CN107403480A - A kind of vehicle trouble method for early warning, system and vehicle - Google Patents
A kind of vehicle trouble method for early warning, system and vehicle Download PDFInfo
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- CN107403480A CN107403480A CN201710452989.3A CN201710452989A CN107403480A CN 107403480 A CN107403480 A CN 107403480A CN 201710452989 A CN201710452989 A CN 201710452989A CN 107403480 A CN107403480 A CN 107403480A
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0816—Indicating performance data, e.g. occurrence of a malfunction
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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Abstract
This application discloses a kind of vehicle trouble method for early warning, including：The vehicle early warning related data collected；Vehicle early warning related data is handled using Outlier Detection Algorithm program, judges whether vehicle trouble warning information；Wherein, Outlier Detection Algorithm program is that statistical model is trained to obtain using the history vehicle early warning related data collected；This method training statistical model obtains Outlier Detection Algorithm program and judged to be predicted, and improves Scalability and data adaptability, adds prediction controllable period and improves the precision of prediction；Disclosed herein as well is a kind of vehicle trouble early warning system and vehicle, has above-mentioned beneficial effect.
Description
Technical field
The application is related to Vehicle Engineering technical field, more particularly to a kind of vehicle trouble method for early warning, system and vehicle.
Background technology
With the development of China's automotive industry and the raising of living standards of the people, vehicle is increasingly entering common family
Front yard.Along with continuing to increase for vehicle, road traffic accident sharply increases.Wherein due to caused by the problem of vehicle part itself
Traffic accident, occupy more than the 30% of traffic accident.Main Means of the vehicle trouble early warning system as vehicle active safety,
Importance in car networking service is growing day by day.
The reference threshold that the existing online fault early warning system of vehicle is based primarily upon single vehicle data is predicted, and its is pre-
The survey cycle is shorter, and accuracy is poor, and its reliability can not meet the user's request of vehicle active safety.Therefore how vehicle is improved
The accuracy of fault pre-alarming, it is those skilled in the art's technical issues that need to address.
The content of the invention
The purpose of the application is to provide a kind of vehicle trouble method for early warning, system and vehicle, and training statistical model obtains different
Normal detection algorithm program judges to be predicted, and improves Scalability and data adaptability, adds prediction controllable period
And improve the precision of prediction.
In order to solve the above technical problems, the application provides a kind of vehicle trouble method for early warning, methods described includes：
The vehicle early warning related data collected；
The vehicle early warning related data is handled using Outlier Detection Algorithm program, judges whether vehicle event
Hinder warning information；Wherein, the Outlier Detection Algorithm program is to statistics using the history vehicle early warning related data collected
Model is trained to obtain.
Optionally, the vehicle early warning related data collected, including：
Receive vehicle operation data and the vehicle CAN bus data that vehicle termination machine is sent；
Receive the vehicle environmental data that environment data-interface is sent.
Optionally, vehicle operation data and the vehicle CAN bus data that vehicle termination machine is sent are received, including：
Vehicle termination machine collects vehicle operation data using global position system；Wherein, the vehicle operation data includes
Vehicle position data, vehicle speed data and direction of traffic data；
The vehicle termination machine collects vehicle CAN bus data using CAN data sink；
Vehicle trouble Warning Service device receives the vehicle operation data and the vehicle CAN bus using TCP length connections
Data.
Optionally, the vehicle environmental data that environment data-interface is sent are received, including：
Environmental data interface obtains vehicle environmental data using HTTP request from environment common services interface；
Vehicle trouble Warning Service device receives the vehicle environmental data using web socket network connections.
Optionally, statistical model is trained using the history vehicle early warning related data collected to obtain the exception
Detection algorithm program, including：
History vehicle early warning related data is mapped to form training dataset { X(1),...,X(m)}；
Establish the Gaussian statistics model p (x for performing vehicle trouble early warning；μ,σ2)；
Each characteristic value x is determined according to the training datasetiCorresponding μiWith
Utilize the μi, it is describedAnd the Gaussian statistics model obtains the Outlier Detection Algorithm program；
Wherein, X(i)For multidimensional characteristic vectors,μ is it is expected, σ is standard deviation, σ2For
Variance, x are variable,M is the size of training dataset.
