CN106515318A - Method for early warning abrasion failure of automobile tires based on big data on Internet of vehicles - Google Patents
Method for early warning abrasion failure of automobile tires based on big data on Internet of vehicles Download PDFInfo
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- CN106515318A CN106515318A CN201611043403.XA CN201611043403A CN106515318A CN 106515318 A CN106515318 A CN 106515318A CN 201611043403 A CN201611043403 A CN 201611043403A CN 106515318 A CN106515318 A CN 106515318A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60C—VEHICLE TYRES; TYRE INFLATION; TYRE CHANGING; CONNECTING VALVES TO INFLATABLE ELASTIC BODIES IN GENERAL; DEVICES OR ARRANGEMENTS RELATED TO TYRES
- B60C11/00—Tyre tread bands; Tread patterns; Anti-skid inserts
- B60C11/24—Wear-indicating arrangements
- B60C11/246—Tread wear monitoring systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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Abstract
The invention relates to a method for early warning the abrasion failure of automobile tires based on big data on the Internet of vehicles. The method comprises the following steps of step 1, acquiring data information of the automobile tires through a big data platform of the Internet of vehicles; step 2, monitoring the data information of the automobile tires by a tire monitoring system; step 3, according to the data information acquired by the tire monitoring system, performing forecasting operation on the abrasion life of the automobile tires through the big data platform of the Internet of vehicles; step 4, performing the abrasion accumulation operation on the automobile tires by utilizing the big data platform of the Internet of vehicles; and step 5, according to the travelling accumulation depletion life mileage situation of the automobile tires, through the big data platform of the Internet of vehicles, giving a driving suggestion or a tire exchanging suggestion to a driver.
Description
Technical field
The present invention relates to a kind of method of the auto tire wear fault pre-alarming based on car networking big data.
Background technology
The application of car networking is progressively deepened at present, with the big number such as front dress installation and cloud computing of the integrated equipment on automobile
According to the rise of means, the intellectuality of automobile, the requirement of hommization experience are appealed by increasing vehicle user.
In daily driving, the safety of automobile tire is most important, but tire is consumable goodss, uses certain mileage,
Tire is just scrapped.In general, tire has one instructs the life-span by what manufacturer provided, but, because what tire was used
The factors such as operating habit, environment, the actual life of tire often have larger gap with the life-span is instructed.It is effectively how
Tire fault early warning is the most important topic of tire wear research field.
There are some solutions to solve above-mentioned technical problem in prior art, but because of design defect in prior art
And do not reach the effect effectively for tire fault early warning.
For example, Chinese invention patent application number 201210064522.9 discloses a kind of automobile tire supervising device and side
Method.The device includes CPU, photographic head, infrared temperature sensor, pressure transducer, controlling switch and display
Screen, photographic head and infrared temperature sensor are arranged on the position of each tire of correspondence automobile on automobile chassis, pressure transducer
Be arranged in correspondence automobile each tire, CPU respectively with photographic head, infrared temperature sensor, pressure sensing
Device, controlling switch are connected with display screen, and the controlling switch and display screen are arranged on vehicle console.The present invention is by shooting
The abrasion condition on head monitoring automobile tire surface and whether there are slight crack and scuffing, automobile is monitored by infrared temperature sensor
The temperature of tire, by the pressure of pressure monitor sensor automobile tire, and relevant information is shown on a display screen, is made
Driver grasps the correlation circumstance of each tire, and judges on this basis, so as to ensure the driving safety of automobile.
For another example, the method that Chinese invention patent application number 200580035574.1 discloses a kind of one group of input power of research,
These input powers are used for analyzing tire wear, and the method comprises the steps：By driving vehicle in wear process and measuring
The data related to multiple power that the vehicle is experienced, so as to using wear process as feature.It is with least one tire
Target vehicle studies vehicle characterization model.The vehicle characterization model is used for calculating the force data that representative is such as exerted oneself：If first
In Vehicle structure, if the vehicle that the wear test course as feature drives as feature, it will by as feature vehicle
The power that experienced of at least one tire.Indoor wear is run by using computer prediction technology or on tire,
Then the force data is used for analyzing tire wear.
