CN116740974B - Highway road condition early warning system based on road network big data - Google Patents
Highway road condition early warning system based on road network big data Download PDFInfo
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- 206010039203 Road traffic accident Diseases 0.000 description 4
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
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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
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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
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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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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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
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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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
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
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Abstract
The invention discloses an expressway road condition early warning system based on road network big data, which relates to the technical field of intelligent traffic and comprises a monitoring center, wherein the monitoring center is connected with an information acquisition module, an information processing module and a road condition early warning module; the information processing module comprises an information analysis unit, an accident positioning unit and an information storage unit; the method comprises the steps of obtaining real-time road condition information of a highway through an information acquisition module, uploading the real-time road condition information to an information processing module for processing, obtaining accident information, constructing an early warning information base, determining the specific position of the accident, and uploading the accident information to the road condition early warning module for early warning; therefore, early warning is carried out on the crowded road section and the severe weather condition, a driver can acquire accurate real-time road condition information of the expressway, and can select smooth safe road sections to drive in time, so that the capacity of the expressway road condition early warning system is greatly improved.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to an expressway road condition early warning system based on road network big data.
Background
The total mileage of the expressway in China breaks through 16 ten thousand kilometers at present, along with the rapid development of national economy and the rapid increase of the quantity of reserved automobiles in China, the traffic safety situation of the expressway in China is very serious, the dangerous road sections of the expressway are used as multiple areas of traffic accidents, the proportion of the number of the traffic accidents to the total number is up to more than 80 percent, the running risk is increased when vehicles running on the expressway meet severe weather, and the traffic safety level of the whole road network is directly influenced; if the vehicle owner can clearly know real-time road condition information of the expressway before driving, reasonable driving arrangement can be made in advance, traffic pressure is effectively relieved, and driving safety of the expressway is improved; therefore, the highway road condition is collected and processed, and the user can acquire the highway road condition early warning information, so that the highway road condition early warning system has important theoretical and practical significance.
How to make early warning on crowded road sections and bad weather conditions by utilizing road network big data, inquiring real-time highway real-time road condition information through a big data information base and an early warning information base, acquiring the accident multiple road section condition in advance, making path planning in advance, and selecting smooth safe road sections for driving in time is a problem to be solved; therefore, the highway road condition early warning system based on road network big data is provided.
Disclosure of Invention
The invention aims to provide an expressway road condition early warning system based on road network big data, which is used for analyzing the real-time condition of expressway road conditions, making early warning on crowded road sections, bad weather conditions and traffic accident occurrence points, inquiring real-time expressway real-time road condition information through an early warning information base, acquiring the condition of accident multiple road sections in advance, making path planning in advance and selecting smooth safe road sections for driving.
The technical problems to be solved by the invention are as follows: how to provide a highway road condition early warning system based on road network big data, which is different from the existing highway early warning system.
The aim of the invention can be achieved by the following technical scheme:
the highway road condition early warning system based on the road network big data comprises a monitoring center, wherein the monitoring center is connected with an information acquisition module, an information processing module and a road condition early warning module; the road condition early warning module is in communication connection with a mobile terminal of a user; the information processing module comprises an information analysis unit, an accident positioning unit and an information storage unit;
analyzing the obtained real-time road condition information of the expressway, judging whether an accident occurs, if so, positioning the specific position of the accident by the accident positioning unit, uploading the accident information to the information storage unit, and establishing an early warning information base by the information storage unit according to the obtained accident information;
the information acquisition module is used for acquiring real-time road condition information of the expressway;
the expressway real-time road condition information comprises expressway road surface real-time video information, the number of vehicles in a shooting range, real-time climate information and license plate photo information;
setting a shooting range of the high-definition cameras, wherein the shooting range is within a radius a of the high-definition cameras, and the shooting ranges between two adjacent high-definition cameras are overlapped; setting a high-definition camera at each interval 2a according to the range; setting a shooting range period of the high-definition camera;
the installed high-definition cameras are marked with the marks K in sequence according to the running direction m Meanwhile, a miniature weather station is arranged in the range of the shooting range of the high-definition camera;
acquiring real-time video information of a highway pavement, the number of vehicles in a shooting range and license plate photo information through a high-definition camera;
acquiring real-time climate information of the expressway through a miniature weather station;
uploading the obtained real-time highway condition information to an information processing module;
the process for analyzing and judging the obtained real-time highway condition information comprises the following steps:
dividing climate information into severe climate information and normal climate information according to real-time climate information acquired by a miniature weather station; the primary climate, the secondary climate and the tertiary climate are recorded as bad climate information;
selecting adjacent high-definition cameras, and marking the high-definition cameras as a point A and a point B in sequence along the running direction of the expressway in a shooting period;
the number of vehicles passing through the point A in an accident period is obtained, and is marked as S1, and license plate photo information of each vehicle in S1 is obtained;
the number of vehicles passing through the point B in an accident period is obtained, the number is marked as S2, and license plate photo information of each vehicle in the S2 is obtained; comparing the number of vehicles passing through the point a and the point B, and setting the vehicle number ratio M, then m=And sets a threshold vehicle number ratio M 0 And M 1 ；
When M 0 ≤M≤M 1 When the road section is a smooth road section;
when M 1 When M is less than the threshold value, the road section has abnormal road conditions; the vehicles passing by the point A in one accident period are marked as i in turn, and the passing time is marked as T A The time mark passing through the point B is T B Obtaining the time T of the vehicle i passing through the AB section i =T B -T A Setting a time difference value T 0 When T i -T i-1 ≥T 0 When the abnormal road condition is a congestion road section; and marking the obtained congestion section information as accident information.
