CN117275250A - Early warning device and method for induction of expressway fog area - Google Patents
Early warning device and method for induction of expressway fog area Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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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
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
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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
- G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
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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
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
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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
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
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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
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses early warning equipment and a method for induction of a fog region of an expressway, which relate to the field of traffic safety and solve the problems that the existing early warning mode for induction of the fog region of the expressway often adopts a manual mode to acquire data of the fog region of the expressway, and when the fog region is formed on the expressway, early warning is started, and a moving path of the fog region cannot be effectively judged, so that a certain hysteresis problem exists in the early warning process of the fog region.
Description
Technical Field
The invention belongs to the field of traffic safety, relates to a sensor technology, and particularly relates to early warning equipment and method for induction of a fog region of a highway.
Background
The expressway fog area is one of the serious hidden dangers of road traffic safety, a driver drives a vehicle to enter the expressway fog area, the probability of traffic accidents is often increased due to the fact that sight is blocked, the expressway fog area induction means that the visibility of the road is reduced due to the occurrence of heavy fog on the expressway, in order to guide the driver to drive safely, a traffic management department can set a corresponding prompt sign on a road surface for reminding the driver to pay attention to the expressway fog area, slowing down and keeping the safe distance between vehicles, observing traffic rules and ensuring driving safety.
The existing expressway fog area induction early warning mode often adopts a manual mode to acquire expressway fog area data, early warning is started when a fog area is formed on an expressway, and a moving path of the fog area cannot be effectively judged, so that certain hysteresis exists in the early warning process of the fog area.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide early warning equipment and method for induction of a fog region of an expressway.
The method comprises the steps of obtaining a high-speed fog region judgment coefficient through obtaining an air temperature value, an air humidity value and a particulate matter concentration value in the air, judging the high-speed fog region by utilizing a high-speed fog region judgment coefficient threshold value, respectively obtaining sensor data, fog region weather data and road condition data of a highway where the fog region is located as fog region data, processing the fog region data to obtain fog region classification data, a fog region moving speed coefficient and a fog region road condition congestion coefficient, calculating to obtain a fog region traffic early warning classification coefficient according to the obtained fog region classification data, the fog region moving speed coefficient and the fog region road condition congestion coefficient, dividing the fog region traffic early warning classification coefficient by utilizing the threshold value to obtain fog region traffic early warning classification data, and respectively formulating a navigation strategy and an induction strategy according to the fog region traffic early warning classification data.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme, and the specific working process of each module of the early warning equipment for the induction of the fog region of the expressway is as follows:
and a data acquisition module: obtaining a high-speed fog region judgment coefficient by obtaining an air temperature value, an air humidity value and a particulate matter concentration value in the air, judging a high-speed fog region by utilizing a high-speed fog region judgment coefficient threshold value, and respectively obtaining sensor data, fog region weather data and road condition data of a highway where the fog region is located as fog region data;
and a data processing module: processing the fog region data to obtain fog region classification data, a fog region moving speed coefficient and a fog region road condition congestion coefficient, calculating to obtain a fog region traffic early warning classification coefficient according to the acquired fog region classification data, the fog region moving speed coefficient and the fog region road condition congestion coefficient, and dividing the fog region traffic early warning classification coefficient by using a threshold value to obtain fog region traffic early warning classification data;
traffic early warning module: respectively making a navigation strategy and an induction strategy according to the traffic early warning grading data of the fog region;
the system further comprises a server, and the data acquisition module, the data processing module and the traffic early warning module are respectively connected with the server.
Further, the data acquisition module acquires fog region data, which is specifically as follows:
the data acquisition module comprises a fog region judgment unit, a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the fog region judgment unit judges whether a fog region appears at a high-speed current position, the first acquisition unit acquires sensor data, the second acquisition unit acquires fog region weather data, the third acquisition unit acquires road condition data, and the sensor data, the fog region weather data and the road condition data are set to be fog region data.
Further, the fog region judging unit judges whether a fog region appears at the high-speed current position or not, and specifically comprises the following steps:
the fog region acquisition unit calculates a high-speed fog region judgment coefficient N according to the air temperature value, the air humidity value and the concentration value of particles in the air;
acquiring a temperature value in a non-fog weather state, an air humidity value in the non-fog weather state and a particulate matter concentration value in air in the non-fog weather state, and calculating to obtain a high-speed fog area judgment coefficient threshold value N1;
setting a threshold value N1 for a high-speed fog region judgment coefficient N, and judging whether a fog region appears at the high-speed current position or not, wherein the method comprises the following steps of:
when N is larger than N1, a fog area appears at the high-speed current position;
When N is less than or equal to N1, no fog area appears at the high-speed current position.
Further, the first acquisition unit acquires sensor data, specifically as follows:
the first acquisition unit acquires the scattered light intensity reflected by the fog region as g1, acquires the reference scattered light intensity g2, and calculates to obtain fog concentration data through g1 and g 2;
the first acquisition unit acquires the infrared light beam intensity h1 passing through the fog region, acquires the reference infrared light beam intensity h2, and calculates to obtain the fog region visibility data through h1 and h 2;
the method comprises the steps that a first acquisition unit acquires longitude and latitude data of a current position, which is acquired by a GPS sensor in a fog area, as the position of the fog area;
and setting fog concentration data in a fog region, visibility data in the fog region and positions of the fog region as sensor data.
Further, the second acquiring unit acquires weather data of the fog region, and the third acquiring unit acquires road condition data, specifically as follows:
the second acquisition unit is used for respectively acquiring the wind speed data of the fog region and the air humidity data of the fog region and setting the wind speed data and the air humidity data of the fog region as the weather data of the fog region;
the third acquisition unit calculates and acquires vehicle flow speed data through n vehicle speeds of the expressway where the fog area is located;
the third acquisition unit acquires the vehicle flow data by using the magnetic induction coil;
The third acquisition unit is used for identifying a speed limit sign board on the right side of the expressway by using a camera to acquire road speed limit data;
and setting traffic flow speed data, traffic flow data and road speed limit data of the expressway where the fog area is located as road condition data.
Further, the data processing module acquires fog area early warning data according to the fog area data, and the method specifically comprises the following steps:
the data processing module comprises a first processing unit, a second processing unit, a third processing unit and a fourth processing unit, wherein the first processing unit acquires fog region classification data, the second processing unit acquires a fog region moving speed coefficient, the third processing unit acquires a fog region road condition congestion coefficient, and the fourth processing unit acquires fog region traffic early warning classification data according to the fog region classification data, the fog region moving speed coefficient and the fog region road condition congestion coefficient.
