CN117549913A - Safe driving early warning system for mixed flow of port and district tank truck - Google Patents
Safe driving early warning system for mixed flow of port and district tank truck Download PDFInfo
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Abstract
The invention discloses a safe driving early warning system for mixed flow of a port and district tank truck, relates to the field of road traffic, and solves the problem of poor early warning effect of the existing safe driving early warning, wherein the early warning system comprises a safe driving early warning system comprising a data acquisition module: acquiring basic data of safe driving, and a data analysis module: analyzing the safe driving basic data to obtain safe driving analysis data, and the data processing module is used for: processing the safe driving basic data and the safe driving analysis data to obtain safe driving early warning grading data, wherein the safe early warning module is used for: according to the safety driving early warning hierarchical data, the safety driving early warning is carried out, and various different data which possibly influence the safety driving early warning are fully acquired in the real road, so that the diversity and universality of early warning data sources are realized, and the early warning effect is improved.
Description
Technical Field
The invention belongs to the field of road traffic, relates to a data analysis technology, and particularly relates to a safe driving early warning system for mixed flow of a port and district tank truck.
Background
Tank trucks are trucks specially used for transporting liquid goods, generally have a cylindrical storage tank structure for loading liquid goods such as petroleum, chemicals, food and the like, tank trucks generally have leakage-proof equipment and safety valves to ensure safety during transportation, and trucks are trucks specially used for transporting containers at ports, freight stations and the like, generally have a large carrying capacity, are capable of towing and transporting heavy containers, and have special designs for facilitating loading and unloading of containers;
in the prior art, in the safety driving early warning of mixed flow of a tank truck and a collection truck, the following defects exist:
1. the condition of the road is not fully considered, and the basic data for carrying out the safe driving early warning lacks diversity and universality;
2. the existing early warning system has a single early warning mode, and can not synchronously early warn a driver in a vehicle, a vehicle outside the vehicle and pedestrians at the same time;
3. the existing early warning system is difficult to effectively predict a moving small object, and the early warning lacks comprehensiveness and reliability;
therefore, the safe driving early warning system for mixed flow of the harbor tank truck is provided.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a safe driving early warning system for mixed flow of a port and district tank truck, the invention obtains safe driving basic data based on the obtained goods basic data, vehicle basic data, driving basic data, road condition basic data and crowd basic data, analyzes the safe driving basic data to obtain safe driving analysis data, calculates a safe driving reference coefficient according to the safe driving basic data and the safe driving analysis data, obtains safe driving early warning grading data by threshold division of the safe driving reference coefficient, and carries out safe driving early warning according to the safe driving early warning grading data;
In order to achieve the above purpose, the present invention adopts the following technical scheme: the specific working process of each module of the safe driving early warning system for mixing the port and district tank truck and the truck is as follows:
and a data acquisition module: acquiring basic data of safe driving;
and a data analysis module: analyzing the safe driving basic data to obtain safe driving analysis data;
and a data processing module: processing the safe driving basic data and the safe driving analysis data to obtain safe driving early warning grading data;
a safety early warning module: carrying out safe driving early warning according to the safe driving early warning grading data;
the system also comprises a server which is respectively connected with the data acquisition module, the data analysis module, the data processing module and the safety early warning module.
Further, the data acquisition module acquires the safe driving basic data, specifically as follows:
the data acquisition module comprises a cargo data unit, a vehicle data unit, a driving unit, a road condition data unit and a crowd unit;
the database stores the number of cargoes loaded by the vehicle, the mass value of the single cargoes, the international maritime dangerous cargo codes corresponding to the cargoes, the age value of a driver, the historical violation data of the vehicle, the historical maintenance record of the vehicle, the historical traffic accident data of the road and the population residence density data of the area where the road is located;
The cargo data unit acquires cargo basic data, and specifically comprises the following steps:
acquiring the number of cargoes loaded by the vehicle, the mass value of the single cargoes and the international maritime dangerous cargo code corresponding to the cargoes according to the database;
acquiring a cargo dangerous reference coefficient, and carrying out parameter assignment on the cargo dangerous reference coefficient according to an international maritime dangerous cargo code corresponding to the cargo;
defining the number of goods, the quality value of the single goods and the goods risk reference coefficient as goods basic data;
the vehicle data unit acquires vehicle basic data, and specifically comprises the following steps:
acquiring the current speed of the vehicle through a speed sensor, and respectively acquiring the front-rear vehicle distance and the left-right vehicle distance through a laser radar;
counting the accumulated maintenance times of the current vehicle through the historical maintenance records of the vehicle;
defining the current speed, the front-rear distance, the left-right distance and the current accumulated maintenance times of the vehicle as basic data of the vehicle;
the driving unit obtains basic data of a driver, and the basic data of the driver are specifically as follows:
acquiring a current driving duration value through a vehicle-mounted driving recorder, and acquiring a driver age value through a database;
acquiring vehicle history violation data through a database, counting the number of violations of the vehicle in the last three years according to the vehicle history violation data, and defining the number of violations of the vehicle in the last three years as driver violation data;
Defining the current driving duration value, the driver age value and the driver violation data as driver basic data;
the road condition data unit acquires road condition basic data;
the crowd unit acquires crowd basic data;
the data acquisition module acquires cargo basic data, vehicle basic data, driver basic data, road condition basic data and crowd basic data, and defines the cargo basic data, the vehicle basic data, the driver basic data, the road condition basic data and the crowd basic data as safe driving basic data.
