Traffic monitoring system and method based on intelligent traffic internet of things

Abstract

The invention provides a traffic monitoring system and method based on an intelligent traffic internet of things, and the method comprises the steps: collecting the traffic flow information of all traffic monitoring points at different sampling moments through the deployment of a data collection device; for each sampling moment, extracting a plurality of transportation junction points and a road network subarea of each transportation junction point, determining the traffic saturation of each road network subarea, and further obtaining the traffic saturation of each road network subarea in each sampling moment; determining the offset of the position information at different sampling moments, and determining the comprehensive traffic indexes of the road network region at different sampling moments according to the traffic saturation of each road network sub-region and the offset of the corresponding transportation junction point; and according to the comprehensive traffic index, traffic situations of the road network area at different sampling moments are subjected to abnormity monitoring, and a monitoring result is sent to an Internet of Things platform through a wireless network. By adopting the scheme provided by the invention, the subarea division can be adjusted in real time, so that the traffic response speed in the road network area is improved.

Description

Technical Field

[0001] The present application relates to the field of traffic monitoring technology, and more specifically, to a traffic monitoring system and method based on intelligent transportation Internet of Things. Background Art

[0002] Traffic monitoring involves a variety of technical means and equipment for real-time collection, analysis and management of information such as traffic flow, vehicle behavior and road conditions. Its main goal is to improve traffic efficiency, reduce congestion, enhance road safety and support traffic management decisions. In terms of hardware, traffic monitoring usually includes video surveillance cameras, radar detectors, infrared sensors, ground sensing coils and other equipment to collect traffic flow, vehicle speed, license plate information and environmental parameters. In terms of software, technologies based on big data analysis, artificial intelligence and the Internet of Things are widely used to process the collected traffic data and generate results such as traffic situation assessment, anomaly detection and optimization suggestions. Traffic monitoring based on the intelligent transportation Internet of Things is an important part of the modern intelligent transportation system. It deeply integrates the Internet of Things devices with traffic monitoring technology to achieve real-time monitoring and management of traffic flow, vehicle dynamics and road conditions. It aims to use a variety of sensor devices (such as cameras, radar sensors, ground sensing coils, vehicle-mounted equipment, etc.) as data collection terminals, and aggregate and analyze the collected traffic data through the Internet of Things platform. In the existing traffic monitoring process based on the intelligent transportation Internet of Things, the traffic flow information in each sub-area is usually collected to build a traffic database, and then the data that deviates significantly from the expectation is mined to realize abnormal monitoring of the traffic situation in the road network area. However, the road network sub-areas divided by the Newman algorithm are relatively fixed, which causes the traffic situation to lag in different road network sub-areas (that is, when the traffic pressure in a road network sub-area suddenly increases, other road network sub-areas may not respond immediately), which leads to the system's insufficient response to traffic changes in the road network area, reducing the efficiency of abnormal monitoring; therefore, how to adjust the sub-area division in real time to improve the traffic response speed in the road network area has become a difficult problem faced by the industry. Summary of the invention

[0003] The present application provides a traffic monitoring system and method based on an intelligent transportation Internet of Things, which can adjust sub-area divisions in real time to improve the traffic response speed in a road network area.

[0004] In a first aspect, the present application provides a traffic monitoring method based on an intelligent transportation Internet of Things, wherein the intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform, and the data acquisition device performs real-time data transmission with the Internet of Things platform via a wireless network. The method comprises the following steps: After the data collection device is deployed at each traffic monitoring point in the road network area, the traffic flow information of all traffic monitoring points at different sampling times is collected; At each sampling moment, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points through the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points; Determine the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then obtain the traffic saturation of each road network sub-area at each sampling moment; Determine the offset of each traffic hub location information between different sampling times, and determine the comprehensive traffic index of the road network area at different sampling times through the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub location information; The traffic situation of the road network area at different sampling times is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things platform via a wireless network.

[0005] In some embodiments, extracting multiple traffic hubs and road network sub-areas corresponding to each traffic hub from all traffic monitoring points through location information and traffic flow information of each traffic monitoring point specifically includes: Based on the impact of different vehicle types on traffic situation and the traffic flow information of each traffic monitoring point, information is integrated to obtain the traffic flow characteristic value of each traffic monitoring point; The degree of convergence of traffic situation between every two traffic monitoring points is determined through the location information of each traffic monitoring point and the vehicle flow characteristic value of each traffic monitoring point; Based on the closeness of the traffic situation between every two traffic monitoring points, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are divided among all traffic monitoring points. In some embodiments, determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area specifically includes: Selecting a road network sub-area as a selected road network sub-area; Determine the traffic carrying capacity in the selected road network sub-area; Determine the traffic saturation of the selected road network sub-area by using the vehicle flow information of all traffic monitoring points in the selected road network sub-area and the traffic carrying capacity; Continue to determine the traffic saturation of the remaining road network sub-areas.

