CN114333313B - Intelligent inspection method based on highway monitoring - Google Patents
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Abstract
The invention discloses an intelligent inspection method based on highway monitoring, which belongs to the technical field of traffic monitoring and intelligent traffic, and aims at traffic flow conditions of accident high-speed road sections and different time periods, the intelligent inspection method controls the default inspection period of a key road section camera, automatically adjusts the position of a monitoring preset point and inspection stay time, and combines the two methods of manual inspection and automatic inspection by a machine to count accident easy-to-send road sections, and starts automatic inspection under the condition that the current focus point of the camera is not in the range of the default stay preset point or is not operated at some other preset point for a long time, so that the time blind area and the space blind area of manual monitoring are reduced, the hysteresis of discovering abnormal traffic conditions is reduced, traffic jam and accidents caused by information service delay are avoided, and the safety and smoothness of highway traffic are ensured.
Description
Technical Field
The invention belongs to the technical field of traffic monitoring and intelligent traffic, and particularly relates to an intelligent inspection method based on highway monitoring.
Background
The expressway is used as a main mark of modern traffic, and is rapidly developed in China in recent years. At present, the traffic mileage of the expressway in China reaches 3.5 kilometers, the rapid development of the expressway brings great economic and social benefits to the society, and the space-time distance between people is greatly shortened. But also presents a significant challenge to highway traffic authorities and traffic participants. Therefore, how to adopt the intelligent inspection method to find the abnormal conditions in time, avoid traffic jam and further influence, ensure the smooth operation of the highway is gradually an important problem in the inspection process of the highway in China.
At present, external field monitoring equipment is generally installed on domestic expressways, but the initiative and the intelligent degree of real-time detection of the road running state are not high, the monitoring management of the expressway running state mainly adopts two methods of manual inspection and automatic machine detection, the manual inspection is realized by staring a monitor to a monitoring picture, the workload of the monitor is increased, the visual fatigue is caused, the accident detection rate is greatly reduced, and the inspection time of the existing automatic detection system is fixed and uniform, the flexibility is lacked, and the efficiency is not high; the pushing of road control information such as lane sealing and shunting is carried out through the experience of a monitor, and a lot of time is consumed for confirming the entry and the release of accident information and information, so that the information service delay is caused. If the accident or traffic jam can not be detected and processed in time, the vehicle can detour and even secondary accidents can be caused, the operation efficiency of the highway is reduced, and huge economic loss is brought to the highway operation company.
Disclosure of Invention
In order to overcome the defects of the conventional highway inspection method, the invention provides an intelligent inspection method based on highway monitoring, which improves the inspection efficiency of a highway and reduces the traffic jam rate.
In order to achieve the technical purpose, the invention adopts the following technical scheme: an intelligent inspection method based on highway monitoring specifically comprises the following steps:
(1) Arranging a camera on a high pole on the side of the highway, and setting a long-range coefficient of a current focus point of the camera according to priori knowledge;
(2) Equally dividing preset point areas of road sections under the control of the cameras, and dividing the lengths of the overlapped areas on the equally divided preset point areas;
(3) Setting default polling time of each preset point area of the camera, and combining the default polling time with the traffic flow weight coefficient of the time period corresponding to the preset point area to obtain a polling period of the camera in the corresponding time period;
(4) Adjusting the inspection residence time of each preset point area according to the long-range coefficient in the step (1);
(5) Traversing all cameras on the highway side, repeating the steps (2) - (4) to obtain preset point areas, routing inspection periods and routing inspection residence time of each preset point area, which are divided on the road section governed by each camera;
(6) Counting preset point areas which are most prone to faults through historical data on the highway, judging whether the current focus point of the camera is in the preset point area which is most prone to faults, and if not, automatically inspecting through the preset point area, the inspection cycle and the inspection residence time of each preset point area which are divided on a road section which is governed by each camera; otherwise, carrying out manual inspection, and if the camera stays in a certain preset point area for more than 1 hour and is not operated, adopting automatic inspection.
Further, the dividing process of the length of the overlapping area in the step (2) is specifically as follows: equally dividing preset point areas of road sections under the control of the cameras, and respectively calculating the maximum length S of vertical focusing of each section of camera in the equally divided preset point areasiMultiplying the long-range coefficient of the camera to obtain the overlapping length U of each section of the preset point area divided equallyi＝Si*FiWherein i represents an index for equally dividing the number of segments, FiAnd representing the perspective coefficient of the camera.
Further, the polling cycle of the camera corresponding to the time period in the step (3) is as follows:
Ti＝T0*Q
wherein, T0Representing default routing inspection time, and Q representing a traffic flow weight coefficient of a time period corresponding to a preset point area; t is a unit ofiAnd representing the polling period of the camera in the corresponding time period.