Uploading the acquired accident information to an accident positioning unit, and marking the mark K according to the real-time video information of the expressway acquired by the information acquisition module m And finding out the corresponding position for installing the high-definition camera, obtaining the specific position of the real-time video information, and further determining the specific position of the accident information.
According to the established early warning information base, obtaining pictures and videos of accident road sections, obtaining the number of times of uploading accident information every 30 days, marking the number of times as P, and setting a threshold value P 0 When P is greater than or equal to P 0 And when the road section is marked as an accident-prone road section, uploading the road section to the road condition early warning module, and sending out early warning.
Uploading the acquired accident information to a road condition early warning module;
the road condition early warning module carries out early warning on the information of the congested road section, and the early warning process comprises the following steps:
obtaining time T of vehicle i passing through AB road section i With i as the abscissa, T i Constructing a two-dimensional function image for an ordinate;
setting the threshold function as T i =0、T i =2 and T i =i；
When the function image is at T i =0 and T i Between =2, indicating that the road section is clear and the vehicle is running normally;
when the function image is in the function T i =0 and function T i When =increasing function between i, this means moderate congestion in the road section; uploading early warning information to the mobile terminal for moderate congestion;
acquiring the distance from a user to a congestion road section, and selecting to continue to run the original road or select other roads to run according to the acquired early warning information when no high-speed entrance of the congestion road section is entered between the distances;
when other roads are selected for driving, marking the selected other roads as selected routes, acquiring the selected routes of other users of the congestion road section, marking the number of the selectable selected routes as M', and carrying out S on the selected routes 1 +M＇=S 2 +M' and satisfies T i -T i-1 ＜T 0 When the route is selected to be smooth; when T is satisfied i -T i-1 ≥T 0 When the selected route is congested, the selected route needs to be reselected until the selected route is smooth;
when a high-speed entrance for entering a congestion section exists between the distances, a user selects other roads to run before entering the high-speed, marks the alternative roads as j, and counts the user selection number H for selecting j; and calculates the route congestion index YD of j j =·（T i -T i-1 ) The method comprises the steps of carrying out a first treatment on the surface of the Constructing a route congestion map, marking the road selected by the user on the map, and simultaneously marking YD corresponding to the road j Marking on a route congestion map, dividing a route congestion index into three sections, and marking j as green, yellow and red respectively, wherein the sections correspond to a clear mode, a moderate congestion mode and a severe congestion mode; and YD is combined with j Marking on a road corresponding to the route congestion map; uploading the route congestion diagram to the mobile terminal, and enabling a user to select a smooth road section to run through the mobile terminal;
when a functionThe image being in a function T i When the function between i and the function i=0 increases, the road section is severely congested; uploading early warning information to a mobile terminal for serious congestion, acquiring the position of a high-speed exit between the vehicle and an accident information place, and driving off a highway;
the road condition early warning module comprises the following steps of:
when the obtained early warning information is the primary climate, uploading the information to a road condition early warning module and displaying the information on the mobile terminal;
when the obtained early warning information is in the secondary climate, uploading the information to a road condition early warning module and displaying the information on the mobile terminal; according to the obtained vehicle position, obtaining the position of a high-speed exit between the vehicle and the accident information, and driving off the expressway;
when the obtained early warning information is in a three-level climate, uploading the information to a road condition early warning module and displaying the information on a mobile terminal; vehicle entrance into the road is prohibited.