Further, the road condition data unit and the crowd unit respectively acquire road condition basic data and crowd basic data, and the method specifically comprises the following steps: the road condition data unit acquires road condition basic data, and specifically comprises the following steps:
identifying a speed limit sign on the right side of the road through a first camera, and obtaining a current road speed limit value;
acquiring road historical traffic accident data according to the database, counting the number of accumulated traffic accidents on the road in the current year by the road historical traffic accident data, and defining the number of accumulated traffic accidents on the road in the current year as road traffic accident data;
monitoring road traffic flow image data in real time through a second camera, and counting the number of vehicles in the view range of the camera by using an image recognition algorithm to obtain the view distance value of the road surface of the second camera;
Calculating the number of vehicles and the road surface visual field distance of the second camera by using a traffic density calculation formula to obtain a traffic density reference value;
defining a current road speed limit value, road traffic accident data and a traffic flow density reference value as road condition basic data;
the crowd unit acquires crowd basic data, and the crowd basic data is specifically as follows:
monitoring road non-motor vehicle image data in real time through a third camera, and counting the number of non-motor vehicles in the visual field range of the camera by utilizing an image recognition algorithm to obtain a road visual field distance value of the third camera;
calculating the number value of the non-motor vehicles and the road surface visual field distance value of the third camera through a non-motor vehicle density calculation formula to obtain a non-motor vehicle density reference value;
acquiring population residence density data of the region where the road is located through a database,
the road pedestrian image data is monitored in real time through the fourth camera, and the number value of pedestrians in the view field range of the camera is counted by utilizing an image recognition algorithm to obtain the road surface view field distance value of the fourth camera;
calculating the pedestrian number value and the third camera road surface visual field distance value through a pedestrian density calculation formula to obtain a pedestrian density reference value;
And defining the density reference value of the non-motor vehicle, the population residence density data of the area where the road is located and the pedestrian density reference value as the crowd basic data.
Further, the data analysis module analyzes the safe driving basic data and acquires safe driving analysis data, which is specifically as follows:
the data analysis module comprises a cargo analysis unit, a vehicle analysis unit, a driving analysis unit and a road condition analysis unit;
the cargo analysis unit acquires cargo risk reference values;
the vehicle analysis unit acquires a vehicle risk reference value;
the driving analysis unit acquires a driving risk reference value;
the road condition analysis unit acquires a road condition complexity reference value;
the data analysis module acquires a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value;
and defining the cargo risk reference value, the vehicle risk reference value, the driving risk reference value and the road condition complexity reference value as safe driving analysis data.
Further, the cargo analysis unit and the vehicle analysis unit respectively acquire cargo risk reference values and vehicle risk reference values, specifically as follows:
The cargo analysis unit acquires cargo risk reference values, and the cargo risk reference values are specifically as follows:
acquiring the number of cargos, the quality value of a single cargo and a cargo risk reference coefficient according to the cargo basic data;
calculating the number of cargos, the mass value of the single cargos and the cargo risk reference coefficient through a cargo risk calculation formula to obtain a cargo risk reference value;
the vehicle analysis unit acquires a vehicle risk reference value, and specifically comprises the following steps:
acquiring the current speed, the front-rear distance, the left-right distance and the accumulated maintenance times of the current vehicle according to the basic data of the vehicle;
and calculating the current vehicle speed, the front-rear vehicle distance, the left-right vehicle distance and the current vehicle accumulated maintenance times through a vehicle risk calculation formula to obtain a vehicle risk reference value.
Further, the driving analysis unit and the road condition analysis unit respectively acquire a driving risk reference value and a road condition complexity reference value, which are specifically as follows:
the driving analysis unit acquires a driving risk reference value, and the driving risk reference value is specifically as follows:
acquiring a current driving duration value, a driver age value and driver violation data according to the driver basic data;
calculating the current driving duration value, the driver age value and the driver violation data through a driver risk calculation formula to obtain a driving risk reference value;
The road condition analysis unit acquires the road condition complexity reference value, and the road condition complexity reference value is specifically as follows:
acquiring a current road speed limit value, road traffic accident data and a traffic flow density reference value according to the road condition basic data;
and calculating the current road speed limit value, the road traffic accident data and the traffic density reference value through a road condition complexity reference value calculation formula to obtain a road condition complexity reference value.
Further, the data processing module processes the safe driving basic data and the safe driving analysis data to obtain safe driving early warning grading data, and the data processing module is specifically as follows:
the data processing module comprises a data processing unit and an early warning grading unit;
the data processing unit processes the safe driving basic data to obtain a safe driving reference coefficient;
the early warning grading unit performs threshold division on the safe driving reference coefficient to obtain safe driving early warning grading data
The data processing module acquires the safe driving early warning grading data.
Further, the data processing unit acquires a safe driving reference coefficient, which is specifically as follows:
acquiring a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value according to the safe driving analysis data;
Calculating a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value through a preliminary early warning reference coefficient calculation formula to obtain a preliminary early warning reference coefficient;
acquiring a cargo risk standard reference value, a vehicle risk standard reference value, a driver risk standard reference value and a road condition complexity standard reference value;
calculating a cargo risk standard reference value, a vehicle risk standard reference value, a driver risk standard reference value and a road condition complexity standard reference value through a safe driving preliminary coefficient threshold calculation formula to obtain a preliminary early warning reference coefficient threshold;
acquiring a non-motor vehicle density reference value, population living density data of an area where a road is located and a pedestrian density reference value according to the safe driving basic data;
and obtaining a non-motor vehicle density reference value, population residence density data of the region where the road is located and a pedestrian density reference value from the preliminary early warning reference coefficient and the safe driving basic data, and calculating the safe driving early warning reference coefficient through a safe driving early warning reference coefficient calculation formula.
Further, the early warning grading unit acquires early warning grading data of safe driving, which is specifically as follows:
Acquiring a preliminary early warning reference coefficient threshold, a non-motor vehicle density standard reference value, population residence density standard data and a pedestrian density standard reference value;
the preliminary early warning reference coefficient threshold value is obtained, and the safe driving early warning reference coefficient threshold value is obtained through calculation of a safe driving early warning reference threshold value calculation formula by non-motor vehicle density standard reference values, population residence density standard data and pedestrian density standard reference values;
the safe driving early warning reference coefficient is obtained, and the safe driving early warning reference coefficient is divided into sections by using a safe driving early warning reference coefficient threshold, and the method specifically comprises the following steps:
when Ay is more than or equal to Ay1, judging that the vehicle is driven and early-warned;
when Ay1 is more than Ay is more than 0, judging that the vehicle is normally driven;
and defining a judgment result obtained according to the safe driving early warning reference coefficient and the safe driving early warning reference coefficient threshold value as safe driving early warning grading data.