[0006] In some embodiments, determining the offset of the location information of each transportation hub point between different sampling moments specifically includes: Select a sampling time as the selected sampling time; Get the adjacent sampling time of the selected sampling time; Determine the offset of the position information of each transportation hub point between the selected sampling time and the adjacent sampling time by selecting the position information of each transportation hub point at the sampling time and the position information of each transportation hub point at the adjacent sampling time; Continue to determine the offset of the location information of each transportation hub point between the remaining sampling moments and the corresponding adjacent sampling moments.

[0007] In some embodiments, determining the comprehensive traffic index of the road network area at different sampling times by using the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub location information specifically includes: Setting a saturation factor and an offset factor according to the area type of the road network area; Select a sampling time as the selected sampling time; Select a road network area at the selected sampling time as the selected road network sub-area; Get the traffic hub corresponding to the selected road network sub-area; Determine the traffic index component of the selected road network sub-area at the selected sampling time according to the traffic saturation of the selected road network sub-area, the offset of the location information of the traffic hub point, the saturation factor and the offset factor; Continue to determine the traffic index components of the remaining road network sub-areas at the selected sampling time, and then determine the comprehensive traffic index of the road network area at the selected sampling time; Continue to determine the comprehensive traffic index of the road network area in the remaining sampling moments.

[0008] In some embodiments, performing abnormal monitoring of the traffic situation of the road network area at different sampling times according to the comprehensive traffic index specifically includes: Setting a traffic anomaly threshold value of the comprehensive traffic index in the road network area; Anomaly judgment is performed on the comprehensive traffic index corresponding to the road network area at different sampling times by using the traffic anomaly threshold; Based on the judgment result, abnormal traffic conditions in the road network area at different sampling times are monitored.

[0009] In some embodiments, the data collection devices include radar sensors, global positioning system devices, and traffic cameras.

[0010] In a second aspect, the present application provides a traffic monitoring system based on an intelligent transportation Internet of Things, wherein the intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform, and the data acquisition device performs real-time data transmission with the Internet of Things platform via a wireless network, and the system includes: A collection module is used to collect the traffic flow information of all traffic monitoring points at different sampling times after deploying the data collection device at each traffic monitoring point in the road network area; A processing module, for extracting, at each sampling moment, a plurality of traffic hubs and a road network sub-area corresponding to each traffic hub from all traffic monitoring points through the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes a plurality of traffic monitoring points; The processing module is further used to determine the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then obtain the traffic saturation of each road network sub-area at each sampling moment; The processing module is further used to determine the offset of the location information of each traffic hub point between different sampling moments, and determine the comprehensive traffic index of the road network area at different sampling moments through the traffic saturation of each road network sub-area and the offset of the location information of the corresponding traffic hub point; The execution module is used to perform abnormal monitoring of the traffic situation in the road network area at different sampling times according to the comprehensive traffic index, and send the monitoring results to the Internet of Things platform via a wireless network.

[0011] In a third aspect, the present application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned traffic monitoring method based on intelligent transportation Internet of Things when executing the computer program.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the above-mentioned traffic monitoring method based on intelligent transportation Internet of Things.

[0013] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the traffic monitoring system and method based on the intelligent transportation Internet of Things provided in the present application, data collection devices are deployed at each traffic monitoring point in the road network area to collect traffic flow information of all traffic monitoring points at different sampling times; for each sampling time, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points through the position information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points; the traffic saturation of each road network sub-area is determined based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then the traffic saturation of each road network sub-area at each sampling time is obtained; the offset of the position information of each traffic hub point between different sampling times is determined, and the comprehensive traffic index of the road network area at different sampling times is determined through the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub point position information; the traffic situation of the road network area at different sampling times is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things platform via a wireless network.

