CN106515318B - A method of the auto tire wear fault pre-alarming based on car networking big data - Google Patents
A method of the auto tire wear fault pre-alarming based on car networking big data Download PDFInfo
- Publication number
- CN106515318B CN106515318B CN201611043403.XA CN201611043403A CN106515318B CN 106515318 B CN106515318 B CN 106515318B CN 201611043403 A CN201611043403 A CN 201611043403A CN 106515318 B CN106515318 B CN 106515318B
- Authority
- CN
- China
- Prior art keywords
- tire
- big data
- car networking
- networking big
- data platform
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Geometry (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Automation & Control Theory (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Tires In General (AREA)
Abstract
A method of the auto tire wear fault pre-alarming based on car networking big data includes the following steps：Step 1, the data information that automobile tire is acquired by car networking big data platform；Step 2 carries out the data information monitoring to automobile tire by tyre monitoring system；Step 3 carries out prediction operation by car networking big data platform according to the collected data information of tyre monitoring system to the auto tire wear service life；Step 4 carries out the accumulative operation of auto tire wear using car networking big data platform；Step 5, being driven a vehicle according to automobile tire adds up consumption life mileage situation by car networking big data platform to driver's proposition drive advice or tire changing suggestion.
Description
Technical field
The present invention relates to a kind of methods of the auto tire wear fault pre-alarming based on car networking big data.
Background technology
The application of car networking is gradually deepened at present, with big numbers such as installation and cloud computing of the preceding dress integrated equipment on automobile
According to the rise of means, the requirement of intelligence, the hommization experience of automobile is appealed by more and more vehicle users.
In daily driving, the safety of automobile tire is most important, however tire is consumables, uses certain mileage,
Tire is just scrapped.In general, the service life is instructed by what manufacturer provided there are one tire meetings, still, because what tire was used
Often there is larger gap in the actual life of operating habit, the factors such as environment, tire with the service life is instructed.How be effectively
Tire fault early warning is the most important topic of tire wear research field.
Some solutions exist in the prior art to solve above-mentioned technical problem, but in the prior art because of design defect
And the effect effectively for tire fault early warning is not achieved.
For example, Chinese invention patent application number 201210064522.9 discloses a kind of automobile tire monitoring device and side
Method.The device includes central processing unit, camera, infrared temperature sensor, pressure sensor, control switch and display
Screen, the position that each tire of automobile is corresponded on automobile chassis, pressure sensor is arranged in camera and infrared temperature sensor
Setting in the corresponding each tire of automobile, central processing unit respectively with camera, infrared temperature sensor, pressure sensing
Device, control switch are connected with display screen, and the control switch and display screen are arranged on vehicle console.The present invention passes through camera 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 shows by the pressure of pressure monitor sensor automobile tire, and by relevant information, makes on a display screen
Driver grasps the correlation circumstance of each tire, and judges on this basis, 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 for analyzing tire wear, and this method comprises the following steps：By driving vehicle in wear process and measuring
The relevant data of multiple power undergone with the vehicle, to using wear process as feature.For at least one tire
Target vehicle studies vehicle characterization model.The vehicle characterization model is used for calculating the force data for representing and such as exerting oneself：If first
It is driven as the vehicle of feature in Vehicle structure, in the wear test course as feature, it will by as feature vehicle
The power that is undergone of at least one tire.Indoor wear is run by using computer prediction technology or on tire,
Then by the force data for analyzing tire wear.
Based on problem as above, the present invention provides a kind of side of the auto tire wear fault pre-alarming based on car networking big data
Method is the method with big data, by the essential information of vehicle tyre, the driving behavior data of driver, travel
Together with situation is counted with ambient temperature situations, according to the abrasion principle and major influence factors of tire, by big data platform
Verification experimental verification after, the early warning system method of a set of science obtained.Importantly, can be by the result situation of tire wear
Direct tradition passs user's client terminal, such as SMS, the forms such as mobile portable phone voice broadcast, by the result of tire wear
Intuitively it is sent to very much user.
The technical issues of present invention can solve is carried out timely and effectively to the automobile worn tire close to service life
It reminds and replaces 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 security risk is caused using the tire being seriously worn.