Further, the safety early warning module carries out safety driving early warning according to the safety driving early warning hierarchical data, and the safety driving early warning module specifically comprises the following steps:
the safety early warning module comprises an in-vehicle early warning unit and an out-vehicle early warning unit;
the in-vehicle early warning unit carries out in-vehicle early warning according to the safe driving early warning grading data, and specifically comprises the following steps:
Aiming at a normally driven vehicle, no in-vehicle early warning is carried out;
aiming at a driving early warning vehicle, a first early warning indicator lamp flashes, a loudspeaker issues alarm broadcast to early warn a driver, and the driver is warned through communication equipment;
the off-board early warning unit carries out off-board early warning according to the early warning grading data, and the off-board early warning unit specifically comprises the following steps:
aiming at a normally driven vehicle, no off-vehicle early warning is carried out;
aiming at the driving early-warning vehicle, the second early-warning indicator lights flash, and the LED display screen issues alarm information to the vehicle outside the vehicle and pedestrians.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. according to the method, various different data which possibly influence the safe driving early warning in the real road are fully acquired, so that the diversity and universality of early warning data sources are realized;
2. according to the invention, the synchronous early warning is carried out in a mode of combining the in-vehicle early warning and the out-vehicle early warning, so that the flexibility and the high efficiency of the early warning mode are ensured.
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 view of the road driving of the tank truck and the truck 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 safe driving early warning system for the mixed flow of the port and district tank truck comprises a data acquisition module, a data analysis module, a data processing module and a safe early warning module, wherein the data acquisition module, the data analysis module, the data processing module and the safe early warning module are respectively connected with a server;
the system also comprises a database, wherein the database stores the quantity of cargoes loaded by the vehicle, the quality value of the single cargoes, the international maritime dangerous cargo codes corresponding to the cargoes, the age value of a driver, the historical violation data of the vehicle, the historical maintenance record of the vehicle, the historical traffic accident data of the road and the population residence density data of the area where the road is located;
the data acquisition module acquires basic data of safe driving;
The data acquisition module comprises a cargo data unit, a vehicle data unit, a driving unit, a road condition data unit and a crowd unit, wherein the cargo data unit acquires cargo basic data, the vehicle data unit acquires vehicle basic data, the driving unit acquires driving basic data, the road condition data unit acquires road condition basic data, the crowd unit acquires crowd basic data, the vehicle data unit comprises a speed sensor and a laser radar, the driving unit comprises a vehicle-mounted driving recorder, the road condition data unit comprises a first camera and a second camera, and the crowd unit comprises a third camera and a fourth camera;
the cargo data unit acquires cargo basic data, and specifically comprises the following steps:
acquiring the number of cargoes loaded by the vehicle, the mass value of the single cargoes and the international maritime dangerous cargo code corresponding to the cargoes according to the database;
acquiring a cargo dangerous reference coefficient, and carrying out parameter assignment on the cargo dangerous reference coefficient according to an international maritime dangerous cargo code corresponding to the cargo, wherein the method comprises the following steps of:
carrying out parameter assignment on the cargo danger according to the international maritime dangerous cargo code corresponding to the cargo, and dividing the cargo into explosives, compressed gas, inflammable liquid, inflammable solid, oxidant, organic peroxide, toxic substances, radioactive substances, corrosives and other dangerous substances;
If the goods are explosives, F1 is used for assigning the dangerous reference coefficient of the goods;
if the goods are compressed gas, F2 is used for assigning the dangerous reference coefficient of the goods;
if the goods are inflammable liquid, F3 is used for assigning the dangerous reference coefficient of the goods;
if the goods are inflammable solids, F4 is used for assigning the dangerous reference coefficient of the goods;
if the goods are oxidant and organic peroxide, using F5 to assign the dangerous reference coefficient of the goods;
if the goods are toxic substances, F6 is used for assigning the dangerous reference coefficient of the goods;
if the goods are radioactive substances, F7 is used for assigning dangerous reference coefficients of the goods;
if the goods are corrosive, assigning the dangerous reference coefficient of the goods by using F8;
if the goods are other dangerous goods, F9 is used for assigning the dangerous reference coefficient of the goods;
wherein F1 > F2 > F3 > F4 > F5 > F6 > F7 > F8 > F9 > 0;
defining the number of goods, the quality value of the single goods and the goods risk reference coefficient as goods basic data;
the vehicle data unit acquires vehicle basic data, and specifically comprises the following steps:
referring to fig. 3, the current speed of the vehicle is obtained through a speed sensor, and the front-rear vehicle distance and the left-right vehicle distance are respectively obtained through a laser radar;
Counting the accumulated maintenance times of the current vehicle through the historical maintenance records of the vehicle;
defining the current speed, the front-rear distance, the left-right distance and the current accumulated maintenance times of the vehicle as basic data of the vehicle;
what needs to be explained here is:
the front-rear vehicle distance referred to herein is the distance between the front obstacle and the vehicle head of the current vehicle plus the distance between the rear obstacle and the vehicle position; the left-right vehicle distance referred to herein is the current vehicle left-side obstacle-to-vehicle body left-side distance plus the right-side obstacle-to-vehicle body right-side distance, and obstacles include, but are not limited to, vehicles, road blocks, guardrails;
the driving unit acquires driving basic data, specifically as follows:
acquiring a current driving duration value through a vehicle-mounted driving recorder, and acquiring a driver age value through a database;
acquiring vehicle history violation data through a database, counting the number of violations of the vehicle in the last three years according to the vehicle history violation data, and defining the number of violations of the vehicle in the last three years as driver violation data;
defining the current driving duration value, the driver age value and the driver violation data as driving basic data;
what needs to be explained here is:
the current driving duration value is the length of time that the driver has been driving in continuous driving, and is generally used for monitoring the working time of the driver to ensure that the driver does not exceed the specified driving duration, thereby ensuring driving safety;
The driver violation data are counted as the number of violations of the vehicle in the last three years, and if the current time of the first road-on interval of the vehicle is not three years, the number of violations of the vehicle from the first road-on is counted as the driver violation data;
the road condition data unit acquires road condition basic data, and specifically comprises the following steps:
identifying a speed limit sign on the right side of the road through a first camera, and obtaining a current road speed limit value;
acquiring road historical traffic accident data according to the database, counting the number of accumulated traffic accidents on the road in the current year by the road historical traffic accident data, and defining the number of accumulated traffic accidents on the road in the current year as road traffic accident data;
monitoring road traffic flow image data in real time through a second camera, and counting the number of vehicles in the view range of the camera by using an image recognition algorithm to obtain the view distance value of the road surface of the second camera;