Optionally, this programme also includes：
User is received to be updated the Outlier Detection Algorithm program feedback information of result.
The application also provides a kind of vehicle, including：
Data acquisition unit, car is sent to for collection vehicle early warning related data, and by the vehicle early warning related data
Fault pre-alarming processor；
The vehicle trouble early warning processor, for the vehicle early warning phase using Outlier Detection Algorithm program to reception
Close data to be handled, judge whether vehicle trouble warning information；Wherein, the Outlier Detection Algorithm program is to utilize to adopt
The history vehicle early warning related data collected is trained to obtain to statistical model.
Optionally, the vehicle trouble early warning processor includes：
Outlier Detection Algorithm procedural training device, for mapping to form training dataset { X history vehicle early warning related data(1),...,X(m)}；Establish the Gaussian statistics model p (x for performing vehicle trouble early warning；μ,σ2)；It is true according to the training dataset
Fixed each characteristic value xiCorresponding μiWithUtilize the μi, it is describedAnd the Gaussian statistics model obtains the exception
Detection algorithm program；
Wherein, X(i)For multidimensional characteristic vectors,μ is it is expected, σ is standard deviation, σ2For
Variance, x are variable,M is the size of training dataset.
Optionally, the vehicle trouble early warning processor includes：
Outlier Detection Algorithm program renovator, for receiving user to the feedback information of result to the abnormality detection
Algorithm routine is updated.
The application also provides a kind of vehicle trouble early warning system, and the system includes：
Vehicle, vehicle trouble is sent to for collection vehicle early warning related data, and by the vehicle early warning related data
Warning Service device；
The vehicle trouble Warning Service device, for the vehicle early warning phase using Outlier Detection Algorithm program to reception
Close data to be handled, judge whether vehicle trouble warning information；Wherein, the Outlier Detection Algorithm program is to utilize to adopt
The history vehicle early warning related data collected is trained to obtain to statistical model.
A kind of vehicle trouble method for early warning provided herein, including：The vehicle early warning related data collected；
Vehicle early warning related data is handled using Outlier Detection Algorithm program, judges whether vehicle trouble warning information；
Wherein, Outlier Detection Algorithm program is that statistical model is trained using the history vehicle early warning related data collected
Arrive.
It can be seen that this method training statistical model obtains Outlier Detection Algorithm program and judged to be predicted, system is improved
Retractility and data adaptability, add prediction controllable period and improve the precision of prediction；Present invention also provides one kind
Vehicle trouble early warning system and vehicle, there is above-mentioned beneficial effect, will not be repeated here.
Brief description of the drawings
, below will be to embodiment or existing in order to illustrate more clearly of the embodiment of the present application or technical scheme of the prior art
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
The embodiment of application, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
The accompanying drawing of offer obtains other accompanying drawings.
The flow chart for the vehicle trouble method for early warning that Fig. 1 is provided by the embodiment of the present application；
The Outlier Detection Algorithm program that Fig. 2 is provided by the embodiment of the present application carries out processing knot to vehicle early warning related data
Fruit schematic diagram；
The structured flowchart for the vehicle trouble early warning system that Fig. 3 is provided by the embodiment of the present application；
The structured flowchart for the vehicle that Fig. 4 is provided by the embodiment of the present application.
Embodiment
The core of the application is to provide a kind of vehicle trouble method for early warning, system and vehicle, and training statistical model obtains different
Normal detection algorithm program judges to be predicted, and improves Scalability and data adaptability, adds prediction controllable period
And improve the precision of prediction.
To make the purpose, technical scheme and advantage of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
In accompanying drawing, the technical scheme in the embodiment of the present application is clearly and completely described, it is clear that described embodiment is
Some embodiments of the present application, rather than whole embodiments.Based on the embodiment in the application, those of ordinary skill in the art
The every other embodiment obtained under the premise of creative work is not made, belong to the scope of the application protection.