[0014] It can be seen that in the present application, firstly, by deploying data collection devices at various traffic monitoring points in the road network area, and then collecting the traffic flow information of all traffic monitoring points at different sampling times, the traffic hubs are dynamically extracted according to the real-time sampling data, and the road network sub-areas matching the current traffic situation are divided, so as to not rely on a fixed road network division method and be able to adapt to the changes in traffic flow distribution at different times. Then, after determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, the traffic load situation of each road network sub-area is quickly quantified, especially for areas with sudden increase in traffic pressure, which can be perceived in time, thereby avoiding the situation of each road network sub-area. The time lag phenomenon between the areas is determined, and then the offset of the location information of each transportation hub point between different sampling times is determined, and the comprehensive traffic index of the road network area at different sampling times is determined through the traffic saturation of each road network sub-area and the offset of the corresponding transportation hub point location information, that is: the division of the road network sub-area is optimized in combination with the offset data to dynamically update the regional boundaries to avoid the problem of untimely response to traffic changes due to fixed division (time lag phenomenon); finally, the traffic situation of the road network area at different sampling times is monitored for abnormalities according to the comprehensive traffic index; in summary, the scheme can adjust the sub-area division in real time to improve the traffic response speed in the road network area. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a traffic monitoring method based on intelligent transportation Internet of Things according to some embodiments of the present application; Figure 2 It is a schematic diagram of the process of dividing the transportation hub points according to some embodiments of the present application; Figure 3 It is a flowchart of implementing abnormality monitoring according to some embodiments of the present application; Figure 4 It is a flow chart of a traffic monitoring system based on the intelligent transportation Internet of Things according to some embodiments of the present application; Figure 5 It is an internal structure diagram of a computer device for implementing a traffic monitoring method based on an intelligent transportation Internet of Things as shown in some embodiments of the present application. DETAILED DESCRIPTION

[0016] In order to better understand the technical solution in this embodiment, the technical solution in this embodiment will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0017] refer to Figure 1 , which is a flow chart of a traffic monitoring method based on the intelligent transportation Internet of Things according to some embodiments of the present application. The traffic monitoring method 100 based on the intelligent transportation Internet of Things mainly includes the following steps: In step 101, the data collection device is deployed at each traffic monitoring point in the road network area, and the traffic flow information of all traffic monitoring points at different sampling times is collected.

[0018] It should be noted that the traffic monitoring point mentioned in this application refers to the specific location of equipment or facilities set up in the traffic network for collecting, monitoring and analyzing information such as traffic flow, vehicle speed, vehicle type, traffic conditions, etc. The data acquisition device includes radar sensors, global positioning system (GPS) equipment and traffic cameras. In addition, the traffic information includes the traffic flow and vehicle type of the corresponding traffic monitoring point within the preset sampling time. The preset sampling time is 10 minutes, and the vehicle types include small cars, buses and non-motor vehicles.

[0019] In step 102, for each sampling moment, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points through the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points.

[0020] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of dividing traffic hubs shown in some embodiments of the present application. The following steps can be used to extract multiple traffic hubs and road network sub-areas corresponding to each traffic hub from all traffic monitoring points through the location information and traffic flow information of each traffic monitoring point: Based on the impact of different vehicle types on traffic situation and the traffic flow information of each traffic monitoring point, information is integrated to obtain the traffic flow characteristic value of each traffic monitoring point; The degree of convergence of traffic situation between every two traffic monitoring points is determined through the location information of each traffic monitoring point and the vehicle flow characteristic value of each traffic monitoring point; Based on the closeness of the traffic situation between every two traffic monitoring points, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are divided among all traffic monitoring points.

[0021] In some embodiments, based on the impact of different vehicle types on the traffic situation and the traffic flow information of each traffic monitoring point, information integration is performed to obtain the traffic flow characteristic value of each traffic monitoring point, which can be achieved by the following steps: Selecting a traffic monitoring point as a selected traffic monitoring point; Obtain the traffic flow of different vehicle types from the traffic flow information of the selected traffic monitoring point; Based on the impact of different vehicle types on the traffic situation, the traffic flow weight of each vehicle type on the traffic situation is set; Determine the traffic flow characteristic value of the selected traffic monitoring point according to the traffic flow weight of each vehicle type to the traffic situation and the traffic flow of each vehicle type; Continue to determine the traffic flow characteristic values ​​of the remaining traffic monitoring points.

[0022] In some preferred embodiments, the space occupied lengths of different vehicle types can be used as the impact of different vehicle types on the traffic situation. Therefore, the traffic flow weight of each vehicle type on the traffic situation is set based on the impact of different vehicle types on the traffic situation, that is, the traffic flow weight of each vehicle type on the traffic situation is set based on the space occupied lengths of different vehicle types. As a preferred embodiment, when the space occupied length of the vehicle type is large, for example: the space occupied length of the bus is 12 meters, the traffic flow weight of the bus on the traffic situation is set to 0.6; the space occupied length of the motorcycle is 2 meters, then the traffic flow weight of the motorcycle on the traffic situation is set to 0.1, and the traffic flow weight is a constant with a value range of (0, 1).

