CN106682795A - Data analysis based automobile part information processing method - Google Patents
Data analysis based automobile part information processing method Download PDFInfo
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- CN106682795A CN106682795A CN201510745284.1A CN201510745284A CN106682795A CN 106682795 A CN106682795 A CN 106682795A CN 201510745284 A CN201510745284 A CN 201510745284A CN 106682795 A CN106682795 A CN 106682795A
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- 238000007405 data analysis Methods 0.000 title claims abstract description 29
- 230000010365 information processing Effects 0.000 title claims abstract description 19
- 238000003672 processing method Methods 0.000 title claims abstract description 17
- 230000007613 environmental effect Effects 0.000 claims description 9
- 230000000977 initiatory effect Effects 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 2
- 238000004378 air conditioning Methods 0.000 claims 1
- 230000005540 biological transmission Effects 0.000 claims 1
- 238000000034 method Methods 0.000 description 19
- 238000004891 communication Methods 0.000 description 13
- 230000008569 process Effects 0.000 description 11
- 238000004458 analytical method Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 7
- 230000003993 interaction Effects 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000007664 blowing Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000003449 preventive effect Effects 0.000 description 3
- 230000004913 activation Effects 0.000 description 2
- 230000004888 barrier function Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
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Abstract
The invention relates to a data analysis based automobile part information processing method, which comprises the steps of collecting historical operation data, historical fault data from a certain part of multiple automobiles and historical environment data of corresponding vehicles; forming a fault model for the part based o the collected historical data, wherein the fault model represents the relevance between the operation data of the part and the environment data of the automobiles and a fault; and providing content services related with the part for a user based on the fault model of the certain part. According to the automobile part information processing method, content services related with a corresponding part can be provided accurately based on the fault model.
Description
Technical field
The present invention relates to big data analysis application technology, the more particularly to auto parts and components based on data analysis
Information processing method.
Background technology
Currently, the quality analysis and control of the parts of automobile, is all by artificial receipts after failure occurs
The collection type of component failure, phenomenon carry out statistical analysis to study the quality of parts and possible change
Enter measure.But, the state after this mode can only occur according to failure is analyzed, and conjecture failure is former
Cause.For the reason for initiating failure and how to cause, can not effectively not analyzed, and after drawing
It is continuous to be effectively improved mode.
The content of the invention
The problem that the present invention is solved is to provide a kind of auto parts and components information processing side based on data analysis
Method, is targetedly predicted with providing the generation of failure.
In order to solve the above problems, auto parts and components information processing method of the present invention based on data analysis,
Including：
Collection comes from history data, the historical failure data of a certain parts of many automobiles, with
And the history environment data residing for corresponding vehicle；
Fault model for the parts, the fault model are formed based on collected historical data
Characterize the relevance of the environmental data residing for the service data and the vehicle of the parts and failure；
Content service about the parts is provided a user with based on the fault model of a certain parts.
Compared with prior art, such scheme has advantages below：By collecting vehicle within the duration
The data of parts, are studied using big data.In view of a certain parts failure generally all and at that time
The running status of vehicle local environment, parts itself is relevant, thus the operation number based on parts at that time
The fault model obtained according to the environmental data residing for, fault data and vehicle can pointedly embody failure
The factor and process of generation.So as to just can accurately provide relevant corresponding zero based on fault model
The content service of part, for example, predicts that the generation of failure, offer avoid drive advice of failure etc..
Description of the drawings
Fig. 1 is a kind of embodiment of the present invention based on the auto parts and components information processing method of data analysis
Schematic diagram；
Fig. 2 is a kind of embodiment configuration diagram for realizing the inventive method；
Fig. 3 is the signal of high in the clouds and many automobile interworking in a kind of embodiment for realize the inventive method
Figure.
Specific embodiment
In the following description, elaborate many details to make person of ordinary skill in the field
The present invention is appreciated more fully.But, for the technical staff in art it is evident that originally
Realizing for invention can not be with some in these details.However, it should be understood that of the invention
It is not limited to introduced specific embodiment.On the contrary, it may be considered that any with following feature and key element
Combine to implement the present invention, regardless of whether whether they are related to different embodiments.Aspect therefore, below,
Feature, embodiment and advantage are used for illustrative purposes only and are not construed as key element or the restriction of claim,
Unless clearly proposed in the claims.