The present embodiment carries out vehicle early warning Correlative data analysis method using Outlier Detection Algorithm program, that is, utilizes abnormal inspection
The instant analysis method of the car networking vehicle trouble prediction of non_monitor algorithm is surveyed, the problem of for existing early warning system, utilization is different
Vehicle early warning related data (such as vehicle operation data, vehicle CAN bus data and environment is uniformly processed in normal detection algorithm program
Data), to extend predetermined period, improve predictablity rate；Specifically it refer to Fig. 1, the car that Fig. 1 is provided by the embodiment of the present application
The flow chart of fault early warning method；This method can include：
S100, the vehicle early warning related data collected.
Specifically, the present embodiment is not defined to the particular content of vehicle early warning related data, can be existing skill
The single vehicle data or a variety of vehicle datas of art collection.Preferably, when user selects polytype vehicle number
During according to as vehicle early warning related data, vehicle early warning related data dimension can be made broader, and then add prediction
Precision and controllable period.I.e. the selection of vehicle early warning related data can be by user according to actually required precision of prediction and hardware meter
Calculation ability is selected.Such as vehicle operation data, vehicle CAN bus data and vehicle environmental data etc. can be included.
Further, the present embodiment does not limit the device of vehicle early warning relevant data acquisition, and it can be according to actual vehicle
Early warning relevant data acquisition demand is selected.Such as vehicle CAN bus data can be gathered by CAN data sink
Obtain.Due to not being defined to the device of vehicle early warning relevant data acquisition, corresponding the present embodiment is not also adopted to reception
The reception mode of the vehicle early warning related data collected is defined.It can carry out adaptation Sexual behavior mode as the case may be.Should
As long as step can get required vehicle early warning related data.Such as when vehicle early warning related data includes vehicle
During CAN data, the collection of corresponding data can be carried out by the device on vehicle, because the device on vehicle can more hold
Easily collect accurate vehicle CAN bus data.It can pass through ring when vehicle early warning related data includes vehicle environmental data
Border common services interface (such as Chinese weather net common interface) obtains vehicle corresponding data.That is the determination of acquisition mode can be with
Using the accuracy of data and the convenience of gatherer process as reference frame.In order to ensure the reliability of the data of collection and accurate
Property, optionally, the vehicle early warning related data collected can include：
Receive vehicle operation data and the vehicle CAN bus data that vehicle termination machine is sent.
Specifically, increase vehicle termination machine in vehicle to collect vehicle operation data and vehicle CAN bus data.
To obtain the current vehicle early warning related data of vehicle in time.The present embodiment does not limit vehicle operation data and vehicle CAN is total
Line data acquisition modes, specific vehicle operation data and vehicle CAN bus data collection content are not limited yet.Such as it can lead to
Vehicle operation data can also be obtained by global position system by crossing the instrument board acquisition vehicle operation data of vehicle.It is further
Ensure the convenience of data acquisition, optionally, receive vehicle operation data and vehicle CAN bus number that vehicle termination machine is sent
According to can include：Vehicle termination machine collects vehicle operation data using global position system；Wherein, vehicle operation data includes car
Position data, vehicle speed data and direction of traffic data；Vehicle termination machine utilizes CAN data sink collecting cart
CAN data.I.e. vehicle termination machine is collected into the number such as the position of vehicle operation, speed, direction using global position system
According to.The CAN data that vehicle termination machine is collected into the engine correlation of vehicle using CAN line data sinks are related to vehicle
CAN data.
Receive the vehicle environmental data that environment data-interface is sent.
Specifically, the present embodiment does not limit vehicle environmental data acquisition modes, specific vehicle environmental number is not limited yet
According to collection content.Such as can with environmental data interface using HTTP request from environment common services interface (such as Chinese weather net
Common interface) obtain vehicle environmental data.I.e. environmental data interface service utilizes HTTP request on demand from environment public service (example
Such as Chinese weather net common interface) in obtain the ambient parameter relevant with vehicle be vehicle environmental data.Here environmental data
Interface can be arranged on vehicle or be arranged in vehicle trouble Warning Service device, the present embodiment to this and without
Limit.
Executive agent in the present embodiment can be the processor in vehicle trouble Warning Service device or vehicle.