Invention content
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, includes the following steps：
Step 1, the data information that automobile tire is acquired by car networking big data platform；
Step 2 carries out the data information monitoring to automobile tire by tyre monitoring system；
Step 3, according to the collected data information of tyre monitoring system by car networking big data platform to automobile tire
Wear-out life carries out prediction operation, and operational formula is：F (XE)=∑ * F (XD), wherein F (XE) is the prediction of tyre life
Mileage, F (XD) are tire plant projected life mileage, and ∑ is the metewand of car networking big data platform, and the metewand is
The coefficient that assessment obtains is carried out according to ten thousand kilometers of driving situations of running car 1-4；
Step 4 carries out the accumulative operation of auto tire wear using car networking big data platform, and 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 when driving
Temperature coefficient, Nq, which is represented as travelling regional rainwater acid-base value, tire accumulated static tire turns to number and tire is accumulative is hit
The combined influence factor coefficient of number, F (XB) represent the practical mileage of each road conditions, and F (XD) was represented in tire plant projected life
Journey, F (X) represent driving and add up consumption life mileage, and the value range of the F (XD) is ten thousand kilometers of (3.5-14)；
Step 5, being driven a vehicle according to automobile tire adds up consumption life mileage situation by car networking big data platform to driving
Member proposes drive advice or tire changing suggestion.
Further, the numberical range of F (XD) is ten thousand kilometers of 4-20 in the step 3, derives from car networking big data
Platform.
Further, the numberical range of metewand is 0.6-1.5 in the step 3, derives from car networking big data
Platform.
Further, the value range of Na is 1.08-1.4 in the step 4；The value range of Nb is in the step 4
0.95-1.15；The value range of Nc is 1.09-1.15 in the step 4；The value range of Nq is 0.01- in the step 4
0.09。
Further, in the step 5, when F (X)=N*0.5, wherein N are 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 that data are sent 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
Service life, tyre monitoring system will judge that data are sent to car networking big data platform, and car networking big data platform prompts 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 car networking big data platform is sent to, car networking big data platform prompts re-type immediately.
Further, after car networking big data platform judgement tire reaches service life, tire often travels 500 kilometers,
The primary re-type immediately of car networking big data platform prompt.
Further, the tyre monitoring system includes the CAN bus and automobile TSP of vehicle self-carrying.
Further, in the step 1 data information include tyre model, tire version number, Vehicle Identify Number, tire trading company,
Brake deceleration degree, road conditions and ambient temperature data.
Further, data information includes tire version number, Vehicle Identify Number, automobile equipment number, data packing in the step 2
Time, GPS longitudes, GPS latitudes, the directions GPS, GPS velocity, traveling kilometrage, the tire pressure of automobile tire, steering wheel
Rotating speed, steering wheel angle, speed, brake deceleration degree, transverse acceleration, normal acceleration, engine speed, car load and sky
Temperature degree.
Compared with prior art, superior effect of the invention is：
1, the method for the auto tire wear fault pre-alarming of the present invention based on car networking big data, by that will count greatly
According to collected information be monitored for the tire of driving individual, can effectively be tire wear fault pre-alarming.
2, the method for the auto tire wear fault pre-alarming of the present invention based on car networking big data, by the way that needle is arranged
Life prediction operation to tire wear, the service life for recognizing tire that can be relatively accurate.
3, the method for the auto tire wear fault pre-alarming of the present invention based on car networking big data, by the way that needle is arranged
Operation is added up to the abrasion of automobile tire, the remaining life for recognizing automobile that can be relatively accurate and service life.
4, the method for the auto tire wear fault pre-alarming of the present invention based on car networking big data, by by number
Predictive computer analysis is carried out according to library, the principal element for finding tire wear problem that can be relatively accurate, and can draw
Lead the service life of the extension tire of driver's science.
Description of the drawings
Fig. 1 is that the flow of the method for the auto tire wear fault pre-alarming of the present invention based on car networking big data is shown
It is intended to.
Specific implementation mode
The specific embodiment of the invention is described in further detail below.