Based on as above problem, the present invention provides a kind of side of the auto tire wear fault pre-alarming based on car networking big data
Method, which is the method with big data, by the essential information of vehicle tyre, the driving behavior data of human pilot, travel
Together with situation is counted with ambient temperature situations, according to the abrasion principle and major influence factors of tire, through big data platform
Verification experimental verification after, the early warning system method of a set of science for drawing.Importantly, can be by the result situation of tire wear
Directly tradition passs user client terminal, such as SMS, and the form such as mobile portable phone voice broadcast, by the result of tire wear
User is intuitively sent to very.
The technical problem that the present invention can solve the problem that is, to the automobile worn tire for being close to service life, to carry out timely and effectively
Remind and change suggestion, solve general driver and have no ability to distinguish the degree of wear, judge the blind area of tire wear situation, prevent
Only excessively potential safety hazard is caused using the tire of heavy wear.
The content of the invention
The present invention is achieved through the following technical solutions：A kind of auto tire wear failure based on car networking big data
The method of early warning, comprises the steps：
Step 1, the data message for gathering automobile tire by car networking big data platform；
Step 2, carry out monitoring the data message of automobile tire by tyre monitoring system；
Step 3, the data message collected according to tyre monitoring system are by car networking big data platform to automobile tire
Wear-out life is predicted computing, and its operational formula is：F (XE)=∑ * F (XD), wherein, predictions of the F (XE) for tyre life
Mileage, F (XD) is tire plant projected life mileage, metewand of the ∑ for car networking big data platform, and the metewand is
The coefficient for drawing is estimated according to ten thousand kilometers of driving situations of running car 1-4；
Step 4, the accumulative computing of auto tire wear is carried out using car networking big data platform, its operational formula is：F(X)
=【Na1*Nb1*Nc1】F1(XB)+【Na2*Nb2*Nc2】F2(XB)+······+【Nan*Nbn*Ncn】Fn(XB)+
Nq*F (XD), wherein, Na represents brake emergency situation coefficient, and Nb represents each pavement behavior coefficient, and Nc represents environment during driving
Temperature coefficient, Nq are represented as travelling that regional rainwater acid-base value, tire accumulated static tire turn to number of times and tire is accumulative is hit
The combined influence factor coefficient of number of times, F (XB) represent the actual mileage of each road conditions, and F (XD) was represented in tire plant projected life
Journey, F (X) represent the accumulative consumption life mileage of driving, and the span of F (XD) is ten thousand kilometers of (3.5-14)；
Step 5, accumulative consumption life mileage situation of being driven a vehicle according to automobile tire are by car networking big data platform to driving
Member proposes drive advice or tire changing suggestion.
Further, in the step 3, the numerical range of F (XD) is ten thousand kilometers of 4-20, and which derives from car networking big data
Platform.
Further, in the step 3, the numerical range of metewand is 0.6-1.5, and which derives from car networking big data
Platform.
Further, in the step 4, the span of Na is 1.08-1.4；In the step 4, the span of Nb is
0.95-1.15；In the step 4, the span of Nc is 1.09-1.15；In the step 4, the span of Nq is 0.01-
0.09。
Further, in the step 5, as F (X)=N*0.5, wherein N is the natural number being not zero, and F (X)<[F
(XE) when -1], tyre monitoring system judges that tire used is in good condition, and tyre monitoring system will judge data is activation to car networking
Big data platform, as [F (XE) -0.3]≤F (X) and F (X)≤[F (XE) -0.2], tyre monitoring system judges that tire is close to
Service life, tyre monitoring system will judge data is activation to car networking big data platform, and car networking big data platform is pointed out more
Tire is changed, as F (X)=F (XE), tyre monitoring system judges that tire reaches service life, and tyre monitoring system is by resulting number
According to sending to car networking big data platform, car networking big data platform points out re-type immediately.
Further, after car networking big data platform judges that tire reaches service life, tire often travels 500 kilometers,
Car networking big data platform points out once re-type immediately.