[0023] In specific implementation, the traffic flow characteristic value of the selected traffic monitoring point is determined according to the flow weight of each vehicle type to the traffic situation and the traffic flow of each vehicle type. The following method can be used, namely: first, a vehicle type is selected as the selected vehicle type, and then the traffic flow weight of the selected vehicle type is obtained, and the traffic flow weight is multiplied by the traffic flow of the selected vehicle type in the selected traffic monitoring point, and the results corresponding to the remaining vehicle types are continued to be determined. Finally, the value obtained by adding the results corresponding to all vehicle types is used as the traffic flow characteristic value of the fixed traffic monitoring point. In other embodiments, other methods can also be used for determination, which are not limited here.

[0024] It should be noted that the traffic flow characteristic value described in the present application is a comprehensive quantitative indicator of the traffic flow at the traffic monitoring point, reflecting the complexity of the traffic situation at the corresponding traffic monitoring point. The larger the traffic flow characteristic value, the higher the complexity of the traffic situation at the corresponding traffic monitoring point; the smaller the traffic flow characteristic value, the lower the complexity of the traffic situation at the corresponding traffic monitoring point.

[0025] In specific implementation, the following method can be used to determine the degree of closeness of the traffic situation between every two traffic monitoring points through the location information of each traffic monitoring point and the vehicle flow characteristic value of each traffic monitoring point, namely: first, after the location information and vehicle flow characteristic value of each traffic monitoring point are used to form a three-dimensional coordinate, the three-dimensional coordinates of all traffic monitoring points are used as input, and the Euclidean distance algorithm in the prior art is used to calculate the Euclidean distance between every two traffic monitoring points, and then the corresponding Euclidean distance is used as the degree of closeness of the traffic situation between every two traffic monitoring points. In other embodiments, other methods can also be used to determine it, which is not limited here.

[0026] It should be noted that the degree of convergence of traffic situations described in the present application is an indicator representing the degree of similarity of traffic situations between two traffic monitoring points. The greater the degree of convergence of traffic situations, the higher the degree of similarity of traffic situations between the corresponding two traffic monitoring points. The smaller the degree of convergence of traffic situations, the lower the degree of similarity of traffic situations between the corresponding two traffic monitoring points.

[0027] In specific implementation, based on the closeness of the traffic situation between every two traffic monitoring points, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are divided among all traffic monitoring points. This can be achieved in the following manner: first, after taking the closeness of the traffic situation between every two traffic monitoring points as the similarity index between every two traffic monitoring points in the K-means clustering algorithm, the existing K-means clustering algorithm is used to extract multiple center points and point clusters of each center point from all traffic monitoring points, and then each center point is used as a traffic hub point, and the area composed of the point clusters of each center point is used as the road network sub-area corresponding to each traffic hub point. In other embodiments, other methods can also be used for implementation, which are not limited here.

[0028] It should be noted that the present application extracts multiple central points from all traffic monitoring points as the traffic hub points in the present application, and thus the areas divided in the road network area with each traffic hub point as the center are used as corresponding road network sub-areas. The present application reflects the real-time changes of traffic flow in the road network area through the changes in the positions of the traffic hub points at different sampling times.

[0029] In step 103, the traffic saturation of each road network sub-area is determined based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then the traffic saturation of each road network sub-area at each sampling moment is obtained.

[0030] In some embodiments, determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area can be achieved by using the following steps: Selecting a road network sub-area as a selected road network sub-area; Determine the traffic carrying capacity in the selected road network sub-area; Determine the traffic saturation of the selected road network sub-area by using the vehicle flow information of all traffic monitoring points in the selected road network sub-area and the traffic carrying capacity; Continue to determine the traffic saturation of the remaining road network sub-areas.

[0031] In specific implementation, the traffic carrying capacity in the selected road network sub-area can be determined in the following manner, namely: first, all roads in the selected road network sub-area are obtained, then the traffic flow threshold of each road is obtained, and finally, the traffic flow thresholds of all roads in the selected road network sub-area are added together as the traffic carrying capacity in the selected road network sub-area. In other embodiments, other methods can also be used for determination, which are not limited here.