Further, the first processing unit acquires mist zone classification data, specifically as follows:
acquiring a fog region classification coefficient N according to fog concentration data, fog region visibility data and fog region wind speed data of a fog region;
setting a first fog region classification interval, a second fog region classification interval, a third fog region classification interval and a fourth fog region classification interval according to the fog region classification coefficient N, respectively corresponding to a slight fog grade, a moderate fog grade, a severe fog grade and a severe fog grade, and setting thresholds N1, N2 and N3 for judgment;
When N is more than 0 and less than or equal to N1, judging that the first fog region classification section corresponds to the light fog grade;
when N1 is more than N and less than or equal to N2, judging that the second fog region classification section corresponds to the medium fog grade;
when N2 is more than N and less than or equal to N3, judging that the third fog region classification section corresponds to the severe fog grade;
when N3 is less than N, judging that the fourth fog region classification section corresponds to the serious fog grade;
and taking the fog region classification intervals corresponding to different fog regions as fog region classification data.
Further, the second processing unit obtains a fog area moving speed coefficient, and the third processing unit obtains a fog area road condition congestion coefficient, specifically as follows:
the second processing unit calculates and obtains a fog region moving speed coefficient according to the fog region wind speed data and the fog region air humidity data;
and the third processing unit calculates and obtains the road condition congestion coefficient of the fog area according to the traffic flow speed data, the traffic flow data and the road speed limit data.
Further, the fourth processing unit acquires traffic early warning grading data of the fog region and transmits the traffic early warning grading data to the traffic early warning module, and the traffic early warning module receives the traffic early warning data of the fog region, carries out high-speed induction and issues early warning, and is specifically as follows:
acquiring fog region traffic early warning classification data according to the fog region classification data, the fog region moving speed coefficient and the fog region road condition congestion coefficient;
Setting a first traffic early warning section, a second traffic early warning section, a third traffic early warning section and a fourth traffic early warning section according to a traffic early warning grading coefficient Y in a fog region, and respectively corresponding to low risk early warning, medium risk early warning and high risk early warning and setting a threshold value for judgment, wherein the method comprises the following steps of:
when Y is more than 0 and less than or equal to Y1, judging a first traffic early warning interval, and corresponding to low risk early warning;
when Y1 is more than Y and less than or equal to Y2, judging a second traffic early warning interval corresponding to the traffic risk early warning;
when Y2 is more than Y and less than or equal to Y3, judging a third traffic early warning interval, and corresponding to high risk early warning;
when Y3 is less than Y, judging a fourth traffic early warning interval, and corresponding to extremely high risk early warning;
setting fog areas corresponding to the first traffic early warning interval, the second traffic early warning interval, the third traffic early warning interval and the fourth traffic early warning interval as fog area traffic early warning classification data and conveying the fog area traffic early warning classification data to a traffic early warning module;
the traffic early warning module comprises a data communication unit, a vehicle navigation unit and a traffic management system unit, wherein the data communication unit uploads traffic early warning data of a fog area to the vehicle navigation unit and the traffic management system unit;
the vehicle-mounted navigation unit formulates a navigation strategy according to the traffic early warning grading data of the fog area;
And the traffic management system unit establishes an induction strategy according to the traffic early warning classification data of the fog region.
Further, the early warning method for the induction of the fog region of the expressway comprises the following steps:
step S1: acquiring fog region data;
step S11: judging the high-speed fog region, which comprises the following specific steps:
step S111: acquiring an air temperature value WD through a temperature sensor, acquiring an air humidity value SD through a first humidity sensor, acquiring a particulate matter concentration value ND in air through an air quality sensor, and according to a formulaObtaining a high-speed fog region judgment coefficient N, wherein a1, a2 and a3 are proportionality coefficients, and a1, a2 and a3 are all larger than 0;
step S112: selecting a fog-free weather state according to weather forecast, and respectively acquiring an air temperature value WD1, an air humidity value SD1 and a particulate matter concentration value ND1 in air in the fog-free weather state by using a temperature sensor, a first humidity sensor and an air quality sensor;
step S113: according to the formulaCalculating to obtain a high-speed fog region judgment coefficient threshold value N1, wherein a1, a2 and a3 are proportionality coefficients, and a1, a2 and a3 are all larger than 0;
step S114: setting a threshold value N1 for a high-speed fog region judgment coefficient N, and judging whether a fog region appears at the high-speed current position or not, wherein the method comprises the following steps of:
When N is larger than N1, a fog area appears at the high-speed current position;
when N is less than or equal to N1, no fog area appears at the high-speed current position;
step S12: the method comprises the following specific steps of:
step S121: by means of laser scatteringThe sensor reflects a beam of laser beam to the fog area, receives scattered light reflected by the fog area, obtains the intensity of the scattered light to be g1, selects a non-fog weather state according to weather forecast in the same way, obtains reference scattered light intensity g2 by using the laser scattering sensor, and uses a formulaObtaining mist concentration data WQND;
step S122: an infrared sensor is utilized to reflect a beam of infrared laser beam to a fog region, an infrared receiving point is arranged in the fog region, the infrared light beam passing through the fog region is received, the intensity of the infrared light beam is obtained to be h1, a fog-free weather state is selected in the same way, the infrared sensor is utilized to obtain the reference infrared light beam intensity h2, and a formula is utilizedObtaining fog region visibility data NJD;
step S123: the GPS sensors are arranged on two sides of the expressway, when the fog area judging unit judges that the fog area appears at the high-speed current position, the GPS sensor in the fog area acquires longitude and latitude data of the current position as the position of the fog area;
Step S124: setting fog concentration data of a fog region, visibility data of the fog region and positions of the fog region as sensor data;
step S13: acquiring fog region wind speed data by using a wind speed sensor, acquiring fog region air humidity data by using a second humidity sensor, and setting the fog region wind speed data and the fog region air humidity data as fog region weather data;
step S14: the method comprises the following specific steps of:
step S141: the radar speed measuring area is utilized to obtain that n vehicle speeds of the expressway where the fog area is located are v1, v2 and v3 … … vn respectively, and a formula is utilizedAcquiring traffic flow speed data vn;
step S142: installing the ground induction coils below the highway pavement, arranging two continuous ground induction coils as a first ground induction coil and a second ground induction coil respectively, wherein the installation distance of the first ground induction coil and the second ground induction coil is s, when a vehicle passes through the first ground induction coil and the second ground induction coil, the first ground induction coil and the second ground induction coil respectively generate electromagnetic signals, the number of the acquired electromagnetic signals is C, and the number of automobiles passing through the installation distance s of the first ground induction coil and the second ground induction coil is C/2 according to the formula Obtaining traffic flow data L;
step S143: identifying a right speed limit sign board of the expressway by using a camera to obtain road speed limit data XS;
step S144: setting traffic flow speed data, traffic flow data and road speed limit data of a highway where the fog area is located as road condition data;
