CN117831267A - Road monitoring system based on multisource data fusion - Google Patents
Road monitoring system based on multisource data fusion Download PDFInfo
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Abstract
The invention discloses a road monitoring system based on multi-source data fusion, which belongs to the technical field of road safety and comprises an infrastructure layer, a network layer, a data layer, a supporting layer, an application service layer, an application layer and a presentation layer, wherein the infrastructure layer is used for providing front-end perception data for the data layer, and the front-end perception data comprises original high-speed perception data and newly-added high-speed perception data; the application service layer comprises a three-dimensional model, the three-dimensional model is used for data fusion in the data layer, and the data in the data layer comprises front-end perception data and newly-added industry enterprise data; the application layer comprises traffic flow monitoring, large transport vehicle monitoring and dangerous goods vehicle monitoring. The method has the advantages that through fusion processing and intelligent research and judgment of multi-source information in the transportation process of large-scale and dangerous goods, consistency supervision is carried out on the permission and safety of vehicles, and visual and controllable management of the large-scale and dangerous goods vehicles on roads or bridges is realized.
Description
Technical Field
The invention relates to the technical field of road safety, in particular to a road monitoring system based on multi-source data fusion.
Background
The expressway operation management needs to monitor the running condition of the road in real time, accurately monitors and predicts the running condition of the road section in the scenes of daily monitoring, maintenance construction, accident handling, plan evaluation, training exercise, road network coordination and the like, and particularly needs to strengthen the monitoring and supervision on special vehicles (dangerous goods transport vehicles and large transport vehicles) running on the road.
The existing expressway monitoring system is complete in construction and mainly comprises traffic event detection, traffic management, traffic parameter acquisition, information release, power supply of outfield monitoring equipment, lane management and control, full-digital BIM+GIS management and maintenance, intelligent ice and snow removal, guardrail collision sensing, dangerous driving behavior detection, traffic event rapid sensing, intelligent evidence collection of traffic accidents and other systems, and provides services for monitoring and management of expressways. A large number of front-end devices such as cameras, ETC portal sensing devices, radar integrated devices, license plate recognition devices, video recognition devices, variable information boards, spraying devices, sensing devices and the like are erected for supporting the system to run the expressway roadsides and the road surfaces, and a large amount of data resources are acquired and generated by the devices. The resources play an important role in the expressway monitoring and charging system, but the use conditions and effects of various data resources are different, and partial data play different roles due to the difference of accuracy, so that inaccuracy and inconsistency among the data also cause the fact that the existing road monitoring system is not accurate and comprehensive in detection, and accidents and events cannot be prevented accurately.
Disclosure of Invention
The invention aims to provide a road monitoring system based on multi-source data fusion, which has the advantages of realizing the important monitoring of important vehicles, realizing the fusion application with a highway monitoring system and preventing accidents and incidents, and aims to solve the problems that the traditional road monitoring system is not accurate and comprehensive in detection due to inaccuracy and inconsistency among data
The invention realizes the aim through the following technical scheme, and the role multi-scene manned transmission method comprises an infrastructure layer, a network layer, a data layer, a supporting layer, an application service layer, an application layer and a presentation layer, wherein the infrastructure layer is used for providing front-end perception data for the data layer, and the front-end perception data comprises original high-speed perception data and newly-added high-speed perception data;
the application service layer comprises a three-dimensional model, the three-dimensional model is used for data fusion in the data layer, and the data in the data layer comprises front-end perception data and newly-added industry enterprise data;
the application layer comprises traffic flow monitoring, large transportation vehicle monitoring, dangerous goods vehicle monitoring, traffic event handling, double-spectrum detection and early warning, digital twin, ship bridge anti-collision, bridge health monitoring and daily management.
Preferably, the original high-speed sensing data comprises video images, a thunder and vision integrated device, ETC, license plate recognition, variable information board and meteorological data; the new high-speed sensing data comprise ship bridge anti-collision, vehicle temperature sensing and laser radar; the enterprise data of the newly added industry comprises large-part permission, large-part vehicle positioning, dangerous goods electronic waybills and bridge health monitoring.
Preferably, the positioning method for positioning the large vehicle and the dangerous goods vehicle comprises the following steps:
acquiring data storage time of a vehicle and recording time of satellite positioning data;
calculating data delay time of the data storage time and the recording time of satellite positioning data, and calculating the driving distance of the vehicle under the data delay time according to the driving speed of the vehicle;
determining the position pile number of the vehicle on the expressway through the longitude and latitude, adding the offset caused by the driving distance, and calculating the actual position and the pile number of the vehicle;
and displaying and marking on the map.