Compared with the prior art, the invention has the beneficial effects that: acquiring real-time road condition information of the expressway by using road network big data, analyzing the acquired real-time road condition information of the expressway, judging whether an accident occurs, if so, positioning the specific position of the accident by using the accident positioning unit, uploading the accident information to an information storage unit, and establishing an early warning information base according to the acquired accident information; early warning is carried out on severe weather conditions and accident-prone road sections;
different driving selections are designed for road sections with different crowding degrees, and when no high-speed entrance exists between the distance from the user to the crowded road section, the user acquires early warning information through the mobile terminal and selects other unblocked roads to drive; when a high-speed entrance exists between the distance from the user to the congested road section, the congestion mode of other roads selected by the user is intuitively displayed by constructing a route congestion diagram, and the smooth safe road section is selected to run, so that the congestion degree of the current congested road section is prevented from being increased, and the capacity of the highway road condition early warning system is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of the present invention.
Description of the embodiments
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, 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.
In order to overcome the defects of the conventional highway road condition early warning system, the invention provides the highway road condition early warning system based on the road network big data, which is used for analyzing the real-time condition of the highway road condition and making early warning on crowded road sections, bad weather conditions and traffic accident occurrence points, so that drivers can acquire accurate highway real-time road condition information, select smooth safe road sections in time to drive, and improve the capacity of the highway road condition early warning system;
as shown in FIG. 1, the highway road condition early warning system based on road network big data comprises a monitoring center, wherein the monitoring center is connected with an information acquisition module, an information processing module and a road condition early warning module; the road condition early warning module is in communication connection with a mobile terminal of a user;
the information processing module comprises an information analysis unit, an accident positioning unit and an information storage unit;
analyzing the obtained real-time road condition information of the expressway, judging whether an accident occurs, if so, positioning the specific position of the accident by the accident positioning unit, uploading the accident information to the information storage unit, and establishing an early warning information base by the information storage unit according to the obtained accident information;
the information acquisition module is used for acquiring real-time road condition information of the expressway;
the information acquisition module comprises a high-definition camera and a miniature weather station;
setting a shooting range of the high-definition cameras, wherein the shooting range is within a radius a of the high-definition cameras, and the shooting ranges between two adjacent high-definition cameras are overlapped;
setting a high-definition camera at each interval 2a according to the range; setting a shooting range period of the high-definition camera;
the installed high-definition cameras are marked with the marks K in sequence according to the running direction m M=1, 2,3, … …, n, n is a positive integer, and the video information acquired by each high-definition camera is marked with a high-definition camera label; meanwhile, a miniature weather station is installed according to the range of the high-definition camera;
the expressway real-time road condition information comprises expressway road surface real-time video information, the number of vehicles in a shooting range, real-time climate information and license plate photo information;
acquiring real-time video information of a highway pavement, vehicle number information in a shooting range and license plate photo information through a high-definition camera;
acquiring real-time climate information of the expressway through a miniature weather station;
uploading the obtained real-time highway condition information to an information processing module;
the process of analyzing and judging the obtained real-time road condition information of the expressway and determining the information as accident information comprises the following steps:
dividing climate information into severe climate information and normal climate information according to real-time climate information acquired by a miniature weather station;
marking the temperature as t, setting a threshold temperature t 0 When t is less than t 0 When the road surface is frozen;
marking the wind speed as W, and setting a threshold wind speed W 0 When W is greater than or equal to W 0 When the road surface is strong wind;
obtaining the visibility of the expressway according to the mini weather station, marking the visibility of the expressway as N, and setting a threshold visibility N 0 When N is less than or equal to N 0 When the road surface visibility is low;
for t < t 0 Or W 1 W is less than or equal to W, one item is marked as primary climate, and two items are marked as secondary climate;
for t < t 0 、N≤N 0 Or W 1 W is less than or equal to W, three terms are satisfied to mark the three as tertiary climate;
and t is set to 0 ≤t、W 0 W and N > N 0 Marking as normal climate;
the primary climate, the secondary climate and the tertiary climate are recorded as bad climate information; marking the acquired severe weather information as accident information;
selecting adjacent high-definition cameras, and marking the high-definition cameras as a point A and a point B in sequence along the running direction of the expressway in a shooting period;