calculating the number of vehicles and the road surface visual field distance of the second camera by using a traffic density calculation formula to obtain a traffic density reference value;
the specific configuration reference of the traffic density calculation formula is as follows:；
wherein Cl is a vehicle flow density reference value, cs is a vehicle number value, and Cd2 is a road surface visual field distance value of the second camera;
What needs to be explained here is:
the camera road surface visual field distance value refers to the road length which can be covered by the camera in the horizontal direction;
for example: if the road surface visual field distance of one camera is 100 meters, the camera can monitor a road with the length of 100 meters in the horizontal direction, which means that the camera can effectively monitor vehicles in the length, and vehicles in areas beyond the range cannot be covered by the camera;
defining a current road speed limit value, road traffic accident data and a traffic flow density reference value as road condition basic data;
the crowd unit acquires crowd basic data, and the crowd basic data comprises the following specific steps:
monitoring road non-motor vehicle image data in real time through a third camera, and counting the number of non-motor vehicles in the visual field range of the camera by utilizing an image recognition algorithm to obtain a road visual field distance value of the third camera;
calculating the number value of the non-motor vehicles and the road surface visual field distance value of the third camera through a non-motor vehicle density calculation formula to obtain a non-motor vehicle density reference value;
the specific configuration of the non-motor vehicle density calculation formula is as follows:；
fj is a non-motor vehicle density reference value, fs is a non-motor vehicle number value, and Cd3 is a third camera road surface visual field distance value;
Acquiring population residence density data of the region where the road is located through a database,
the road pedestrian image data is monitored in real time through the fourth camera, and the number value of pedestrians in the view field range of the camera is counted by utilizing an image recognition algorithm to obtain the road surface view field distance value of the fourth camera;
calculating the pedestrian number value and the third camera road surface visual field distance value through a pedestrian density calculation formula to obtain a pedestrian density reference value;
the specific configuration reference of the pedestrian density calculation formula is as follows:；
wherein Xr is a pedestrian density reference value, rs is a pedestrian number value, and Cd4 is a fourth camera road surface visual field distance value;
defining a non-motor vehicle density reference value, population living density data of an area where a road is located and a pedestrian density reference value as crowd basic data;
the data acquisition module acquires cargo basic data, vehicle basic data, driving basic data, road condition basic data and crowd basic data, defines the cargo basic data, the vehicle basic data, the driving basic data, the road condition basic data and the crowd basic data as safe driving basic data, and transmits the safe driving basic data to the data analysis module and the safety early warning module;
The data analysis module analyzes the safe driving basic data to obtain safe driving analysis data;
the data analysis module comprises a cargo analysis unit, a vehicle analysis unit, a driving analysis unit and a road condition analysis unit, wherein the data analysis module acquires cargo basic data, vehicle basic data, driving basic data and road condition basic data according to safe driving analysis data, the cargo analysis unit analyzes the cargo basic data to obtain cargo dangerous reference values, the vehicle analysis unit analyzes the vehicle basic data to obtain vehicle dangerous reference values, the driving analysis unit analyzes the driving basic data to obtain driving dangerous reference values, and the road condition analysis unit analyzes the road condition basic data to obtain road condition complexity reference values;
the cargo analysis unit analyzes the cargo basic data to obtain cargo risk reference values, and the cargo risk reference values are specifically as follows:
acquiring the number of cargos, the quality value of a single cargo and a cargo risk reference coefficient according to the cargo basic data;
calculating the number of cargos, the mass value of the single cargos and the cargo risk reference coefficient through a cargo risk calculation formula to obtain a cargo risk reference value;
the specific configuration reference of the cargo risk calculation formula is as follows: ；
Wherein Hw is a cargo risk reference value and Hw is greater than 0, hs is the number of cargoes, dz is the mass value of a single cargo, wx is a cargo risk reference coefficient, a1 is a set proportionality coefficient and a1 is greater than 0;
the vehicle analysis unit analyzes the basic data of the vehicle to obtain a vehicle risk reference value, and the vehicle risk reference value is specifically as follows:
acquiring the current speed, the front-rear distance, the left-right distance and the accumulated maintenance times of the current vehicle according to the basic data of the vehicle;
calculating the current vehicle speed, the front-rear vehicle distance, the left-right vehicle distance and the current vehicle accumulated maintenance times through a vehicle risk calculation formula to obtain a vehicle risk reference value;
the specific configuration of the vehicle risk calculation formula is referred as follows:；
wherein Cw is a vehicle risk reference value and is larger than 0, vc is a current vehicle speed, qh is a front-rear vehicle distance, zy is a left-right vehicle distance, xc is a current vehicle accumulated maintenance frequency, a2 and a3 are set proportionality coefficients, and a2 and a3 are both larger than 0;
the driving analysis unit analyzes the driving basic data to obtain a driving risk reference value, and the driving risk reference value is specifically as follows:
acquiring a current driving duration value, a driver age value and driver violation data according to the driving basic data;
calculating the current driving duration value, the driver age value and the driver violation data through a driver risk calculation formula to obtain a driving risk reference value;
Carrying out numerical judgment on the current driving duration numerical value, wherein the judgment result is divided into a current driving duration numerical value which is more than 4 hours and a current driving duration numerical value which is less than 4 hours;
if the current driving duration value is greater than 4 hours, the specific configuration reference of the driver risk calculation formula is as follows:；
if the current driving duration value is less than 4 hours, the specific configuration reference of the driver risk calculation formula is as follows:；
wherein, jy is a driving risk reference value and Jy is larger than 0, sc is a current driving duration value, nl is a driver age value, wz is driver violation data, b1 is a set proportionality coefficient and b1 is larger than 0;
the road condition analysis unit analyzes the road condition basic data to obtain a road condition complexity reference value, and the road condition complexity reference value is specifically as follows:
acquiring a current road speed limit value, road traffic accident data and a traffic flow density reference value according to the road condition basic data;
calculating the current road speed limit value, the road traffic accident data and the traffic density reference value through a road condition complexity reference value calculation formula to obtain a road condition complexity reference value;
the specific configuration reference of the road condition complexity reference value calculation formula is as follows:；
wherein Lk is a road condition complexity reference value and is larger than 0, xs is a current road speed limit value, sg is road traffic accident data, cm is a traffic density reference value, b2 is a set proportionality coefficient and b2 is larger than 0;
The data analysis module acquires a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value;
defining the cargo risk reference value, the vehicle risk reference value, the driving risk reference value and the road condition complexity reference value as safe driving analysis data, and transmitting the safe driving analysis data to a data processing module;
the data processing module processes the safe driving basic data and the safe driving analysis data to obtain safe driving early warning grading data;