As shown in Figure 1, a kind of method of the auto tire wear fault pre-alarming based on car networking big data, including walk as follows
Suddenly：
Step 1, the data information that automobile tire is acquired by car networking big data platform；
Step 2 carries out the data information monitoring to automobile tire by tyre monitoring system；
Step 3, according to the collected data information of tyre monitoring system by car networking big data platform to automobile tire
Wear-out life carries out prediction operation, and operational formula is：F (XE)=∑ * F (XD), wherein F (XE) is the prediction of tyre life
Mileage, F (XD) are tire plant projected life mileage, and ∑ is the metewand of car networking big data platform, and the metewand is
The coefficient that assessment obtains is carried out according to ten thousand kilometers of driving situations of running car 1-4；
Step 4 carries out the accumulative operation of auto tire wear using car networking big data platform, and 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 when driving
Temperature coefficient, Nq, which is represented as travelling regional rainwater acid-base value, tire accumulated static tire turns to number and tire is accumulative is hit
The combined influence factor coefficient of number, F (XB) represent the practical mileage of each road conditions, and F (XD) was represented in tire plant projected life
Journey, F (X) represent driving and add up consumption life mileage, and the value range of the F (XD) is ten thousand kilometers of (3.5-14)；
Step 5, being driven a vehicle according to automobile tire adds up consumption life mileage situation by car networking big data platform to driving
Member proposes drive advice or tire changing suggestion.
The car networking big data platform is to be directed to 300,000 or more same level vehicles, in order to complete to influence on tire wear
The data of relevant parameter item are transmitted in the research of parameter in real time, are collected, and are arranged, operation and accumulative, to the number formed
According to empirical features.
Collected data information is sent to car networking big data platform by the tyre monitoring system.
The abrasion condition of automobile tire, is influenced by several factors.Such as manufacturer, the specification of tire, tire
Speed class, load-carrying index, the pavement behavior of traveling, the environment temperature of traveling and the brake custom of driver etc., for example, together
The same configuration (assuming that the Life of Tyre of manufacturer's design is 70,000 kilometers) of a vehicle, first opens the vehicle, may only open
50000 kilometers of tires are just worn totally；And second opens the vehicle and may open 8.5 ten thousand kilometers of tires and just worn, and needs replacing new tire,
With the same configuration of a vehicle, there are 3.5 ten thousand kilometers of individuals to use difference for different people driving, according to above-mentioned influence
Factor carries out budget, car networking big data shows to driver by car networking big data using tyre life situation
The abrasion condition of tire, the situation of bringing to a halt with driver, the pavement behavior and vehicle that vehicle is travelled tire when driving
Residing environment temperature has strong correlation, counts the coefficient of part correlation obtained below for car networking big data platform, as follows
Table：
Table 1
Further, the numberical range of F (XD) is ten thousand kilometers of 4-20 in the step 3, derives from car networking big data
Platform.
Further, the numberical range of metewand is 0.6-1.5 in the step 3, derives from car networking big data
Platform.
Further, the value range of Na is 1.08-1.4 in the step 4；The value range of Nb is in the step 4
0.95-1.15；The value range of Nc is 1.09-1.15 in the step 4；The value range of Nq is 0.01- in the step 4
0.09。
Further, in the step 5, when F (X)=N*0.5, wherein N are 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 that data are sent 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
Service life, tyre monitoring system will judge that data are sent to car networking big data platform, and car networking big data platform prompts 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 car networking big data platform is sent to, car networking big data platform prompts re-type immediately.
Further, after car networking big data platform judgement tire reaches service life, tire often travels 500 kilometers,
The primary re-type immediately of car networking big data platform prompt.
Further, the tyre monitoring system includes the CAN bus of vehicle self-carrying, automobile TSP and onboard sensor.
The tire accumulated static tire turns to number：When speed is 0, it is primary static that steering wheel angle is more than 120 ° of notes
Tire turns to.
Further, data information includes but is not limited to tyre model, tire version number, Vehicle Identify Number, wheel in the step 1
Tire trading company, brake deceleration degree, road conditions and ambient temperature data.
The data information of the step 1 is provided by manufacturer.
Further, in the step 2 data information include but be not limited to tire version number, Vehicle Identify Number, automobile equipment number,
Data be packaged the time, GPS longitudes, GPS latitudes, the directions GPS, GPS velocity, traveling kilometrage, automobile tire tire pressure,
Rotating speed, steering wheel angle, speed, brake deceleration degree, transverse acceleration, normal acceleration, engine speed, the automobile of steering wheel
Load and air themperature.