When the executive agent in the present embodiment is vehicle trouble Warning Service device, vehicle trouble Warning Service device receives vehicle operation number
According to, vehicle CAN bus data and vehicle environmental data.The present embodiment does not limit the mode of data transfer.Further for
Ensure the reliability of data transfer, can be carried out data transmission by Transmission Control Protocol and web socket networks.Optionally, car
Fault pre-alarming server by utilizing TCP length connection receives vehicle operation data and vehicle CAN bus data, utilizes web socket
Network connection receives vehicle environmental data.I.e. vehicle termination machine is grown after connection sends data to vehicle trouble early warning using TCP
Platform system (i.e. vehicle trouble Warning Service device).Environmental data interface pushes vehicle environmental data to vehicle using web socket
Fault pre-alarming background system.Here with internet environment information interface, extract real-time data, the prediction for improving system is smart
Degree.
S110, using Outlier Detection Algorithm program vehicle early warning related data is handled, judge whether vehicle
Fault pre-alarming information；Wherein, Outlier Detection Algorithm program is to counting mould using the history vehicle early warning related data collected
Type is trained to obtain.
Specifically, the executive agent in the present embodiment can be in vehicle trouble Warning Service device or vehicle
Processor.They can include two processing modules or be interpreted as processing apparatus, such as be connect comprising vehicle early warning related data
Receive module, and vehicle early warning related-data handling module.Wherein, vehicle early warning related-data handling module is different using training
Normal detection algorithm program handles vehicle operation data, vehicle CAN bus data and vehicle environmental data in real time, judges vehicle event
Whether barrier may occur.Specifically, collecting and uploading the vehicle termination machine of vehicle operation data and vehicle CAN bus data, obtain
After taking the environmental data interface service of respective environment data, integrated data processing to carry out the vehicle trouble early warning of vehicle trouble judgement
Platform system.The present embodiment is not defined to specific statistical model.Such as can be Gaussian statistics model.Use Gauss point
Cloth judges to be predicted, and improves Scalability and data adaptability, adds prediction controllable period.
Wherein, statistical model is trained using the history vehicle early warning related data collected to obtain abnormality detection calculation
Method program can include：
History vehicle early warning related data is mapped to form training dataset { X(1),...,X(m)}；
Establish the Gaussian statistics model p (x for performing vehicle trouble early warning；μ,σ2)；
Each characteristic value x is determined according to training datasetiCorresponding μiWith
Utilize μi、And Gaussian statistics model obtains Outlier Detection Algorithm program；
Wherein, X(i)For multidimensional characteristic vectors,μ is it is expected, σ is standard deviation, σ2For
Variance, x are variable,M is the size of training dataset.
Said process is illustrated with vehicle operation data, vehicle CAN bus data and vehicle environmental data instance below
Particular content：
The first step：History vehicle early warning related data is mapped to form training dataset { X(1),...,X(m)}；Wherein X(i)
For multidimensional characteristic vectors, it is made up of three parts：Vehicle operation data, vehicle CAN data and real-time vehicle environmental data are (therein
Each characteristic value X(i))。
Second step：Establish the Gaussian statistics model for the prediction for carrying out vehicle trouble.Gauss model
There are two parameters, that is, it is expected (mean) μ and standard deviation sigma, σ2For variance.
3rd step：Each characteristic value x is determined by training datasetiCorresponding μiWithWherein, M is the size of training dataset.
4th step：The on-line prediction of vehicle trouble is carried out by training Gaussian statistics model i.e. Outlier Detection Algorithm program
Calculating is handled, and judges whether vehicle trouble warning information.It refer to Fig. 2, the situation corresponding data meeting that possible breakdown occurs
Deviate reasonable data collection.
The vehicle trouble method for early warning provided based on above-mentioned technical proposal, the embodiment of the present application, this method training Gauss system
Meter model obtains Outlier Detection Algorithm program and judged to be predicted, broadening by vehicle its data and vehicle environmental data
Data dimension, Scalability and data adaptability are improved, add prediction controllable period and improve the precision of prediction；
And machine learning is carried out using reasonable statistical model, it is progressive to improve the precision of prediction, while prediction result has been decoupled to single
The dependence of data.
Based on above-described embodiment, this method can also include：
User is received to be updated Outlier Detection Algorithm program the feedback information of result.
