CN115019514B - Highway inspection system based on car networking - Google Patents
Highway inspection system based on car networking Download PDFInfo
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- CN115019514B CN115019514B CN202210948440.4A CN202210948440A CN115019514B CN 115019514 B CN115019514 B CN 115019514B CN 202210948440 A CN202210948440 A CN 202210948440A CN 115019514 B CN115019514 B CN 115019514B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/08—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
Abstract
The invention discloses a road patrol system based on the Internet of vehicles, which relates to the technical field of road patrol and comprises a data acquisition module, a data analysis module and a cloud service platform; wherein: the data acquisition module is used for acquiring the running state information of vehicles on the road, and the running state information comprises running image data and running speed data; the data analysis module is used for analyzing and processing the driving image data and the driving speed data acquired by the data acquisition module and sending an analysis and processing result to the cloud service desk; the cloud service platform is used for recording the analysis processing result of the data analysis module and carrying out early warning according to the analysis processing result, the early warning module pushes an early warning signal sent by the cloud service platform to an internet map and displays the early warning signal in the internet map so as to remind a driver in the navigation process, and a manager is guided to carry out targeted traffic safety control by counting the high-incidence places of road accidents.
Description
Technical Field
The invention relates to the technical field of road patrol, in particular to a road patrol system based on the Internet of vehicles.
Background
The purpose of road inspection is to carry out general inspection on the appearance conditions of roads, roadbeds, bridges, tunnels and facilities along the roads, to master the problems of all components of road assets in time, particularly to avoid traffic accidents caused by accidents such as road congestion, roadbed collapse, roadbed water damage, road pit grooves, road surface subsidence and the like which affect the driving safety.
More than seven congestion accidents on expressways are related to traffic accidents, but due to the particularity of expressways, the fast rate of the existing expressway accidents is far less than that in urban areas, and the congestion severity is also aggravated. In case of an accident, at least one lane, sometimes even two, three or even all lanes need to be occupied, so that the traffic efficiency is greatly reduced, and the traffic speed on a highway is so high that several kilometers of traffic jam can be caused in several minutes.
Although the condition of whether the early warning happens in front of the road can be inquired through the mobile terminal navigation map, the navigation map cannot display the accident on which lane in the same section, and therefore the early warning is difficult to be carried out on vehicles behind the road, and the condition of congestion is easily aggravated.
Disclosure of Invention
The invention aims to provide a road patrol system based on the Internet of vehicles, which solves the following technical problems:
although the condition of whether the early warning happens in front of the road can be inquired through the mobile terminal navigation map, the navigation map cannot display the accident on which lane in the same section, and therefore the early warning is difficult to be carried out on vehicles behind the road, and the condition of congestion is easily aggravated.
The purpose of the invention can be realized by the following technical scheme:
a road patrol system based on the Internet of vehicles comprises a data acquisition module, a data analysis module and a cloud service platform;
wherein:
the data acquisition module is used for acquiring the running state information of vehicles on the road, and the running state information comprises running image data and running speed data;
the data analysis module is used for analyzing the driving image data and the driving speed data acquired by the data acquisition module to obtain a processing result, and sending the processing result to the cloud service desk;
and the cloud service platform is used for recording the analysis processing result of the data analysis module and judging whether to send out an early warning signal according to the analysis processing result.
Preferably, the data acquisition module comprises a plurality of groups of monitoring devices which are sequentially arranged on two sides of the highway at equal intervals;
the monitoring equipment is used for carrying out camera shooting and speed measurement on vehicles running on the highway, and running image data and running speed data are obtained.
Preferably, the processing result comprises a first analysis result and a second analysis result, and the data analysis module comprises a first analysis unit and a second analysis unit;
the first analysis unit judges whether the vehicle runs on a corresponding lane at a normal speed or not based on the acquired running image data and the running speed data to obtain a first analysis result;
and the second analysis unit judges whether the vehicle runs on the corresponding lane according to the driving direction or not based on the acquired running image data to obtain a second analysis result.
Preferably, the first analysis unit performs the steps of:
dividing the road into a plurality of sections { X) according to the positions of the monitoring devices 1 、X 2 、……、X n And according to different lanes of the road { Y } 1 、Y 2 、……、Y m Will { X } 1 、X 2 、……、X n Is further refined into { X } 1Y1 、X 2Y2 、……、X nYm }；
Presetting corresponding lanes Y based on different lanes m Maximum speed running threshold V max And a minimum speed running speed V min ；
Acquiring speed set { V) of all vehicles passing through monitoring equipment based on each group of monitoring equipment 1 、V 2 、……、V α And V max And V min And comparing and judging.