The data are packaged the time and are：At interval of the regular hour, CAN bus is required by car networking big data platform
The data of data acquisition, transmit and give car networking big data platform, and interlude is referred to as data and is packaged the time.
The data information of the step 2 is provided by the manufacturer and CAN bus on vehicle, automobile TSP and onboard sensor.
Present invention is not limited to the embodiments described above, without departing from the essence of the present invention, this field skill
Any deformation, improvement, the replacement that art personnel are contemplated that 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, which is characterized in that including walking as follows
Suddenly：
Step 1, the data information that automobile tire is acquired by car networking big data platform；
Step 2 carries out the data information monitoring to automobile tire by tyre monitoring system；
Step 3, according to the collected data information of tyre monitoring system by car networking big data platform to auto tire wear
Service life carries out prediction operation, and operational formula is：F (XE)=∑ * F (XD), wherein F (XE) is the prediction mileage of 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
Ten thousand kilometers of driving situations of vehicle traveling 1-4 carry out the coefficient that assessment obtains；
Step 4 carries out the accumulative operation of auto tire wear using car networking big data platform, and 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 temperature when driving
Coefficient, Nq are represented as travelling regional rainwater acid-base value, tire accumulated static tire steering number and the accumulative number that is hit of tire
Combined influence factor coefficient, F (XB) represents the practical mileage of each road conditions, and F (XD) represents tire plant projected life mileage, F
(X) it represents driving and adds up consumption life mileage, the value range of the 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 carry 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 according to claim 1 based on car networking big data, feature
It is, the numberical range of F (XD) is ten thousand kilometers of 4-20 in the step 3, derives from car networking big data platform.
3. the method for the auto tire wear fault pre-alarming according to claim 1 based on car networking big data, feature
It is, the numberical range of metewand is 0.6-1.5 in the step 3, derives from car networking big data platform.
4. the method for the auto tire wear fault pre-alarming according to claim 1 based on car networking big data, feature
It is, the value range of Na is 1.08-1.4 in the step 4；The value range of Nb is 0.95-1.15 in the step 4；Institute
The value range for stating Nc in step 4 is 1.09-1.15；The value range of Nq is 0.01-0.09 in the step 4.
5. the method for the auto tire wear fault pre-alarming according to claim 1 based on car networking big data, feature
It is, in the step 5, when F (X)=N*0.5, wherein N are 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 that data are sent to car networking big data platform, when
[F (XE) -0.3]≤F (X) and when F (X)≤[F (XE) -0.2], tyre monitoring system judges tire close to service life, tire
Monitoring system will judge that data are sent to car networking big data platform, and car networking big data platform prompts re-type, as F (X)
When=F (XE), tyre monitoring system judges that tire reaches service life, and tyre monitoring system will judge that data are sent to car networking
Big data platform, car networking big data platform prompt re-type immediately.
6. the method for the auto tire wear fault pre-alarming according to claim 5 based on car networking big data, feature
It is, after car networking big data platform judgement tire reaches service life, tire often travels 500 kilometers, car networking big data
The primary re-type immediately of platform prompt.
7. the method for the auto tire wear fault pre-alarming according to claim 1 based on car networking big data, feature
It is, the tyre monitoring system includes the CAN bus and automobile TSP of vehicle self-carrying.
8. the method for the auto tire wear fault pre-alarming according to claim 1 based on car networking big data, feature
It is, data information includes tyre model, tire version number, Vehicle Identify Number, tire trading company, brake deceleration degree, road in the step 1
Road situation and ambient temperature data.
