CN117238139B - Real-time road condition early warning system based on meteorological data - Google Patents
Real-time road condition early warning system based on meteorological data Download PDFInfo
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Abstract
The invention discloses a real-time road condition early warning system based on meteorological data, which relates to the technical field of road traffic early warning and comprises a road surface monitoring module, a vehicle-mounted terminal, a cloud server, a road condition early warning module and a driving analysis module; the road surface monitoring module comprises road side base stations and road side sensors which are distributed on two sides of a road and are used for sensing the positions of automobiles, the flow of the automobiles and the meteorological data of the road surface; the road condition early warning module is used for collecting traffic flow information of each road section and road surface meteorological data for comprehensive analysis, calculating to obtain road condition early warning indexes and providing references for a driver to select a route; the cloud server is used for carrying out maximum estimated vehicle speed assessment by combining the vehicle position and the road condition early warning indexes of all road sections so as to remind a driver of reasonably adjusting the driving speed; the driving analysis module is used for acquiring the related information around the vehicle to analyze the jam coefficient, and reminding a driver to improve the attention, slow down and improve the traffic safety if the jam coefficient is larger than a preset jam threshold value.
Description
Technical Field
The invention relates to the technical field of road traffic early warning, in particular to a real-time road condition early warning system based on meteorological data.
Background
In extreme weather, traffic accidents which can cause rear-end accidents and secondary accidents easily occur in expressway driving. Vehicles which keep running on the expressway in rainy, snowy and foggy weather are easy to cause accidents, and then the vehicles cannot run at a reduced speed in time due to the attention, the environment and other reasons, so that the occurrence of continuous accidents is easy to cause. At present, the early warning of rear-end accidents and secondary accidents on highways mainly comprises the steps of obtaining vehicle data through radar ranging and gps and providing warning information for nearby vehicles through broadcasting, but the early warning of vehicles around the accident vehicles cannot be carried out in time when the accident occurs.
At present, the expressway is combined with the meteorological monitoring requirement, the conventional meteorological information such as air temperature, air humidity, wind speed, wind direction and the like is low in information quantity, analysis and monitoring of the collected meteorological information cannot be realized, and the pavement condition of the expressway in ice and snow weather cannot be accurately reflected in real time; based on the defects, the invention provides a road condition real-time early warning system based on meteorological data.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a real-time road condition early warning system based on meteorological data.
In order to achieve the above objective, according to an embodiment of the first aspect of the present invention, a real-time road condition early warning system based on meteorological data is provided, which includes a road surface monitoring module, a vehicle-mounted terminal, a cloud server, a road condition early warning module and a driving analysis module;
The road surface monitoring module comprises road side base stations and road side sensors which are distributed on two sides of a road; the vehicle-mounted terminal is used for sending the vehicle model to the road side base station, sensing the vehicle speed, sensing the distance between the vehicle and surrounding vehicles, and combining the road side base station to position the vehicle and the vehicle flow;
The road side sensor is used for collecting road surface meteorological data in real time and transmitting the collected data to the cloud server through the road side base station; the road surface meteorological data comprise road surface temperature, road surface humidity, road area water quantity or snow accumulation quantity;
The road condition early warning module is used for collecting traffic flow information and road surface meteorological data of each road section for comprehensive analysis and calculating to obtain a road condition early warning index Ly; issuing early warning instructions of different levels according to the road condition early warning index Ly to provide references for a driver to select a route;
The road condition early warning module is used for stamping a time stamp on the road condition early warning index Ly of each road section and uploading the time stamp to the cloud server; the cloud server is used for carrying out maximum estimated vehicle speed Rt evaluation by combining the vehicle position and road condition early warning indexes Ly of each road section, and transmitting the maximum estimated vehicle speed Rt to the vehicle-mounted terminal for display so as to remind a driver of reasonably adjusting the driving speed;
The driving analysis module is connected with the vehicle-mounted terminal and is used for acquiring vehicle periphery related information to conduct blockage factor Ds analysis, wherein the vehicle periphery related information comprises vehicle speed, distance between a vehicle and surrounding vehicles and vehicle speed of the surrounding vehicles;
If the jam coefficient Ds is larger than the preset jam threshold, the current running area of the vehicle is indicated to be relatively jammed, and a jam signal is generated to the vehicle-mounted terminal so as to remind a driver of improving attention and slowing down.