the data processing module comprises a data processing unit and an early warning grading unit, the data processing unit processes the safe driving basic data to obtain a safe driving reference coefficient, and the early warning grading unit performs threshold division on the safe driving reference coefficient to obtain safe driving early warning grading data;
the data processing unit processes the safe driving basic data to obtain a safe driving reference coefficient, and the safe driving reference coefficient is specifically as follows:
acquiring a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value according to the safe driving analysis data;
calculating a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value through a preliminary early warning reference coefficient calculation formula to obtain a preliminary early warning reference coefficient;
The specific configuration reference of the preliminary early warning reference coefficient calculation formula is as follows:；
wherein Yc is a preliminary early warning reference coefficient, hw is a cargo risk reference value, cw is a vehicle risk reference value, jy is a driving risk reference value, lk is a road condition complexity reference value, c1 is a set proportionality coefficient, and c1 is greater than 0;
acquiring a cargo risk standard reference value, a vehicle risk standard reference value, a driver risk standard reference value and a road condition complexity standard reference value;
calculating a cargo risk standard reference value, a vehicle risk standard reference value, a driver risk standard reference value and a road condition complexity standard reference value through a safe driving preliminary coefficient threshold calculation formula to obtain a preliminary early warning reference coefficient threshold;
the specific configuration reference of the safe driving preliminary coefficient threshold value calculation formula is as follows:
；
wherein Yc1 is a preliminary early warning reference coefficient threshold, hw1 is a cargo risk standard reference value, cw1 is a vehicle risk standard reference value, jy1 is a driver risk standard reference value, lk1 is a road condition complexity standard reference value, c1 is a set scale factor, and c1 is greater than 0;
what needs to be explained here is: the cargo risk standard reference value, the vehicle risk standard reference value, the driver risk standard reference value and the road condition complexity standard reference value are all reference values carried out by related management departments;
Acquiring a non-motor vehicle density reference value, population living density data of an area where a road is located and a pedestrian density reference value according to the safe driving basic data;
acquiring a non-motor vehicle density reference value, population residence density data of an area where a road is located and a pedestrian density reference value from the preliminary early warning reference coefficient and the safe driving basic data, and calculating the safe driving early warning reference coefficient through a safe driving early warning reference coefficient calculation formula;
the specific configuration reference of the safe driving early warning reference coefficient calculation formula is as follows:；
ay is a safe driving early warning reference coefficient, yc is a preliminary early warning reference coefficient, fj is a non-motor vehicle density reference value, xr is pedestrian density reference value defined as crowd basic data, jz is population residence density data of an area where a road is located, c2 and c3 are set proportionality coefficients, and both c2 and c3 are larger than 0;
the early warning grading unit carries out threshold division on the safe driving reference coefficient to obtain safe driving early warning grading data;
acquiring a preliminary early warning reference coefficient threshold, a non-motor vehicle density standard reference value, population residence density standard data and a pedestrian density standard reference value;
the preliminary early warning reference coefficient threshold value is obtained, and the safe driving early warning reference coefficient threshold value is obtained through calculation of a safe driving early warning reference threshold value calculation formula by non-motor vehicle density standard reference values, population residence density standard data and pedestrian density standard reference values;
The specific configuration reference of the safe driving early warning reference threshold calculation formula is as follows:
；
ay1 is a safe driving early warning reference coefficient threshold, yc1 is a preliminary early warning reference coefficient threshold, fj1 is a non-motor vehicle density standard reference value, xr1 is a pedestrian density standard reference value, jz1 is man-hole residence density standard data, c2 and c3 are set proportionality coefficients, and both c2 and c3 are larger than 0;
what needs to be explained here is: the non-motor vehicle density standard reference value, population residence density standard data and pedestrian density standard reference value are specific reference values set by related departments;
the safe driving early warning reference coefficient is obtained, and the safe driving early warning reference coefficient is divided into sections by using a safe driving early warning reference coefficient threshold, and the method specifically comprises the following steps:
when Ay is more than or equal to Ay1, judging that the vehicle is driven and early-warned;
when Ay1 is more than Ay is more than 0, judging that the vehicle is normally driven;
defining a judgment result obtained according to the safe driving early warning reference coefficient and the safe driving early warning reference coefficient threshold value as safe driving early warning grading data;
the data processing module acquires the safe driving early warning grading data and transmits the safe driving early warning grading data to the safe early warning module;
the safety early warning module carries out safety early warning according to the safety driving early warning grading data;
The safety early warning module comprises an in-vehicle early warning unit and an out-vehicle early warning unit, wherein the in-vehicle early warning unit carries out in-vehicle early warning according to the safety driving early warning grading data, the out-vehicle early warning unit carries out-vehicle early warning according to the early warning grading data, the in-vehicle early warning unit comprises a first early warning indicator lamp, a loudspeaker and communication equipment, and the out-vehicle early warning unit comprises a second early warning indicator LED display screen;
the in-vehicle early warning unit carries out in-vehicle early warning according to the safe driving early warning grading data, and specifically comprises the following steps:
aiming at a normally driven vehicle, no in-vehicle early warning is carried out;
aiming at a driving early warning vehicle, a first early warning indicator lamp flashes, a loudspeaker issues alarm broadcast to early warn a driver, and the driver is warned through communication equipment;
the off-board early warning unit carries out off-board early warning according to the early warning grading data, and the off-board early warning unit specifically comprises the following steps:
aiming at a normally driven vehicle, no off-vehicle early warning is carried out;
aiming at a driving early warning vehicle, a second early warning indicator lamp flashes, and an LED display screen issues alarm information to vehicles outside the vehicle and pedestrians;
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 the same conception, the invention provides a safe driving early warning method for mixed flow of a collection truck of a port tank truck, which comprises the following steps:
step S1: acquiring basic data of safe driving;
step S11: the method comprises the following steps of:
step S111: acquiring the number of cargoes loaded by the vehicle, the mass value of the single cargoes and the international maritime dangerous cargo code corresponding to the cargoes according to the database;
step S112: acquiring a cargo dangerous reference coefficient, and carrying out parameter assignment on the cargo dangerous reference coefficient according to an international maritime dangerous cargo code corresponding to the cargo;
step S113: defining the number of goods, the quality value of the single goods and the goods risk reference coefficient as goods basic data;
step S12: the method comprises the following steps of:
step S121: acquiring the current speed of the vehicle through a speed sensor, and respectively acquiring the front-rear vehicle distance and the left-right vehicle distance through a laser radar;
