[0036]The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.
[0037]Seefigure 1As shown, a vehicle driving safety intelligent monitoring and early warning management system based on big data includes a vehicle driving parameter acquisition module, a vehicle tire pressure monitoring module, a database, a vehicle parameter database, a vehicle real-time load detection module, a modeling analysis module, and a remote A server, an early warning module and a display terminal. The vehicle driving parameter acquisition module is respectively connected to the vehicle parameter database, the modeling analysis module and the remote server, and the modeling analysis module is respectively connected to the vehicle tire pressure monitoring module, the database, and the vehicle real-time load detection module , The remote server is connected with the display terminal, and the remote server is connected with the warning module and the display terminal respectively.
[0038]The vehicle driving parameter acquisition module is used to acquire the driving parameters of the vehicle according to the logo pattern and license plate number of the vehicle to be monitored, and send the acquired driving parameters of the vehicle to be monitored to the modeling analysis module and the remote server respectively, wherein The driving parameters of the vehicle include the road time, the approved load weight and the standard tire pressure of the tire. The method for obtaining the driving parameters of the vehicle includes the following steps:
[0039]Step S1: Obtain the logo pattern of the vehicle to be monitored, compare and match the acquired logo pattern of the vehicle to be monitored with the vehicle model corresponding to each logo pattern in the car parameter database, and screen the vehicle model corresponding to the logo pattern;
[0040]Step S2: Compare and match the selected vehicle model corresponding to the vehicle to be monitored with the approved load capacity and the standard tire pressure in the characteristic parameters corresponding to the various vehicle models in the vehicle parameter database, and screen the approved load capacity and tire pressure corresponding to the vehicle model Tire standard tire pressure;
[0041]Step S3: Connect to the vehicle registration information database of the vehicle management office, obtain the license plate number of the vehicle to be monitored, and match the license plate number of the vehicle to be monitored with the registration time corresponding to each license plate number registered in the vehicle registration information database, and output the The time when the vehicle corresponds to the sign is the time on the road.
[0042]The automobile parameter database is used to store the vehicle model corresponding to each vehicle logo pattern and the characteristic parameters corresponding to each vehicle model, wherein the characteristic parameters include the approved load capacity and the standard tire pressure of the tire.
[0043]This embodiment obtains the on-road time, the approved load capacity and the standard tire pressure of the vehicle according to the logo pattern and license plate number of the vehicle to be monitored, and provides a comparison reference of standard parameters for subsequent statistics on the comprehensive risk coefficient of tires and analysis of the risk level of vehicle overload data.
[0044]The vehicle tire pressure monitoring module is used to monitor the tire pressure of each tire of the vehicle to be monitored, obtain the tire pressure of each tire of the vehicle, and send it to the modeling analysis module. The specific steps of the tire pressure monitoring are as follows:
[0045]Step 1: Count the number of tires on the left and right sides of the vehicle to be monitored;
[0046]Step 2: Number the tires on the left side of the vehicle to be monitored according to the preset sequence, and mark them as 1, 2...i...n, and then the tires on the left side of the vehicle to be monitored are numbered according to the same Mark in the order of numbering, respectively marked as 1', 2'...i'...n';
[0047]Step 3: Use tire pressure sensors to monitor the marked tires to obtain the tire pressure of each tire of the vehicle. The tire pressure values of each tire on the left side of the vehicle constitute the left tire pressure set P(p1,p2,. ..,pi,...,pn), where pi is the tire pressure of the i-th tire on the left side of the vehicle, and the obtained tire pressure values of each tire on the right side of the vehicle constitute the right side tire pressure set P′(p′1, p'2,...,p'i,...,p'n), p'i is the tire pressure of the i-th tire on the right side of the vehicle.
[0048]In this embodiment, the number of tires of the vehicle to be monitored is counted and numbered to provide convenience for subsequent tire pressure monitoring of each tire.
[0049]The vehicle real-time load detection module includes a weighing sensor for detecting the real-time load of the vehicle to be monitored, and sends the obtained real-time load of the vehicle to be monitored to the modeling analysis module.