Further, the tyre monitoring system includes CAN and the automobile TSP of vehicle self-carrying.
Further, in the step 1, data message includes but is not limited to tyre model, tire version number, Vehicle Identify Number, wheel
Tire trading company, brake deceleration degree, road conditions and ambient temperature data.
Further, in the step 2 data message include but be not limited to tire version number, Vehicle Identify Number, automobile equipment number,
Data packing the time, GPS longitudes, GPS latitudes, GPS directions, GPS velocity, traveling kilometrage, the tire pressure of automobile tire,
The rotating speed of steering wheel, steering wheel angle, speed, brake deceleration degree, transverse acceleration, normal acceleration, engine speed, automobile
Load and air themperature.
Compared with prior art, superior effect of the invention is：
1st, the method for the auto tire wear fault pre-alarming based on car networking big data of the present invention, by counting greatly
Carry out being monitored for the individual tire of driving according to the information for collecting, can effectively be tire wear fault pre-alarming.
2nd, the method for the auto tire wear fault pre-alarming based on car networking big data of the present invention, by arranging pin
Biometry computing to tire wear, the service life for recognizing tire that can be relatively accurate.
3rd, the method for the auto tire wear fault pre-alarming based on car networking big data of the present invention, by arranging pin
Computing is added up to the abrasion of automobile tire, the residual life for recognizing automobile that can be relatively accurate and service life.
4th, the method for the auto tire wear fault pre-alarming based on car networking big data of the present invention, by by number
Analyze according to the computer of being predicted property of storehouse, the principal element for finding tire wear problem that can be relatively accurate, it is possible to draw
Lead the service life of the prolongation tire of driver's science.
Description of the drawings
Fig. 1 is that the flow process of the method for the auto tire wear fault pre-alarming based on car networking big data of the present invention is shown
It is intended to.
Specific embodiment
Below the specific embodiment of the invention is described in further detail.
As shown in figure 1, a kind of method of the auto tire wear fault pre-alarming based on car networking big data, including following step
Suddenly：
Step 1, the data message for gathering automobile tire by car networking big data platform；
Step 2, carry out monitoring the data message of automobile tire by tyre monitoring system；
Step 3, the data message collected according to tyre monitoring system are by car networking big data platform to automobile tire
Wear-out life is predicted computing, and its operational formula is：F (XE)=∑ * F (XD), wherein, predictions of the F (XE) for tyre life
Mileage, F (XD) is tire plant projected life mileage, metewand of the ∑ for car networking big data platform, and the metewand is
The coefficient for drawing is estimated according to ten thousand kilometers of driving situations of running car 1-4；
Step 4, the accumulative computing of auto tire wear is carried out using car networking big data platform, its operational formula is：F(X)
=【Na1*Nb1*Nc1】F1(XB)+【Na2*Nb2*Nc2】F2(XB)+······+【Nan*Nbn*Ncn】Fn(XB)+
Nq*F (XD), wherein, Na represents brake emergency situation coefficient, and Nb represents each pavement behavior coefficient, and Nc represents environment during driving
Temperature coefficient, Nq are represented as travelling that regional rainwater acid-base value, tire accumulated static tire turn to number of times and tire is accumulative is hit
The combined influence factor coefficient of number of times, F (XB) represent the actual mileage of each road conditions, and F (XD) was represented in tire plant projected life
Journey, F (X) represent the accumulative consumption life mileage of driving, and the span of F (XD) is ten thousand kilometers of (3.5-14)；
Step 5, accumulative consumption life mileage situation of being driven a vehicle according to automobile tire are by car networking big data platform to driving
Member proposes drive advice or tire changing suggestion.
The car networking big data platform is for more than 300,000 same level vehicles, in order to complete that tire wear is affected
The data of relevant parameter item are transmitted by the research of parameter in real time, are collected, and are arranged, computing and accumulative, so as to the number for being formed
According to empirical features.
The tyre monitoring system sends the data message for collecting to car networking big data platform.