step S15: the data acquisition module is used for transmitting the sensor data, the fog weather data and the road condition data as the fog data to the data processing module;
step S2: acquiring fog region early warning data according to the fog region data;
step S21: the method comprises the following specific steps of:
step S211: acquiring fog concentration data in a fog region, visibility data in the fog region and wind speed data in the fog region;
step S212: acquiring a fog region classification coefficient N according to fog concentration data, fog region visibility data and fog region wind speed data of a fog region;
step S213: setting a first fog region classification interval, a second fog region classification interval, a third fog region classification interval and a fourth fog region classification interval according to the fog region classification coefficient N, respectively corresponding to a slight fog grade, a moderate fog grade, a severe fog grade and a severe fog grade, and setting thresholds N1, N2 and N3 for judgment;
when N is more than 0 and less than or equal to N1, judging that the first fog region classification section corresponds to the light fog grade;
When N1 is more than N and less than or equal to N2, judging that the second fog region classification section corresponds to the medium fog grade;
when N2 is more than N and less than or equal to N3, judging that the third fog region classification section corresponds to the severe fog grade;
when N3 is less than N, judging that the fourth fog region classification section corresponds to the serious fog grade;
step S214: taking the fog region classification intervals corresponding to different fog regions as fog region classification data WQFJ;
step S22: calculating to obtain a fog region moving speed coefficient according to the fog region wind speed data and the fog region air humidity data;
step S22: acquiring road condition congestion coefficients of a fog area according to traffic flow speed data, traffic flow data and road speed limit data;
step S23: acquiring fog region traffic early warning classification data according to fog region classification data, a fog region moving speed coefficient and a fog region road condition congestion coefficient, wherein the method comprises the following specific steps of:
step S231: according to the fog region classification data WQFJ, the fog region moving speed coefficient M and the fog region road condition congestion coefficient LK, calculating to obtain a fog region traffic early warning classification coefficient
Step S232: setting a first traffic early warning section, a second traffic early warning section, a third traffic early warning section and a fourth traffic early warning section according to a traffic early warning grading coefficient Y in a fog region, and respectively corresponding to low risk early warning, medium risk early warning and high risk early warning and setting a threshold value for judgment, wherein the method comprises the following steps of:
When Y is more than 0 and less than or equal to Y1, judging a first traffic early warning interval, and corresponding to low risk early warning;
when Y1 is more than Y and less than or equal to Y2, judging a second traffic early warning interval corresponding to the traffic risk early warning;
when Y2 is more than Y and less than or equal to Y3, judging a third traffic early warning interval, and corresponding to high risk early warning;
when Y3 is less than Y, judging a fourth traffic early warning interval, and corresponding to extremely high risk early warning;
step S233: setting fog areas corresponding to the first traffic early warning interval, the second traffic early warning interval, the third traffic early warning interval and the fourth traffic early warning interval as fog area traffic early warning classification data, and transmitting the fog area traffic early warning data to a traffic early warning unit.
Step S3: carrying out high-speed induction and early warning distribution according to traffic early warning data in a fog area;
step S31: compressing and packaging the transported traffic early warning classification data in the fog region, establishing connection with a 5G network by using a first 5G module, performing data subpackaging on the traffic early warning classification data in the fog region, and forwarding the traffic early warning classification data in the fog region to a second 5G module and a third 5G module by using the 5G network of the first 5G module;
step S32: the navigation strategy is formulated according to the traffic early warning grading data in the fog area, and the specific steps are as follows:
step S321: aiming at low risk early warning, the vehicle-mounted navigation unit reminds a driver of a current high-speed error zone, keeps proper vehicle distance and running speed, and displays the position of a fog zone on a navigation interface so as to help the driver to make a corresponding decision, provide alternative routes and avoid areas where traffic jams or accidents possibly exist;
Step S322: aiming at the early warning of the traffic risk, the vehicle-mounted navigation unit reminds a driver of paying attention to the position of a fog region, reduces the speed of the vehicle, turns on a fog lamp, displays the position and the range of the fog region on a navigation screen, provides real-time traffic condition update, and adjusts a navigation route according to the real-time traffic condition so as to avoid possible traffic jam or accident;
step S323: aiming at high risk early warning, the vehicle-mounted navigation unit warns a driver to avoid entering a fog area, suggests to find a safe parking place to wait for the fog area to dissipate, displays the position of the fog area on a navigation screen, provides real-time traffic condition update, and provides an emergency contact phone so that the driver can seek help when encountering an emergency;
step S324: aiming at extremely high risk early warning, a driver is strongly warned to avoid entering a fog area, a nearest safe parking place is automatically searched for waiting for the fog area to dissipate, the position of the fog area is displayed on a navigation screen, real-time traffic condition update is provided, and emergency contact phones and emergency rescue services are provided so that the driver can seek help when encountering an emergency.
Step S33: an induction strategy is formulated according to the traffic early warning grading data of the fog area, and the induction strategy is specifically as follows:
Step S331: aiming at low risk early warning, the traffic management system unit utilizes a high-speed electronic guideboard to issue early warning information to inform a driver of a high-speed fog area, and the driver is careful to keep the speed and the distance;
step S332: aiming at the early warning of the traffic risk, the traffic management system unit reminds a driver of the current high-speed occurrence of a fog zone by using a high-speed electronic card, informs the driver of the reduction of the vehicle speed, keeps the vehicle distance, turns on a fog lamp, and arranges traffic police to perform law enforcement patrol so as to ensure the high-speed traffic order and traffic safety;
step S333: aiming at high risk early warning, the traffic management system unit reduces road speed limit, adopts traffic control measures to limit high speed on vehicles, reduces high-speed traffic flow, utilizes a high-speed electronic guideboard to display fog area early warning information, and provides real-time traffic road condition update;
step S334: aiming at extremely high risk early warning, the traffic management system module guides the high-speed vehicles to get at a high speed nearby or enter a server for risk avoidance by using the high-speed guideboard and the patrol car.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. according to the invention, the high-speed fog region judgment coefficient is obtained, so that the high-speed fog region is judged, and compared with the judgment of the high-speed fog region according to meteorological observation, the accuracy of the high-speed fog region judgment result is greatly improved;
2. According to the invention, different types of data are obtained by utilizing a plurality of modes to classify the high-speed fog region, and the fog region traffic early warning classification coefficient is obtained by combining the fog region moving speed and the fog region road condition congestion coefficient, so that the pertinence and the high efficiency of the early warning strategy and the induction strategy are greatly improved.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is an overall system block diagram of the present invention;
FIG. 2 is a diagram of steps for implementing the present invention;
FIG. 3 is a schematic diagram of the position of the magnetic induction coil according to the present invention.
Detailed Description
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.