Preferably, when V min ≤V α ≤V max And alpha is an element [1, n is a positive integer]Determining the section X nYm The vehicles in the corresponding lanes are in a normal driving state;
when V is α ＜V min When alpha is an element [1, n is a positive integer]Determining the section X nYm The vehicles in the corresponding lane are in an abnormal driving state;
when V is α ＞V max When alpha is an element [1, n is a positive integer]Determining the section X nYm The vehicle in the lane is in an overspeed state in the corresponding lane.
Preferably, the second analysis unit performs the steps of:
acquiring all vehicles in section { X based on each group of monitoring equipment 1Y1 、X 2Y2 、……、X nYm Driving image data in (1);
preprocessing the shot image, adjusting the direction of the image according to the direction of a road traffic marking, fitting the automobile in the image into a rectangle, and mapping the image in a two-dimensional plane coordinate system;
calculating a first slope K1 of two sides of the rectangle and a second slope K2 of the lane line according to the image mapped by the rectangle and the lane line in the two-dimensional plane coordinate system;
the driving state of the vehicle in the section lane is determined based on the first slope K1 and the second slope K2.
Preferably, when | K1-K2 | is | > & gt, it is determined that the driving direction of the vehicle deviates from the lane line;
determining that there is no abnormality in the traveling direction of the vehicle when | K1-K2 | = [;
and is a preset threshold value.
Preferably, the cloud service platform is used for recording and storing the first analysis result and the second analysis result in the database.
Preferably, the early warning device further comprises an early warning module, wherein the early warning module is used for pushing an early warning signal sent by the cloud service platform to an internet map, and displaying the early warning signal in the internet map so as to remind a driver in the navigation process.
Preferably, the data acquisition module is further configured to acquire road vehicle information, where the road vehicle information includes vehicle speed data, duration data, and difference data, the speed data is a driving speed of a vehicle during a road driving process, the duration data is a duration of a vehicle staying in a non-parking area of the road, the difference data is a difference between an average vehicle speed of the vehicle staying area and an average vehicle speed of a vehicle leaving the vehicle staying area, the road vehicle is labeled as i, i =1,2, … …, n, n is a positive integer, and a specific analysis and detection process is as follows:
step S1: acquiring the driving speed of a vehicle in the road driving process, and marking the driving speed of the vehicle in the road driving process as JSi;
step S2: acquiring the stay time of a vehicle in a non-parking area in a road, and marking the stay time of the vehicle in the non-parking area in the road as TCi;
and step S3: acquiring the difference value between the average vehicle speed of the vehicle staying area and the average vehicle speed of the driven-out vehicle staying area, and marking the difference value between the average vehicle speed of the vehicle staying area and the average vehicle speed of the driven-out vehicle staying area as PJi;
and step S4: data analysis module passes through formula
Acquiring accident analysis coefficients Xi of road vehicles, wherein a1, a2 and a3 are proportional coefficients, and a1 is greater than a2 and is greater than a3 and is greater than 0;
step S5: setting an accident analysis coefficient threshold, comparing the accident analysis coefficient Xi of the road vehicle with the accident analysis coefficient threshold, analyzing the road vehicle, judging whether the vehicle has an accident or not, generating an early warning signal in advance, improving the road vehicle management efficiency and reducing traffic jam;
if the accident analysis coefficient Xi of the road vehicle is larger than or equal to the accident analysis coefficient threshold value, judging that the corresponding road vehicle has an accident, generating an accident early warning signal and sending the accident early warning signal and the corresponding road vehicle to the cloud service platform;
and if the accident analysis coefficient Xi of the road vehicle is less than the accident analysis coefficient threshold value, judging that no accident occurs in the corresponding road vehicle.