As background technology is referred to, prior art for parts quality analysis only in accordance with fail result come
Conjecture failure cause.This mode without reliable data due to supporting, thus whether its analysis result is accurate
Really remain to be discussed.Because current many vehicles have been assembled multiple sensors, what these sensors had can
To obtain the running status of parts, what is had can monitor the phase of vehicle environment residing in the process of moving
Close data, etc..Therefore, according to the present invention it is possible to obtain parts generation using these sensors
Before failure, failure when, the related data after failure, according to these data, failure generation should be able to be obtained
Relation between related data, so as to obtain the model of accurate description component failure.
Specifically, with reference to shown in Fig. 1, according to auto parts and components information processing of the present invention based on data analysis
A kind of embodiment of method, it includes：
Step 10, collection comes from history data, the historical failure of a certain parts of many automobiles
Data, and the history environment data residing for corresponding vehicle；
Step 20, based on collected historical data the fault model for the parts is formed, described
Fault model characterizes the pass of the environmental data residing for the service data and the vehicle of the parts and failure
Connection property；
Step 30, based on the fault model of a certain parts the content clothes about the parts are provided a user with
Business.
It should be noted that in order to obtain accurate fault model, needs go the phase for obtaining many automobiles
Data are closed, to avoid because the chance cause of some automobiles is (such as purely for the transient error behaviour of user
Failure caused by making) and produce the modeling result of mistake.Thus, current big data application mode will make
Obtain output result of the invention more accurate.Also, in order that output result is more accurate, except obtaining very
Outside the related data of many automobiles, to same automobile, it is also possible to obtain above-mentioned in distance
History data, historical failure data and history environment data.
Include multiple subsystems in current automotive system, be respectively used to perform the various functions of automobile.
For example, engine system is responsible for controlling the running of engine；Chassis and brakes are responsible for the braking of automobile
And the stable control of vehicle body in vehicle traveling process；Body control system is responsible for the antitheft and auto lamp of automobile
The control of light；And the sensor of various internal or external data snooping functions is provided, etc..For this purpose,
Each vehicle sub-systems can all have an electronic control unit (ECU) to be responsible for realizing needed for respective function
Communication, data processing etc. are operated.Also, due to being all configured with vehicle bus in current automotive system,
Each electronic control unit can easily by the service data of related components, event in respective system
The environmental data that barrier data and sensor are detected uploads to vehicle bus.It is other by vehicle bus
Vehicle sub-systems are also obtained in that related data.The realization of the inventive method is also based on this mode to grasp
Make.
Fig. 2 is illustrated to realize a kind of framework of embodiment of the inventive method.With reference to shown in Fig. 2, vapour
The electronic control unit of the subsystems of car all establishes communication connection with vehicle bus, vehicle-mounted end also with
Vehicle bus establish communication connection.
Vehicle-mounted end includes：
Vehicle bus communication module, it provides vehicle bus communication interface, to set up vehicle-mounted end and vehicle
The communication connection of bus；
Data communication module, it provides the communication interface of automobile access network, to set up vehicle-mounted end and cloud
The communication connection of end analysis platform；
Data acquisition module, by the communication of vehicle bus communication module and vehicle bus, can be from vehicle
The information that the electronic control unit of other vehicle sub-systems is uploaded on vehicle bus is obtained in bus, these
The service data of related components in each vehicle sub-systems, fault data and environment number are just included in information
According to；
Message processing module, arranges to the Various types of data that data acquisition module is obtained, for example will be all kinds of
Data carry out classifying packing etc. by parts；After arrangement, by data communication module by related data
It is sent to high in the clouds Data Analysis Platform；Subsequently, for the data that high in the clouds Data Analysis Platform is issued are solved
Analysis, process, and by the data is activation after process to human-computer interaction module；Certainly, pacify for information transfer
Full demand, message processing module can be to be encrypted after disposal data to data, only will be encrypted
Data is activation give high in the clouds Data Analysis Platform；
High in the clouds Data Analysis Platform, obtain vehicle-mounted end send parts service data, fault data and
After environmental data, stored and these data are analyzed, to form the fault model of parts,
The data type of correlative factor of the fault model model comprising initiating failure, and the data type pair
In the impact relation (can be characterized by formula or other forms) that failure occurs；And, quantify meter
Calculate correlative factor change for the impact that failure occurs, and and then predict failure generation time (failure
Prediction)；Further, the preventive suggestions for component failure can also be formed and (avoids the driving of failure
Suggestion), the quality report (such as comprising parts production suggestion) of relevant parts, etc.；High in the clouds number
Fault model, failure prediction data, preventive suggestions, quality report formed according to analysis platform etc. can be below
It is sent to vehicle-mounted end and thinks that message processing module is obtained；
Human-computer interaction module, its data sent according to message processing module, by image and/or sound
Mode presents to user；For example, failure predication is audibly reminded user；By preventive suggestions scheming
Image space formula is shown to user, etc..