[0032] It should be noted that the traffic flow threshold described in the present application refers to the maximum load threshold on the road. As a preferred embodiment, the traffic flow threshold can be obtained according to the type of road. For example, when the road is a main urban road, the traffic flow threshold of the road is set to 1500. When the road is a secondary urban road, the traffic flow threshold of the road is set to 1000. In other embodiments, other methods can be used to obtain it, which is not limited here.

[0033] In specific implementation, the traffic saturation of a selected road network sub-area can be determined by using the traffic information of all traffic monitoring points in the selected road network sub-area and the traffic carrying capacity. That is, first, the traffic flow characteristic value of each traffic monitoring point in the traffic flow information of all traffic monitoring points in the selected road network sub-area is obtained, and then, the result of adding up all the traffic flow characteristic values ​​and comparing them with the traffic carrying capacity is used as the traffic saturation of the selected road network sub-area.

[0034] It should be noted that the traffic carrying capacity described in this application refers to the maximum vehicle flow that can pass safely and efficiently in a specific road network sub-area within a unit time. In addition, the traffic saturation is used to characterize the relationship between traffic flow and road carrying capacity, which reflects the degree of traffic pressure in the road network sub-area. The greater the traffic saturation, the higher the degree of traffic pressure in the road network sub-area, and the smaller the traffic saturation, the lower the degree of traffic pressure in the road network sub-area.

[0035] In step 104, the offset of the location information of each traffic hub point between different sampling times is determined, and the comprehensive traffic index of the road network area at different sampling times is determined by the traffic saturation of each road network sub-area and the offset of the location information of the corresponding traffic hub point.

[0036] In some embodiments, determining the offset of the location information of each transportation hub point between different sampling moments may be achieved by using the following steps: Select a sampling time as the selected sampling time; Get the adjacent sampling time of the selected sampling time; Determine the offset of the position information of each transportation hub point between the selected sampling time and the adjacent sampling time by selecting the position information of each transportation hub point at the sampling time and the position information of each transportation hub point at the adjacent sampling time; Continue to determine the offset of the location information of each transportation hub point between the remaining sampling moments and the corresponding adjacent sampling moments.

[0037] In specific implementation, the offset of the position information of each transportation hub point between the selected sampling moment and the adjacent sampling moment can be determined by the position information of each transportation hub point in the selected sampling moment and the position information of each transportation hub point in the adjacent sampling moment. The method is as follows: first, a transportation hub point is selected at the selected sampling moment, and then, the position information of the transportation hub point at the adjacent sampling moment is obtained. Finally, after the position information of the transportation hub point and the position information at the adjacent sampling moment are used as input, the Euclidean distance algorithm in the prior art is used to calculate the Euclidean distance between the transportation hub point at the selected sampling moment and the adjacent sampling moment, and the Euclidean distance is used as the offset of the position information of the transportation hub point between the selected sampling moment and the adjacent sampling moment.

[0038] It should be noted that the offset of the location information described in this application refers to the spatial displacement of the geographical location of the transportation hub due to changes in traffic conditions between different sampling times, which characterizes the degree of change of the traffic situation between different sampling times. The larger the offset of the location information, the higher the degree of change of the traffic situation between different sampling times. The smaller the offset of the location information, the lower the degree of change of the traffic situation between different sampling times.

[0039] In some embodiments, determining the comprehensive traffic index of the road network area at different sampling times by the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub location information can be achieved by the following steps: Setting a saturation factor and an offset factor according to the area type of the road network area; Select a sampling time as the selected sampling time; Select a road network area at the selected sampling time as the selected road network sub-area; Get the traffic hub corresponding to the selected road network sub-area; Determine the traffic index component of the selected road network sub-area at the selected sampling time according to the traffic saturation of the selected road network sub-area, the offset of the location information of the traffic hub point, the saturation factor and the offset factor; Continue to determine the traffic index components of the remaining road network sub-areas at the selected sampling time, and then determine the comprehensive traffic index of the road network area at the selected sampling time; Continue to determine the comprehensive traffic index of the road network area in the remaining sampling moments.