The invention has the beneficial effects that:
(1) The invention divides the road into a plurality of sections { X 1 、X 2 、……、X n And according to different lanes of the road { Y } 1 、Y 2 、……、Y m Will { X } 1 、X 2 、……、X n Is further refined into { X } 1Y1 、X 2Y2 、……、X nYm Judging whether vehicles in each group of lanes in the refined section run on corresponding lanes at normal speed by arranging a plurality of groups of monitoring equipment, and further analyzing which lane on the road has an accident through a first analysis unit so as to facilitate subsequent treatment, wherein compared with the traditional road patrol equipment, the patrol system is more accurate in monitoring;
(2) The road vehicle information is analyzed through the early warning detection unit, so that the road vehicle is detected, the real-time speed of the vehicle on the road, the stay time of the vehicle in a non-parking area in the road and the difference value between the average speed of the vehicle stay area and the average speed of the vehicle staying area are obtained, the accident analysis coefficient Xi of the road vehicle is obtained through a formula, and the accident analysis coefficient Xi of the road vehicle is compared with an accident analysis coefficient threshold value; the road vehicle is analyzed, whether the vehicle has an accident or not is judged, and an early warning signal is generated in advance, so that the road vehicle management efficiency is improved, and traffic jam is reduced;
(3) The early warning module pushes an early warning signal sent by the cloud service platform to an internet map, and the early warning signal is displayed in the internet map so as to remind a driver in the navigation process, and a manager is guided to carry out targeted traffic safety management by counting the high-incidence places of road accidents; analyzing historical traffic accident causes and determining the closely related accident inducing factors of each traffic accident multi-occurrence point, wherein the factors comprise the following steps: the method comprises the steps of providing accident occurrence probability of each point by combining real-time states of all factors influencing traffic safety with meteorological environment, traffic flow, driving behaviors and road structures, and early warning the position with high probability.
Drawings
The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a module schematic diagram of a road patrol system based on the internet of vehicles.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, the invention relates to a road patrol system based on internet of vehicles, which comprises a data acquisition module, a data analysis module and a cloud service platform;
wherein:
the data acquisition module is used for acquiring the running state information of vehicles on the road, and the running state information comprises running image data and running speed data;
the data analysis module is used for analyzing the driving image data and the driving speed data acquired by the data acquisition module to obtain a processing result, and sending the processing result to the cloud service desk;
and the cloud service platform is used for recording the analysis processing result of the data analysis module and judging whether to send out an early warning signal according to the analysis processing result.
In a preferred embodiment of the present invention, in practical applications, the data acquisition module is used to acquire driving state information of vehicles on a road, where the driving state information includes driving image data and driving speed data, the driving image data and the driving speed data acquired by the data acquisition module are analyzed and processed, and an analysis processing result is sent to the cloud service platform, and the cloud service platform records the analysis processing result of the data analysis module and performs early warning according to the analysis processing result.
The data acquisition module comprises a plurality of groups of monitoring equipment which are sequentially arranged on two sides of the highway at equal intervals; the monitoring equipment is used for carrying out camera shooting and speed measurement on vehicles running on the road, and running image data and running speed data;
the method comprises the following steps that (1) monitoring equipment is arranged at one position of 200-500 meters at intervals on one side of a highway along the highway and comprises 2 gun camera monitoring equipment, 1 dome camera monitoring equipment and 1 fisheye monitoring equipment, and each group of monitoring equipment synchronously tests the speed of vehicles running on the highway; storing timestamp information of a video stream of an event occurring on a road to generate a corresponding picture and a short video;
in one implementation, the processing result includes a first analysis result and a second analysis result, and the data analysis module includes a first analysis unit and a second analysis unit;
the first analysis unit judges whether the vehicle runs on a corresponding lane at a normal speed or not based on the acquired running image data and the running speed data to obtain a first analysis result;
and the second analysis unit judges whether the vehicle runs on the corresponding lane according to the driving direction or not based on the acquired running image data to obtain a second analysis result.