Refer to as aforementioned, be to cause the output result of the inventive method more accurate, big data can be applied
The mode of process.Thus, in a kind of embodiment of the present invention, between each automobile and high in the clouds Data Analysis Platform
Interworking can be as shown in Figure 3.Assume that each automobile is all employed and the vehicle-mounted end identical shown in Fig. 2
Structure (also can adopt completely different structures) certainly, and with reference to shown in Fig. 2 and Fig. 3, each automobile will be respective
Parts service data, fault data and environmental data be uploaded to high in the clouds Data Analysis Platform, work as high in the clouds
Data platform complete data analysis obtain fault model, just can going through with combination failure model and automobile
History data carry out failure predication, generate drive advice (for example, to avoid failure which should be avoided drive row
For), then related data is issued in corresponding automobile, so that corresponding information is in by the vehicle-mounted end in automobile
Now give user.So as to user can make in advance preparation to failure, also enhance traffic safety；
Additionally, can also form the quality report comprising production suggestion according to fault model, vehicle system is supplied to
Manufacturer or subsupplier business are made, to improve the quality of product.
Process is described further to be realized to the inventive method below by way of concrete application example.
By taking tire as an example, can be summarized as using the process of the present invention：According in a large number use same brand and
Such as road conditions during the history tire pressure delta data of the tire of model, historical failure data and its use,
The history environment such as temperature data are forming the fault model of the tire of the brand model.
So that the framework that Fig. 2, Fig. 3 illustrate realizes the present invention as an example, it is assumed that be assembled with tire on current automobile
Pressure monitoring system (TPMS, Tire Pressure Monitoring System).In simple terms, tire pressure
Monitoring system includes：The tyre pressure sensor being assemblied on tire, antenna integrated, it can be to tire pressure, tire temperature
Detected Deng tire operating condition；And electronic control unit, receive tyre pressure sensor signal to obtain
Tire operating condition data, and such as tire pressure and tire temperature letter are provided by the process to operating condition data
The tire pressure monitoring functions such as breath shows, tire fault identification, tire fault warning.This electronic control unit is same
Sample can be set up with vehicle bus (such as CAN) and communicate to connect, by tire operating condition data, failure
Data are uploaded to vehicle bus.So as to vehicle-mounted end also can obtain tire operating condition number from vehicle bus
According to, fault data.Similarly, the vehicle speed data detected by wheel speed sensors for assembling on current automobile,
Also can be directly or indirectly through corresponding subsystem (such as chassis and brakes, body control system etc.)
Vehicle bus are uploaded to, and are obtained for vehicle-mounted end.
Additionally, current automobile has also been increasingly being equipped with the various biographies for outside vehicle environment detection
Sensor, by these sensors such as direct environment data such as gas epidemic disaster can be detected.These are straight
Connecing environmental data can also be uploaded to vehicle bus, and be obtained by vehicle-mounted end.Also, connect due to having
Enter the function of network, automobile can also obtain the indirect environmental data such as real-time traffic information (such as by car
The message processing module for carrying end is obtained by data communication module networking).
After above-mentioned data are obtained, above-mentioned data can be uploaded to high in the clouds data analysis by message processing module
Platform.High in the clouds Data Analysis Platform, will be per brand model together after the mass data for uploading is obtained
Data and the corresponding fault data such as tire pressure data, speed, temperature, road conditions of tire when breaking down
Associate.Then, analyzed by the data for having associated, find the same tire fault (example of generation
Such as blow out) when data related to failure.For example, compared by mass data and found, speed is more than 110km/
Hour, tire pressure would generally be raised so that blow out extremely, now it is considered that this speed be to blow out it is related
Data.Again for example, compared by mass data and found, when temperature is more than 35 degrees Celsius, tire pressure is usual
Abnormal rising is understood so that blowing out, now it is considered that this temperature is and related data of blowing out.
After the data related to each failure are obtained, so that it may set up the brand model tire therefore
Barrier model.Specifically, the content of the fault model contains the related data class for causing various tire faults
Type and the data type are for the impact relation that corresponding tire fault occurs..Thus, can by fault model
What situation to be easier to what type of failure in the tire for predicting the brand model.High in the clouds
Data Analysis Platform can select the vehicle-mounted end by each automobile is issued to based on the prediction data of fault model.