Further, the specific analysis steps of the road condition early warning module are as follows:
For a certain road section, counting the traffic flow information of the road section as L1; the number of lanes of the road section is obtained and marked as C1; collecting pavement meteorological data of the road section; sequentially marking the road surface temperature, the road surface humidity, the road area water quantity and the road surface snow quantity as W1, W2, W3 and W4;
Calculating the road surface influence coefficient LM by using a formula lm=l1× (w2×b2+w3×b3+w4×b4)/(c1×w1×b1), wherein b1, b2, b3, b4 are all preset coefficient factors;
acquiring real-time microclimate data of the road section through a meteorological platform; the real-time microclimate data comprise rainfall values, snowfall, wind power information and road section visibility;
sequentially marking rainfall value, snowfall amount, wind power information and road section visibility as G1, G2, G3 and G4; calculating to obtain weather sensitivity QM by using a formula QM= (G1×g1+G2×g2+G3×g3)/(G4×g4), wherein G1, G2, G3 and G4 are all preset coefficient factors;
in a preset time period, counting the total number of traffic accidents of the road section as an accident frequency P1; and calculating the road condition early warning index Ly by using a formula Ly=LMXg5+QM Xg6+P1 Xg7, wherein g5, g6 and g7 are all preset coefficient factors.
Further, according to the road condition early warning index Ly, the early warning instructions of different levels are issued, specifically:
comparing the road condition early warning index Ly with a preset early warning threshold value; the preset early warning threshold comprises X1 and X2; and X1 is less than X2;
When Ly is greater than X2, the road section running danger is extremely large, and a red early warning instruction is issued for the road section; when X1 is more than Ly and less than or equal to X2, indicating that the road section running danger is general, and issuing a yellow early warning instruction aiming at the road section; when Ly is less than or equal to X1, the running danger of the road section is extremely small, and a green early warning instruction is issued for the road section; the green early warning instruction represents that the road section can safely run.
Further, the specific evaluation step of the cloud server includes:
acquiring a road section matched with the position of an automobile, and acquiring a road condition early warning index of the road section and marking the road condition early warning index as Lyt; obtaining the highest speed limit of the road section as R1;
calculating the maximum estimated vehicle speed Rt of the road section by using a formula Rt=f×R1× (1-Lyt×g8), wherein g8 is a preset coefficient factor; f is a preset equalization coefficient.
Further, the specific analysis steps of the driving analysis module are as follows:
Acquiring vehicle periphery association information, and marking all running vehicles in a radius r1 area as associated vehicles by taking the center of the current running vehicle as the center of a circle; wherein r1 is a preset value;
counting the number of associated vehicles to Cz; the method comprises the steps of obtaining the distance between a current running vehicle and an associated vehicle, marking the distance as a vehicle distance GDi, and marking the speed of the associated vehicle as Vi; i represents an i-th associated vehicle; wherein GDi corresponds to Vi one by one;
Calculating to obtain a vehicle distance average GDz according to an average calculation formula; marking the maximum vehicle distance value as GDmax and the minimum vehicle distance value as GDmin; calculating to obtain a space difference ratio Gb by using a formula Gb= (GDmax-GDmin)/GDz;
calculating a vehicle speed average value Vz according to an average value calculation formula; marking the maximum value of the vehicle speed as Vmax and the minimum value of the vehicle speed as Vmin; calculating to obtain a vehicle speed difference ratio Vb by using a formula vb= (Vmax-Vmin)/Vz;
And carrying out normalization processing on the number of related vehicles, the distance difference ratio and the vehicle speed difference ratio, taking the values of the normalization processing, and calculating the jam coefficient Ds by using a formula Ds= (Cz multiplied by a 1)/(1-Gb) multiplied by (1-Vb) multiplied by a 2), wherein a1 and a2 are preset coefficient factors.
Further, the road side sensor comprises a road surface temperature sensor, a road surface humidity sensor, a water film sensor, a voltage level film sensor and an optical fiber sensor;
the road surface temperature sensor and the road surface humidity sensor are respectively used for monitoring the temperature and the humidity of a road surface; the water film sensor is used for monitoring the water film thickness of the road surface; the piezoelectric level film sensor and the optical fiber sensor are used for monitoring the thickness of ice and snow.