Specifically, being handled according to Outlier Detection Algorithm program vehicle early warning related data, car is judged whether
The result of fault pre-alarming information is fed back and (judges whether the vehicle trouble warning information of processing is correct), that is, utilizes
The feedback information of user is updated to Outlier Detection Algorithm program.It is accurate so as to improve Outlier Detection Algorithm Programmable detection
Property.
The vehicle trouble method for early warning provided based on above-mentioned technical proposal, the embodiment of the present application, this method training Gauss system
Meter model obtains Outlier Detection Algorithm program and judged to be predicted, broadening by vehicle its data and vehicle environmental data
Data dimension, Scalability and data adaptability are improved, add prediction controllable period and improve the precision of prediction；
And machine learning is carried out using reasonable statistical model, it is progressive to improve the precision of prediction, while prediction result has been decoupled to single
The dependence of data.And the accuracy of Outlier Detection Algorithm program is improved by feedback mechanism.
The vehicle trouble early warning system and vehicle provided below the embodiment of the present application is introduced, vehicle described below
Fault early warning system and vehicle can be mutually to should refer to above-described vehicle trouble method for early warning.
It refer to Fig. 3, the structured flowchart for the vehicle trouble early warning system that Fig. 3 is provided by the embodiment of the present application；The system
It can include：
Vehicle 100, for collection vehicle early warning related data, and it is pre- that vehicle early warning related data is sent into vehicle trouble
Alert server；
Vehicle trouble Warning Service device 200, for the vehicle early warning dependency number using Outlier Detection Algorithm program to reception
According to being handled, vehicle trouble warning information is judged whether；Wherein, Outlier Detection Algorithm program is to be gone through using what is collected
History vehicle early warning related data is trained to obtain to Gaussian statistics model.
Based on above-described embodiment, the vehicle 100 can include：
Vehicle termination machine, for collecting vehicle operation data using global position system；Wherein, vehicle operation data includes
Vehicle position data, vehicle speed data and direction of traffic data；It is total that vehicle CAN is collected using CAN data sink
Line number evidence；
Environmental data interface, for obtaining vehicle environmental data from environment common services interface using HTTP request.
Based on above-described embodiment, vehicle trouble Warning Service device 200 can include：
TCP interfaces, for receiving vehicle operation data and vehicle CAN bus data using TCP length connections；
Web socket interfaces, for receiving vehicle environmental data using web socket network connections.
Based on above-mentioned any embodiment, vehicle trouble Warning Service device 200 can include：Outlier Detection Algorithm procedural training
Device；Wherein, Outlier Detection Algorithm procedural training device, for mapping to form training dataset { X history vehicle early warning related data(1),...,X(m)}；Establish the Gaussian statistics model p (x for performing vehicle trouble early warning；μ,σ2)；Determined according to training dataset every
Individual characteristic value xiCorresponding μiWithUtilize μi、And Gaussian statistics model obtains Outlier Detection Algorithm program；
Wherein, X(i)For multidimensional characteristic vectors,μ is it is expected, σ is standard deviation, σ2For
Variance, x are variable,M is the size of training dataset.
Based on above-described embodiment, vehicle trouble Warning Service device 200 can also include：
Outlier Detection Algorithm program renovator, for receiving user to the feedback information of result to Outlier Detection Algorithm
Program is updated.
It refer to Fig. 4, the structured flowchart for the vehicle that Fig. 4 is provided by the embodiment of the present application；The vehicle can include：
Data acquisition unit 300, vehicle is sent to for collection vehicle early warning related data, and by vehicle early warning related data
Fault pre-alarming processor；
Vehicle trouble early warning processor 400, for the vehicle early warning dependency number using Outlier Detection Algorithm program to reception
According to being handled, vehicle trouble warning information is judged whether；Wherein, Outlier Detection Algorithm program is to be gone through using what is collected
History vehicle early warning related data is trained to obtain to Gaussian statistics model.
Based on above-described embodiment, data acquisition unit 300 can include：
Vehicle termination machine, for collecting vehicle operation data using global position system；Wherein, vehicle operation data includes
Vehicle position data, vehicle speed data and direction of traffic data；It is total that vehicle CAN is collected using CAN data sink
Line number evidence；
Environmental data interface, for obtaining vehicle environmental data from environment common services interface using HTTP request.