[0040] Preferably, the present application sets the saturation factor and the offset factor by the area type of the road network area, that is: the values ​​of the saturation factor and the offset factor are set according to the different scenario ratios in the road network area. For example, in the highway scenario, the main focus is on the traffic load situation, and too high traffic saturation will lead to potential congestion and safety risks; in the urban main road scenario, due to the large traffic volume and moderate vehicle speed, congestion and traffic fluctuations are prone to occur, so it is necessary to pay attention to both the traffic load situation and the flow trend; in the urban secondary road or branch road scenario, due to the small traffic volume, but the complex dynamic changes of vehicles, and the greater impact of adjacent roads or events, more attention is needed. Pay attention to dynamic changes of vehicles and emergencies. Therefore, preferably, when the proportion of highway scenes in the road network area is relatively large, the saturation factor is set to 0.8, and the offset factor is set to 0.2; when the proportion of urban main road scenes in the road network area is relatively large, the saturation factor is set to 0.6, and the offset factor is set to 0.4; when the proportion of urban secondary roads or branch road scenes in the road network area is relatively large, the saturation factor is set to 0.4, and the offset factor is set to 0.6. In other embodiments, other methods can also be used for setting, which are not limited here.

[0041] It should be noted that the traffic index component described in the present application represents the degree of fluctuation of the traffic operation status of the road network sub-area at the corresponding sampling moment. The larger the traffic index component is, the higher the degree of fluctuation of the traffic operation status of the road network sub-area at the corresponding sampling moment is, and the smaller the traffic index component is, the lower the degree of fluctuation of the traffic operation status of the road network sub-area at the corresponding sampling moment is; as a preferred embodiment, determining the traffic index component of the selected road network sub-area at the selected sampling moment according to the traffic saturation of the selected road network sub-area, the offset of the traffic hub point location information, the saturation factor and the offset factor can be implemented in the following manner, namely: first, add the result of multiplying the traffic saturation of the selected road network sub-area by the saturation factor and the result of multiplying the offset of the corresponding traffic hub point location information of the selected road network sub-area by the offset factor, and use the result obtained as the traffic index component of the selected road network sub-area at the selected sampling moment.

[0042] It should be noted that the comprehensive traffic index described in the present application characterizes the degree of fluctuation of the traffic operation conditions in the road network area at the corresponding sampling time. The larger the comprehensive traffic index is, the higher the degree of fluctuation of the traffic operation conditions in the road network area at the corresponding sampling time. The smaller the comprehensive traffic index is, the lower the degree of fluctuation of the traffic operation conditions in the road network area at the corresponding sampling time. As a preferred embodiment, determining the comprehensive traffic index of the road network area at the selected sampling time can be achieved in the following manner, namely: first, obtaining the traffic index components of each road network sub-area at the selected sampling time, and then taking the average value of all traffic index components as the comprehensive traffic index of the road network area at the selected sampling time. In other embodiments, other methods can also be used to determine it, which will not be repeated here.

[0043] In step 105, the traffic situation of the road network area at different sampling times is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things platform via a wireless network.

[0044] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of a process for implementing abnormal monitoring shown in some embodiments of the present application. The abnormal monitoring of the traffic situation of the road network area at different sampling times according to the comprehensive traffic index can be implemented by the following steps: First, in 1051, a traffic anomaly threshold of a comprehensive traffic index in the road network area is set; Then, in 1052, the traffic anomaly threshold is used to determine the anomaly of the comprehensive traffic index corresponding to the road network area at different sampling times; Finally, in 1053, based on the judgment result, the traffic situation of the road network area at different sampling times is monitored for abnormalities.

[0045] It should be noted that, after obtaining the historical traffic flow information of all traffic monitoring points in the road network area, the present application respectively converts the historical traffic flow information into the traffic flow information in the present application, repeats the above process of determining the comprehensive traffic index from the traffic flow information, and obtains multiple historical comprehensive traffic indexes of the road network area. Subsequently, the average value and standard deviation of each historical comprehensive traffic index are determined, and then the standard deviation is multiplied by the natural constant 2 and the result is added to the average value as the traffic anomaly threshold. In other embodiments, other methods can also be used for setting, which are not limited here.

[0046] In specific implementation, the traffic anomaly threshold is used to make an abnormal judgment on the comprehensive traffic index corresponding to the road network area at different sampling times. This can be achieved in the following manner, namely: first, the comprehensive traffic index of the road network area at a sampling time is obtained; when the comprehensive traffic index is greater than or equal to the traffic anomaly threshold, the traffic situation of the road network area at the sampling time is judged as an abnormal mode; when the comprehensive traffic index is less than the traffic anomaly threshold, the traffic situation of the road network area at the sampling time is judged as a normal mode, and the above steps are repeated to make an abnormal judgment on the comprehensive traffic index of the road network area at the remaining sampling times.