[0050]The modeling analysis module receives the tire pressure of each tire of the vehicle sent by the vehicle tire pressure monitoring module, receives the travel parameters of the vehicle sent by the vehicle travel parameter acquisition module, and extracts the tire standard tire pressure from the received vehicle travel parameters. The tire pressure of each tire of the received vehicle is compared with the standard tire pressure of the tire to obtain the comparison value of each tire pressure. The comparison value of the tire pressure of each tire on the left side of the vehicle constitutes the left side tire pressure comparison set ΔP(Δp1, Δp2,...,Δpi,...,Δpn), the obtained tire pressure comparison values of each tire on the right side of the vehicle constitute the right tire pressure comparison set ΔP′(Δp′1,Δp′2,...,Δp ′I,...,Δp′n), the modeling analysis module calculates the comprehensive risk coefficient of tires according to the comparison set of left side tire pressure and the right side tire pressure comparison set of the vehicle Where Δpi is the difference between the tire pressure of the i-th tire on the left side of the vehicle and the standard tire pressure of the tire, and Δp′i is the difference between the tire pressure of the i-th tire on the right side of the vehicle and the standard tire pressure of the tire Value, the larger the comprehensive risk coefficient of tires, the higher the risk of vehicle driving. The modeling analysis module sends the statistical comprehensive risk coefficients of tires to the remote server.
[0051]The comprehensive risk coefficient of tires calculated in this embodiment provides the correlation coefficient of the comprehensive risk coefficient of tires for later calculation of the comprehensive risk coefficient of vehicle driving.
[0052]At the same time, the modeling analysis module can also analyze and count dangerous tires. The specific analysis process includes the following steps:
[0053]Step H1: Compare the obtained tire pressure of each tire of the vehicle with the standard tire pressure of the tire. If the tire pressure of a certain tire is greater than the standard tire pressure of the tire, it indicates that the tire has a risk of overpressure, and statistics have overpressure. If the tire pressure of a tire is less than the standard tire pressure, it indicates that the tire has a risk of underpressure. Count the number of the tire with the risk of underpressure, and perform step H3 ;
[0054]Step H2: Subtract the standard tire pressure from the tire pressure of each tire that is in danger of tire pressure overpressure to obtain the tire pressure overpressure value of each tire, and perform the calculation with the tire pressure overpressure value corresponding to each preset overpressure risk level By comparison, filter the overpressure risk level of each tire with the risk of tire pressure overpressure, and send the number of each tire with the risk of tire pressure overpressure and the corresponding overpressure risk level to the display terminal;
[0055]Step H3: Subtract the standard tire pressure from the tire pressure of each tire that is at risk of underpressure to obtain the underpressure value of each tire, and perform the calculation with the underpressure value corresponding to each preset risk level of underpressure In contrast, the underpressure risk level of each tire with the risk of underpressure is screened, and the number of each tire with the risk of underpressure and the corresponding underpressure risk level are sent to the display terminal.
[0056]The tire pressure hazard levels corresponding to the dangerous tires calculated in this embodiment can predict the dangerous situation of the tires in advance, and provide sufficient time for the subsequent measures to take protection.
[0057]The modeling analysis module also receives the vehicle's real-time load capacity sent by the vehicle's real-time load detection module, and extracts the vehicle's approved load from the received vehicle driving parameters, and compares the received vehicle's real-time load with the vehicle's approved load. If the real-time load of the vehicle is greater than the approved load of the vehicle, it indicates that the vehicle is overloaded. At this time, the real-time load of the vehicle is subtracted from the approved load of the vehicle to obtain the overloaded weight, and the obtained overloaded weight is corresponding to each overload level stored in the database Compare the overload weight range, filter the overload level corresponding to the overload weight, and send it to the remote server.
[0058]The database is used to store the overload weight range corresponding to each overload level and the overload risk coefficient corresponding to each overload level D=1, 2, 3, store the preset comprehensive risk coefficient of the vehicle driving standard, and store the corresponding overvoltage risk level Tire pressure overpressure value, store the tire pressure underpressure value corresponding to each underpressure danger level, and store the weight coefficient corresponding to the tire comprehensive risk factor and the vehicle overload risk factor.