Preferably, the large transport vehicle monitoring realizes normal key monitoring and abnormal alarm reminding by verifying large license, and the method for verifying large license comprises the following steps:
s1, capturing and acquiring basic information of a large-scale vehicle through an area, wherein the basic information comprises license plates and positions, and the basic information is acquired through large-scale transport vehicle positioning data, expressway portal data and expressway charging data;
s2, acquiring large-piece transportation permission data of the large-piece vehicle in S1, matching the basic information of the large-piece vehicle with the large-piece transportation permission data, and if the basic information of the large-piece vehicle is matched with the large-piece transportation permission data, verifying that the large-piece vehicle passes the matching, otherwise, verifying that the large-piece vehicle does not pass the matching.
Preferably, the large transport vehicle monitoring further comprises a permitted line monitoring, and the method for the permitted line monitoring comprises the following steps:
acquiring basic information of a large vehicle, wherein the basic information comprises license plates and positions;
verifying the large license of the large vehicle, wherein the verified large license information comprises a license state and a license time, and verifying a license route if the license state and the license time are matched;
and if any one of the permission state, the permission time and the permission route is verified to be not passed, alarming and uploading monitoring data.
Preferably, the monitoring method for monitoring the dangerous goods vehicle comprises the following steps:
judging whether the dangerous goods vehicle enters the area to be monitored or not according to satellite positioning data of the dangerous goods vehicle;
acquiring basic information of dangerous goods vehicles entering a monitoring area, wherein the basic information comprises a permission state and goods information;
and monitoring satellite positioning data of dangerous goods vehicles in real time, and alarming and uploading basic information of the vehicles if the vehicles are detected to be parked.
Compared with the prior art, the invention has the beneficial effects that: the existing perception data and the complementary perception data resources are fused with other data resources in the traffic industry, and multi-dimensional risk factor perception is achieved through multi-source data fusion, so that safety perception and monitoring capability in road or bridge traffic are improved. Through comparison and analysis of various data resources, the accuracy and the precision of a perception result are improved, vehicle flow data, vehicle positioning data and event perception data are analyzed, the perception precision of bridge road monitoring in the three aspects is improved, the visual and controllable management of large and dangerous goods transportation vehicles on roads or bridges is realized, and accidents and events are prevented.
Drawings
Fig. 1 is a schematic diagram of an overall architecture of a road monitoring system according to the present invention.
FIG. 2 is a schematic diagram of a data fusion framework according to the present invention.
FIG. 3 is a schematic illustration of a large transport vehicle monitoring process of the present invention.
Fig. 4 is a schematic diagram of a permissible route monitoring flow of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the road monitoring system based on multi-source data fusion comprises an infrastructure layer, a network layer, a data layer, a supporting layer, an application service layer, an application layer and a presentation layer, wherein the infrastructure layer is used for providing front-end perception data for the data layer, the front-end perception data comprise original high-speed perception data and newly-added high-speed perception data, the infrastructure layer comprises video monitoring, radar perception equipment, a double-spectrum camera and ETC equipment, and the front-end perception data are acquired through the front-end equipment.
The network layer comprises a traffic private network, a monitoring network and a cloud, the network layer is utilized to store data to a data center of the data layer, the data layer comprises a resource pool and a data service, and the resource pool stores emergencies, travel time, traffic flow, space/time occupancy, engineering plans, weather environment, vehicle speed and electromechanical equipment states and data shared by other data ports such as: the province hall shares data and other road sections and ferry data, and the data service provides services such as resource catalogs, data service, data management, data quality, data monitoring and the like.
The support layer is used for supporting the architecture of the whole system and comprises a platform service and a platform support, wherein the platform service provides data service, functional service and engine service of the system, and the platform support provides data storage, data calculation, a power calculation terminal and IT supporting facilities. The application service layer comprises a three-dimensional model, wherein the three-dimensional model is used for data fusion in the data layer, the data in the data layer comprises front-end perception data and newly-added industry enterprise data, and the application service layer further comprises an event detection service, a high-precision map, an event reference, a master control service and a global tracking service. The application layer comprises traffic flow monitoring, large transportation vehicle monitoring, dangerous goods vehicle monitoring, traffic event handling, double-spectrum detection and early warning, digital twin, ship bridge anti-collision, bridge health monitoring and daily management. The presentation layer can be a mobile terminal or a work portal website for interactive use of a user.