[0047] It should be noted that based on the judgment result, abnormal monitoring of the traffic situation in the road network area at different sampling times is realized, that is: when the traffic situation in the road network area is in normal mode, the processing is skipped and the judgment of the next sampling time is waited for; when the traffic situation in the road network area is in abnormal mode, an alarm is sent to the traffic monitoring center of the road network area to prompt possible traffic abnormalities, and the comprehensive traffic index, sampling time and related information of the corresponding sampling time are recorded into the traffic monitoring center, thereby realizing abnormal monitoring of the traffic situation in the road network area at different sampling times. In addition, the present application utilizes wireless network technology (for example: 5G technology) to wirelessly transmit the collected data (comprehensive traffic index) to a cloud server (Internet of Things platform) for storage.

[0048] In addition, in another aspect of the present application, in some embodiments, the present application provides a traffic monitoring system based on an intelligent transportation Internet of Things, wherein the intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform, and the data acquisition device performs real-time data transmission with the Internet of Things platform via a wireless network, referring to Figure 4 , which is a schematic diagram of the structure of a traffic monitoring system based on the intelligent transportation Internet of Things according to some embodiments of the present application. The traffic monitoring system 200 based on the intelligent transportation Internet of Things includes: a collection module 201, a processing module 202 and an execution module 203, which are respectively described as follows: The acquisition module 201 in this application is mainly used to deploy data acquisition devices at various traffic monitoring points in the road network area, and collect traffic information of all traffic monitoring points at different sampling times; Processing module 202, in the present application, is mainly used to extract multiple traffic hubs and road network sub-areas corresponding to each traffic hub from all traffic monitoring points at each sampling moment through the location information and vehicle flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points; In addition, the processing module 202 in the present application is also used to determine the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then obtain the traffic saturation of each road network sub-area at each sampling time; In addition, the processing module 202 in the present application is also used to determine the offset of the location information of each traffic hub point between different sampling times, and determine the comprehensive traffic index of the road network area at different sampling times through the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub point location information; Execution module 203. In the present application, execution module 203 is mainly used to perform abnormal monitoring of the traffic situation in the road network area at different sampling times based on the comprehensive traffic index, and send the monitoring results to the Internet of Things platform via the wireless network.

[0049] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned traffic monitoring method based on the intelligent transportation Internet of Things.

[0050] In some embodiments, reference Figure 5 , which is an internal structure diagram of a computer device according to a traffic monitoring method based on the intelligent transportation Internet of Things shown in some embodiments of the present application. The traffic monitoring method based on the intelligent transportation Internet of Things in the above embodiments can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0051] The processor 301 may be a general-purpose central processing unit (CPU), or an application specific integrated circuit (ASIC) or one or more for controlling the execution of the traffic monitoring method based on the intelligent transportation Internet of Things in the present application.

[0052] The communication bus 302 is used to transmit information between the above components.

[0053] The memory 303 may be a read only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0054] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The traffic monitoring method based on the intelligent transportation Internet of Things in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0055] The communication interface 304 uses any transceiver-like system for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0056] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0057] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0058] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned traffic monitoring method based on the intelligent transportation Internet of Things.

[0059] In summary, in the traffic monitoring system and method based on the intelligent transportation Internet of Things disclosed in the embodiment of the present application, data collection devices are deployed at each traffic monitoring point in the road network area, so as to collect the traffic flow information of all traffic monitoring points at different sampling times; for each sampling time, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points through the position information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points; the traffic saturation of each road network sub-area is determined based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then the traffic saturation of each road network sub-area at each sampling time is obtained; the offset of the position information of each traffic hub point between different sampling times is determined, and the comprehensive traffic index of the road network area at different sampling times is determined through the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub point position information; the traffic situation of the road network area at different sampling times is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things through a wireless network; the sub-area division can be adjusted in real time to improve the traffic response speed in the road network area.

[0060] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0061] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A traffic monitoring method based on intelligent transportation Internet of Things, wherein: The intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform. The data acquisition device performs real-time data transmission with the Internet of Things platform via a wireless network. The method for traffic monitoring based on the intelligent transportation Internet of Things includes the following steps: After the data collection device is deployed at each traffic monitoring point in the road network area, the traffic flow information of all traffic monitoring points at different sampling times is collected; At each sampling moment, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points through the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes multiple traffic monitoring points; Determine the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then obtain the traffic saturation of each road network sub-area at each sampling moment; Determine the offset of each traffic hub location information between different sampling times, and determine the comprehensive traffic index of the road network area at different sampling times through the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub location information; The traffic situation of the road network area at different sampling times is monitored for abnormalities based on the comprehensive traffic index, and the monitoring results are sent to the Internet of Things platform via a wireless network.