step S122: counting the accumulated maintenance times of the current vehicle through the historical maintenance records of the vehicle;
step S123: defining the current speed, the front-rear distance, the left-right distance and the current accumulated maintenance times of the vehicle as basic data of the vehicle;
Step S13: the driving basic data is acquired, and the driving basic data is specifically as follows:
step S131: acquiring a current driving duration value through a vehicle-mounted driving recorder, and acquiring a driver age value through a database;
step S132: acquiring vehicle history violation data through a database, counting the number of violations of the vehicle in the last three years according to the vehicle history violation data, and defining the number of violations of the vehicle in the last three years as driver violation data;
step S133: defining the current driving duration value, the driver age value and the driver violation data as driving basic data;
step S14: the road condition data unit acquires road condition basic data, and specifically comprises the following steps:
step S141: identifying a speed limit sign on the right side of the road through a first camera, and obtaining a current road speed limit value;
step S142: acquiring road historical traffic accident data according to the database, counting the number of accumulated traffic accidents on the road in the current year by the road historical traffic accident data, and defining the number of accumulated traffic accidents on the road in the current year as road traffic accident data;
step S143: monitoring road traffic flow image data in real time through a second camera, and counting the number of vehicles in the view range of the camera by using an image recognition algorithm to obtain the view distance value of the road surface of the second camera;
Step S144: calculating the number of vehicles and the road surface visual field distance of the second camera by using a traffic density calculation formula to obtain a traffic density reference value;
step S15: the crowd basic data is obtained by the following specific steps:
step S151: monitoring road non-motor vehicle image data in real time through a third camera, and counting the number of non-motor vehicles in the visual field range of the camera by utilizing an image recognition algorithm to obtain a road visual field distance value of the third camera;
step S152: calculating the number value of the non-motor vehicles and the road surface visual field distance value of the third camera through a non-motor vehicle density calculation formula to obtain a non-motor vehicle density reference value;
step S153: acquiring population residence density data of the region where the road is located through a database,
step S154: the road pedestrian image data is monitored in real time through the fourth camera, and the number value of pedestrians in the view field range of the camera is counted by utilizing an image recognition algorithm to obtain the road surface view field distance value of the fourth camera;
step S155: calculating the pedestrian number value and the third camera road surface visual field distance value through a pedestrian density calculation formula to obtain a pedestrian density reference value;
step S156: defining a non-motor vehicle density reference value, population living density data of an area where a road is located and a pedestrian density reference value as crowd basic data;
Step S16: the data acquisition module acquires cargo basic data, vehicle basic data, driving basic data, road condition basic data and crowd basic data, and defines the cargo basic data, the vehicle basic data, the driving basic data, the road condition basic data and the crowd basic data as safe driving basic data;
step S2: analyzing the safe driving basic data to obtain safe driving analysis data;
step S21: analyzing the cargo basic data to obtain cargo risk reference values, wherein the cargo risk reference values are as follows:
step S211: acquiring the number of cargos, the quality value of a single cargo and a cargo risk reference coefficient according to the cargo basic data;
step S212: calculating the number of cargos, the mass value of the single cargos and the cargo risk reference coefficient through a cargo risk calculation formula to obtain a cargo risk reference value;
step S22: the vehicle basic data is analyzed to obtain a vehicle risk reference value, which is specifically as follows:
step S221: acquiring the current speed, the front-rear distance, the left-right distance and the accumulated maintenance times of the current vehicle according to the basic data of the vehicle;
step S222: calculating the current vehicle speed, the front-rear vehicle distance, the left-right vehicle distance and the current vehicle accumulated maintenance times through a vehicle risk calculation formula to obtain a vehicle risk reference value;
Step S23: the driving basic data is analyzed to obtain a driving risk reference value, and the driving risk reference value is specifically as follows:
step S231: acquiring a current driving duration value, a driver age value and driver violation data according to the driving basic data;
step S232: calculating the current driving duration value, the driver age value and the driver violation data through a driver risk calculation formula to obtain a driving risk reference value;
step S24: analyzing the road condition basic data to obtain a road condition complexity reference value, which is specifically as follows:
step S241: acquiring a current road speed limit value, road traffic accident data and a traffic flow density reference value according to the road condition basic data;
step S242: calculating the current road speed limit value, the road traffic accident data and the traffic density reference value through a road condition complexity reference value calculation formula to obtain a road condition complexity reference value;
step S25: the data analysis module acquires a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value;
step S26: defining a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value as safe driving analysis data;
Step S3: processing the safe driving basic data and the safe driving analysis data to obtain safe driving early warning grading data;
step S31: processing the safe driving basic data to obtain a safe driving reference coefficient, wherein the safe driving reference coefficient is specifically as follows:
step S311: acquiring a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value according to the safe driving analysis data;
step S312: calculating a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value through a preliminary early warning reference coefficient calculation formula to obtain a preliminary early warning reference coefficient;
step S313: acquiring a cargo risk standard reference value, a vehicle risk standard reference value, a driver risk standard reference value and a road condition complexity standard reference value;
step S314: calculating a cargo risk standard reference value, a vehicle risk standard reference value, a driver risk standard reference value and a road condition complexity standard reference value through a safe driving preliminary coefficient threshold calculation formula to obtain a preliminary early warning reference coefficient threshold;
Step S315: acquiring a non-motor vehicle density reference value, population living density data of an area where a road is located and a pedestrian density reference value according to the safe driving basic data;
step S316: acquiring a non-motor vehicle density reference value, population residence density data of an area where a road is located and a pedestrian density reference value from the preliminary early warning reference coefficient and the safe driving basic data, and calculating the safe driving early warning reference coefficient through a safe driving early warning reference coefficient calculation formula;
step S32: threshold division is carried out on the safe driving reference coefficient to obtain safe driving early warning grading data;
step S321: acquiring a preliminary early warning reference coefficient threshold, a non-motor vehicle density standard reference value, population residence density standard data and a pedestrian density standard reference value;
step S322: the preliminary early warning reference coefficient threshold value is obtained, and the safe driving early warning reference coefficient threshold value is obtained through calculation of a safe driving early warning reference threshold value calculation formula by non-motor vehicle density standard reference values, population residence density standard data and pedestrian density standard reference values;