Further, the system also comprises a Bluetooth playing module, the Bluetooth playing module is connected with the cloud server, the Bluetooth playing function is used for playing the road surface meteorological data and traffic flow in a voice mode, and providing references for a driver to change routes.
Compared with the prior art, the invention has the beneficial effects that:
1. The road surface monitoring module comprises road side base stations and road side sensors which are distributed on two sides of a road; the road side base station is used for sensing the position and the flow of the automobile; the road side sensor is used for collecting road surface meteorological data in real time; the Bluetooth playing module is used for playing the road surface meteorological data and traffic flow through a Bluetooth playing function, so that a driver can know the traffic flow and meteorological information of each route at any time, and a reference is provided for the driver to change the route; the road condition early warning module is used for collecting traffic flow information and road surface meteorological data of each road section, comprehensively analyzing, calculating to obtain a road condition early warning index Ly, and issuing early warning instructions of different levels according to the road condition early warning index Ly, so that a driver can know the driving danger level of the corresponding road section at a glance, and providing a reference for the driver to select a route, thereby improving traffic safety;
2. The cloud end server is used for carrying out maximum estimated vehicle speed Rt evaluation by combining the vehicle position and road condition early warning indexes Ly of each road section, and transmitting the maximum estimated vehicle speed Rt to the vehicle-mounted terminal for display so as to remind a driver of reasonably adjusting the driving speed; the driving analysis module is used for acquiring vehicle surrounding related information to analyze the jam coefficient Ds, wherein the vehicle surrounding related information comprises the vehicle speed, the distance between the vehicle and surrounding vehicles and the speed of the surrounding vehicles; if the jam coefficient Ds is larger than the preset jam threshold, the current running area of the vehicle is indicated to be relatively jammed, and a jam signal is generated to the vehicle-mounted terminal so as to remind a driver to improve attention, slow down and avoid vehicle collision.
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 system block diagram of a real-time road condition early warning system based on meteorological data 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.
As shown in FIG. 1, the real-time road condition early warning system based on meteorological data comprises a road surface monitoring module, a vehicle-mounted terminal, a cloud server, a Bluetooth playing module, a road condition early warning module and a driving analysis module;
The road surface monitoring module comprises road side base stations and road side sensors which are distributed on two sides of a road; the road side base station is used for sensing the position and the flow of the automobile; the road side sensor is used for collecting road surface meteorological data in real time;
In this embodiment, the road side sensor includes a road surface temperature sensor, a road surface humidity sensor, a water film sensor, a voltage level film sensor, and an optical fiber sensor; the road surface temperature sensor and the road surface humidity sensor are respectively used for monitoring the temperature and the humidity of a road surface, the water film sensor is used for monitoring the water film thickness of the road surface, and the piezoelectric level film sensor and the optical fiber sensor are used for monitoring the ice and snow thickness; the pavement meteorological data comprise pavement temperature, pavement humidity, pavement ponding or snow accumulation;
The vehicle-mounted terminal is used for sending the vehicle model to the road side base station, sensing the vehicle speed, sensing the distance between the vehicle and surrounding vehicles, and combining the road side base station to position the vehicle and the vehicle flow; the perceived data is transmitted to a cloud server through a road side base station; the data transmitted by the road side base station comprises road surface meteorological data, automobile positions and automobile flow;
The Bluetooth playing module is connected with the cloud server and is used for playing the road surface meteorological data and the traffic flow through the Bluetooth playing function, so that a driver can know the traffic flow and the meteorological information of each route at any time, and a reference is provided for the driver to change the route;
The road condition early warning module is used for collecting traffic flow information and road surface meteorological data of each road section for comprehensive analysis and calculating to obtain a road condition early warning index Ly; the specific analysis steps are as follows:
For a certain road section, counting the traffic flow information of the road section as L1; acquiring the number of lanes of the road section and marking as C1; collecting road surface meteorological data of a road section, and marking road surface temperature, road surface humidity, road area water quantity and road surface snow quantity as W1, W2, W3 and W4 in sequence;
calculating the road surface influence coefficient LM by using a formula lm=l1× (w2×b2+w3×b3+w4×b4)/(c1×w1×b1), wherein b1, b2, b3, b4 are all preset coefficient factors; wherein the larger the road surface influence coefficient LM is, the larger the obstacle to the running of the vehicle is;
Acquiring real-time microclimate data of a road section through a meteorological platform; the real-time microclimate data comprises rainfall value, snowfall amount, wind power information and road section visibility;
sequentially marking rainfall value, snowfall amount, wind power information and road section visibility as G1, G2, G3 and G4; calculating to obtain weather sensitivity QM by using a formula QM= (G1×g1+G2×g2+G3×g3)/(G4×g4), wherein G1, G2, G3 and G4 are all preset coefficient factors;
in a preset time period, counting the total number of traffic accidents of a road section as an accident frequency P1, and calculating by using a formula Ly=LMXg5+QM Xg6+P1 Xg7 to obtain a road condition early warning index Ly, wherein g5, g6 and g7 are preset coefficient factors;
Comparing the road condition early warning index Ly with a preset early warning threshold value; the preset early warning threshold comprises X1 and X2; and X1 is less than X2;
When Ly is greater than X2, the road section running danger is extremely large, and a red early warning instruction is issued for the road section; when X1 is more than Ly and less than or equal to X2, the road section running danger is indicated to be general, and a yellow early warning instruction is issued for the road section; when Ly is less than or equal to X1, the running danger of the road section is extremely small, and a green early warning instruction is issued for the road section; the green early warning instruction represents that the road section can safely run;
By setting the green early warning instruction, the yellow early warning instruction and the red early warning instruction, a driver can know the driving danger level of the corresponding road section at a glance, and a reference is provided for the driver to select a route; the vehicle can be better protected, and the traffic safety is improved;
the road condition early warning module is used for marking a time stamp on the road condition early warning index Ly of each road section and uploading the time stamp to the cloud server; the cloud server is used for carrying out maximum estimated vehicle speed assessment by combining the vehicle position and the road condition early warning index Ly of each road section, and specifically comprises the following steps:
Acquiring a road section matched with the position of an automobile, acquiring a road condition early warning index of the road section and marking the road condition early warning index as Lyt; obtaining the highest speed limit of a road section as R1;
calculating the maximum estimated vehicle speed Rt of the road section by using a formula Rt=R1× (1-Lyt×g8), wherein g8 is a preset coefficient factor; such as a value 0.0002365; lyt×g8 has a value of less than 1;
the cloud server is used for transmitting the maximum estimated vehicle speed Rt to the vehicle-mounted terminal for display so as to remind a driver of reasonably adjusting the driving speed;
The driving analysis module is connected with the vehicle-mounted terminal and is used for acquiring vehicle surrounding related information to analyze the jam coefficient Ds, wherein the vehicle surrounding related information comprises the vehicle speed, the distance between the vehicle and surrounding vehicles and the speed of the surrounding vehicles; the specific analysis steps are as follows:
Acquiring vehicle periphery association information, and marking all running vehicles in a radius r1 area as associated vehicles by taking the center of the current running vehicle as the center of a circle; wherein r1 is a preset value;
counting the number of associated vehicles to Cz; the method comprises the steps of obtaining the distance between a current running vehicle and an associated vehicle, marking the distance as a vehicle distance GDi, and marking the speed of the associated vehicle as Vi; i represents an i-th associated vehicle; wherein GDi corresponds to Vi one by one;
Calculating to obtain a vehicle distance average GDz according to an average calculation formula; marking the maximum vehicle distance value as GDmax and the minimum vehicle distance value as GDmin; calculating to obtain a space difference ratio Gb by using a formula Gb= (GDmax-GDmin)/GDz;
calculating a vehicle speed average value Vz according to an average value calculation formula; marking the maximum value of the vehicle speed as Vmax and the minimum value of the vehicle speed as Vmin; calculating to obtain a vehicle speed difference ratio Vb by using a formula vb= (Vmax-Vmin)/Vz;
Normalizing the number of related vehicles, the distance difference ratio and the vehicle speed difference ratio, taking the values of the normalized numbers, and calculating by using a formula Ds= (Cz×a1)/((1-Gb) × (1-Vb) ×a2) to obtain a blockage coefficient Ds, wherein a1 and a2 are preset coefficient factors;