Or, it is also possible to it is handled as follows：When the current environment residing for certain car of discovery, vehicle operational mode symbol
During the factor of the possibility initiating failure described in conjunction fault model, issue failure predication to the automobile and remind,
User is presented to by the human-computer interaction module of vehicle-mounted end.As a example by finding that current temperature may cause to blow out,
Failure predication remind mode can be：With text importing or voice at the human-computer interaction module of vehicle-mounted end
The description below --- " current temperature may cause to blow out, and continue the probability of blowing out for travelling longer than X hours for report
For Y%, please don't continue to drive！”.
Illustrated by described above and practical application, auto parts and components of the present invention based on data analysis
Information processing method exists following significantly different relative to the failure analysis methods of prior art：1) number is collected
According to type it is different：Not only as prior art collects result data (historical failure data), process is also collected
Data (history data, history environment data)；2) mode of Data Collection is different：By automobile
The each subsystem being equipped with is collected in vehicle traveling process, rather than if existing only a few is after failure generation
Just collect；3) history data and history environment number high with the failure degree of correlation is found based on mass data
According to, and be derived from causing the factor of corresponding failure, rather than such as prior art based on failure by rule of thumb guessing
Survey reason.Therefore, the present invention can provide the content service for being more accurately directed to parts, for example accurately
Failure predication result.
Although the present invention is disclosed as above with preferred embodiment, the present invention is not limited to this.Any
Art personnel, the various changes made without departing from the spirit and scope of the present invention and modification,
Should include in protection scope of the present invention, therefore protection scope of the present invention should be limited with claim
Scope be defined.
Claims (9)
1. a kind of auto parts and components information processing method based on data analysis, it is characterised in that include：
Collection comes from history data, the historical failure data of a certain parts of many automobiles, with
And the history environment data residing for corresponding vehicle；
Fault model for the parts, the fault model are formed based on collected historical data
Characterize the relevance of the environmental data residing for the service data and the vehicle of the parts and failure；
Content service about the parts is provided a user with based on the fault model of a certain parts.
2. the auto parts and components information processing method of data analysis is based on as claimed in claim 1, and its feature exists
In the content service includes：The factor of initiating failure is obtained according to fault model；Finding currently to deposit
When the factor of initiating failure is met, there is provided remind about the failure predication of the parts.
3. the auto parts and components information processing method of data analysis is based on as claimed in claim 2, and its feature exists
In, the failure predication remind include it is following any one or combine：It is several that offer parts break down
The possibility time that rate, offer parts break down.
4. the auto parts and components information processing method of data analysis is based on as claimed in claim 1, and its feature exists
In the content service includes：Offer avoids the drive advice of component failure.
5. the auto parts and components information processing method of data analysis is based on as claimed in claim 1, and its feature exists
In the content service includes：Quality report about parts is provided.
6. the auto parts and components information processing method of data analysis is based on as claimed in claim 1, and its feature exists
In the history data includes：The mode setting information of electronic component, each function mould of parts
The state-detection data of block.
7. the auto parts and components information processing method of data analysis is based on as claimed in claim 1, and its feature exists
In the historical failure data includes：The fault message of parts when time of failure, failure occur.
8. the auto parts and components information processing method of data analysis is based on as claimed in claim 1, and its feature exists
Include following any one or combinations in, history environment data：Temperature, humidity, weather, road conditions,
Vehicle location, front-and-rear vehicle distance, distance travelled.
9. the auto parts and components information processing method of data analysis is based on as claimed in claim 1, and its feature exists
In, parts include it is following any one：Tire, air-conditioning, engine, power transmission,
Brake system.