9. the method for the auto tire wear fault pre-alarming according to claim 1 based on car networking big data, feature
It is, data information includes tire version number in the step 2, Vehicle Identify Number, automobile equipment number, data are packaged the time, GPS is passed through
Degree, GPS latitudes, the directions GPS, GPS velocity, traveling kilometrage, the tire pressure of automobile tire, the rotating speed of steering wheel, direction
Disk corner, speed, brake deceleration degree, transverse acceleration, normal acceleration, engine speed, car load and air themperature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611043403.XA CN106515318B (en) | 2016-11-23 | 2016-11-23 | A method of the auto tire wear fault pre-alarming based on car networking big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611043403.XA CN106515318B (en) | 2016-11-23 | 2016-11-23 | A method of the auto tire wear fault pre-alarming based on car networking big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106515318A CN106515318A (en) | 2017-03-22 |
| CN106515318B true CN106515318B (en) | 2018-07-20 |
Family
ID=58357932
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611043403.XA Active CN106515318B (en) | 2016-11-23 | 2016-11-23 | A method of the auto tire wear fault pre-alarming based on car networking big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106515318B (en) |
Families Citing this family (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108170121A (en) * | 2017-12-18 | 2018-06-15 | 温州大学瓯江学院 | One kind monitors big data vehicle safety control system based on cloud |
| KR102406182B1 (en) * | 2018-01-30 | 2022-06-07 | 현대자동차주식회사 | Vehicle predictive control system and method based on big data |
| CN108777067B (en) * | 2018-06-07 | 2021-04-02 | 郑州云海信息技术有限公司 | Road health degree monitoring method and system |
| IT201800006322A1 (en) * | 2018-06-14 | 2019-12-14 | SYSTEM AND METHOD FOR MONITORING THE CONSUMPTION OF TREAD | |
| IT201800007884A1 (en) * | 2018-08-06 | 2020-02-06 | Bridgestone Europe Nv Sa | SYSTEM AND METHOD FOR MONITORING THE CONSUMPTION OF TREAD |
| JP7348198B2 (en) * | 2018-10-05 | 2023-09-20 | 株式会社ブリヂストン | Tire wear estimation method |
| CN111098644A (en) * | 2018-10-26 | 2020-05-05 | 上汽通用汽车有限公司 | Intelligent tire management system, automobile and method |
| CN109472885B (en) * | 2018-11-14 | 2021-09-03 | 广州小鹏汽车科技有限公司 | Tire safety management method and device, tire safety management equipment and automobile |
| CN110262489A (en) * | 2019-06-21 | 2019-09-20 | 重庆市农业科学院 | For three-dimensional vegetable cultivation AGV navigation magnetic stripe layout method |
| DE102019213644A1 (en) * | 2019-09-09 | 2021-03-11 | Continental Reifen Deutschland Gmbh | Method for determining a tread depth of a vehicle tire |
| CN110816538B (en) * | 2019-09-27 | 2021-07-02 | 惠州市德赛西威汽车电子股份有限公司 | Vehicle tire monitoring method and system based on data analysis |
| CN111332312A (en) * | 2020-03-27 | 2020-06-26 | 杭州鸿泉物联网技术股份有限公司 | Automobile risk pre-control method and system |
| CN111516705B (en) * | 2020-05-12 | 2021-03-26 | 广东工贸职业技术学院 | Automobile high-speed driving safety early warning method and system based on tire working conditions |
| CN111907265B (en) * | 2020-08-17 | 2022-12-02 | 科大讯飞股份有限公司 | Tire wear condition judgment method, device, equipment and storage medium |
| CN111949010B (en) * | 2020-08-27 | 2021-08-03 | 安徽锐途物联科技有限公司 | Automobile maintenance management and control system based on Internet of things |
| CN112113504B (en) * | 2020-09-18 | 2023-01-31 | 深圳市道通科技股份有限公司 | Brake disc wear diagnosis method and wear diagnosis system |
| CN117649225B (en) * | 2024-01-30 | 2024-04-05 | 江苏路安车联网研究院有限公司 | Internet of vehicles safety barrier control fortune dimension management system |
| CN117901884B (en) * | 2024-03-19 | 2024-06-11 | 武汉理工大学 | Tire wear real-time detection method, device and medium |
| CN117928983B (en) * | 2024-03-22 | 2024-06-18 | 山东北骏重工有限公司 | Mining transport vehicle operation fault diagnosis system based on data analysis |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002040296A1 (en) * | 2000-11-20 | 2002-05-23 | Dufournier Technologies Sas | Method and device for detecting tyre wear or the like |
| CN103674573A (en) * | 2012-09-03 | 2014-03-26 | 株式会社普利司通 | System for predicting tire casing life |