Comparing the jam coefficient Ds with a preset jam threshold; if the jam coefficient Ds is larger than the preset jam threshold, the current running area of the vehicle is indicated to be relatively jammed, and a jam signal is generated to the vehicle-mounted terminal so as to remind a driver to improve attention, slow down and avoid vehicle collision.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows:
The road condition real-time early warning system based on meteorological data comprises a road side base station and a road side sensor which are distributed on two sides of a road when the road condition real-time early warning system works; the road side base station is used for sensing the position and the flow of the automobile; the road side sensor is used for collecting road surface meteorological data in real time; the Bluetooth playing module is used for playing the road surface meteorological data and traffic flow through a Bluetooth playing function, so that a driver can know the traffic flow and meteorological information of each route at any time, and a reference is provided for the driver to change the route; the road condition early warning module is used for collecting traffic flow information and road surface meteorological data of each road section, comprehensively analyzing, calculating to obtain a road condition early warning index Ly, and issuing early warning instructions of different levels according to the road condition early warning index Ly, so that a driver can know the driving danger level of the corresponding road section at a glance, and providing a reference for the driver to select a route, thereby improving traffic safety;
The cloud server is used for carrying out maximum estimated vehicle speed Rt evaluation by combining the vehicle position and road condition early warning indexes Ly of each road section, and transmitting the maximum estimated vehicle speed Rt to the vehicle-mounted terminal for display so as to remind a driver of reasonably adjusting the driving speed; the driving analysis module is used for acquiring vehicle surrounding related information to analyze the jam coefficient Ds, wherein the vehicle surrounding related information comprises the vehicle speed, the distance between the vehicle and surrounding vehicles and the speed of the surrounding vehicles; if the jam coefficient Ds is larger than the preset jam threshold, the current running area of the vehicle is indicated to be relatively jammed, and a jam signal is generated to the vehicle-mounted terminal so as to remind a driver to improve attention, slow down and avoid vehicle collision.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
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 (3)
1. The road condition real-time early warning system based on the meteorological data is characterized by comprising a road surface monitoring module, a vehicle-mounted terminal, a cloud server, a road condition early warning module and a driving analysis module;
The road surface monitoring module comprises road side base stations and road side sensors which are distributed on two sides of a road; the vehicle-mounted terminal is used for sending the vehicle model to the road side base station, sensing the vehicle speed, sensing the distance between the vehicle and surrounding vehicles, and combining the road side base station to position the vehicle and the vehicle flow;
The road side sensor is used for collecting road surface meteorological data in real time and transmitting the collected data to the cloud server through the road side base station; the road surface meteorological data comprise road surface temperature, road surface humidity, road area water quantity or snow accumulation quantity;
The road condition early warning module is used for collecting traffic flow information and road surface meteorological data of each road section for comprehensive analysis and calculating to obtain a road condition early warning index Ly; issuing early warning instructions of different levels according to the road condition early warning index Ly to provide references for a driver to select a route; the specific analysis steps are as follows:
For a certain road section, counting the traffic flow information of the road section as L1; the number of lanes of the road section is obtained and marked as C1; collecting pavement meteorological data of the road section; sequentially marking the road surface temperature, the road surface humidity, the road area water quantity and the road surface snow quantity as W1, W2, W3 and W4;
Calculating the road surface influence coefficient LM by using a formula lm=l1× (w2×b2+w3×b3+w4×b4)/(c1×w1×b1), wherein b1, b2, b3, b4 are all preset coefficient factors;
acquiring real-time microclimate data of the road section through a meteorological platform; the real-time microclimate data comprise rainfall values, snowfall, wind power information and road section visibility;
sequentially marking rainfall value, snowfall amount, wind power information and road section visibility as G1, G2, G3 and G4; calculating to obtain weather sensitivity QM by using a formula QM= (G1×g1+G2×g2+G3×g3)/(G4×g4), wherein G1, G2, G3 and G4 are all preset coefficient factors;