As shown in fig. 2, the frame of data fusion is shown, and the original high-speed sensing data comprises video images, a thunder and vision integrated system, ETC, license plate recognition, variable information board and meteorological data; the new high-speed sensing data comprise ship bridge anti-collision, vehicle temperature sensing and laser radar; the enterprise data of the newly added industry comprises large license, large vehicle positioning, dangerous goods electronic waybill and bridge health monitoring, the original high-speed sensing data and the newly added high-speed sensing data can all acquire corresponding data through equipment of an infrastructure layer, the enterprise data of the newly added industry are acquired from a database of an enterprise in a data sharing mode, the data resources are fused by supplementing sensing data resources and other data resources of the traffic industry on the basis of analyzing the existing highway data resources, new technology and new sensing means are researched, the accuracy of road sensing is improved, the functions of established achievements and resources are fully exerted, and the application depth of the data is the object of the multi-source data fusion.
In the whole monitoring system, in order to correct the positions of large vehicles and dangerous goods vehicles, the traditional positioning method needs to be improved, and the positioning method for positioning the large vehicles and the dangerous goods vehicles comprises the following steps:
acquiring data storage time of a vehicle and recording time of satellite positioning data;
calculating data delay time of the data storage time and the recording time of satellite positioning data, and calculating the driving distance of the vehicle under the data delay time according to the driving speed of the vehicle;
determining the position pile number of the vehicle on the expressway through the longitude and latitude, adding the offset caused by the driving distance, and calculating the actual position and the pile number of the vehicle;
and displaying and marking on the map.
As shown in fig. 3, the large-scale transport vehicle monitoring realizes normal key monitoring and abnormal alarm reminding by verifying large-scale permission, and the method for verifying large-scale permission comprises the following steps:
s1, capturing and acquiring basic information of a large-scale vehicle through an area, wherein the basic information comprises license plates and positions, and the basic information is acquired through large-scale transport vehicle positioning data, expressway portal data and expressway charging data;
s2, acquiring large-piece transportation permission data of the large-piece vehicle in S1, matching the basic information of the large-piece vehicle with the large-piece transportation permission data, and if the basic information of the large-piece vehicle is matched with the large-piece transportation permission data, verifying that the large-piece vehicle passes the matching, otherwise, verifying that the large-piece vehicle does not pass the matching.
As shown in fig. 4, the large transport vehicle monitoring further includes a licensed line monitoring method, where the licensed line monitoring method includes:
acquiring basic information of a large vehicle, wherein the basic information comprises license plates and positions, such as heavy common transportation vehicles threAXXXXXX, positioned in a large bridge range;
verifying the large license of the large vehicle, wherein the verified large license information comprises a license state and a license time, and verifying a license route if the license state and the license time are matched;
and if any one of the permission state, the permission time and the permission route is verified to be not passed, alarming and uploading monitoring data.
The monitoring method for monitoring the dangerous goods vehicle comprises the following steps:
judging whether the dangerous goods vehicle enters the area to be monitored or not according to satellite positioning data of the dangerous goods vehicle;
acquiring basic information of dangerous goods vehicles entering a monitoring area, wherein the basic information comprises a permission state and goods information;
and monitoring satellite positioning data of dangerous goods vehicles in real time, and alarming and uploading basic information of the vehicles if the vehicles are detected to be parked.
By monitoring the large transport vehicles and the dangerous goods transport vehicles, the accident that the vehicles run at high speed and in the process trunk with extremely high rate can be effectively avoided.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the 7 embodiments of the disclosure may be suitably combined to form other embodiments as would be understood by one of ordinary skill in the art.
Claims (6)
1. The road monitoring system based on multi-source data fusion comprises an infrastructure layer, a network layer, a data layer, a supporting layer, an application service layer, an application layer and a presentation layer, wherein the infrastructure layer is characterized in that the infrastructure layer is used for providing front-end perception data for the data layer, and the front-end perception data comprises original high-speed perception data and newly-added high-speed perception data;
the application service layer comprises a three-dimensional model, the three-dimensional model is used for data fusion in the data layer, and the data in the data layer comprises front-end perception data and newly-added industry enterprise data;
the application layer comprises traffic flow monitoring, large transportation vehicle monitoring, dangerous goods vehicle monitoring, traffic event handling, double-spectrum detection and early warning, digital twin, ship bridge anti-collision, bridge health monitoring and daily management.
2. The road monitoring system based on multi-source data fusion according to claim 1, wherein the original high-speed sensing data comprises video images, a thunder integration, ETC, license plate recognition, variable information board and meteorological data; the new high-speed sensing data comprise ship bridge anti-collision, vehicle temperature sensing and laser radar; the enterprise data of the newly added industry comprises large-part permission, large-part vehicle positioning, dangerous goods electronic waybills and bridge health monitoring.