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| CN201510745284.1A CN106682795A (en) | 2015-11-05 | 2015-11-05 | Data analysis based automobile part information processing method |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109298902A (en) * | 2017-07-24 | 2019-02-01 | Sap欧洲公司 | Big Data-Driven Telematics with AR/VR User Interface |
| CN110600112A (en) * | 2019-08-12 | 2019-12-20 | 东软医疗系统股份有限公司 | Method, device and equipment for discovering quality problems of parts |
| CN110637327A (en) * | 2017-06-20 | 2019-12-31 | 宝马股份公司 | Method and apparatus for content push |
| CN111105522A (en) * | 2019-12-10 | 2020-05-05 | 郑州嘉晨电器有限公司 | Vehicle health prediction system and method |
| CN111142499A (en) * | 2019-12-19 | 2020-05-12 | 大众问问(北京)信息科技有限公司 | Vehicle fault detection method, device and equipment |
| CN112041819A (en) * | 2018-06-29 | 2020-12-04 | 罗伯特·博世有限公司 | Method for monitoring and identifying sensor faults in an electric drive system |
| CN113033860A (en) * | 2019-12-25 | 2021-06-25 | 宁波吉利汽车研究开发有限公司 | Automobile fault prediction method and device, electronic equipment and storage medium |
| CN113268640A (en) * | 2020-02-17 | 2021-08-17 | 中国航发商用航空发动机有限责任公司 | Metal filing fault analysis system and method |
| CN113687642A (en) * | 2021-08-13 | 2021-11-23 | 合肥维天运通信息科技股份有限公司 | Intelligent early warning method and system for potential hazards of logistics vehicles |
| CN114076680A (en) * | 2020-08-17 | 2022-02-22 | 北京福田康明斯发动机有限公司 | Engine assembly detection method, system, storage medium and electronic device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120296514A1 (en) * | 2011-05-16 | 2012-11-22 | Ford Motor Company | System and Method of Conducting Vehicle Usage Data Analysis |
| WO2013156791A1 (en) * | 2012-04-19 | 2013-10-24 | Project Vanguard Limited | Machine analytic system and components thereof |
| CN103455026A (en) * | 2013-08-23 | 2013-12-18 | 王绍兰 | Method and device for diagnosis and early warning of vehicle faults |
-
2015
- 2015-11-05 CN CN201510745284.1A patent/CN106682795A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120296514A1 (en) * | 2011-05-16 | 2012-11-22 | Ford Motor Company | System and Method of Conducting Vehicle Usage Data Analysis |
| WO2013156791A1 (en) * | 2012-04-19 | 2013-10-24 | Project Vanguard Limited | Machine analytic system and components thereof |
| CN103455026A (en) * | 2013-08-23 | 2013-12-18 | 王绍兰 | Method and device for diagnosis and early warning of vehicle faults |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110637327A (en) * | 2017-06-20 | 2019-12-31 | 宝马股份公司 | Method and apparatus for content push |
| US11453412B2 (en) | 2017-06-20 | 2022-09-27 | Bayerische Motoren Werke Aktiengesellschaft | Method and device for pushing content |
| CN109298902A (en) * | 2017-07-24 | 2019-02-01 | Sap欧洲公司 | Big Data-Driven Telematics with AR/VR User Interface |
| CN112041819A (en) * | 2018-06-29 | 2020-12-04 | 罗伯特·博世有限公司 | Method for monitoring and identifying sensor faults in an electric drive system |
| CN110600112A (en) * | 2019-08-12 | 2019-12-20 | 东软医疗系统股份有限公司 | Method, device and equipment for discovering quality problems of parts |
| CN111105522A (en) * | 2019-12-10 | 2020-05-05 | 郑州嘉晨电器有限公司 | Vehicle health prediction system and method |
| CN111142499B (en) * | 2019-12-19 | 2021-11-30 | 大众问问(北京)信息科技有限公司 | Vehicle fault detection method, device and equipment |
| CN111142499A (en) * | 2019-12-19 | 2020-05-12 | 大众问问(北京)信息科技有限公司 | Vehicle fault detection method, device and equipment |
| CN113033860A (en) * | 2019-12-25 | 2021-06-25 | 宁波吉利汽车研究开发有限公司 | Automobile fault prediction method and device, electronic equipment and storage medium |
| CN113268640A (en) * | 2020-02-17 | 2021-08-17 | 中国航发商用航空发动机有限责任公司 | Metal filing fault analysis system and method |
| CN113268640B (en) * | 2020-02-17 | 2022-09-13 | 中国航发商用航空发动机有限责任公司 | Metal filing fault analysis system and method |
| CN114076680A (en) * | 2020-08-17 | 2022-02-22 | 北京福田康明斯发动机有限公司 | Engine assembly detection method, system, storage medium and electronic device |
| CN113687642A (en) * | 2021-08-13 | 2021-11-23 | 合肥维天运通信息科技股份有限公司 | Intelligent early warning method and system for potential hazards of logistics vehicles |
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Address after: 200082 538 Dalian Road, Yangpu District, Shanghai Applicant after: Mainland Investment (China) Co., Ltd. Address before: 200082 538 Dalian Road, Yangpu District, Shanghai Applicant before: Continental Automotive Asia Pacific (Beijing) Co., Ltd. |
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Application publication date: 20170517 |