step S323: the safe driving early warning reference coefficient is obtained, and the safe driving early warning reference coefficient is divided into sections by using a safe driving early warning reference coefficient threshold, and the method specifically comprises the following steps:
When Ay is more than or equal to Ay1, judging that the vehicle is driven and early-warned;
when Ay1 is more than Ay is more than 0, judging that the vehicle is normally driven;
step S324: defining a judgment result obtained according to the safe driving early warning reference coefficient and the safe driving early warning reference coefficient threshold value as safe driving early warning grading data;
step S4: carrying out safety precaution according to the safety driving precaution grading data;
step S41: and carrying out in-vehicle early warning according to the safe driving early warning grading data, wherein the method comprises the following steps of:
step S411: aiming at a normally driven vehicle, no in-vehicle early warning is carried out;
step S412: aiming at a driving early warning vehicle, a first early warning indicator lamp flashes, a loudspeaker issues alarm broadcast to early warn a driver, and the driver is warned through communication equipment;
step S42: and carrying out off-vehicle early warning according to the early warning grading data, wherein the off-vehicle early warning is specifically as follows:
step S421: aiming at a normally driven vehicle, no off-vehicle early warning is carried out;
step S422: aiming at a driving early warning vehicle, a second early warning indicator lamp flashes, and an LED display screen issues alarm information to vehicles outside the vehicle and pedestrians;
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. Safe driving early warning system of district tank car collection truck mixed flow, its characterized in that includes:
and a data acquisition module: acquiring cargo basic data, vehicle basic data, driving basic data, road condition basic data and crowd basic data, and synthesizing the cargo basic data, the vehicle basic data, the driving basic data, the road condition basic data and the crowd basic data to obtain safe driving basic data;
and a data analysis module: analyzing the cargo basic data to obtain cargo hazard reference values, analyzing the vehicle basic data to obtain vehicle hazard reference values, analyzing the driving basic data to obtain driving hazard reference values, analyzing the road condition basic data to obtain road condition complexity reference values, and synthesizing the cargo hazard reference values, the vehicle hazard reference values, the driver hazard reference values and the road condition complexity reference values to obtain safe driving analysis data;
and a data processing module: calculating a safe driving reference coefficient according to the safe driving basic data and the safe driving analysis data, and obtaining safe driving early warning grading data by carrying out threshold division on the safe driving reference coefficient;
a safety early warning module: and carrying out safe driving early warning according to the safe driving early warning grading data.
2. The safe driving early warning system of mixed flow of the harbor tank truck according to claim 1, wherein the data acquisition module acquires the safe driving basic data, specifically as follows:
the data acquisition module comprises a cargo data unit, a vehicle data unit, a driving unit, a road condition data unit and a crowd unit;
the system also comprises a database, wherein the database stores the quantity of cargoes loaded by the vehicle, the quality value of the single cargoes, the international maritime dangerous cargo codes corresponding to the cargoes, the age value of a driver, the historical violation data of the vehicle, the historical maintenance record of the vehicle, the historical traffic accident data of the road and the population residence density data of the area where the road is located;
the cargo data unit acquires cargo basic data, and specifically comprises the following steps:
acquiring the number of cargoes loaded by the vehicle, the mass value of the single cargoes and the international maritime dangerous cargo code corresponding to the cargoes according to the database;
acquiring a cargo dangerous reference coefficient, and carrying out parameter assignment on the cargo dangerous reference coefficient according to an international maritime dangerous cargo code corresponding to the cargo;
defining the number of goods, the quality value of the single goods and the goods risk reference coefficient as goods basic data;
The vehicle data unit acquires vehicle basic data, and specifically comprises the following steps:
acquiring the current speed of the vehicle through a speed sensor, and respectively acquiring the front-rear vehicle distance and the left-right vehicle distance through a laser radar;
counting the accumulated maintenance times of the current vehicle through the historical maintenance records of the vehicle;
defining the current speed, the front-rear distance, the left-right distance and the current accumulated maintenance times of the vehicle as basic data of the vehicle;
the driving unit obtains basic data of a driver, and the basic data of the driver are specifically as follows:
acquiring a current driving duration value through a vehicle-mounted driving recorder, and acquiring a driver age value through a database;
acquiring vehicle history violation data through a database, counting the number of violations of the vehicle in the last three years according to the vehicle history violation data, and defining the number of violations of the vehicle in the last three years as driver violation data;
defining the current driving duration value, the driver age value and the driver violation data as driver basic data;
the road condition data unit acquires road condition basic data;
the crowd unit acquires crowd basic data;
the data acquisition module acquires cargo basic data, vehicle basic data, driver basic data, road condition basic data and crowd basic data, and defines the cargo basic data, the vehicle basic data, the driver basic data, the road condition basic data and the crowd basic data as safe driving basic data.
3. The safe driving early warning system for mixed flow of port and tank truck according to claim 2, wherein the road condition data unit obtains road condition basic data, specifically as follows:
identifying a speed limit sign on the right side of the road through a first camera, and obtaining a current road speed limit value;
acquiring road historical traffic accident data according to the database, counting the number of accumulated traffic accidents on the road in the current year by the road historical traffic accident data, and defining the number of accumulated traffic accidents on the road in the current year as road traffic accident data;
monitoring road traffic flow image data in real time through a second camera, and counting the number of vehicles in the view range of the camera by using an image recognition algorithm to obtain the view distance value of the road surface of the second camera;
calculating the number of vehicles and the road surface visual field distance of the second camera by using a traffic density calculation formula to obtain a traffic density reference value;
defining a current road speed limit value, road traffic accident data and a traffic flow density reference value as road condition basic data;
the crowd unit acquires crowd basic data, and the crowd basic data comprises the following specific steps:
monitoring road non-motor vehicle image data in real time through a third camera, and counting the number of non-motor vehicles in the visual field range of the camera by utilizing an image recognition algorithm to obtain a road visual field distance value of the third camera;
Calculating the number value of the non-motor vehicles and the road surface visual field distance value of the third camera through a non-motor vehicle density calculation formula to obtain a non-motor vehicle density reference value;
acquiring population residence density data of the region where the road is located through a database,
the road pedestrian image data is monitored in real time through the fourth camera, and the number value of pedestrians in the view field range of the camera is counted by utilizing an image recognition algorithm to obtain the road surface view field distance value of the fourth camera;
calculating the pedestrian number value and the third camera road surface visual field distance value through a pedestrian density calculation formula to obtain a pedestrian density reference value;
and defining the density reference value of the non-motor vehicle, the population residence density data of the area where the road is located and the pedestrian density reference value as the crowd basic data.
