the name of the urban area, constructing an urban area traffic artery map model, and obtaining road information data;

[0007] Step S2: setting a camera data network, obtaining traffic artery intersection nodes and each connecting road corresponding to the traffic artery intersection nodes, and obtaining road video image data corresponding to each connecting road, and then generating an intersection road data set;

[0008] Step S3: constructing a data processing model in the urban area traffic data center, obtaining a road vehicle passing quantity set corresponding to each connecting road according to the intersection road data set, and then obtaining a congestion data value range corresponding to each connecting road;

[0009] Step S4: obtaining a congestion state level corresponding to each connecting road according to the congestion data value range, and then constructing an urban area traffic network system;

[0010] Step S5: acquiring scheduling data according to the urban area traffic network system, and managing and scheduling the traffic arteries corresponding to the urban area according to the scheduling data.

[0011] Further, the setting process of the urban area traffic data center comprises:

[0012] setting the urban area traffic data center corresponding to the urban area name according to the urban area name.

[0013] The urban area traffic data center is used for storing road traffic information data; the road traffic information data comprises road information data and traffic information data; the road information data comprises position data, road video image data and road state data; and the traffic data comprises congestion data and scheduling data.

[0014] Further, the construction process of the urban area traffic artery map model comprises:

[0015] acquiring each traffic artery name corresponding to the urban area name and sending the traffic artery name to the urban area traffic data center for storage to generate an urban area traffic artery database;

[0016] acquiring the traffic artery map corresponding to each traffic artery name of the urban area traffic artery database according to the traffic navigation system; marking each road intersection in each traffic artery map to generate a traffic artery intersection, acquiring the connected road marked as a connected road corresponding to the traffic artery intersection, and acquiring the node corresponding to the unit length of the connected road from the traffic artery intersection, and then intercepting the connected road according to the node to acquire the traffic artery intersection map, merging each traffic artery intersection map corresponding to the traffic artery name to generate the traffic artery map, and further merging each traffic artery map corresponding to the urban area name to construct the urban area traffic artery map model;

[0017] The traffic navigation system is used for acquiring position data and road state data.

[0018] Further, the acquisition process of the road information data comprises:

[0019] generating a traffic artery intersection node from the traffic artery intersection, acquiring the connected road corresponding to the traffic artery intersection node based on the urban area traffic artery map model, and then acquiring the position data and road state data corresponding to the connected road and sending the position data and road state data to the urban area traffic data center for storage; the road state data comprises congestion state, congestion state waiting time and people flow data.

[0020] Further, the setting process of the camera data network comprises:

[0021] According to the position data corresponding to the connecting road, the traffic camera corresponding to the connecting road is acquired, and a traffic camera node is generated, and then the traffic camera nodes corresponding to the traffic artery intersection nodes are connected to generate an intersection camera node data network, which is used to acquire road video image data corresponding to the connecting road and send to the urban area traffic data center for storage;

[0022] The intersection camera node data networks corresponding to the traffic artery intersection nodes are connected to generate a traffic artery camera node data network, and then the traffic artery camera node data networks are connected to generate a camera data network, and the urban area traffic data center is connected by wireless communication.

[0023] Further, the intersection road data set generation process includes:

[0024] The intersection road data set is generated by data connection of the road information data and the road video image data corresponding to the connecting road connected to the traffic artery intersection node in the urban area traffic data center.

[0025] Further, the data processing model construction process includes:

[0026] According to the intersection road data set, the congestion state waiting time corresponding to the congestion state is obtained, denoted as T, and a plurality of to-and-fro data collection time points are set within the congestion state waiting time, denoted as i, wherein i=1, 2, 3,..., j, and j is a positive integer, the road video image data corresponding to the connecting road is obtained according to the to-and-fro data collection time points, and then the road vehicle to-and-fro quantity corresponding to the to-and-fro data collection time points is obtained, denoted as N i , and the value is a positive integer greater than 0; the road vehicle to-and-fro quantities corresponding to each of the congestion state waiting time are merged to generate a road vehicle to-and-fro quantity set;

[0027] According to the flow data, the road vehicle to-and-fro quantity threshold of the connecting road corresponding to the to-and-fro data collection time point is obtained, and according to the road vehicle to-and-fro quantity set, the congestion data value range of the connecting road is obtained and then the data processing model is constructed.

[0028] Further, the congestion data value range acquisition process includes:

[0029]

[0030] Among them, and respectively represent the maximum value and the minimum value of the congestion data value range corresponding to the connecting road in the congestion state waiting time; represents the road vehicle to-and-fro quantity threshold corresponding to the to-and-fro data collection time point, and h represents the error coefficient corresponding to the people flow data, and h = 1, 2, 3.