the number of vehicles passing through the point A in an accident period is obtained, the number is marked as S1, and license plate photo information of each vehicle is obtained at the same time;
the number of vehicles passing through the point B in an accident period is obtained, the number is marked as S2, and license plate photo information of each vehicle is obtained at the same time;
comparing the number of vehicles passing through the point a and the point B, and setting the vehicle number ratio M, then m=And sets a threshold vehicle number ratio M 0 And M 1 ；
When M 0 ≤M≤M 1 When the road section is a smooth road section;
when M 1 When M is less than the threshold value, the road section has abnormal road conditions; sequentially marking vehicles passing through the point A in one shooting period as i, wherein i=1, 2,3, … …, n and n are positive integers;
when the vehicle i passes the point A, the marking time is T A When the vehicle i passes the point B, the marking time is T B Obtaining a vehicle iTime T of passing through AB section i =T B -T A Wherein T is i =T 1 ，T 2 ，T 3 ，……，T n N is a positive integer; setting a time difference T 0 When T i -T i-1 ≥T 0 When the abnormal road condition is a congestion road section; marking the obtained congestion road section information as accident information;
the specific position process of the accident information determined by the accident positioning unit is as follows:
uploading the acquired accident information to an accident positioning unit, and marking the mark K according to the real-time video information of the expressway acquired by the information acquisition module m Finding out the corresponding position for installing the high-definition camera, obtaining the specific position of the real-time video information, and further determining the specific position of the accident information;
uploading the accident information and the specific position of the accident information to an information storage unit according to the obtained accident information and the specific position of the accident information, and constructing an early warning information base;
according to the established early warning information base, obtaining pictures and videos of accident road sections, obtaining the number of times of uploading accident information every 30 days, marking the number of times as P, and setting a threshold value P 0 When P is greater than or equal to P 0 When the road section is marked as an accident multiple road section, the accident multiple road section is uploaded to a road condition early warning module, and early warning is sent out in advance;
uploading the acquired accident information to a road condition early warning module;
the road condition early warning module is used for carrying out early warning on accident information obtained by the information processing module, marking the received accident information as early warning information and carrying out information interaction sharing with a user through the mobile terminal; the mobile terminal comprises a smart phone, a notebook computer, a tablet personal computer and a vehicle-mounted computer;
the road condition early warning module sends real-time road condition information and early warning information of the expressway to the mobile terminal;
according to the license plate photo information of the vehicle, positioning the position of the vehicle through video information, and comparing the position with the position of the high-definition camera to obtain the distance from the vehicle to the accident information;
when the acquired early warning information is the congestion road section information, the early warning process of the early warning module is as follows:
obtaining time T of vehicle i passing through AB road section i With i as the abscissa, T i Constructing a two-dimensional function image for an ordinate;
setting the threshold function as T i =0、T i =2 and T i =i；
When the function image is at T i =0 and T i Between =2, indicating that the road section is clear and the vehicle is running normally;
when the function image is in the function T i =0 and function T i When =increasing function between i, this means moderate congestion in the road section; uploading early warning information to the mobile terminal for moderate congestion;
acquiring the distance from a user to a congestion road section, and selecting to continue to run the original road or select other roads to run according to the acquired early warning information when no high-speed entrance of the congestion road section is entered between the distances; when other roads are selected for driving, marking the selected other roads as selected routes, acquiring the selected routes of other users of the congestion road section, marking the number of the selected routes of the users as M', and carrying out S on the selected routes 1 +M＇=S 2 +M' and satisfies T i -T i-1 ＜T 0 When, the selected route is clear; when T is satisfied i -T i-1 ≥T 0 When the selected route is congestion, the selected route needs to be re-selected until the selected route is smooth;
when a high-speed entrance for entering a congestion section exists between the distances, a user needing to enter the section of road selects other roads to run before entering the high-speed entrance according to the obtained early warning information, and the roads which can be selected by the user are marked as j, j=1, 2,3, … …, n and n are positive integers; counting the user selection number of the road j selected by the user, and marking the user selection number of the road j as H;
calculating a route congestion index of the selected road j, and calculating a route congestion index YD j =·（T i -T i-1 ) Constructing a route congestion map, marking the road selected by the user on the map, and simultaneously marking the YD corresponding to the road j Marking on a route congestion map, and setting route congestion index thresholds YD0, YD1, YD2 and YD3;
when YD0 is less than or equal to YD j When YD1 is not more than, marking the road j as green, then the road is in a smooth mode, and YD is applied j Marking on a road corresponding to the route congestion map;