Example 1
Referring to fig. 1 and 2, the present invention provides a technical solution: the early warning equipment and the method for the induction of the expressway fog area comprise a data acquisition module, a data processing module, a data communication module and an early warning module, wherein the data acquisition module, the data processing module, the data communication module and the early warning module are respectively connected with a server;
The data acquisition module acquires fog region data;
the data acquisition module comprises a fog region judgment unit, a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the fog region acquisition unit comprises a temperature sensor, a first humidity sensor and an air quality sensor; the method comprises the steps that a first acquisition unit acquires fog concentration data of a fog region, visibility data of the fog region and position data of the fog region to obtain sensor data; the second acquisition unit acquires fog region wind speed data and fog region air humidity data to obtain fog region weather data; the third acquisition unit acquires traffic flow speed data, traffic flow data and road speed limit data of the expressway where the fog area is located, and road condition data is obtained;
the fog region judging unit judges the high-speed fog region, and specifically comprises the following steps:
the fog region acquisition unit comprises a temperature sensor, a first humidity sensor and an air quality sensor, wherein the temperature sensor acquires an air temperature value WD, the first humidity sensor acquires an air humidity value SD, and the air quality sensor acquires a particulate matter concentration value ND in air, and the temperature sensor acquires a particulate matter concentration value ND according to a formulaObtaining a high-speed fog region judgment coefficient N, wherein a1, a2 and a3 are proportionality coefficients, and a1, a2 and a3 are all larger than 0;
Setting a threshold value N1 for a high-speed fog region judgment coefficient N, and judging whether a fog region appears at the current position of the high speed;
specific:
when N is larger than N1, a fog area appears at the high-speed current position;
when N is less than or equal to N1, no fog area appears at the high-speed current position;
the threshold value N1 is obtained, and the specific process is as follows:
selecting a non-fog weather state according to weather forecast, and respectively acquiring a temperature value WD1 in the non-fog weather state, an air humidity value SD1 in the non-fog weather state and a particulate matter concentration value ND1 in air in the non-fog weather state by using a temperature sensor, a first humidity sensor and an air quality sensor;
according to the formulaCalculating to obtain a high-speed fog region judgment coefficient threshold value N1, wherein a1, a2 and a3 are proportionality coefficients, and a1, a2 and a3 are all larger than 0;
the first acquisition unit comprises a laser scattering sensor, a wind speed sensor, an infrared sensor and a GPS sensor, and acquires fog region concentration data, fog region visibility data and fog region positions, and the specific steps are as follows:
(1) The method comprises the steps of utilizing a laser scattering type sensor to reflect a beam of laser beam to a fog area, receiving scattered light reflected by the fog area, obtaining the intensity of the scattered light to be g1, utilizing the same mode to select a fog-free weather state according to weather forecast, utilizing the laser scattering type sensor to obtain reference astigmatism intensity g2, and utilizing a formula Obtaining mist concentration data WQND;
(2) An infrared sensor is utilized to reflect a beam of infrared laser beam to a fog region, an infrared receiving point is arranged in the fog region and is received to pass through the fog regionIn the same manner, a haze-free weather state is selected, a reference infrared beam intensity h2 is obtained by an infrared sensor, and a formula is usedObtaining fog region visibility data NJD;
(3) The GPS sensors are arranged on two sides of the expressway, when the fog area judging unit judges that the fog area appears at the high-speed current position, the GPS sensor in the fog area acquires longitude and latitude data of the current position as the position of the fog area;
setting fog concentration data of a fog region, visibility data of the fog region and positions of the fog region as sensor data;
the second acquisition unit comprises a wind speed sensor and a second humidity sensor, wherein the wind speed sensor acquires wind speed data of a fog region, the second humidity sensor acquires air humidity data of the fog region, and the wind speed data of the fog region and the air humidity data of the fog region are set as weather data of the fog region;
the third acquisition unit comprises a ground induction coil and a camera, wherein the ground induction coil acquires traffic flow data of a highway where a fog area is located, and the camera acquires road speed limit data, and the third acquisition unit comprises the following specific steps:
The radar speed measuring area is utilized to obtain that n vehicle speeds of the expressway where the fog area is located are v1, v2 and v3 … … vn respectively, and a formula is utilizedAcquiring traffic flow speed data vn;
referring to fig. 3, a ground induction coil is installed under a highway pavement, two continuous ground induction coils are respectively provided as a first ground induction coil and a second ground induction coil, the installation distance of the first ground induction coil and the second ground induction coil is s, when a vehicle passes through the first ground induction coil and the second ground induction coil, the first ground induction coil and the second ground induction coil respectively generate an electromagnetic signal, the number of the acquired electromagnetic signals is C, and the number of automobiles passing through the installation distance s of the first ground induction coil and the second ground induction coil is C/2 according to a formulaObtaining traffic flow data L;
identifying a right speed limit sign board of the expressway by using a camera to obtain road speed limit data XS;
setting traffic flow speed data, traffic flow data and road speed limit data of a highway where the fog area is located as road condition data;
the data acquisition module is used for transmitting the sensor data, the fog weather data and the road condition data as the fog data to the data processing module;
it should be noted that: according to the weather forecast, whether fog appears in the day is judged by checking the related information of the weather forecast, if the related information of the fog is not mentioned in the weather forecast, such as the possibility of fog appearance or warning related to the fog is not mentioned, the weather state without the fog in the day can be judged, the weather state without the fog in the day is the weather state without the fog in the weather forecast broadcasting day, and the noon time of the day is selected;
The data processing module acquires fog region early warning data according to the fog region data;
the data processing module comprises a first processing unit, a second processing unit and a third processing unit, and is used for respectively acquiring fog region classification data, fog region moving speed coefficients and fog region traffic early warning data;
the first processing unit calculates and acquires fog region classification data according to fog concentration data, fog region visibility data and fog region wind speed data, and the method comprises the following steps of:
(1) Acquiring fog concentration data in a fog region, visibility data in the fog region and wind speed data in the fog region;
(2) According to the formulaAcquiring a mist zone grading coefficient N;
(3) Setting a first fog region classification interval, a second fog region classification interval, a third fog region classification interval and a fourth fog region classification interval according to the fog region classification coefficient N, respectively corresponding to a slight fog grade, a moderate fog grade, a severe fog grade and a severe fog grade, and setting thresholds N1, N2 and N3 for judgment;
when N is more than 0 and less than or equal to N1, judging that the first fog region classification section corresponds to the light fog grade;
when N1 is more than N and less than or equal to N2, judging that the second fog region classification section corresponds to the medium fog grade;
when N2 is more than N and less than or equal to N3, judging that the third fog region classification section corresponds to the severe fog grade;
When N3 is less than N, judging that the fourth fog region classification section corresponds to the serious fog grade;