| CN105701070A (en) * | 2016-02-25 | 2016-06-22 | 安徽佳通乘用子午线轮胎有限公司 | Tire predicted mileage estimation method based on road test data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2996192B1 (en) * | 2012-10-02 | 2015-05-01 | Eurodrive Services And Distrib N V | METHOD FOR DETERMINING THE WEAR CONDITION OF A PIECE AND INFORMATION OF A CLIENT |
-
2016
- 2016-11-23 CN CN201611043403.XA patent/CN106515318B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002040296A1 (en) * | 2000-11-20 | 2002-05-23 | Dufournier Technologies Sas | Method and device for detecting tyre wear or the like |
| CN103674573A (en) * | 2012-09-03 | 2014-03-26 | 株式会社普利司通 | System for predicting tire casing life |
| EP2703194B1 (en) * | 2012-09-03 | 2015-08-19 | Bridgestone Corporation | System for predicting tire casing life |
| CN105701070A (en) * | 2016-02-25 | 2016-06-22 | 安徽佳通乘用子午线轮胎有限公司 | Tire predicted mileage estimation method based on road test data |
Non-Patent Citations (1)
| Title |
|---|
| 汽车轮胎磨损机理的研究;彭旭东;《润滑与密封》;19991231(第6期);50-52 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106515318A (en) | 2017-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106515318B (en) | A method of the auto tire wear fault pre-alarming based on car networking big data | |
| US10545499B2 (en) | Determining driver engagement with autonomous vehicle | |
| JP6671248B2 (en) | Abnormality candidate information analyzer | |
| CA3026415C (en) | Traveling-based insurance ratings | |
| EP2828781B1 (en) | A system and a method for improved car prognosis | |
| CN109885040B (en) | Vehicle driving control right distribution system in man-machine driving | |
| CN103043057B (en) | Abnormal driving based on vehicle position information judges and warning system | |
| Aljaafreh et al. | Driving style recognition using fuzzy logic | |
| US10445758B1 (en) | Providing rewards based on driving behaviors detected by a mobile computing device | |
| CN107346565B (en) | Vehicle data processing method and device and terminal equipment | |
| US20070005404A1 (en) | System and method for providing driving insurance | |
| GB2545317A (en) | Incapacitated driving detection and prevention | |
| US10249112B2 (en) | Vehicle state monitoring apparatus, vehicle state monitoring system, and vehicle state monitoring method | |
| CN102163368A (en) | System and method for identifying and monitoring unsafe driving behavior | |
| US20210300132A1 (en) | Tire state estimation system and method utilizing a physics-based tire model | |
| US20210027556A1 (en) | Abnormality sign diagnosis apparatus and abnormality sign diagnosis method | |
| EP4046888A1 (en) | A device, a method and a computer program for determining the driving behavior of a driver | |
| CN107672600A (en) | A kind of pilotless automobile security system and method for controlling security | |
| Zinebi et al. | Driver behavior analysis methods: Applications oriented study | |
| Hermawan et al. | Acquisition, modeling, and evaluating method of driving behavior based on OBD-II: A literature survey | |
| KR101823994B1 (en) | High-risk driver's behavior analysis system | |
| Xiang et al. | Prediction of dangerous driving behavior based on vehicle motion state and passenger feeling using Cloud Model and Elman Neural Network | |
| Ponomarev et al. | Adaptation and personalization in driver assistance systems | |
| Silva et al. | Traffic expression through ubiquitous and pervasive sensorization: Smart cities and assessment of driving behaviour | |
| Karkouch et al. | Cads: A connected assistant for driving safe |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: Method for early warning abrasion failure of automobile tires based on big data on Internet of vehicles Effective date of registration: 20191011 Granted publication date: 20180720 Pledgee: Beijing Yizhuang International Financing Guarantee Co., Ltd. Pledgor: Rainbow Wireless (Beijing) New Technology Co., Ltd. Registration number: Y2019990000319 |
|
| PC01 | Cancellation of the registration of the contract for pledge of patent right | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Date of cancellation: 20201030 Granted publication date: 20180720 Pledgee: Beijing Yizhuang International Financing Guarantee Co.,Ltd. Pledgor: RAINBOW WIRELESS (BEIJING) NEW TECHNOLOGY Co.,Ltd. Registration number: Y2019990000319 |