The abrasion condition of automobile tire, is affected by several factors.Such as manufacturer, the specification of tire, tire
Speed class, load-carrying index, the pavement behavior of traveling, brake custom of the ambient temperature of traveling and human pilot etc., for example, together
The same configuration (assuming that the Life of Tyre that manufacturer designs is 70,000 kilometers) of a vehicle, first drives the car, only may open
50000 kilometers of tires just wear and tear totally；And second is driven the car and may open 8.5 ten thousand kilometers of tires and just worn and torn, and needs more to renew tire,
With the same configuration of a vehicle, different people drives the individuality for having 3.5 ten thousand kilometers and uses difference, according to above-mentioned impact
Factor, by car networking big data, carries out budget to the use tyre life situation of human pilot, and car networking big data shows,
The tire when abrasion condition of tire, the situation of bringing to a halt with human pilot, the pavement behavior travelled by vehicle, and vehicle are travelled
Residing ambient temperature has strong correlation, the coefficient of partial correlation for drawing for car networking big data platform statistics below, as follows
Table：
Table 1
Further, in the step 3, the numerical range of F (XD) is ten thousand kilometers of 4-20, and which derives from car networking big data
Platform.
Further, in the step 3, the numerical range of metewand is 0.6-1.5, and which derives from car networking big data
Platform.
Further, in the step 4, the span of Na is 1.08-1.4；In the step 4, the span of Nb is
0.95-1.15；In the step 4, the span of Nc is 1.09-1.15；In the step 4, the span of Nq is 0.01-
0.09。
Further, in the step 5, as F (X)=N*0.5, wherein N is the natural number being not zero, and F (X)<[F
(XE) when -1], tyre monitoring system judges that tire used is in good condition, and tyre monitoring system will judge data is activation to car networking
Big data platform, as [F (XE) -0.3]≤F (X) and F (X)≤[F (XE) -0.2], tyre monitoring system judges that tire is close to
Service life, tyre monitoring system will judge data is activation to car networking big data platform, and car networking big data platform is pointed out more
Tire is changed, as F (X)=F (XE), tyre monitoring system judges that tire reaches service life, and tyre monitoring system is by resulting number
According to sending to car networking big data platform, car networking big data platform points out re-type immediately.
Further, after car networking big data platform judges that tire reaches service life, tire often travels 500 kilometers,
Car networking big data platform points out once re-type immediately.
Further, the tyre monitoring system includes the CAN of vehicle self-carrying, automobile TSP and onboard sensor.
The tire accumulated static tire turns to number of times：When speed is 0, steering wheel angle is once static more than 120 ° of notes
Tire is turned to.
Further, in the step 1, data message includes but is not limited to tyre model, tire version number, Vehicle Identify Number, wheel
Tire trading company, brake deceleration degree, road conditions and ambient temperature data.
The data message of the step 1 is provided by manufacturer.
Further, in the step 2 data message include but be not limited to tire version number, Vehicle Identify Number, automobile equipment number,
Data packing the time, GPS longitudes, GPS latitudes, GPS directions, GPS velocity, traveling kilometrage, the tire pressure of automobile tire,
The rotating speed of steering wheel, steering wheel angle, speed, brake deceleration degree, transverse acceleration, normal acceleration, engine speed, automobile
Load and air themperature.
The data packing time is：At interval of the regular hour, CAN is by required for car networking big data platform
The data of data acquisition, transmit and give car networking big data platform, and interlude is referred to as the data packing time.
The data message of the step 2 is provided by manufacturer and the CAN on car, automobile TSP and onboard sensor.
The present invention is not limited to above-mentioned embodiment, in the case of the flesh and blood without departing substantially from the present invention, this area skill
Any deformation that art personnel are contemplated that, improvement, replacement each fall within protection scope of the present invention.