it can be understood that: WQND is fog concentration data, NJD is fog visibility data and WF is fog wind speed data, a1, a2 and a3 are set proportionality coefficients, a1, a2 and a3 are all larger than 0, N1, N2 and N3 are set fog classification coefficient standard data, 0< N1< N2< N3, and the fog classification coefficient corresponding to the first fog classification interval is smaller than the fog classification coefficient corresponding to the second fog classification interval; the fog region classification coefficient corresponding to the second fog region classification interval is smaller than the fog region classification coefficient corresponding to the third fog region classification interval; the fog region classification coefficient corresponding to the third fog region classification interval is smaller than the fog region classification coefficient corresponding to the fourth fog region classification interval; taking the fog region classification intervals corresponding to different fog regions as fog region classification data WQFJ;
the second processing unit obtains a fog region moving speed coefficient according to the fog region wind speed data and the fog region air humidity data, and the method specifically comprises the following steps:
calculating to obtain a fog moving speed coefficient according to a formula M=b1×WF (1-WS×b2), wherein M is a fog moving speed coefficient value, WF is fog wind speed data, WS is fog air humidity data, b1 and b2 are set proportionality coefficients, and b1 and b2 are both larger than 0, and the larger the fog moving coefficient is, the faster the fog moving speed is; the smaller the fog area moving coefficient is, the smaller the moving speed of the fog area is;
The third processing unit obtains road condition congestion coefficients of the fog areas according to the traffic flow speed data, the traffic flow data and the road speed limit data, and the road condition congestion coefficients are specifically as follows:
according to the formulaCalculating to obtain a road condition congestion coefficient of a fog region, wherein LK is the road condition congestion coefficient of the fog region, vn is vehicle flow speed data, L is vehicle flow data, XS is road speed limit data, c1 and c2 are set proportion coefficients, and both c1 and c2 are larger than 0;
it should be noted that: the larger the road condition congestion coefficient of the fog area is, the more the road condition of the fog area is congested; the smaller the road condition congestion coefficient of the fog area is, the smoother the road condition of the fog area is;
the fourth acquisition unit acquires fog region traffic early warning classification data according to fog region classification data, a fog region moving speed coefficient and a fog region road condition congestion coefficient, and the method specifically comprises the following steps:
(1) Acquiring fog region classification data WQFJ, a fog region moving speed coefficient M and a fog region road condition congestion coefficient LK, and respectively carrying out parameter assignment on the WQFJ by using J1, J2, J3 and J4 aiming at a first fog region classification interval, a second fog region classification interval, a third fog region classification interval and a fourth fog region classification interval in the fog region classification data, wherein J1 is more than 0 and less than J2 and J3 is more than 0 and less than J4;
(2) According to the formulaCalculating to obtain a traffic early warning grading coefficient Y in a fog region;
(3) Setting a first traffic early warning section, a second traffic early warning section, a third traffic early warning section and a fourth traffic early warning section according to a traffic early warning grading coefficient Y in a fog region, and respectively corresponding to low risk early warning, medium risk early warning and high risk early warning and setting a threshold value for judgment, wherein the method comprises the following steps of:
when Y is more than 0 and less than or equal to Y1, judging a first traffic early warning interval, and corresponding to low risk early warning;
when Y1 is more than Y and less than or equal to Y2, judging a second traffic early warning interval corresponding to the traffic risk early warning;
when Y2 is more than Y and less than or equal to Y3, judging a third traffic early warning interval, and corresponding to high risk early warning;
when Y3 is less than Y, judging a fourth traffic early warning interval, and corresponding to extremely high risk early warning;
it is understandably that: WQFJ is fog region classification data, M is a fog region moving speed coefficient, LK is a fog region road condition congestion coefficient, Y is a fog region traffic early warning classification coefficient, d1, d2 and d3 are set proportion coefficients, d1, d2 and d3 are all larger than 0, Y1, Y2 and Y3 are set consumption intention coefficient standard data, 0< Y1< Y2< Y3, and the fog region traffic early warning classification coefficient corresponding to the first traffic early warning region is smaller than the fog region traffic early warning classification coefficient corresponding to the second traffic early warning region; the traffic early warning classification coefficient of the fog area corresponding to the second traffic early warning interval is smaller than the traffic early warning classification coefficient of the fog area corresponding to the third traffic early warning interval; the traffic early warning classification coefficient of the fog area corresponding to the third traffic early warning interval is smaller than the traffic early warning classification coefficient of the fog area corresponding to the fourth traffic early warning interval;
Setting fog areas corresponding to the first traffic early warning interval, the second traffic early warning interval, the third traffic early warning interval and the fourth traffic early warning interval as fog area traffic early warning classification data, and conveying the fog area traffic early warning data to a traffic early warning classification unit.
It should be noted that:
the fog region moving speed refers to a speed at which the high-fog weather on the expressway moves away from a high speed.
The traffic early warning module receives traffic early warning data in a fog area, and carries out high-speed induction and early warning distribution;
the traffic early warning module comprises a data communication unit, a vehicle navigation unit and a traffic management system unit, wherein the data communication unit, the vehicle navigation unit and the traffic management system unit are respectively internally provided with a first 5G module, a second 5G module and a third 5G module;
the data communication unit uploads traffic early warning data of the fog area to the vehicle navigation unit and the traffic management system unit, and the traffic early warning data of the fog area are specifically as follows:
the data unit compresses and encapsulates the transported traffic early warning classified data in the fog region, establishes connection with a 5G network by using a first 5G module, packetizes the traffic early warning classified data in the fog region, forwards the traffic early warning classified data in the fog region to a second 5G module and a third 5G module by using the 5G network of the first 5G module, and the vehicle-mounted navigation unit and the traffic management system unit receive the traffic early warning classified data in the fog region;
The vehicle-mounted navigation unit formulates a navigation strategy according to the traffic early warning grading data in the fog area, and the specific steps are as follows:
(1) Aiming at low risk early warning, the vehicle-mounted navigation unit reminds a driver of a current high-speed error zone, keeps proper vehicle distance and running speed, and displays the position of a fog zone on a navigation interface so as to help the driver to make a corresponding decision, provide alternative routes and avoid areas where traffic jams or accidents possibly exist;
(2) Aiming at the early warning of the traffic risk, the vehicle-mounted navigation unit reminds a driver of paying attention to the position of a fog region, reduces the speed of the vehicle, turns on a fog lamp, displays the position and the range of the fog region on a navigation screen, provides real-time traffic condition update, and adjusts a navigation route according to the real-time traffic condition so as to avoid possible traffic jam or accident;
(3) Aiming at high risk early warning, the vehicle-mounted navigation unit warns a driver to avoid entering a fog area, suggests to find a safe parking place to wait for the fog area to dissipate, displays the position of the fog area on a navigation screen, provides real-time traffic condition update, and provides an emergency contact phone so that the driver can seek help when encountering an emergency;
(4) Aiming at extremely high risk early warning, a driver is strongly warned to avoid entering a fog area, a nearest safe parking place is automatically searched for waiting for the fog area to dissipate, the position of the fog area is displayed on a navigation screen, real-time traffic condition update is provided, and emergency contact phones and emergency rescue services are provided so that the driver can seek help when encountering an emergency.