when YD1 is less than or equal to YD j Marking the road j as yellow when the ratio of the road j to the YD2 is less than or equal to the ratio of the road j to the road j, marking the road j as a medium congestion mode, and marking the route congestion index YD j Marking on a road corresponding to the route congestion map;
when YD2 is less than or equal to YD j When YD3 is not more than, marking the road j as red, then the road is in a heavy congestion mode, and the route congestion index YD is obtained j Marking on a road corresponding to the route congestion map;
uploading the route congestion diagram to the mobile terminal, and enabling a user to select a smooth road section to run through the mobile terminal so as to avoid aggravating the congestion degree of the current congestion road section;
when the function image is in the function T i When the function between i and the function i=0 increases, the road section is severely congested; uploading early warning information to a mobile terminal for serious congestion, acquiring the position of a high-speed exit between the vehicle and an accident information place, and driving off a highway;
when the acquired early warning information is severe weather information, the early warning process of the early warning module is as follows:
when the obtained early warning information is the primary climate, uploading the information to a road condition early warning module and displaying the information on the mobile terminal;
when the obtained early warning information is in the secondary climate, uploading the information to a road condition early warning module and displaying the information on the mobile terminal; according to the obtained vehicle position, obtaining the position of a high-speed exit between the vehicle and the accident information, and driving off the expressway;
when the obtained early warning information is in a three-level climate, uploading the information to a road condition early warning module and displaying the information on a mobile terminal; vehicle entrance into the road is prohibited.
The invention provides an expressway road condition early warning system based on road network big data, which comprises a monitoring center, wherein the monitoring center is connected with an information acquisition module, an information processing module and a road condition early warning module; the information processing module comprises an information analysis unit, an accident positioning unit and an information storage unit. The method comprises the steps that real-time highway condition information is obtained through an information collecting module and is uploaded to an information processing module, accident information in the real-time highway condition information is obtained through an information analysis unit in the information processing module, a specific position of an accident is obtained through an accident positioning unit, and an early warning information base is built by an information storage unit through the uploaded accident information; and uploading the processed accident information to a road condition early warning module, wherein the road condition early warning module receives the accident information, displays the accident place, the accident occurrence time and the real-time video information, is in communication connection with a mobile terminal of a user, and the user acquires the accident information of the road condition early warning module through the mobile terminal. The highway road condition early warning system based on the road network big data is realized, the road network big data is utilized to make early warning on the crowded road section of the road surface and the bad weather condition, a driver is enabled to acquire accurate real-time road condition information of the highway, smooth safe road section driving is timely selected, and the capacity of the highway road condition early warning system is greatly improved.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (2)
1. The highway road condition early warning system based on the road network big data comprises a monitoring center, and is characterized in that the monitoring center is connected with an information acquisition module, an information processing module and a road condition early warning module; the road condition early warning module is in communication connection with a mobile terminal of a user;
the information acquisition module is used for acquiring real-time road condition information of the expressway;
the information acquisition module comprises a high-definition camera and a miniature weather station, and the real-time highway road condition information comprises real-time video information of highway pavement, the number of vehicles in a shooting range, real-time weather information and license plate photo information;
setting a shooting range of the high-definition cameras, wherein the shooting range between two adjacent high-definition cameras is overlapped; the installed high-definition cameras are marked with the marks K in sequence according to the running direction m Setting a miniature weather station according to the range of the high-definition camera;
acquiring real-time video information of a highway pavement, the number of vehicles in a shooting range and license plate photo information through a high-definition camera;
acquiring real-time climate information of the expressway through a miniature weather station;
the information processing module comprises an information analysis unit, an accident positioning unit and an information storage unit;
analyzing the obtained real-time road condition information of the expressway through an information analysis unit, judging whether an accident occurs, if so, positioning the specific position of the accident through an accident positioning unit, uploading the accident information to an information storage unit, and establishing an early warning information base according to the obtained accident information by the information storage unit;
the process for analyzing and judging the obtained real-time highway condition information comprises the following steps:
dividing climate information into severe climate information and normal climate information according to real-time climate information acquired by a miniature weather station; the primary climate, the secondary climate and the tertiary climate are recorded as bad climate information;