[0031] Further, the construction process of the urban area traffic network system comprises:

[0032] obtaining the congestion data according to the range of the congestion data value;

[0033] that is, the specific formula is:

[0034]

[0035] setting a congestion data threshold S T , and comparing with the congestion data:

[0036] if 0 < S T ≤ S T , the corresponding connection road congestion state level is generated as a first-class congestion state;

[0037] if S T > S T , the corresponding connection road congestion state level is generated as a second-class congestion state;

[0038] connecting the range of the congestion data value and the corresponding congestion state level to generate a congestion state level data set, and then sending to the urban area traffic route map model and displaying on the connection road of the corresponding traffic route intersection node to construct the urban area traffic network system.

[0039] Further, the acquisition process of the scheduling data comprises:

[0040] The scheduling data comprises the traffic brigade contact information and the scheduling time;

[0041] obtaining the location data corresponding to the second-class congestion state through the urban area traffic network system, obtaining the traffic brigade contact information of the nearest traffic brigade according to the location data, and then notifying the corresponding traffic brigade to go to the corresponding location data for traffic scheduling according to the scheduling time.

[0042] Compared with the prior art, the beneficial effects of the present application are: according to the corresponding city area traffic data center of the city area setting, and the wireless communication connection traffic navigation system; based on the traffic navigation system, the city area traffic road map model is constructed, and the road information data is obtained; the camera data network is set, the road video image data is obtained and sent to the city area traffic data center for storage, and the intersection road data set is generated; then the data processing model is constructed in the city area traffic data center, the corresponding road vehicle number set of the intersection road data set is obtained, and the congestion data value range is obtained; then the congestion state level is obtained, and the city area traffic network system is constructed; finally, based on the city area traffic network system, the scheduling data is obtained, and the corresponding traffic road of the city area is managed and scheduled according to the scheduling data; the present application realizes the real-time monitoring and scheduling of the city road, and improves the road utilization rate and vehicle passing efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only represent some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0044] Figure 1 The flowchart of the present application.

[0045] Figure 2 The principle diagram of the present application for obtaining the city area traffic network system. DETAILED DESCRIPTION

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only represent some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0047] As shown in Figure 1 , a road safety monitoring method based on Internet of Things, the method comprises the following steps:

[0048] As shown in Figure 1 , a road safety monitoring method based on Internet of Things, the method comprises the following steps:

[0049] Embodiment 1,

[0050] Step S1: according to the corresponding city area traffic data center of the city area name setting, constructing the city area traffic road map model, and obtaining the road information data;

[0051] Step S2: Set up a camera data network, obtain traffic intersection nodes and each connecting road corresponding to the traffic intersection nodes, and obtain road video image data corresponding to each connecting road, and then generate an intersection road data set;

[0052] Step S3: Construct a data processing model in the urban area traffic data center, obtain a road vehicle number set corresponding to each connecting road according to the intersection road data set, and then obtain a congestion data value range corresponding to each connecting road;

[0053] Step S4: Obtain a congestion state level corresponding to each connecting road according to the congestion data value range, and then construct an urban area traffic network system;

[0054] Step S5: Obtain scheduling data according to the urban area traffic network system, and manage and schedule the corresponding traffic arteries of the urban area according to the scheduling data.

[0055] Embodiment 2,

[0056] As Figure 2 shown, the present embodiment is a further limitation of embodiment 1, and the step S1 is implemented by the following process:

[0057] The setting process of the urban area traffic data center includes:

[0058] The urban area traffic data center is used to store road traffic information data; the road traffic information data includes road information data and traffic information data; the road information data includes location data, road video image data and road state data; the traffic data includes congestion data and scheduling data;

[0059] Obtain the name of the city area, and obtain each traffic artery name corresponding to the city area name and send it to the corresponding city area traffic data center for storage to generate a city area traffic artery database;

[0060] The traffic navigation system is used to obtain location data and road state data;

[0061] It should be further pointed out that in specific embodiments, the traffic navigation system includes but is not limited to Baidu Map, Gaode Map, etc.