Claims (9)
1. a kind of method of the auto tire wear fault pre-alarming based on car networking big data, it is characterised in that including following step
Suddenly：
Step 1, the data message for gathering automobile tire by car networking big data platform；
Step 2, carry out monitoring the data message of automobile tire by tyre monitoring system；
Step 3, the data message collected according to tyre monitoring system are by car networking big data platform to auto tire wear
Life-span is predicted computing, and its operational formula is：F (XE)=∑ * F (XD), wherein, prediction mileages of the F (XE) for tyre life,
F (XD) is tire plant projected life mileage, and ∑ is the metewand of car networking big data platform, and the metewand is according to vapour
Car traveling ten thousand kilometers of driving situations of 1-4 are estimated the coefficient for drawing；
Step 4, the accumulative computing of auto tire wear is carried out using car networking big data platform, its operational formula is：F (X)=
【Na1*Nb1*Nc1】F1(XB)+【Na2*Nb2*Nc2】F2(XB)+······+【Nan*Nbn*Ncn】Fn(XB)+Nq*F
(XD), wherein, Na represents brake emergency situation coefficient, and Nb represents each pavement behavior coefficient, and Nc represents ambient temperature during driving
Coefficient, Nq are represented as travelling regional rainwater acid-base value, tire accumulated static tire steering number of times and the accumulative number of times that is hit of tire
Combined influence factor coefficient, F (XB) represents the actual mileage of each road conditions, and F (XD) represents tire plant projected life mileage, F
(X) represent the accumulative consumption life mileage of driving, the span of F (XD) is ten thousand kilometers of (3.5-14)；
Step 5, accumulative consumption life mileage situation of being driven a vehicle according to automobile tire are carried to driver by car networking big data platform
Go out drive advice or tire changing suggestion.
2. the method for the auto tire wear fault pre-alarming based on car networking big data according to claim 1, its feature
It is that in the step 3, the numerical range of F (XD) is ten thousand kilometers of 4-20, and which derives from car networking big data platform.
3. the method for the auto tire wear fault pre-alarming based on car networking big data according to claim 1, its feature
It is that in the step 3, the numerical range of metewand is 0.6-1.5, and which derives from car networking big data platform.
4. the method for the auto tire wear fault pre-alarming based on car networking big data according to claim 1, its feature
It is that the span of Na is 1.08-1.4 in the step 4；In the step 4, the span of Nb is 0.95-1.15；Institute
The span for stating Nc in step 4 is 1.09-1.15；In the step 4, the span of Nq is 0.01-0.09.
5. the method for the auto tire wear fault pre-alarming based on car networking big data according to claim 1, its feature
It is that, in the step 5, as F (X)=N*0.5, wherein N is the natural number being not zero, and F (X)<When [F (XE) -1], tire
Monitoring system judges that tire used is in good condition, and tyre monitoring system will judge data is activation to car networking big data platform, when
When [F (XE) -0.3]≤F (X) and F (X)≤[F (XE) -0.2], tyre monitoring system judges that tire is close to service life, tire
Monitoring system will judge data is activation to car networking big data platform, and the prompting re-type of car networking big data platform, as F (X)
During=F (XE), tyre monitoring system judges that tire reaches service life, and tyre monitoring system will judge data is activation to car networking
Big data platform, the prompting re-type immediately of car networking big data platform.
6. the method for the auto tire wear fault pre-alarming based on car networking big data according to claim 5, its feature
It is that, after car networking big data platform judges that tire reaches service life, tire often travels 500 kilometers, car networking big data
Platform points out once re-type immediately.
7. the method for the auto tire wear fault pre-alarming based on car networking big data according to claim 1, its feature
It is that the tyre monitoring system includes the CAN of vehicle self-carrying and automobile TSP.
8. the method for the auto tire wear fault pre-alarming based on car networking big data according to claim 1, its feature
It is that data message includes but be not limited to tyre model, tire version number, Vehicle Identify Number, tire trading company, brake in the step 1
Deceleration, road conditions and ambient temperature data.
9. the method for the auto tire wear fault pre-alarming based on car networking big data according to claim 1, its feature
It is, when in the step 2, data message includes but be not limited to tire version number, Vehicle Identify Number, automobile equipment number, data packing
Between, GPS longitudes, GPS latitudes, GPS directions, GPS velocity, traveling kilometrage, the tire pressure of automobile tire, steering wheel turn
Speed, steering wheel angle, speed, brake deceleration degree, transverse acceleration, normal acceleration, engine speed, car load and air
Temperature.
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