2. The method according to claim 1, characterized in that Through the location information and traffic flow information of each traffic monitoring point, multiple traffic hubs and the road network sub-areas corresponding to each traffic hub are extracted from all traffic monitoring points, including: Based on the impact of different vehicle types on traffic situation and the traffic flow information of each traffic monitoring point, information is integrated to obtain the traffic flow characteristic value of each traffic monitoring point; The degree of convergence of traffic situation between every two traffic monitoring points is determined through the location information of each traffic monitoring point and the vehicle flow characteristic value of each traffic monitoring point; Based on the closeness of the traffic situation between every two traffic monitoring points, multiple traffic hubs and road network sub-areas corresponding to each traffic hub are divided among all traffic monitoring points.

3. The method according to claim 1, characterized in that Determining the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area specifically includes: Selecting a road network sub-area as a selected road network sub-area; Determine the traffic carrying capacity in the selected road network sub-area; Determine the traffic saturation of the selected road network sub-area by using the traffic flow information of all traffic monitoring points in the selected road network sub-area and the traffic carrying capacity; Continue to determine the traffic saturation of the remaining road network sub-areas.

4. The method according to claim 1, characterized in that Determining the offset of the location information of each traffic hub between different sampling times specifically includes: Select a sampling time as the selected sampling time; Get the adjacent sampling time of the selected sampling time; Determine the offset of the position information of each transportation hub point between the selected sampling time and the adjacent sampling time by selecting the position information of each transportation hub point at the sampling time and the position information of each transportation hub point at the adjacent sampling time; Continue to determine the offset of the location information of each transportation hub point between the remaining sampling moments and the corresponding adjacent sampling moments.

5. The method according to claim 1, characterized in that Determining the comprehensive traffic index of the road network area at different sampling times by using the traffic saturation of each road network sub-area and the offset of the corresponding traffic hub location information specifically includes: Setting a saturation factor and an offset factor according to the area type of the road network area; Select a sampling time as the selected sampling time; Select a road network area at the selected sampling time as the selected road network sub-area; Get the traffic hub corresponding to the selected road network sub-area; Determine the traffic index component of the selected road network sub-area at the selected sampling time according to the traffic saturation of the selected road network sub-area, the offset of the location information of the traffic hub point, the saturation factor and the offset factor; Continue to determine the traffic index components of the remaining road network sub-areas at the selected sampling time, and then determine the comprehensive traffic index of the road network area at the selected sampling time; Continue to determine the comprehensive traffic index of the road network area in the remaining sampling moments.

6. The method according to claim 1, characterized in that The abnormal monitoring of the traffic situation of the road network area at different sampling times according to the comprehensive traffic index specifically includes: Setting a traffic anomaly threshold value of the comprehensive traffic index in the road network area; Anomaly judgment is performed on the comprehensive traffic index corresponding to the road network area at different sampling times by using the traffic anomaly threshold; Based on the judgment result, abnormal traffic conditions in the road network area at different sampling times are monitored.

7. The method according to claim 1, characterized in that The data collection device includes a radar sensor, a global positioning system device and a traffic camera.

8. A traffic monitoring system based on intelligent transportation Internet of Things, wherein: The intelligent transportation Internet of Things includes a data acquisition device and an Internet of Things platform. The data acquisition device performs real-time data transmission with the Internet of Things platform via a wireless network. The system is characterized in that: The collection module is used to collect the traffic flow information of all traffic monitoring points at different sampling times after deploying data collection devices at each traffic monitoring point in the road network area; A processing module, for extracting, at each sampling moment, a plurality of traffic hubs and a road network sub-area corresponding to each traffic hub from all traffic monitoring points through the location information and traffic flow information of each traffic monitoring point, wherein the road network sub-area includes a plurality of traffic monitoring points; The processing module is further used to determine the traffic saturation of each road network sub-area based on the traffic flow information of all traffic monitoring points in each road network sub-area, and then obtain the traffic saturation of each road network sub-area at each sampling moment; The processing module is further used to determine the offset of the location information of each traffic hub point between different sampling moments, and determine the comprehensive traffic index of the road network area at different sampling moments through the traffic saturation of each road network sub-area and the offset of the location information of the corresponding traffic hub point; The execution module is used to perform abnormal monitoring of the traffic situation in the road network area at different sampling times according to the comprehensive traffic index, and send the monitoring results to the Internet of Things platform via a wireless network.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the traffic monitoring method based on the intelligent transportation Internet of Things described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the traffic monitoring method based on the intelligent transportation Internet of Things as described in any one of claims 1 to 7 are implemented.

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