The traffic management system unit formulates an induction strategy according to the traffic early warning classification data of the fog area, and specifically comprises the following steps:
(1) Aiming at low risk early warning, the traffic management system unit utilizes a high-speed electronic guideboard to issue early warning information to inform a driver of a high-speed fog area, and the driver is careful to keep the speed and the distance;
(2) Aiming at the early warning of the traffic risk, the traffic management system unit reminds a driver of the current high-speed occurrence of a fog zone by using a high-speed electronic card, informs the driver of the reduction of the vehicle speed, keeps the vehicle distance, turns on a fog lamp, and arranges traffic police to perform law enforcement patrol so as to ensure the high-speed traffic order and traffic safety;
(3) Aiming at high risk early warning, the traffic management system unit reduces road speed limit, adopts traffic control measures to limit high speed on vehicles, reduces high-speed traffic flow, utilizes a high-speed electronic guideboard to display fog area early warning information, and provides real-time traffic road condition update;
(4) Aiming at extremely high risk early warning, the traffic management system module guides the high-speed vehicles to get at a high speed nearby or enter a server for risk avoidance by using the high-speed guideboard and the patrol car.
In the present application, if a corresponding calculation formula appears, the above calculation formulas are all dimensionality-removed and numerical calculation, and the size of the weight coefficient, the scale coefficient and other coefficients existing in the formulas is a result value obtained by quantizing each parameter, so long as the proportional relation between the parameter and the result value is not affected.
Example two
Based on another concept of the same invention, an early warning method for induction of a fog region of a highway is provided, which comprises the following steps:
step S1: acquiring fog region data;
step S11: judging the high-speed fog region, which comprises the following specific steps:
step S111: acquiring an air temperature value WD through a temperature sensor, acquiring an air humidity value SD through a first humidity sensor, acquiring a particulate matter concentration value ND in air through an air quality sensor, and according to a formulaObtaining a high-speed fog region judgment coefficient N, wherein a1, a2 and a3 are proportionality coefficients, and a1, a2 and a3 are all larger than 0;
step S112: selecting a fog-free weather state according to weather forecast, and respectively acquiring an air temperature value WD1, an air humidity value SD1 and a particulate matter concentration value ND1 in air in the fog-free weather state by using a temperature sensor, a first humidity sensor and an air quality sensor;
step S113: according to the formulaCalculating to obtain a high-speed fog region judgment coefficient threshold value N1, wherein a1, a2 and a3 are proportionality coefficients, and a1, a2 and a3 are all larger than 0;
step S114: setting a threshold value N1 for a high-speed fog region judgment coefficient N, and judging whether a fog region appears at the high-speed current position or not, wherein the method comprises the following steps of:
When N is larger than N1, a fog area appears at the high-speed current position;
when N is less than or equal to N1, no fog area appears at the high-speed current position;
step S12: the method comprises the following specific steps of:
step S121: the method comprises the steps of utilizing a laser scattering type sensor to reflect a beam of laser beam to a fog area, receiving scattered light reflected by the fog area, obtaining the intensity of the scattered light to be g1, utilizing the same mode to select a fog-free weather state according to weather forecast, utilizing the laser scattering type sensor to obtain reference astigmatism intensity g2, and utilizing a formulaObtaining mist concentration data WQND;
step S122: an infrared sensor is utilized to reflect a beam of infrared laser beam to a fog region, an infrared receiving point is arranged in the fog region, the infrared light beam passing through the fog region is received, the intensity of the infrared light beam is obtained to be h1, a fog-free weather state is selected in the same way, the infrared sensor is utilized to obtain the reference infrared light beam intensity h2, and a formula is utilizedObtaining fog region visibility data NJD;
step S123: the GPS sensors are arranged on two sides of the expressway, when the fog area judging unit judges that the fog area appears at the high-speed current position, the GPS sensor in the fog area acquires longitude and latitude data of the current position as the position of the fog area;
Step S124: setting fog concentration data of a fog region, visibility data of the fog region and positions of the fog region as sensor data;
step S13: acquiring fog region wind speed data by using a wind speed sensor, acquiring fog region air humidity data by using a second humidity sensor, and setting the fog region wind speed data and the fog region air humidity data as fog region weather data;
step S14: the method comprises the following specific steps of:
step S141: the radar speed measuring area is utilized to obtain that n vehicle speeds of the expressway where the fog area is located are v1, v2 and v3 … … vn respectively, and a formula is utilizedAcquiring traffic flow speed data vn;
step S142: installing the ground induction coils below the highway pavement, arranging two continuous ground induction coils as a first ground induction coil and a second ground induction coil respectively, wherein the installation distance of the first ground induction coil and the second ground induction coil is s, when a vehicle passes through the first ground induction coil and the second ground induction coil, the first ground induction coil and the second ground induction coil respectively generate electromagnetic signals, the number of the acquired electromagnetic signals is C, and the number of automobiles passing through the installation distance s of the first ground induction coil and the second ground induction coil is C/2 according to the formula Obtaining traffic flow data L;
step S143: identifying a right speed limit sign board of the expressway by using a camera to obtain road speed limit data XS;
step S144: setting traffic flow speed data, traffic flow data and road speed limit data of a highway where the fog area is located as road condition data;
step S15: the data acquisition module is used for transmitting the sensor data, the fog weather data and the road condition data as the fog data to the data processing module;
step S2: acquiring fog region early warning data according to the fog region data;
step S21: the method comprises the following specific steps of:
step S211: acquiring fog concentration data in a fog region, visibility data in the fog region and wind speed data in the fog region;
step S212: acquiring a fog region classification coefficient N according to fog concentration data, fog region visibility data and fog region wind speed data of a fog region;
step S213: setting a first fog region classification interval, a second fog region classification interval, a third fog region classification interval and a fourth fog region classification interval according to the fog region classification coefficient N, respectively corresponding to a slight fog grade, a moderate fog grade, a severe fog grade and a severe fog grade, and setting thresholds N1, N2 and N3 for judgment;
when N is more than 0 and less than or equal to N1, judging that the first fog region classification section corresponds to the light fog grade;
When N1 is more than N and less than or equal to N2, judging that the second fog region classification section corresponds to the medium fog grade;
when N2 is more than N and less than or equal to N3, judging that the third fog region classification section corresponds to the severe fog grade;
when N3 is less than N, judging that the fourth fog region classification section corresponds to the serious fog grade;
step S214: taking the fog region classification intervals corresponding to different fog regions as fog region classification data WQFJ;
step S22: calculating to obtain a fog region moving speed coefficient according to the fog region wind speed data and the fog region air humidity data;
step S22: acquiring road condition congestion coefficients of a fog area according to traffic flow speed data, traffic flow data and road speed limit data;
step S23: acquiring fog region traffic early warning classification data according to fog region classification data, a fog region moving speed coefficient and a fog region road condition congestion coefficient, wherein the method comprises the following specific steps of:
step S231: according to the fog region classification data WQFJ, the fog region moving speed coefficient M and the fog region road condition congestion coefficient LK, calculating to obtain a fog region traffic early warning classification coefficient
Step S232: setting a first traffic early warning section, a second traffic early warning section, a third traffic early warning section and a fourth traffic early warning section according to a traffic early warning grading coefficient Y in a fog region, and respectively corresponding to low risk early warning, medium risk early warning and high risk early warning and setting a threshold value for judgment, wherein the method comprises the following steps of:
When Y is more than 0 and less than or equal to Y1, judging a first traffic early warning interval, and corresponding to low risk early warning;
when Y1 is more than Y and less than or equal to Y2, judging a second traffic early warning interval corresponding to the traffic risk early warning;
when Y2 is more than Y and less than or equal to Y3, judging a third traffic early warning interval, and corresponding to high risk early warning;
when Y3 is less than Y, judging a fourth traffic early warning interval, and corresponding to extremely high risk early warning;
step S233: setting fog areas corresponding to the first traffic early warning interval, the second traffic early warning interval, the third traffic early warning interval and the fourth traffic early warning interval as fog area traffic early warning classification data, and transmitting the fog area traffic early warning data to a traffic early warning unit.