[0062] The acquisition process of the road information data includes:

[0063] The traffic navigation system obtains a database of traffic arteries in a city area, obtains a corresponding traffic artery map according to the name of each traffic artery, marks each road intersection in the corresponding traffic artery map to generate a traffic artery intersection, obtains a connected road mark corresponding to the connected road of the traffic artery intersection, and obtains a node corresponding to the unit length of the connected road from the traffic artery intersection, and then intercepts the connected road according to the node to obtain a traffic artery intersection map, merges each traffic artery intersection map corresponding to the traffic artery name to generate a traffic artery map, and then merges each traffic artery map corresponding to the city area name to construct a city area traffic artery map model, and the city area traffic artery map model is updated in real time along with the traffic navigation system to obtain road information data corresponding to the connected road.

[0064] It should be further explained that, in the specific implementation process, the unit length is used to represent the congestion state range of each connected road corresponding to the traffic artery intersection that needs to be monitored in real time, wherein the unit length can be set according to actual needs; it should be further explained that the traffic artery intersection map is updated in real time along with the traffic navigation system to obtain road information data corresponding to the connected road, and then the city area traffic artery map model is updated, further, the existing traffic navigation system usually inputs a geographic location name through a Place API interface to obtain corresponding map information data, wherein the map information data includes but is not limited to road information data, i.e. road congestion, accident information, weather influence, etc.

[0065] The traffic artery intersection is generated into a traffic artery intersection node, the connected road corresponding to the traffic artery intersection node is obtained based on the city area traffic artery map model, and then the position data and road state data corresponding to the connected road are obtained and sent to the city area traffic data center for storage; the road state data includes congestion state, congestion state waiting time and people flow data.

[0066] Embodiment 3,

[0067] This embodiment is a further limitation of embodiment 1, and the step S2 is realized by the following process:

[0068] The setting process of the camera data network includes:

[0069] According to the position data corresponding to the connected road, a traffic camera corresponding to the connected road is obtained, and a traffic camera node is generated, and then the traffic camera node corresponding to the traffic artery intersection node is connected to generate an intersection camera node data network, which is used to obtain road video image data corresponding to the connected road and send it to the city area traffic data center for storage;

[0070] The traffic important road intersection node corresponding intersection camera node data network is connected to generate a traffic important road camera node data network, and then each traffic important road camera node data network is connected to generate a camera data network, and is wirelessly connected to a city area traffic data center;

[0071] It should be further explained that in specific embodiments, the road video images obtained by the camera data network are updated in real time, and the road video images include but are not limited to road vehicle traffic images, illegal vehicle images, etc.

[0072] The generation process of the intersection road data set includes:

[0073] The intersection road data set is generated by connecting the road information data and the road video image data corresponding to the connecting road of the traffic important road intersection node connection in the city area traffic data center, and is recorded as {position data, road state data, road video image data}

[0074] It should be further explained that in the specific implementation process, the intersection road data set is stored in the city area traffic data center, and then the road information data in the intersection road data set is processed to obtain a processing result corresponding to the traffic important road intersection node, which is sent to the city area traffic road map model and the corresponding traffic important road intersection node data connection, so that the safety of the city road corresponding city important road intersection can be better monitored in real time.

[0075] Embodiment 4,

[0076] This embodiment is a further limitation of embodiment 1, and the step S3 is realized by the following process:

[0077] The construction process of the data processing model includes:

[0078] According to the intersection road data set, the congestion state waiting time corresponding to the congestion state is obtained, recorded as T, and a plurality of data collection time points are set in the congestion state waiting time, recorded as i, wherein i=1, 2, 3,..., j, and j is a positive integer, the road video image data corresponding to the connecting road is obtained according to the data collection time point, and then the road vehicle traffic quantity corresponding to the data collection time point is obtained, recorded as N i , and the value is a positive integer greater than 0; the road vehicle traffic quantity corresponding to each road vehicle traffic quantity in the congestion state waiting time is merged to generate a road vehicle traffic quantity set, recorded as {N1, N2, N3,..., N j}

[0079] It needs to be further explained that in the specific implementation process, the road video image data corresponding to the connecting road in the intersection camera node data network corresponding to the node of the traffic junction intersection is acquired through the camera data network, and according to the road vehicle passing quantity corresponding to the data collection time point, the road vehicle passing quantity corresponding to the connecting road can be directly acquired, so in the embodiment, it is not specifically described;

[0080] According to the road vehicle passing quantity threshold corresponding to the data collection time point of the connecting road acquired by the people flow data, and according to the road vehicle passing quantity set, the congestion data value range corresponding to the connecting road is acquired Further, the data processing model is constructed;

[0081] That is, the specific formula is:

[0082]