Further, the traffic flow weight coefficient of the time period corresponding to the corresponding preset point area is obtained by the following method:
(a) Collecting historical data of the management record ledger and the event report on the expressway, and counting the number of the camera piles with abnormal conditions on the expressway and the corresponding time distribution to obtain the accident occurrence coefficient Q corresponding to each road section in each time period1And giving out an accident occurrence experience coefficient Q of each road section in each time period according to prior knowledge2；
(b) Acquiring traffic demand OD information of each road section on the expressway at each time period by adopting a multi-source data analysis technology, and calculating a traffic flow coefficient Q of each road section in each time period according to the acquired traffic demand OD information3；
(c) The accident occurrence coefficient Q obtained according to the step (a)1Given accident occurrence experience coefficientQ2And the traffic flow coefficient Q obtained in the step (b)3Calculating a traffic flow weight coefficient Q = 1/(Q) of each section of each time period1+Q2+Q3)。
Further, the vehicle flow coefficient Q3The calculation process specifically comprises the following steps:
wherein x represents the traffic demand OD traffic volume of a certain highway section in a certain time period, mu represents the average value of the traffic flow coefficient of the section in the corresponding time period, and sigma represents the traffic flow coefficient of the section in the corresponding time period2And the variance of the traffic flow coefficient of the road section in the corresponding time period is represented.
Further, the area inspection residence time of the preset points in the step (4) is as follows: pi＝P0/Fi
Wherein, PiIndicating the inspection residence time of the area at the preset point, FiA distance coefficient representing a camera, i an index for equally dividing the number of segments, P0Indicating a default frequency for sending a move camera command.
Compared with the prior art, the invention has the following beneficial effects: according to the intelligent inspection method based on the highway monitoring, the default inspection period of the camera of the key road section is controlled according to the traffic flow conditions of the accident high-speed road section and different time periods, so that the efficiency of real-time monitoring of the camera is maximized; the accuracy and timeliness of accident finding are improved by automatically adjusting the monitoring preset point position and the patrol inspection residence time; the automatic inspection mode is combined with manual inspection, the workload of a monitor is reduced, an accident-prone road section is obtained through analysis, when the current focus point of a camera is not in the accident-prone road section or is not operated at some other preset point for a long time, automatic inspection is started, the manual monitoring time and position blind areas are reduced, the hysteresis for finding abnormal traffic conditions is reduced, traffic jam and accidents caused by information service delay are avoided, and therefore the safety and smoothness of highway traffic are guaranteed. The intelligent inspection method based on the highway monitoring has good effects of improving the inspection efficiency of the highway and reducing traffic jam, and has wide application range and good application prospect in the aspect of intelligent inspection of the highway in China.
Drawings
FIG. 1 is a flow chart of an intelligent inspection method based on highway monitoring;
FIG. 2 is a diagram illustrating division of preset point regions;
Detailed Description
The technical solution of the invention is further explained below with reference to the drawings and the embodiments.
Fig. 1 is a flowchart of an intelligent inspection method based on highway monitoring, and the intelligent inspection method specifically includes the following steps:
(1) Arranging a camera on a high pole on the side of the highway, and setting a long-range coefficient of a current focus point of the camera according to priori knowledge;
(2) Equally dividing preset point areas of a road section governed by a camera, and dividing the length of an overlapping area on the equally divided preset point areas; the specific process is as follows: equally dividing preset point areas of road sections governed by the cameras, and respectively calculating the maximum length S of vertical focusing of each section of camera in the equally divided preset point areasiMultiplying the long-range coefficient of the camera to obtain the overlapping length U of each section of the preset point area divided equallyi＝Si*FiWherein i represents an index for equally dividing the number of segments, FiThe perspective coefficients of the camera are shown, as shown in fig. 2, the Sb1-Se1 segment, the Sb2-Se2 segment and the Sb3-Se3 segment all represent preset point areas divided equally, U1Represents the overlapping length of the Sb1-Se1 section preset point region and the Sb2-Se2 section preset point region, U2Represents the overlapping length of the Sb2-Se2 segment preset point region and the Sb3-Se3 segment preset point region, U3The overlapping length of the Sb3-Se3 segment preset point area and the preset point area of the subsequent adjacent segment is shown. Because the routing inspection area is large in the long shot, the target is small and unclear in the image captured by the camera, and whether the target is abnormal or not can be judged again in the other overlapped Zhang Xianglin image by increasing the overlapping length, so that the maximum target is obtainedAnd realizing efficient configuration of resources to the extent.