[0059]The remote server receives the overload level corresponding to the overload weight of the vehicle sent by the modeling analysis module, extracts the overload risk coefficient corresponding to each overload level of the vehicle in the database, and compares the received overload level of the vehicle with the overload risk corresponding to each overload level of the vehicle The coefficient is compared, and the overload risk coefficient corresponding to the overload level of the vehicle is selected;
[0060]The overload risk coefficient corresponding to the vehicle overload level obtained in this embodiment provides a correlation coefficient of the vehicle overload risk coefficient for later calculation of the comprehensive risk coefficient of vehicle driving.
[0061]At the same time, the remote server also receives the vehicle driving parameters sent by the vehicle driving parameter acquisition module, and extracts the vehicle on the road time from the vehicle driving parameters, and counts the vehicle on the road time. The statistical method of the vehicle on the road time is to first obtain the vehicle driving at this time Time, and then subtract the vehicle’s on-road time from the obtained vehicle travel time at this time to get the vehicle’s on-road time. The remote server also receives the comprehensive tire risk factor sent by the modeling analysis module, and based on the comprehensive tire risk factor, vehicle overload risk factor and vehicle Length of time on the road, statistics of comprehensive risk factors of vehicle driving In the formula, T is the length of time on the road, ξ is the comprehensive risk factor of tires, RDExpressed as the overload risk coefficient corresponding to the D-th overload level of the vehicle, D=1, 2, 3, A represents the weight coefficient corresponding to the comprehensive risk coefficient of tires, B represents the weight coefficient corresponding to the vehicle overload risk coefficient, statistical vehicle driving The comprehensive risk coefficient realizes the quantitative display of the comprehensive risk of vehicle driving. The larger the comprehensive risk coefficient of vehicle driving, the higher the comprehensive risk degree of vehicle driving. The remote server integrates the statistical comprehensive risk coefficient of vehicle driving with preset vehicle driving standards. The risk coefficient is compared, and if it is greater than the preset comprehensive risk coefficient of the vehicle driving standard, an early warning instruction is sent to the early warning module, and the statistical comprehensive risk coefficient of vehicle driving is sent to the display terminal.
[0062]The early warning module receives the early warning instructions sent by the remote server, performs early warning, reminds the driver to pay attention, improves the driver’s danger vigilance, and enables the driver to be aware of driving dangers in time, maximizing the protection of the driver’s personal safety .
[0063]The display terminal is installed in the driver's driving box, and it respectively receives the comprehensive risk factor of vehicle driving sent by the remote server and the tire number and corresponding overpressure risk level and the corresponding overpressure risk level sent by the modeling analysis module. The number of each tire with the risk of under-pressure and the corresponding under-pressure danger level are displayed, so that the driver can intuitively understand the overall dangerous situation of the vehicle and the dangerous situation of the tire, and then take targeted measures according to the dangerous situation of each tire , In order to reduce the occurrence of dangerous accidents and ensure driving safety.
[0064]The invention detects the tire pressure and real-time load of each tire of the vehicle to be monitored, and combines the detection results to count the tire comprehensive risk coefficient and the vehicle overload risk coefficient, thereby obtaining the vehicle driving comprehensive risk coefficient, and at the same time, the situation exceeds the preset value. Early warning of the vehicle under the vehicle can effectively and intelligently monitor the factors that cause the driver to drive passively and dangerously during the driving process. It has the characteristics of high intelligence and strong operational practicability. The comprehensive risk coefficient of vehicle driving can predict the vehicle ahead The dangerous situation in which the driver is in a passive dangerous driving situation is avoided, and the personal safety of the driver is maximized.
[0065] The above content is merely an example and description of the structure of the present invention. Those skilled in the art make various modifications or additions to the specific embodiments described or substitute similar methods, as long as they do not deviate from the structure of the invention or Anything beyond the scope defined by the claims shall fall within the protection scope of the present invention.