[0083] Among them, and respectively represent the maximum value and the minimum value of the congestion data value range corresponding to the connecting road in the congestion state waiting time; represents the road vehicle passing quantity threshold corresponding to the data collection time point, and h represents the error coefficient corresponding to the people flow data, and h=1, 2, 3;

[0084] It needs to be further explained that in the specific implementation process, the congestion state and the corresponding congestion state waiting time can be directly acquired by using the traffic navigation system, so it is not necessary to make further description in the embodiment; further, if the user positioning route is acquired by using Baidu map, the vehicle quantity passing through the traffic junction intersection in unit time is acquired according to the positioning route, which is recorded as the people flow quantity, the larger the people flow quantity is, the more the road vehicle passing quantity is, and if the road vehicle passing quantity becomes less, the congestion state is formed; further, because the traffic navigation system can collect these data, but the vehicles passing through the traffic junction intersection may not use the traffic navigation system, and the vehicles passing through the traffic junction intersection using the traffic navigation system may not be all vehicles, so there is a certain error between the people flow data acquired by using the traffic navigation system and the actual people flow data, and further, the error coefficient needs to be set according to the people flow data to set the corresponding road vehicle passing quantity threshold; and the value can be determined according to the specific implementation process.

[0085] Embodiment 5,

[0086] This embodiment is a further limitation of embodiment 1, and the step S4 is realized by the following process:

[0087] The congestion state level acquisition process includes:

[0088] obtaining congestion data according to the range of congestion data values;

[0089] That is, the specific formula is:

[0090]

[0091] Setting a congestion data threshold S T ′, and compared with the congestion data:

[0092] If 0<S T ≤S T ′, the corresponding connection road congestion state level is generated as a first-class congestion state;

[0093] If S T >S T ′, the corresponding connection road congestion state level is generated as a second-class congestion state;

[0094] The construction process of the urban area traffic network system comprises:

[0095] The congestion data value range is connected with the corresponding congestion state level to generate a congestion state level data set, and is sent to the urban area traffic data center and the corresponding connection road, and is further sent to the urban area traffic route map model and displayed on the connection road at the corresponding traffic route intersection node, thereby constructing the urban area traffic network system.

[0096] Embodiment 6,

[0097] This embodiment is a further limitation of embodiment 1, and the step S5 is implemented by the following process:

[0098] The scheduling data includes the traffic police contact information and the scheduling time;

[0099] The position data corresponding to the second-class congestion state is obtained through the urban area traffic network system, and the traffic police contact information closest to the position data is obtained according to the position data, and the corresponding traffic police is notified to go to the corresponding position data for traffic scheduling according to the scheduling time.

[0100] The features and exemplary embodiments of various aspects of the present application will be described in detail above, in order to make the purpose, technical scheme and advantages of the present application more clear and clear, the above combines the drawings and specific embodiments, and the present application is further described in detail; It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application; for those skilled in the art, the present application can be implemented without some of these specific details; the above description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0101] The above examples are only used to illustrate the technical method of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present application.

Claims

1. A road safety monitoring method based on Internet of Things, characterized in that, The method comprises the following steps: Step S1: setting a corresponding urban area traffic data center according to the urban area name, constructing an urban area traffic route map model, and obtaining road information data; Step S2: setting a camera data network, obtaining traffic route intersection nodes and each connecting road corresponding to the traffic route intersection nodes, and obtaining road video image data corresponding to each connecting road, and then generating intersection road data sets; Step S3: constructing a data processing model in the urban area traffic data center, obtaining road vehicle traffic volume sets corresponding to each connecting road according to the intersection road data sets, and then obtaining congestion data value ranges corresponding to each connecting road; Step S4: obtaining congestion state levels corresponding to each connecting road according to the congestion data value ranges, and then constructing an urban area traffic network system; Step S5: obtaining scheduling data according to the urban area traffic network system, and managing and scheduling the corresponding traffic routes of the urban area according to the scheduling data.

2. The road safety monitoring method based on the Internet of Things according to claim 1, characterized in that, The setting process of the urban area traffic data center comprises: Setting an urban area traffic data center corresponding to the urban area name according to the urban area name; The urban area traffic data center is used for storing road traffic information data; the road traffic information data comprises road information data and traffic information data; the road information data comprises position data, road video image data, and road state data; the traffic data comprises congestion data and scheduling data.