Based on above-described embodiment, vehicle trouble early warning processor 400 can include：
Outlier Detection Algorithm procedural training device, for mapping to form training dataset { X history vehicle early warning related data(1),...,X(m)}；Establish the Gaussian statistics model p (x for performing vehicle trouble early warning；μ,σ2)；Determined according to training dataset every
Individual characteristic value xiCorresponding μiWithUtilize μi、And Gaussian statistics model obtains Outlier Detection Algorithm program；
Wherein, X(i)For multidimensional characteristic vectors,μ is it is expected, σ is standard deviation, σ2For
Variance, x are variable,M is the size of training dataset.
Based on above-described embodiment, vehicle trouble early warning processor 400 can also include：
Outlier Detection Algorithm program renovator, for receiving user to the feedback information of result to Outlier Detection Algorithm
Program is updated.
Based on above-mentioned any embodiment, the vehicle can also include：
Warning device, for when vehicle trouble warning information be present, being alarmed.To remind user to grasp vehicle in time
Failure situation, corresponding countermeasure can be made in time to ensure vehicle safe driving.Here specific warning device is not limited
Type, can be voice message equipment, or indicator lamp display device or other warning devices.
Each embodiment is described by the way of progressive in specification, and what each embodiment stressed is and other realities
Apply the difference of example, between each embodiment identical similar portion mutually referring to.For device disclosed in embodiment
Speech, because it is corresponded to the method disclosed in Example, so description is fairly simple, related part is referring to method part illustration
.
Professional further appreciates that, with reference to the unit of each example of the embodiments described herein description
And algorithm steps, can be realized with electronic hardware, computer software or the combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software, the composition and step of each example are generally described according to function in the above description.These
Function is performed with hardware or software mode actually, application-specific and design constraint depending on technical scheme.Specialty
Technical staff can realize described function using distinct methods to each specific application, but this realization should not
Think to exceed scope of the present application.
Directly it can be held with reference to the step of method or algorithm that the embodiments described herein describes with hardware, processor
Capable software module, or the two combination are implemented.Software module can be placed in random access memory (RAM), internal memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
A kind of vehicle trouble method for early warning, system and vehicle provided herein are described in detail above.This
Apply specific case in text to be set forth the principle and embodiment of the application, the explanation of above example is only intended to
Help understands the present processes and its core concept.It should be pointed out that for those skilled in the art,
On the premise of not departing from the application principle, some improvement and modification can also be carried out to the application, these are improved and modification also falls
Enter in the application scope of the claims.
Claims (10)
1. a kind of vehicle trouble method for early warning, it is characterised in that methods described includes：
The vehicle early warning related data collected；
The vehicle early warning related data is handled using Outlier Detection Algorithm program, judges whether that vehicle trouble is pre-
Alert information；Wherein, the Outlier Detection Algorithm program is to statistical model using the history vehicle early warning related data collected
It is trained to obtain.
2. according to the method for claim 1, it is characterised in that the vehicle early warning related data collected, including：
Receive vehicle operation data and the vehicle CAN bus data that vehicle termination machine is sent；
Receive the vehicle environmental data that environment data-interface is sent.
3. according to the method for claim 2, it is characterised in that receive vehicle operation data and the car that vehicle termination machine is sent
CAN data, including：
Vehicle termination machine collects vehicle operation data using global position system；Wherein, the vehicle operation data includes vehicle
Position data, vehicle speed data and direction of traffic data；
The vehicle termination machine collects vehicle CAN bus data using CAN data sink；
Vehicle trouble Warning Service device receives the vehicle operation data and the vehicle CAN bus data using TCP length connections.
4. according to the method for claim 2, it is characterised in that the vehicle environmental data that environment data-interface is sent are received,
Including：
Environmental data interface obtains vehicle environmental data using HTTP request from environment common services interface；
Vehicle trouble Warning Service device receives the vehicle environmental data using web socket network connections.