The first analysis unit performs the following steps:
the number of lane lines where the vehicle is located can be obtained through the lane line information recognized by the monitoring equipment, namely the condition of the relevant lane where the vehicle is located in the current road can be obtained, and meanwhile, the highway is divided into a plurality of sections { X ] according to the positions of the monitoring equipment 1 、X 2 、……、X n And according to different lanes of the road { Y } 1 、Y 2 、……、Y m Will { X } 1 、X 2 、……、X n Is further refined into { X } 1Y1 、X 2Y2 、……、X nYm }; along the road, for the sections with more accidents, the arrangement density of the monitoring equipment is higher, one group of monitoring equipment can be arranged at intervals of 200 meters, and for the sections with relatively less accidents, one group of monitoring equipment can be arranged at intervals of 400-500 meters, so that the cost is saved;
presetting corresponding lanes Y based on different lanes m Maximum speed running threshold V of max And a minimum speed running speed V min (ii) a For a high-speed three-lane route, a maximum speed driving threshold value V is set on the left lane max =120km/h, minimum speed driving speed V min =110km/h, maximum speed driving threshold V for intermediate lane max =110km/h, minimum speed driving speed V min =90km/h, right lane maximum speed driving threshold V max =90km/h, minimum speed driving speed V min =70km/h；
Speed set { V) when all vehicles pass through monitoring equipment is obtained based on each group of monitoring equipment 1 、V 2 、……、V α And V max And V min And comparing and judging.
When V is min ≤V α ≤V max ，α∈[1，n]Determining the section X nYm In which the vehicle is in the corresponding vehicleThe interior of the lane is in a normal driving state;
when V is α ＜V min When alpha is in [1,n ]]Determining the section X nYm The vehicles in the corresponding lane are in an abnormal driving state;
when V is α ＞V max When alpha is in [1,n ]]Determining the section X nYm The vehicles in the corresponding lane are in an overspeed state;
in a preferred embodiment of the present invention, although it is possible to inquire whether an early warning is generated in front of a road by moving a navigation map of a terminal in the related art, the navigation map cannot display which lane in the same section an accident has occurred, and the present invention divides the road into a plurality of sections { X } 1 、X 2 、……、X n And according to different lanes of the road { Y } 1 、Y 2 、……、Y m Will { X } 1 、X 2 、……、X n Is further refined into { X } 1Y1 、X 2Y2 、……、X nYm And judging whether vehicles in each group of lanes in the refined section run on corresponding lanes at normal speed by arranging a plurality of groups of monitoring equipment, and further analyzing which lane on the road has an accident through the first analysis unit so as to facilitate subsequent treatment.
The second analysis unit performs the following steps:
all vehicles in section { X ] are obtained based on each group of monitoring equipment 1Y1 、X 2Y2 、……、X nYm Driving image data in (1);
preprocessing the shot image, adjusting the direction of the image according to the direction of a road traffic marking, fitting the automobile in the image into a rectangle, and mapping the image in a two-dimensional plane coordinate system;
calculating a first slope K1 of two sides of the rectangle and a second slope K2 of the lane line according to the image mapped by the rectangle and the lane line in the two-dimensional plane coordinate system;
the driving state of the vehicle in the section lane is determined based on the first slope K1 and the second slope K2.
Determining that the traveling direction of the vehicle deviates from the lane line when | K1-K2 | is | >;
determining that there is no abnormality in the traveling direction of the vehicle when | K1-K2 | = [;
is a preset threshold;
in a preferred embodiment of the invention, an image is mapped in a two-dimensional plane coordinate system by acquiring driving image data of a vehicle on a road, the horizontal direction is taken as an X-axis direction, the vertical direction is taken as a Y-axis direction vertical to the X-axis direction, slopes on two sides of a rectangle are calculated in the two-dimensional plane coordinate system to obtain a first slope K1, meanwhile, during mapping, a lane line in a lane where the vehicle is located is synchronously mapped in the two-dimensional plane coordinate system, and slopes of the lane lines on two sides of the position where the rectangle is located are calculated to obtain a second slope K2, so that when the vehicle drives in a corresponding lane in a normal driving direction, the first slope K1 is the same as the second slope K2, and when the vehicle deviates in the corresponding lane, an absolute difference exists between the first slope K1 and the second slope K2, the invention calculates and compares the slopes of the vehicle and the corresponding lane lines when the vehicle drives, so as to obtain an analysis result;
under the condition that the driving direction of the vehicle deviates from the lane line, a warning instruction is sent to the vehicle through the vehicle-mounted system, and the double flashing lamps are turned on at the same time to remind the front vehicle and the rear vehicle that the vehicle deviates from the lane, and the front vehicle and the rear vehicle do corresponding safe operation to avoid collision with the vehicle; the double-flashing light can also be replaced by other light reminding modes;