4. The safe driving early warning system for mixed flow of the harbor tank truck and the tank truck according to claim 1, wherein the data analysis module comprises a cargo analysis unit, a vehicle analysis unit, a driving analysis unit and a road condition analysis unit;
the cargo analysis unit acquires cargo risk reference values;
the vehicle analysis unit acquires a vehicle risk reference value;
the driving analysis unit acquires a driving risk reference value;
The road condition analysis unit acquires a road condition complexity reference value;
the data analysis module acquires a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value;
and defining the cargo risk reference value, the vehicle risk reference value, the driving risk reference value and the road condition complexity reference value as safe driving analysis data.
5. The safe driving early warning system for mixed flow of port and tank truck according to claim 4, wherein the cargo analysis unit acquires cargo risk reference values, specifically as follows:
acquiring the number of cargos, the quality value of a single cargo and a cargo risk reference coefficient according to the cargo basic data;
calculating the number of cargos, the mass value of the single cargos and the cargo risk reference coefficient through a cargo risk calculation formula to obtain a cargo risk reference value;
the vehicle analysis unit acquires a vehicle risk reference value, and specifically comprises the following steps:
acquiring the current speed, the front-rear distance, the left-right distance and the accumulated maintenance times of the current vehicle according to the basic data of the vehicle;
and calculating the current vehicle speed, the front-rear vehicle distance, the left-right vehicle distance and the current vehicle accumulated maintenance times through a vehicle risk calculation formula to obtain a vehicle risk reference value.
6. The safe driving early warning system for mixed flow of port and tank truck according to claim 4, wherein the driving analysis unit obtains a driving risk reference value, specifically as follows:
acquiring a current driving duration value, a driver age value and driver violation data according to the driver basic data;
calculating the current driving duration value, the driver age value and the driver violation data through a driver risk calculation formula to obtain a driving risk reference value;
the road condition analysis unit acquires the road condition complexity reference value, and specifically comprises the following steps:
acquiring a current road speed limit value, road traffic accident data and a traffic flow density reference value according to the road condition basic data;
and calculating the current road speed limit value, the road traffic accident data and the traffic density reference value through a road condition complexity reference value calculation formula to obtain a road condition complexity reference value.
7. The safe driving early warning system for mixed flow of port and tank truck according to claim 1, wherein the safe driving early warning grading data is obtained, and the safe driving early warning grading data is specifically as follows:
the data processing module comprises a data processing unit and an early warning grading unit;
The data processing unit processes the safe driving basic data to obtain a safe driving reference coefficient;
the early warning grading unit performs threshold division on the safe driving reference coefficient to obtain safe driving early warning grading data
The data processing module acquires the safe driving early warning grading data.
8. The safe driving early warning system for mixed flow of the harbor tank truck according to claim 7, wherein the data processing unit obtains the safe driving reference coefficient, specifically as follows:
acquiring a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value according to the safe driving analysis data;
calculating a cargo risk reference value, a vehicle risk reference value, a driving risk reference value and a road condition complexity reference value through a preliminary early warning reference coefficient calculation formula to obtain a preliminary early warning reference coefficient;
acquiring a cargo risk standard reference value, a vehicle risk standard reference value, a driver risk standard reference value and a road condition complexity standard reference value;
calculating a cargo risk standard reference value, a vehicle risk standard reference value, a driver risk standard reference value and a road condition complexity standard reference value through a safe driving preliminary coefficient threshold calculation formula to obtain a preliminary early warning reference coefficient threshold;
Acquiring a non-motor vehicle density reference value, population living density data of an area where a road is located and a pedestrian density reference value according to the safe driving basic data;
and obtaining a non-motor vehicle density reference value, population residence density data of the region where the road is located and a pedestrian density reference value from the preliminary early warning reference coefficient and the safe driving basic data, and calculating the safe driving early warning reference coefficient through a safe driving early warning reference coefficient calculation formula.
9. The safe driving early warning system for mixed flow of the harbor tank truck according to claim 7, wherein the early warning classification unit acquires safe driving early warning classification data, specifically as follows:
acquiring a preliminary early warning reference coefficient threshold, a non-motor vehicle density standard reference value, population residence density standard data and a pedestrian density standard reference value;
the preliminary early warning reference coefficient threshold value is obtained, and the safe driving early warning reference coefficient threshold value is obtained through calculation of a safe driving early warning reference threshold value calculation formula by non-motor vehicle density standard reference values, population residence density standard data and pedestrian density standard reference values;
the safe driving early warning reference coefficient is obtained, and the safe driving early warning reference coefficient is divided into sections by using a safe driving early warning reference coefficient threshold, and the method specifically comprises the following steps:
When Ay is more than or equal to Ay1, judging that the vehicle is driven and early-warned;
when Ay1 is more than Ay is more than 0, judging that the vehicle is normally driven;
and defining a judgment result obtained according to the safe driving early warning reference coefficient and the safe driving early warning reference coefficient threshold value as safe driving early warning grading data.
10. The safe driving early warning system of mixed flow of the port and tank truck according to claim 1, wherein the safe driving early warning module carries out safe driving early warning according to safe driving early warning grading data, and the safe driving early warning system is specifically as follows:
the safety early warning module comprises an in-vehicle early warning unit and an out-vehicle early warning unit;
the in-vehicle early warning unit carries out in-vehicle early warning according to the safe driving early warning grading data, and specifically comprises the following steps:
aiming at a normally driven vehicle, no in-vehicle early warning is carried out;
aiming at a driving early warning vehicle, a first early warning indicator lamp flashes, a loudspeaker issues alarm broadcast to early warn a driver, and the driver is warned through communication equipment;
the off-board early warning unit carries out off-board early warning according to the early warning grading data, and the off-board early warning unit specifically comprises the following steps:
aiming at a normally driven vehicle, no off-vehicle early warning is carried out;
aiming at the driving early-warning vehicle, the second early-warning indicator lights flash, and the LED display screen displays alarm information for vehicles outside the vehicle and pedestrians.
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