(3) Because the important attention that needs when the traffic stream blocks up, can lead to the camera when patrolling and examining the slew velocity slow down, video image attention time is long simultaneously to lead to patrolling and examining the cycle and change, through setting up the regional acquiescence of every preset point of camera is patrolled and examined time, combines together with the traffic stream weight coefficient that corresponds the regional corresponding time quantum of preset point, obtains the cycle of patrolling and examining of camera in the corresponding time quantum:
Ti＝T0*Q
wherein, T0Representing default routing inspection time, and Q representing a traffic flow weight coefficient of a time period corresponding to a preset point area; t isiAnd representing the polling period of the camera in the corresponding time period.
The traffic flow weight coefficient corresponding to the time period corresponding to the preset point area is obtained by the following method:
(a) Collecting historical data of the management record ledger and the event report on the expressway, and counting the number of the camera piles with abnormal conditions on the expressway and the corresponding time distribution to obtain the accident occurrence coefficient Q corresponding to each road section in each time period1And giving out an accident occurrence experience coefficient Q of each road section in each time period according to prior knowledge2；
(b) The method comprises the steps of acquiring traffic demand OD information of each road section on the expressway at each time period by adopting a multi-source data analysis technology, determining the real-time traffic flow congestion condition, and calculating a traffic flow coefficient Q of each road section in each time period according to the acquired traffic demand OD information3：
Wherein x represents the traffic demand OD traffic volume of a certain highway section in a certain time period, mu represents the average value of the traffic flow coefficient of the section in the corresponding time period, and sigma represents the traffic flow coefficient of the section in the corresponding time period2And the variance of the traffic flow coefficient of the road section in the corresponding time period is represented.
(c) The accident occurrence coefficient Q obtained according to the step (a)1Given empirical factor Q of accident occurrence2And the traffic flow coefficient Q obtained in the step (b)3Calculating a traffic flow weight coefficient Q = 1/(Q) of each section of each time period1+Q2+Q3)。
(4) The camera has a large rotation distance and long rotation time in a long-distance scene, and the inspection frequency is slower; the rotating distance of the camera is small during close-range, the camera rotates quickly, and the polling frequency is fast, so that the polling residence time of each preset point area is adjusted according to the long-range coefficient in the step (1): pi＝P0/Fi；
Wherein, PiShowing the inspection residence time of the area at the preset point, FiRepresenting the perspective coefficient of the camera, i representing the index of the number of equally divided segments, P0Indicating a default frequency for sending a move camera command.
(5) Traversing all cameras on the highway side, repeating the steps (2) to (4) to obtain preset point areas, routing inspection periods and routing inspection residence time of each preset point area divided on the road section governed by each camera;
(6) Counting preset point areas which are most prone to faults through historical data on the highway, judging whether the current focus point of the camera is in the preset point area which is most prone to faults, and if not, automatically inspecting through the preset point area, the inspection cycle and the inspection residence time of each preset point area which are divided on a road section which is governed by each camera; otherwise, manual inspection is carried out, if the camera stays in a certain preset point area for more than 1 hour and is not operated, automatic inspection is adopted, so that the inspection efficiency of the highway is improved, and a good effect on reducing traffic jam is achieved.
Examples
In a road section controlled by a certain monitoring camera on a certain expressway, dividing one time period into segments from 06 to 09 according to historical event statistical analysis, normalizing the number of all events to obtain an accident occurrence experience coefficient of 0.7, obtaining the current OD traffic volume analysis traffic capacity, normalizing to obtain a real-time traffic flow coefficient of 0.5, judging the accident occurrence experience coefficient of the road section to be 0.8 through artificial experience, and calculating to obtain a comprehensive traffic flow coefficientThe resultant coefficient Q = 1/(Q)1+Q2+Q3) =0.64. The default polling time of the camera is 10 minutes, and the actual polling time of the current time period is 10 × 0.64 ≈ 6 minutes. The length of the path governed by the camera is 1km, the starting point of a first preset point is calculated from the farthest forward position, and the maximum visible distance is monitored to be | S1I =0.2km, the current perspective coefficient F1=0.2, the overlap region U1= S1F 1=0.2 x 0.2=0.04, the stay time at the preset point is Pt = P0/Fi =10/0.2=50, and the start point, the overlap region, and the stay time at the other preset points are calculated in this order.
