CN114333313A - 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, belonging to the technical field of traffic monitoring and intelligent traffic, the intelligent inspection method controls the default inspection period of the camera of the key road section according to the traffic flow conditions of the accident high-speed road section and different time periods, the accident-prone road section is counted by automatically adjusting the position of the monitoring preset point and the patrol inspection residence time and combining the two methods of manual patrol inspection and automatic machine patrol inspection, under the condition that the current focus point of the camera is not in the interval range of the default stay preset point or is not operated at some other preset point for a long time, the automatic inspection is started, so that the time blind area and the space blind area of manual monitoring are reduced, the lag of finding abnormal traffic conditions is reduced, and traffic jam and accidents caused by information service delay are avoided, thereby ensuring the safety and smoothness of highway traffic.
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 the 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 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;
(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, and 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, and if the camera stays in a certain preset point area for more than 1 hour and is not operated, automatic inspection is adopted.
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 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.
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 isiAnd 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 empirical factor Q of accident occurrence2And the traffic flow coefficient Q obtained in the step (b)3Calculating the traffic flow weight coefficient Q of each section of each time period as 1/(Q)1+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, 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.
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.
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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 road sections under the control of the cameras, and dividing the lengths of the overlapped areas on the equally divided preset point areas; the specific process is as follows: equally dividing preset point areas of road sections under the control of the camera, and respectively solving shooting of each section in the equally divided preset point areasMaximum length S of vertical focusing of image headiMultiplying 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, FiShowing the perspective coefficients of the camera, as shown in fig. 2, the segments Sb1-Se1, Sb2-Se2 and Sb3-Se3 all show preset point areas divided equally, U1Denotes the overlap length, U, of the Sb1-Se1 segment preset point region and the Sb2-Se2 segment preset point region2Denotes the overlap length, U, of the Sb2-Se2 segment preset point region and the Sb3-Se3 segment preset point region3Indicating the overlap length of the Sb3-Se3 segment preset point region with the subsequent adjacent segment preset point region. The inspection area is large in the long-range view, the target in the image captured by the camera is small and unclear, and whether the target is abnormal or not can be judged again in the other overlapped adjacent image by increasing the overlapping length, so that the efficient allocation of resources is realized to the maximum extent.
(3) Because the important attention that needs when the traffic jam, 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 default of every preset point of camera is patrolled and examined the time, combines together with the traffic flow 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 the fact of each road section of each time period according to the prior knowledgeEmpirical coefficient of occurrence Q2；
(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 the traffic flow weight coefficient Q of each section of each time period as 1/(Q)1+Q2+Q3)。
(4) The camera has large rotation distance and long rotation time in long-range view, 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, 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 move camera command.
(5) Traversing all cameras on the highway side, and 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 governed by a certain monitoring camera on a certain expressway, a time period is divided into 06:50-09:15 by statistical analysis of historical events, an accident occurrence experience coefficient is obtained after the number of all events is normalized and is 0.7, the current OD traffic volume analysis traffic capacity is obtained and is subjected to normalization processing to obtain a real-time traffic flow coefficient of 0.5, the road section accident occurrence experience coefficient is judged to be 0.8 by artificial experience, and a comprehensive coefficient Q & ltSUB & gt 1 & lt/Q & gt is obtained by calculation1+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 | S1The current perspective coefficient F1 is calculated to be 0.2, the overlap region U1 is S1 is F1 is 0.2 is 0.04, the preset point stay time is Pt 0/Fi is 10/0.2 is 50, and the other preset point start points, the overlap region, and the stay time are calculated in this order.
Under the condition of not adopting intelligent routing inspection, 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 (6)
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 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 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;
(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, and 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, 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 the road section governed by the camera into preset point areas, and respectively solving the averageMaximum length S of vertical focusing of each section of camera in equally divided preset point areaiMultiplying 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 inspection cycle of the cameras corresponding to the time periods 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 isiAnd representing the polling period of the camera in the corresponding time period.
4. The intelligent inspection method based on the highway monitoring according to claim 1, wherein 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 empirical factor Q of accident occurrence2And the traffic flow coefficient Q obtained in the step (b)3Calculating the traffic flow weight coefficient Q of each section of each time period as 1/(Q)1+Q2+Q3)。
5. The intelligent inspection method based on highway monitoring according to claim 4, 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.
6. The intelligent inspection method based on highway monitoring according to claim 1, wherein 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, 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.
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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 |