5. according to the method described in claim any one of 1-4, it is characterised in that related using the history vehicle early warning collected
Data are trained to obtain the Outlier Detection Algorithm program to statistical model, including：
History vehicle early warning related data is mapped to form training dataset { X(1),...,X(m)}；
Establish the Gaussian statistics model p (x for performing vehicle trouble early warning；μ,σ2)；
Each characteristic value x is determined according to the training datasetiCorresponding μiWith
Utilize the μi, it is describedAnd the Gaussian statistics model obtains the Outlier Detection Algorithm program；
Wherein, X(i)For multidimensional characteristic vectors,μ is it is expected, σ is standard deviation, σ2For variance,
X is variable,M is the size of training dataset.
6. according to the method for claim 5, it is characterised in that also include：
User is received to be updated the Outlier Detection Algorithm program feedback information of result.
A kind of 7. vehicle, it is characterised in that including：
Data acquisition unit, vehicle event is sent to for collection vehicle early warning related data, and by the vehicle early warning related data
Hinder early warning processor；
The vehicle trouble early warning processor, for the vehicle early warning dependency number using Outlier Detection Algorithm program to reception
According to being handled, vehicle trouble warning information is judged whether；Wherein, the Outlier Detection Algorithm program is to utilize to collect
History vehicle early warning related data statistical model is trained to obtain.
8. vehicle according to claim 7, it is characterised in that the vehicle trouble early warning processor includes：
Outlier Detection Algorithm procedural training device, for mapping to form training dataset { X history vehicle early warning related data(1),...,X(m)}；Establish the Gaussian statistics model p (x for performing vehicle trouble early warning；μ,σ2)；It is true according to the training dataset
Fixed each characteristic value xiCorresponding μiWithUtilize the μi, it is describedAnd the Gaussian statistics model obtains the abnormal inspection
Method of determining and calculating program；
Wherein, X(i)For multidimensional characteristic vectors,μ is it is expected, σ is standard deviation, σ2For variance,
X is variable,M is the size of training dataset.
9. vehicle according to claim 8, it is characterised in that the vehicle trouble early warning processor includes：
Outlier Detection Algorithm program renovator, for receiving user to the feedback information of result to the Outlier Detection Algorithm
Program is updated.
10. a kind of vehicle trouble early warning system, it is characterised in that the system includes：
Vehicle, vehicle trouble early warning is sent to for collection vehicle early warning related data, and by the vehicle early warning related data
Server；
The vehicle trouble Warning Service device, for the vehicle early warning dependency number using Outlier Detection Algorithm program to reception
According to being handled, vehicle trouble warning information is judged whether；Wherein, the Outlier Detection Algorithm program is to utilize to collect
History vehicle early warning related data statistical model is trained to obtain.
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| CN109615726A (en) * | 2018-12-12 | 2019-04-12 | 湖北汽车工业学院 | Information output method, device, dumper system, computer and storage medium |
| CN110389572A (en) * | 2018-04-23 | 2019-10-29 | 上海博泰悦臻电子设备制造有限公司 | Vehicle part failure gives warning in advance method, system and server |
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| CN111988342A (en) * | 2020-09-18 | 2020-11-24 | 大连理工大学 | Online automobile CAN network anomaly detection system |
| CN112486136A (en) * | 2019-09-11 | 2021-03-12 | 中科云谷科技有限公司 | Fault early warning system and method |
| CN112606779A (en) * | 2020-12-24 | 2021-04-06 | 东风汽车有限公司 | Automobile fault early warning method and electronic equipment |
| CN113888775A (en) * | 2020-06-19 | 2022-01-04 | 比亚迪股份有限公司 | Vehicle early warning method, server, storage medium, vehicle early warning system and vehicle |
| US11320813B2 (en) | 2018-10-25 | 2022-05-03 | General Electric Company | Industrial asset temporal anomaly detection with fault variable ranking |
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| CN111988342A (en) * | 2020-09-18 | 2020-11-24 | 大连理工大学 | Online automobile CAN network anomaly detection system |
| CN112606779A (en) * | 2020-12-24 | 2021-04-06 | 东风汽车有限公司 | Automobile fault early warning method and electronic equipment |
| CN117389256A (en) * | 2023-12-11 | 2024-01-12 | 青岛盈智科技有限公司 | Early warning method for truck vehicle state in transportation process |
| CN117389256B (en) * | 2023-12-11 | 2024-03-08 | 青岛盈智科技有限公司 | Early warning method for truck vehicle state in transportation process |
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Application publication date: 20171128 |