selecting adjacent high-definition cameras, and marking the high-definition cameras as a point A and a point B in sequence along the running direction of the expressway in a shooting period;
the number of vehicles passing through the point A in an accident period is obtained, and is marked as S1, and license plate photo information of each vehicle in S1 is obtained;
the number of vehicles passing through the point B in an accident period is obtained, the number is marked as S2, and license plate photo information of each vehicle in the S2 is obtained;
comparing the number of vehicles passing through the point a and the point B, and setting the vehicle number ratio M, then m=And sets a threshold vehicle number ratio M 0 And M 1 ；
When M 0 ≤M≤M 1 When the road section is a smooth road section;
when M 1 When M is less than the threshold value, the road section has abnormal road conditions; the vehicles passing by the point A in one accident period are marked as i in turn, and the passing time is marked as T A The time mark passing through the point B is T B Obtaining the time T of the vehicle i passing through the AB section i =T B -T A Setting a time difference value T 0 When T i -T i-1 ≥T 0 When the abnormal road condition is a congestion road section; marking the obtained congestion road section information as accident information;
the accident positioning unit determines the specific position process of the accident information comprises the following steps:
uploading the acquired accident information to an accident positioning unit, and marking the mark K according to the real-time video information of the expressway acquired by the information acquisition module m Finding out the corresponding position for installing the high-definition camera, obtaining the specific position of the real-time video information, and further determining the specific position of the accident information;
the process of establishing the early warning information base by the information storage unit through the uploaded accident information comprises the following steps:
according to the established early warning information base, obtaining pictures and videos of accident road sections, obtaining the number of times of uploading accident information every 30 days, marking the number of times as P, and setting a threshold value P 0 When P is greater than or equal to P 0 When the road section is marked as an accident-prone road section, the accident-prone road section is uploaded to a road condition early warning module, and early warning is sent out;
the road condition early warning module is used for generating corresponding early warning information according to accident information in the early warning information base and carrying out information interaction sharing on the generated early warning information and a mobile terminal of a user;
the road condition early warning module carries out early warning on the information of the congested road section, and the early warning process comprises the following steps:
obtaining time T of vehicle i passing through AB road section i ；
When the road section is smooth, the vehicle normally runs;
when the road section is moderately congested; acquiring the distance from a user to a congested road section, and selecting to continue to run an original road or selecting other roads to run when no high-speed entrance of the congested road section is entered between the distances;
when other roads are selected for driving, marking the selected other roads as selected routes, acquiring the selected routes of other users of the congestion road section, marking the number of the selectable selected routes as M', and carrying out S on the selected routes 1 +M＇=S 2 +M' and satisfies T i -T i-1 ＜T 0 When the route is selected to be smooth; when T is satisfied i -T i-1 ≥T 0 When the selected route is congested, the selected route needs to be reselected until the selected route is smooth;
when a high-speed entrance for entering a congestion section exists between the distances, a user selects other roads to run before entering the high-speed entrance, marks the alternative roads as j, and counts the user selection number H for selecting j; and calculates the route congestion index YD of j j =·（T i -T i-1 ) The method comprises the steps of carrying out a first treatment on the surface of the Constructing a route congestion map, marking the road selected by the user on the map, and simultaneously marking YD corresponding to the road j Marking on a route congestion map, dividing a route congestion index into three sections, and marking j as green, yellow and red respectively, wherein the sections correspond to a clear mode, a moderate congestion mode and a severe congestion mode; and YD is combined with j Marking on a road corresponding to the route congestion map; uploading the route congestion diagram to the mobile terminal, and enabling a user to select a smooth road section to run through the mobile terminal;
when the road section is severely congested; the position of the high-speed exit between the vehicle and the accident information is acquired, and the vehicle is driven off the expressway.
2. The highway road condition early warning system based on road network big data according to claim 1, wherein the road condition early warning module early warning process of severe weather information comprises:
when the obtained early warning information is the primary climate, uploading the information to a road condition early warning module and displaying the information on the mobile terminal;
when the obtained early warning information is in the secondary climate, uploading the information to a road condition early warning module and displaying the information on the mobile terminal; according to the obtained vehicle position, obtaining the position of a high-speed exit between the vehicle and the accident information, and driving off the expressway;
when the obtained early warning information is in a three-level climate, uploading the information to a road condition early warning module and displaying the information on a mobile terminal; vehicle entrance into the road is prohibited.
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