the cloud service platform is used for recording and storing a first analysis result and a second analysis result in the database, storing the first analysis result and the second analysis result so as to conveniently repeat and analyze the traffic accident and further perform key marking on the road of the section where the road is located, and timely early warning and reminding a driver before the vehicle runs to the section of the road so as to avoid accidents again;
the data acquisition module is further used for acquiring road vehicle information, wherein the road vehicle information comprises vehicle speed data, duration data and difference data, the speed data is the driving speed of a vehicle in the road driving process, the duration data is the duration of the vehicle staying in a non-parking area in the road, the difference data is the difference between the average speed of the vehicle staying area and the average speed of the vehicle leaving the vehicle staying area, the road vehicle is marked as i, i =1,2, … …, n and n are positive integers, and the specific analysis and detection process is as follows:
step S1: acquiring the driving speed of a vehicle in the road driving process, and marking the driving speed of the vehicle in the road driving process as JSi;
step S2: acquiring the stay time of a vehicle in a non-parking area in a road, and marking the stay time of the vehicle in the non-parking area in the road as TCi;
and step S3: acquiring the difference value between the average vehicle speed of the vehicle staying area and the average vehicle speed of the driven-out vehicle staying area, and marking the difference value between the average vehicle speed of the vehicle staying area and the average vehicle speed of the driven-out vehicle staying area as PJi;
and step S4: data analysis module passing formula
Acquiring accident analysis coefficients Xi of road vehicles, wherein a1, a2 and a3 are proportional coefficients, and a1 is greater than a2 and greater than a3 is greater than 0;
step S5: setting an accident analysis coefficient threshold, comparing the accident analysis coefficient Xi of the road vehicle with the accident analysis coefficient threshold, analyzing the road vehicle, judging whether the vehicle has an accident or not, generating an early warning signal in advance, improving the road vehicle management efficiency and reducing traffic jam;
if the accident analysis coefficient Xi of the road vehicle is larger than or equal to the accident analysis coefficient threshold value, judging that the corresponding road vehicle has an accident, generating an accident early warning signal, and sending the accident early warning signal and the corresponding road vehicle to a cloud service platform;
if the accident analysis coefficient Xi of the road vehicle is smaller than the accident analysis coefficient threshold value, judging that no accident occurs in the corresponding road vehicle;
the early warning module is used for pushing an early warning signal sent by the cloud service platform to an internet map, displaying the early warning signal in the internet map, reminding a driver in the navigation process, and guiding a manager to carry out targeted traffic safety management by counting the high-incidence places of the road accidents; analyzing historical traffic accident causes and determining each traffic accident multi-point associated close accident inducing factor, wherein the factors comprise: the method comprises the steps of providing accident occurrence probability of each point by combining real-time states of all factors influencing traffic safety with meteorological environment, traffic flow, driving behaviors and road structures, and early warning the position with high probability.
The above formulas are all calculated by taking the numerical value of the dimension, and the preset coefficient in the formula is set by the person skilled in the art according to the actual situation.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be directly connected or indirectly connected through an intermediate member, or they may be connected through two or more elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Although one embodiment of the present invention has been described in detail, the description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (5)
1. A road patrol system based on the Internet of vehicles is characterized by comprising a data acquisition module, a data analysis module and a cloud service platform;
wherein:
the data acquisition module is used for acquiring the running state information of vehicles on the road, and the running state information comprises running image data and running speed data;
the data analysis module is used for analyzing the driving image data and the driving speed data acquired by the data acquisition module to obtain a processing result and sending the processing result to the cloud service desk;
the cloud service platform is used for recording the analysis processing result of the data analysis module and judging whether to send out an early warning signal according to the analysis processing result;
the processing result comprises a first analysis result and a second analysis result, and the data analysis module comprises a first analysis unit and a second analysis unit;
the first analysis unit judges whether the vehicle runs on a corresponding lane at a normal speed or not based on the acquired running image data and the running speed data to obtain a first analysis result;
the second analysis unit judges whether the vehicle runs on the corresponding lane according to the driving direction or not based on the acquired running image data to obtain a second analysis result;
the first analysis unit performs the following steps:
dividing a road into a plurality of sections { X) according to the position of a monitoring device 1 、X 2 、……、X n And according to different lanes of the road { Y } 1 、Y 2 、……、Y m Will { X } 1 、X 2 、……、X n Is refined into { X } 1Y1 、X 2Y2 、……、X nYm }；
Presetting corresponding lanes Y based on different lanes m Maximum speed running threshold V of max And a minimum speed running speed V min ；
Acquiring speed set { V) of all vehicles passing through monitoring equipment based on each group of monitoring equipment 1 、V 2 、……、V a And V max And V min Comparing and judging;
when V is min ≤V α ≤V max And alpha is an element [1, n is a positive integer]Determining the section X nYm The vehicles in the corresponding lanes are in normalA driving state;
when V is a ＜V min When alpha is an element [1, n is a positive integer]Determining the section X nYm The vehicles in the corresponding lane are in an abnormal driving state;
when V is a ＞V min When alpha is an element [1, n is a positive integer]Determining the section X nYm The vehicles in the corresponding lane are in an overspeed state;
the second analysis unit performs the following steps:
acquiring all vehicles in section { X based on each group of monitoring equipment 1Y1 、X 2Y2 、……、X nYm Driving image data in (1);
preprocessing the shot image, adjusting the direction of the image according to the direction of a road traffic marking, fitting the automobile in the image into a rectangle, and mapping the image in a two-dimensional plane coordinate system;
calculating a first slope K1 of two sides of the rectangle and a second slope K2 of the lane line according to the image mapped by the rectangle and the lane line in the two-dimensional plane coordinate system;
judging the driving state of the vehicle in the section lane based on the first slope K1 and the second slope K2;
determining that the driving direction of the vehicle deviates from a lane line if | K1-K2 | > -;
determining that there is no abnormality in the traveling direction of the vehicle when | K1-K2 | = [;
wherein & is a preset threshold.
2. The road patrol system based on the internet of vehicles as claimed in claim 1, wherein the data acquisition module comprises a plurality of groups of monitoring devices which are sequentially arranged on both sides of the road at equal intervals;
the monitoring equipment is used for shooting and measuring the speed of the vehicles running on the road to obtain running image data and running speed data.
3. The Internet of vehicles based road patrol system of claim 1, wherein the cloud service platform is used for recording and storing the first analysis result and the second analysis result in a database.
4. The road patrol system based on the internet of vehicles as claimed in claim 1, further comprising an early warning module, wherein the early warning module is used for pushing an early warning signal sent by the cloud service platform to an internet map, and displaying the early warning signal in the internet map, so as to remind a driver through the vehicle-mounted vehicle system in a navigation process.
5. The Internet of vehicles-based road patrol system according to claim 4, wherein the data collection module is further configured to collect road vehicle information, the road vehicle information includes vehicle speed data, duration data and difference data, the speed data is driving speed of a vehicle during driving on a road, the duration data is duration of a vehicle staying in an out-of-parking area on the road, the difference data is a difference between an average vehicle speed of the vehicle staying area and an average vehicle speed of a vehicle leaving the vehicle staying area, the road vehicle is labeled as i, i =1,2, … …, n, n is a positive integer, and the specific analysis and detection process is as follows:
step S1: acquiring the driving speed of a vehicle in the road driving process, and marking the driving speed of the vehicle in the road driving process as JSi;
step S2: acquiring the stay time of a vehicle in a non-parking area in a road, and marking the stay time of the vehicle in the non-parking area in the road as TCi;
and step S3: acquiring the difference value between the average vehicle speed of the vehicle staying area and the average vehicle speed of the driven-out vehicle staying area, and marking the difference value between the average vehicle speed of the vehicle staying area and the average vehicle speed of the driven-out vehicle staying area as PJi;
and step S4: data analysis module passing formula
Acquiring accident analysis coefficients Xi of road vehicles, wherein a1, a2 and a3 are proportional coefficients, and a1 is greater than a2 and greater than a3 is greater than 0;
step S5: setting an accident analysis coefficient threshold, comparing the accident analysis coefficient Xi of the road vehicle with the accident analysis coefficient threshold, analyzing the road vehicle, judging whether the vehicle has an accident or not, generating an early warning signal in advance, improving the road vehicle management efficiency and reducing traffic jam;
if the accident analysis coefficient Xi of the road vehicle is larger than or equal to the accident analysis coefficient threshold value, judging that the corresponding road vehicle has an accident, generating an accident early warning signal, and sending the accident early warning signal and the corresponding road vehicle to a cloud service platform;
and if the accident analysis coefficient Xi of the road vehicle is less than the accident analysis coefficient threshold value, judging that no accident occurs in the corresponding road vehicle.
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