Step S3: carrying out high-speed induction and early warning distribution according to traffic early warning data in a fog area;
step S31: compressing and packaging the transported traffic early warning classification data in the fog region, establishing connection with a 5G network by using a first 5G module, performing data subpackaging on the traffic early warning classification data in the fog region, and forwarding the traffic early warning classification data in the fog region to a second 5G module and a third 5G module by using the 5G network of the first 5G module;
step S32: the navigation strategy is formulated according to the traffic early warning grading data in the fog area, and the specific steps are as follows:
step S321: aiming at low risk early warning, the vehicle-mounted navigation unit reminds a driver of a current high-speed error zone, keeps proper vehicle distance and running speed, and displays the position of a fog zone on a navigation interface so as to help the driver to make a corresponding decision, provide alternative routes and avoid areas where traffic jams or accidents possibly exist;
Step S322: aiming at the early warning of the traffic risk, the vehicle-mounted navigation unit reminds a driver of paying attention to the position of a fog region, reduces the speed of the vehicle, turns on a fog lamp, displays the position and the range of the fog region on a navigation screen, provides real-time traffic condition update, and adjusts a navigation route according to the real-time traffic condition so as to avoid possible traffic jam or accident;
step S323: aiming at high risk early warning, the vehicle-mounted navigation unit warns a driver to avoid entering a fog area, suggests to find a safe parking place to wait for the fog area to dissipate, displays the position of the fog area on a navigation screen, provides real-time traffic condition update, and provides an emergency contact phone so that the driver can seek help when encountering an emergency;
step S324: aiming at extremely high risk early warning, a driver is strongly warned to avoid entering a fog area, a nearest safe parking place is automatically searched for waiting for the fog area to dissipate, the position of the fog area is displayed on a navigation screen, real-time traffic condition update is provided, and emergency contact phones and emergency rescue services are provided so that the driver can seek help when encountering an emergency.
Step S33: an induction strategy is formulated according to the traffic early warning grading data of the fog area, and the induction strategy is specifically as follows:
Step S331: aiming at low risk early warning, the traffic management system unit utilizes a high-speed electronic guideboard to issue early warning information to inform a driver of a high-speed fog area, and the driver is careful to keep the speed and the distance;
step S332: aiming at the early warning of the traffic risk, the traffic management system unit reminds a driver of the current high-speed occurrence of a fog zone by using a high-speed electronic card, informs the driver of the reduction of the vehicle speed, keeps the vehicle distance, turns on a fog lamp, and arranges traffic police to perform law enforcement patrol so as to ensure the high-speed traffic order and traffic safety;
step S333: aiming at high risk early warning, the traffic management system unit reduces road speed limit, adopts traffic control measures to limit high speed on vehicles, reduces high-speed traffic flow, utilizes a high-speed electronic guideboard to display fog area early warning information, and provides real-time traffic road condition update;
step S334: aiming at extremely high risk early warning, the traffic management system module guides the high-speed vehicles to get at a high speed nearby or enter a server for risk avoidance by using the high-speed guideboard and the patrol car.
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 (10)
1. An early warning device for induction of a fog region of an expressway, comprising:
and a data acquisition module: obtaining a high-speed fog region judgment coefficient by obtaining an air temperature value, an air humidity value and a particulate matter concentration value in the air, judging a high-speed fog region by utilizing a high-speed fog region judgment coefficient threshold value, and respectively obtaining sensor data, fog region weather data and road condition data of a highway where the fog region is located as fog region data;
and a data processing module: processing the fog region data to obtain fog region classification data, a fog region moving speed coefficient and a fog region road condition congestion coefficient, calculating to obtain a fog region traffic early warning classification coefficient according to the acquired fog region classification data, the fog region moving speed coefficient and the fog region road condition congestion coefficient, and dividing the fog region traffic early warning classification coefficient by using a threshold value to obtain fog region traffic early warning classification data;
traffic early warning module: respectively making a navigation strategy and an induction strategy according to the traffic early warning grading data of the fog region;
the system further comprises a server, and the data acquisition module, the data processing module and the traffic early warning module are respectively connected with the server.
2. The early warning device for highway fog induction according to claim 1, wherein the data acquisition module acquires fog data, specifically as follows:
The data acquisition module comprises a fog region judgment unit, a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the fog region judgment unit judges whether a fog region appears at a high-speed current position, the first acquisition unit acquires sensor data, the second acquisition unit acquires fog region weather data, the third acquisition unit acquires road condition data, and the sensor data, the fog region weather data and the road condition data are set to be fog region data.
3. The early warning device and method for highway fog area induction according to claim 2, wherein the fog area judging unit judges whether a fog area appears at a high-speed current position, and the method is specifically as follows:
the fog region acquisition unit calculates a high-speed fog region judgment coefficient N according to the air temperature value, the air humidity value and the concentration value of particles in the air;
acquiring a temperature value in a non-fog weather state, an air humidity value in the non-fog weather state and a particulate matter concentration value in air in the non-fog weather state, and calculating to obtain a high-speed fog area judgment coefficient threshold value N1;
setting a threshold value N1 for a high-speed fog region judgment coefficient N, and judging whether a fog region appears at the high-speed current position or not, wherein the method comprises the following steps of:
When N is larger than N1, a fog area appears at the high-speed current position;
when N is less than or equal to N1, no fog area appears at the high-speed current position.
4. The early warning device for highway foggy area induction according to claim 2, wherein the first acquisition unit acquires sensor data, specifically as follows:
the first acquisition unit acquires the scattered light intensity reflected by the fog region as g1, acquires the reference scattered light intensity g2, and calculates to obtain fog concentration data through g1 and g 2;
the first acquisition unit acquires the infrared light beam intensity h1 passing through the fog region, acquires the reference infrared light beam intensity h2, and calculates to obtain the fog region visibility data through h1 and h 2;
the method comprises the steps that a first acquisition unit acquires longitude and latitude data of a current position, which is acquired by a GPS sensor in a fog area, as the position of the fog area;
and setting fog concentration data in a fog region, visibility data in the fog region and positions of the fog region as sensor data.