3. The road monitoring system based on multi-source data fusion according to claim 2, wherein the positioning method for positioning the large vehicle and the dangerous goods vehicle comprises the following steps:
acquiring data storage time of a vehicle and recording time of satellite positioning data;
calculating data delay time of the data storage time and the recording time of satellite positioning data, and calculating the driving distance of the vehicle under the data delay time according to the driving speed of the vehicle;
determining the position pile number of the vehicle on the expressway through the longitude and latitude, adding the offset caused by the driving distance, and calculating the actual position and the pile number of the vehicle;
and displaying and marking on the map.
4. The multi-source data fusion-based roadway monitoring system of claim 2, wherein the bulk transportation vehicle monitoring achieves normal key monitoring and abnormal alarm reminding by verifying bulk permissions, the method of verifying bulk permissions comprising:
s1, capturing and acquiring basic information of a large-scale vehicle through an area, wherein the basic information comprises license plates and positions, and the basic information is acquired through large-scale transport vehicle positioning data, expressway portal data and expressway charging data;
s2, acquiring large-piece transportation permission data of the large-piece vehicle in S1, matching the basic information of the large-piece vehicle with the large-piece transportation permission data, and if the basic information of the large-piece vehicle is matched with the large-piece transportation permission data, verifying that the large-piece vehicle passes the matching, otherwise, verifying that the large-piece vehicle does not pass the matching.
5. The multi-source data fusion-based roadway monitoring system of claim 4, wherein the bulk transportation vehicle monitoring further comprises a licensed line monitoring method comprising:
acquiring basic information of a large vehicle, wherein the basic information comprises license plates and positions;
verifying the large license of the large vehicle, wherein the verified large license information comprises a license state and a license time, and verifying a license route if the license state and the license time are matched;
and if any one of the permission state, the permission time and the permission route is verified to be not passed, alarming and uploading monitoring data.
6. The road monitoring system based on multi-source data fusion according to claim 2, wherein the monitoring method for dangerous goods vehicle monitoring comprises the following steps:
judging whether the dangerous goods vehicle enters the area to be monitored or not according to satellite positioning data of the dangerous goods vehicle;
acquiring basic information of dangerous goods vehicles entering a monitoring area, wherein the basic information comprises a permission state and goods information;
and monitoring satellite positioning data of dangerous goods vehicles in real time, and alarming and uploading basic information of the vehicles if the vehicles are detected to be parked.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118629216A (en) * | 2024-08-08 | 2024-09-10 | 四川华体照明科技股份有限公司 | Road target recognition method and system based on radar and vision fusion |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140015045A (en) * | 2012-07-27 | 2014-02-06 | 한국철도기술연구원 | Management method of dangerous article transport car and management apparatus of dangerous article transport car using the method |
| CN105488641A (en) * | 2016-01-07 | 2016-04-13 | 东南大学 | Provincial highway operation management data center system and implementation method thereof |
| CN113658428A (en) * | 2021-08-09 | 2021-11-16 | 南京美慧软件有限公司 | Intelligent active management and control platform for expressway |
| CN113869824A (en) * | 2021-09-28 | 2021-12-31 | 中国电子科技集团公司信息科学研究院 | Logistics transportation management method and device, electronic equipment and storage medium |
| CN113984110A (en) * | 2021-09-29 | 2022-01-28 | 合肥市正茂科技有限公司 | Bridge safety and health monitoring system and method |
-
2022
- 2022-12-31 CN CN202211733770.8A patent/CN117831267A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140015045A (en) * | 2012-07-27 | 2014-02-06 | 한국철도기술연구원 | Management method of dangerous article transport car and management apparatus of dangerous article transport car using the method |
| CN105488641A (en) * | 2016-01-07 | 2016-04-13 | 东南大学 | Provincial highway operation management data center system and implementation method thereof |
| CN113658428A (en) * | 2021-08-09 | 2021-11-16 | 南京美慧软件有限公司 | Intelligent active management and control platform for expressway |
| CN113869824A (en) * | 2021-09-28 | 2021-12-31 | 中国电子科技集团公司信息科学研究院 | Logistics transportation management method and device, electronic equipment and storage medium |
| CN113984110A (en) * | 2021-09-29 | 2022-01-28 | 合肥市正茂科技有限公司 | Bridge safety and health monitoring system and method |
Non-Patent Citations (1)
| Title |
|---|
| 曹更永;田玉玛;: "基于物联网技术的道路危险品运输安全应急管理系统设计", 物流科技, no. 01, 10 January 2013 (2013-01-10), pages 70 - 72 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118629216A (en) * | 2024-08-08 | 2024-09-10 | 四川华体照明科技股份有限公司 | Road target recognition method and system based on radar and vision fusion |
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