3. The method for monitoring road safety based on Internet of Things according to claim 2, characterized in that, The construction process of the urban area traffic route map model comprises: Obtaining each traffic route name corresponding to the urban area name and sending it to the urban area traffic data center for storage to generate an urban area traffic route database; Obtaining traffic route maps corresponding to each traffic route name of the urban area traffic route database according to a traffic navigation system; marking each road intersection in each traffic route map to generate traffic route intersections, obtaining connecting roads connected to the traffic route intersections, and obtaining nodes corresponding to the unit length of the connecting roads from the traffic route intersections, and then cutting the connecting roads according to the nodes to obtain traffic route intersection maps, merging each traffic route intersection map corresponding to the traffic route name to generate a traffic route map, and then merging each traffic route map corresponding to the urban area name to construct an urban area traffic route map model.

4. The road safety monitoring method based on the Internet of Things according to claim 3, characterized in that, The obtaining process of the road information data comprises: Generating traffic route intersection nodes from traffic route intersections, obtaining connecting roads corresponding to the traffic route intersection nodes based on the urban area traffic route map model, and then obtaining position data and road state data corresponding to the connecting roads and sending them to the urban area traffic data center for storage; the road state data comprises congestion state, congestion state waiting time, and people flow data.

5. The method for monitoring road safety based on Internet of Things according to claim 4, characterized in that, The setting process of the camera data network comprises: According to the position data corresponding to the connecting road, the traffic camera corresponding to the connecting road is acquired, and a traffic camera node is generated, and then the traffic camera nodes corresponding to the traffic artery intersection nodes are connected to generate an intersection camera node data network, which is used to acquire road video image data corresponding to the connecting road and send to the urban area traffic data center for storage; The intersection camera node data networks corresponding to the traffic artery intersection nodes are connected to generate a traffic artery camera node data network, and then the traffic artery camera node data networks are connected to generate a camera data network, and are wirelessly connected to the urban area traffic data center.

6. The road safety monitoring method based on the Internet of Things according to claim 5, characterized in that, The generation process of the intersection road data set includes: The intersection road data set is generated by connecting the road information data and the road video image data corresponding to the connecting road connected to the traffic artery intersection node in the urban area traffic data center.

7. The road safety monitoring method based on the Internet of Things according to claim 6, characterized in that, The construction process of the data processing model includes: According to the intersection road data set, the congestion state waiting time corresponding to the congestion state is obtained, denoted as T, and a plurality of to-and-fro data collection time points are set within the congestion state waiting time, denoted as i, wherein i = 1, 2, 3, …, j, and j is a positive integer, the road video image data corresponding to the connecting road is obtained according to the to-and-fro data collection time points, and then the road vehicle to-and-fro quantity corresponding to the to-and-fro data collection time points is obtained, denoted as N i , and the value is a positive integer greater than 0; the road vehicle to-and-fro quantities corresponding to the congestion state waiting time are merged to generate a road vehicle to-and-fro quantity set; According to the person flow data, a road vehicle to-and-fro quantity threshold corresponding to a to-and-fro data collection time point of a connecting road is acquired, and according to a road vehicle to-and-fro quantity set, a congestion data value range corresponding to the connecting road is acquired Further, a data processing model is constructed. 8.The road safety monitoring method based on the Internet of Things according to claim 7, characterized in that, The acquisition process of the congestion data value range includes: wherein, and respectively represent the maximum and minimum values of the corresponding congestion data value range in the waiting time of the connecting road in the congestion state; represents the road vehicle flow threshold value corresponding to the data collection time point, and h represents the error coefficient corresponding to the people flow data, and h = 1, 2, 3. 9.The road safety monitoring method based on the Internet of Things according to claim 7, characterized in that, The construction process of the urban area traffic network system includes: According to the congestion data value range, congestion data is acquired; The specific formula is: Setting a crowd data threshold S T ' and comparing it to the crowd data: If 0 < S T ≤ S T ′, the congestion state level corresponding to the connecting road is generated as a first-class congestion state; If S T > S T ', the congestion level corresponding to the connecting road is generated as a second-level congestion state. The congestion state level data set is generated by connecting the congestion data value range and the corresponding congestion state level, and then is sent to the urban area traffic artery map model and displayed on the connecting road of the corresponding traffic artery intersection node, and the urban area traffic network system is constructed. 10.The road safety monitoring method based on the Internet of Things according to claim 9, characterized in that, The acquisition process of the scheduling data includes: The scheduling data includes the contact information of the traffic police team and the scheduling time; Through the urban area traffic network system, the position data and the scheduling time corresponding to the secondary level congestion state are acquired, the contact information of the traffic police team closest to the position data is acquired according to the position data, and then the corresponding traffic police team is notified to go to the corresponding position data for traffic scheduling according to the scheduling time.

Images