In a preset time period, counting the total number of traffic accidents of the road section as an accident frequency P1; calculating to obtain a road condition early warning index Ly by using a formula Ly=LMXg5+QM Xg6+P1 Xg7, wherein g5, g6 and g7 are all preset coefficient factors;
comparing the road condition early warning index Ly with a preset early warning threshold value; the preset early warning threshold comprises X1 and X2; and X1 is less than X2;
when Ly is greater than X2, the road section running danger is extremely large, and a red early warning instruction is issued for the road section; when X1 is more than Ly and less than or equal to X2, indicating that the road section running danger is general, and issuing a yellow early warning instruction aiming at the road section; when Ly is less than or equal to X1, the running danger of the road section is extremely small, and a green early warning instruction is issued for the road section; the green early warning instruction represents that the road section can safely run;
The road condition early warning module is used for stamping a time stamp on the road condition early warning index Ly of each road section and uploading the time stamp to the cloud server; the cloud server is used for carrying out maximum estimated vehicle speed Rt evaluation by combining the vehicle position and road condition early warning indexes Ly of each road section, and transmitting the maximum estimated vehicle speed Rt to the vehicle-mounted terminal for display so as to remind a driver of reasonably adjusting the driving speed; the specific evaluation steps comprise:
acquiring a road section matched with the position of an automobile, and acquiring a road condition early warning index of the road section and marking the road condition early warning index as Lyt; obtaining the highest speed limit of the road section as R1;
Calculating the maximum estimated vehicle speed Rt of the road section by using a formula Rt=f×R1× (1-Lyt×g8), wherein g8 is a preset coefficient factor; f is a preset equalization coefficient;
The driving analysis module is connected with the vehicle-mounted terminal and is used for acquiring vehicle periphery related information to conduct blockage factor Ds analysis, wherein the vehicle periphery related information comprises vehicle speed, distance between a vehicle and surrounding vehicles and vehicle speed of the surrounding vehicles; the specific analysis steps are as follows:
Acquiring vehicle periphery association information, and marking all running vehicles in a radius r1 area as associated vehicles by taking the center of the current running vehicle as the center of a circle; wherein r1 is a preset value;
counting the number of associated vehicles to Cz; the method comprises the steps of obtaining the distance between a current running vehicle and an associated vehicle, marking the distance as a vehicle distance GDi, and marking the speed of the associated vehicle as Vi; i represents an i-th associated vehicle; wherein GDi corresponds to Vi one by one;
Calculating to obtain a vehicle distance average GDz according to an average calculation formula; marking the maximum vehicle distance value as GDmax and the minimum vehicle distance value as GDmin; calculating to obtain a space difference ratio Gb by using a formula Gb= (GDmax-GDmin)/GDz;
calculating a vehicle speed average value Vz according to an average value calculation formula; marking the maximum value of the vehicle speed as Vmax and the minimum value of the vehicle speed as Vmin; calculating to obtain a vehicle speed difference ratio Vb by using a formula vb= (Vmax-Vmin)/Vz;
Normalizing the number of related vehicles, the distance difference ratio and the vehicle speed difference ratio, taking the values of the normalized numbers, and calculating by using a formula Ds= (Cz×a1)/((1-Gb) × (1-Vb) ×a2) to obtain a blockage coefficient Ds, wherein a1 and a2 are preset coefficient factors;
If the jam coefficient Ds is larger than the preset jam threshold, the current running area of the vehicle is indicated to be relatively jammed, and a jam signal is generated to the vehicle-mounted terminal so as to remind a driver of improving attention and slowing down.
2. The real-time road condition early warning system based on meteorological data according to claim 1, wherein the road side sensor comprises a road surface temperature sensor, a road surface humidity sensor, a water film sensor, a voltage level film sensor and an optical fiber sensor;
the road surface temperature sensor and the road surface humidity sensor are respectively used for monitoring the temperature and the humidity of a road surface; the water film sensor is used for monitoring the water film thickness of the road surface; the piezoelectric level film sensor and the optical fiber sensor are used for monitoring the thickness of ice and snow.
3. The real-time road condition early warning system based on meteorological data according to claim 1, further comprising a Bluetooth playing module, wherein the Bluetooth playing module is connected with the cloud server and is used for playing voice of road surface meteorological data and traffic flow through a Bluetooth playing function and providing references for a driver to reroute.
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