Under the condition of not adopting intelligent patrol, accidents cannot be timely and accurately found, the workload of a monitor is large, time blind areas and space blind areas exist in manual monitoring, and the traffic capacity of the expressway is reduced. After the intelligent routing inspection system is adopted, the real-time property of finding accidents is improved, the hysteresis of finding abnormal traffic conditions is reduced, and traffic jam and accidents caused by information service delay are avoided, so that the safety and smoothness of highway traffic are ensured.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (3)
1. An intelligent inspection method based on highway monitoring is characterized by comprising the following steps:
(1) Arranging a camera on a high pole on the side of the highway, and setting a long-range coefficient of a current focus point of the camera according to priori knowledge;
(2) Equally dividing preset point areas of a road section governed by a camera, and dividing the length of an overlapping area on the equally divided preset point areas;
(3) Setting default inspection time of each preset point area of the camera, and combining the default inspection time with the traffic flow weight coefficient of the time period corresponding to the preset point area to obtain the inspection period of the camera in the corresponding time period;
the traffic flow weight coefficient of the time period corresponding to the corresponding preset point area is obtained by the following method:
(a) Collecting historical data of the management record ledger and event report on the highway, and counting the number of the camera piles with abnormal conditions on the highway and the corresponding time distribution to obtain the accident occurrence coefficient Q corresponding to each road section in each time period1And giving out an accident occurrence empirical coefficient Q of each road section in each time period according to prior knowledge2；
(b) Acquiring traffic demand OD information of each road section on the expressway at each time period by adopting a multi-source data analysis technology, and calculating a traffic flow coefficient Q of each road section in each time period according to the acquired traffic demand OD information3；
(c) The accident occurrence coefficient Q obtained according to the step (a)1Given empirical factor Q of accident occurrence2And the traffic flow coefficient Q obtained in the step (b)3Calculating a traffic flow weight coefficient Q = 1/(Q) of each section of each time period1+Q2+Q3)；
The polling cycle of the camera corresponding to the time period is as follows:
Ti＝T0*Q
wherein, T0Representing default routing inspection time, and Q representing a traffic flow weight coefficient of a time period corresponding to a preset point area; t isiRepresenting the polling period of the camera in the corresponding time period;
(4) Adjusting the inspection residence time of each preset point area according to the long-range coefficient in the step (1);
the area routing inspection residence time of the preset points is as follows: pi＝P0/Fi
Wherein, PiIndicating the inspection residence time of the area at the preset point, FiRepresenting the perspective coefficient of the camera, i representing the index of the number of equally divided segments, P0Indicating a default frequency for sending a moving camera command;
(5) Traversing all cameras on the highway side, repeating the steps (2) to (4) to obtain preset point areas, routing inspection periods and routing inspection residence time of each preset point area divided on the road section governed by each camera;
(6) Counting preset point areas which are most prone to faults through historical data on the highway, judging whether current focus points of the cameras are in the preset point areas which are most prone to faults, and if not, automatically inspecting through preset point areas, inspection cycles and inspection residence time of each preset point area which are divided on road sections governed by each camera; otherwise, manual inspection is carried out, and if the camera stays in a certain preset point area for more than 1 hour and is not operated, automatic inspection is adopted.
2. The intelligent inspection method based on highway monitoring according to claim 1, wherein the division process of the length of the overlapping area in the step (2) is specifically as follows: equally dividing preset point areas of road sections governed by the cameras, and respectively calculating the maximum length S of vertical focusing of each section of camera in the equally divided preset point areasiMultiplying the long-range coefficient of the camera to obtain the overlapping length U of each section of the preset point area divided equallyi＝Si*FiWherein i represents an index for equally dividing the number of segments, FiRepresenting the perspective coefficient of the camera.
3. The intelligent inspection method based on highway monitoring according to claim 1, wherein the traffic flow coefficient Q3The calculation process specifically comprises the following steps:
wherein x represents the traffic demand OD traffic volume of a certain highway section in a certain time period, mu represents the average value of the traffic flow coefficient of the section in the corresponding time period, and sigma represents the traffic flow coefficient of the section in the corresponding time period2And the variance of the traffic flow coefficient of the road section in the corresponding time period is represented.
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| CN117764303B (en) * | 2023-11-17 | 2024-06-21 | 南京公路发展(集团)有限公司 | Road inspection data analysis system and method based on artificial intelligence |
| CN117596386B (en) * | 2023-12-06 | 2024-05-24 | 中云数科(广州)信息科技有限公司 | Intelligent building safety monitoring system |
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Application publication date: 20220412 Assignee: Xiamen Jianxing Zhida Information Technology Co.,Ltd. Assignor: JINLING INSTITUTE OF TECHNOLOGY Contract record no.: X2023980054789 Denomination of invention: An intelligent inspection method based on highway monitoring Granted publication date: 20221101 License type: Common License Record date: 20240103 |