5. The early warning device for highway fog area induction according to claim 3, wherein the second obtaining unit obtains fog area weather data, and the third obtaining unit obtains road condition data, specifically as follows:
the second acquisition unit is used for respectively acquiring the wind speed data of the fog region and the air humidity data of the fog region and setting the wind speed data and the air humidity data of the fog region as the weather data of the fog region;
The third acquisition unit calculates and acquires vehicle flow speed data through n vehicle speeds of the expressway where the fog area is located;
the third acquisition unit acquires the vehicle flow data by using the magnetic induction coil;
the third acquisition unit is used for identifying a speed limit sign board on the right side of the expressway by using a camera to acquire road speed limit data;
and setting traffic flow speed data, traffic flow data and road speed limit data of the expressway where the fog area is located as road condition data.
6. The early warning device for induction of a fog region of an expressway according to claim 1, wherein the data processing module obtains the early warning data of the fog region according to the fog region data, specifically as follows:
the data processing module comprises a first processing unit, a second processing unit, a third processing unit and a fourth processing unit, wherein the first processing unit acquires fog region classification data, the second processing unit acquires a fog region moving speed coefficient, the third processing unit acquires a fog region road condition congestion coefficient, and the fourth processing unit acquires fog region traffic early warning classification data according to the fog region classification data, the fog region moving speed coefficient and the fog region road condition congestion coefficient.
7. The early warning device and method for highway fog area induction according to claim 6, wherein the first processing unit obtains fog area classification data, specifically as follows:
Acquiring a fog region classification coefficient N according to fog concentration data, fog region visibility data and fog region wind speed data of a fog region;
setting a first fog region classification interval, a second fog region classification interval, a third fog region classification interval and a fourth fog region classification interval according to the fog region classification coefficient N, respectively corresponding to a slight fog grade, a moderate fog grade, a severe fog grade and a severe fog grade, and setting thresholds N1, N2 and N3 for judgment;
when N is more than 0 and less than or equal to N1, judging that the first fog region classification section corresponds to the light fog grade;
when N1 is more than N and less than or equal to N2, judging that the second fog region classification section corresponds to the medium fog grade;
when N2 is more than N and less than or equal to N3, judging that the third fog region classification section corresponds to the severe fog grade;
when N3 is less than N, judging that the fourth fog region classification section corresponds to the serious fog grade;
and taking the fog region classification intervals corresponding to different fog regions as fog region classification data.
8. The early warning device and method for highway fog area induction according to claim 6, wherein the second processing unit obtains a fog area moving speed coefficient, and the third processing unit obtains a fog area road condition congestion coefficient, specifically as follows:
the second processing unit calculates and obtains a fog region moving speed coefficient according to the fog region wind speed data and the fog region air humidity data;
And the third processing unit calculates and obtains the road condition congestion coefficient of the fog area according to the traffic flow speed data, the traffic flow data and the road speed limit data.
9. The warning device for induction of a fog region of an expressway according to claim 6, wherein the fourth processing unit obtains and transmits the traffic warning classification data of the fog region to the traffic warning module, and the traffic warning module receives the traffic warning data of the fog region, induces and issues the warning at a high speed, and specifically comprises the following steps:
acquiring fog region traffic early warning classification data according to the fog region classification data, the fog region moving speed coefficient and the fog region road condition congestion coefficient;
setting a first traffic early warning section, a second traffic early warning section, a third traffic early warning section and a fourth traffic early warning section according to a traffic early warning grading coefficient Y in a fog region, and respectively corresponding to low risk early warning, medium risk early warning and high risk early warning and setting a threshold value for judgment, wherein the method comprises the following steps of:
when Y is more than 0 and less than or equal to Y1, judging a first traffic early warning interval, and corresponding to low risk early warning;
when Y1 is more than Y and less than or equal to Y2, judging a second traffic early warning interval corresponding to the traffic risk early warning;
when Y2 is more than Y and less than or equal to Y3, judging a third traffic early warning interval, and corresponding to high risk early warning;
When Y3 is less than Y, judging a fourth traffic early warning interval, and corresponding to extremely high risk early warning;
setting fog areas corresponding to the first traffic early warning interval, the second traffic early warning interval, the third traffic early warning interval and the fourth traffic early warning interval as fog area traffic early warning classification data and conveying the fog area traffic early warning classification data to a traffic early warning module;
the traffic early warning module comprises a data communication unit, a vehicle navigation unit and a traffic management system unit, wherein the data communication unit uploads traffic early warning data of a fog area to the vehicle navigation unit and the traffic management system unit;
the vehicle-mounted navigation unit formulates a navigation strategy according to the traffic early warning grading data of the fog area;
and the traffic management system unit establishes an induction strategy according to the traffic early warning classification data of the fog region.
10. An early warning device and method for highway fog induction, which are suitable for the early warning device for highway fog induction according to any one of claims 1 to 9, and are characterized in that the method comprises:
step S1: acquiring fog region data;
step S2: acquiring fog region early warning data according to the fog region data;
step S3: and carrying out high-speed induction and issuing early warning according to the traffic early warning data in the fog area.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112258853A (en) * | 2020-10-19 | 2021-01-22 | 洛阳云感科技有限公司 | Chain visibility monitoring and early warning system in highway fog zone becomes more meticulous |
| CN112863201A (en) * | 2021-01-07 | 2021-05-28 | 武汉理工大学 | Guiding method based on expressway agglomerate fog area multi-stage early warning system |
| CN114822024A (en) * | 2022-04-19 | 2022-07-29 | 哈尔滨工业大学 | Active safety guidance system for expressway agglomerate fog road section |
| CN114999180A (en) * | 2022-05-12 | 2022-09-02 | 广东省韶关市气象局 | Expressway severe weather traffic early warning system and method based on Internet of things |
| CN116486635A (en) * | 2022-01-11 | 2023-07-25 | 上海三思电子工程有限公司 | Road dough fog detection and early warning method, system, storage medium and terminal |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112258853A (en) * | 2020-10-19 | 2021-01-22 | 洛阳云感科技有限公司 | Chain visibility monitoring and early warning system in highway fog zone becomes more meticulous |
| CN112863201A (en) * | 2021-01-07 | 2021-05-28 | 武汉理工大学 | Guiding method based on expressway agglomerate fog area multi-stage early warning system |
| CN116486635A (en) * | 2022-01-11 | 2023-07-25 | 上海三思电子工程有限公司 | Road dough fog detection and early warning method, system, storage medium and terminal |
| CN114822024A (en) * | 2022-04-19 | 2022-07-29 | 哈尔滨工业大学 | Active safety guidance system for expressway agglomerate fog road section |
| CN114999180A (en) * | 2022-05-12 | 2022-09-02 | 广东省韶关市气象局 | Expressway severe weather traffic early warning system and method based on Internet of things |
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