CN109460003A - Vehicle trouble predicts modeling method and system - Google Patents
Vehicle trouble predicts modeling method and system Download PDFInfo
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- CN109460003A CN109460003A CN201811208768.2A CN201811208768A CN109460003A CN 109460003 A CN109460003 A CN 109460003A CN 201811208768 A CN201811208768 A CN 201811208768A CN 109460003 A CN109460003 A CN 109460003A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
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- Automation & Control Theory (AREA)
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Abstract
The present invention provides a kind of vehicle trouble prediction modeling method and system, belong to technical field of data processing, this method comprises: vehicle-mounted OBD data, maintenance data and weather condition data are uploaded cloud, modeling analysis is carried out to the vehicle trouble being likely to occur, obtains prediction model；Current vehicle health forecast information is reached into corporate client maintenance device by prediction model, vehicle is safeguarded in advance for judging whether, to avoid there is significant trouble；Prediction model is pushed into individual client mobile phone A pp, even if providing instant prediction to vehicle health situation in conjunction with the OBD data generated, the prediction model can be adjusted improvement according to the new data of continuous renewal and client feedback beyond the clouds；Vehicle trouble is predicted by the prediction model, the output of the prediction model includes: to occur a possibility that certain failure in given time, the possible cause that failure occurs.This programme has saved cost while reducing risk.
Description
Technical field
The present invention relates to technical field of data processing more particularly to a kind of vehicle trouble prediction modeling methods and system.
Background technique
Effective prediction vehicle health situation, provides anticipation before there is significant trouble, can to avoid because no early warning therefore
Barrier causes time and economic loss.There are many vehicle-mounted fault diagnosis equipment (OBD) in the market, when can collect vehicle operation
The mass data and fault message of generation.These information combination vehicle maintenance mantenance datas and Weather information, pass through engineering
The method of habit can establish prediction model to vehicle trouble, the instant data and current weather conditions generated when being run by vehicle
Accurate Prediction is carried out to the various failures that vehicle may generate.The relevant patent of existing car fault diagnosis is laid particular emphasis on to going out
Existing failure is analyzed, this patent lay particular emphasis on to the future may appear failure prejudged in advance, to reduce risk, save
Cost.
Summary of the invention
In view of this, the embodiment of the present invention provides a kind of vehicle trouble prediction modeling method and system, at least partly solve
Problems of the prior art.
In a first aspect, a kind of vehicle trouble provided in an embodiment of the present invention predicts modeling method, comprising:
Vehicle-mounted OBD data, maintenance data and weather condition data are uploaded into cloud, the vehicle event to being likely to occur
Barrier carries out modeling analysis, obtains prediction model；
Current vehicle health forecast information is reached into corporate client maintenance device by prediction model, for judging whether
Vehicle is safeguarded in advance, to avoid there is significant trouble；
Prediction model is pushed into individual client mobile phone A pp, even if mentioning in conjunction with the OBD data generated to vehicle health situation
For instant prediction, the prediction model can be adjusted improvement according to the new data of continuous renewal and client feedback beyond the clouds；
Vehicle trouble is predicted by the prediction model, the output of the prediction model includes: in given time
There is a possibility that certain failure, the possible cause that failure occurs.
A kind of specific implementation according to an embodiment of the present invention, each characteristic variable passes through standardized mode in data
Ensure that each characteristic variable participates in modeling under same standard, the standardisation process of feature is as follows:
Wherein X is the original data vector of arbitrary characteristics variable,For the average value of the initial data, σ is the original number
According to standard deviation, X ' be standardization after characteristic variable.
A kind of specific implementation according to an embodiment of the present invention passes through principal component analysis when characteristic variable number is very big
To carry out dimensionality reduction to feature space.
A kind of specific implementation according to an embodiment of the present invention, the specific reduction process include:
Variance matrix R is calculated based on the characteristic after standardization, singular value decomposition is carried out to the chief later
R=U ∑ Vt
Wherein U and V is orthogonal matrix, and Σ is diagonal matrix.The result of singular value decomposition can directly obtain feature space
Dimensionality reduction result:
F=X ' Vk
Wherein VkIt is arranged for the preceding k of matrix V, it includes k feature that F, which is transformed feature space, which is far smaller than original
The feature quantity of beginning data.
A kind of specific implementation according to an embodiment of the present invention, the method also includes:
It is after dimensionality reduction as a result, selecting most accurately prediction model for the machine learning method of next step；
Using including decision tree, random forest, classification analysis, support vector machines, the machine learning methods such as neural network are built
Prediction model between vertical Y and F
Wherein Y is the observation of system, whether for recording system errors.
Second aspect, the embodiment of the invention provides a kind of vehicle trouble Forecasting Model Systems, comprising:
Vehicle-mounted OBD data, maintenance data and weather condition data are uploaded cloud, to may go out by uploading module
Existing vehicle trouble carries out modeling analysis, obtains prediction model；
Current vehicle health forecast information is reached corporate client maintenance device by prediction model by judgment module, is used
In judging whether to safeguard vehicle in advance, to avoid there is significant trouble；
Prediction model is pushed to individual client mobile phone A pp by prediction module, even if in conjunction with the OBD data generated to vehicle
Health status provides instant prediction, and the prediction model can be carried out according to the new data of continuous renewal and client feedback beyond the clouds
Adjustment improves；
Output module predicts vehicle trouble by the prediction model, the output of the prediction model include: to
Fix time it is interior there is a possibility that certain failure, the possible cause that failure occurs.
A kind of specific implementation according to an embodiment of the present invention, each characteristic variable passes through standardized mode in data
Ensure that each characteristic variable participates in modeling under same standard, the standardisation process of feature is as follows:
Wherein X is the original data vector of arbitrary characteristics variable,For the average value of the initial data, σ is the original number
According to standard deviation, X ' be standardization after characteristic variable.
A kind of specific implementation according to an embodiment of the present invention passes through principal component analysis when characteristic variable number is very big
To carry out dimensionality reduction to feature space.
A kind of specific implementation according to an embodiment of the present invention, the specific reduction process include:
Variance matrix R is calculated based on the characteristic after standardization, singular value decomposition is carried out to the chief later
R=U ∑ Vt
Wherein U and V is orthogonal matrix, and Σ is diagonal matrix.The result of singular value decomposition can directly obtain feature space
Dimensionality reduction result:
F=X ' Vk
Wherein VkIt is arranged for the preceding k of matrix V, it includes k feature that F, which is transformed feature space, which is far smaller than original
The feature quantity of beginning data.
A kind of specific implementation according to an embodiment of the present invention, the system are also used to:
It is after dimensionality reduction as a result, selecting most accurately prediction model for the machine learning method of next step；
Using including decision tree, random forest, classification analysis, support vector machines, the machine learning methods such as neural network are built
Prediction model between vertical Y and F
Wherein Y is the observation of system, whether for recording system errors.
Prediction model can be established to vehicle trouble by the method for machine learning, what is generated when running by vehicle is instant
Data and current weather conditions carry out Accurate Prediction to the various failures that vehicle may generate.This patent is laid particular emphasis on can to future
The failure that can occur is prejudged in advance, to reduce risk, save the cost.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this field
For those of ordinary skill, without creative efforts, it can also be obtained according to these attached drawings other attached drawings.
Fig. 1 is modeling procedure schematic diagram；
Fig. 2 is that vehicle trouble provided in an embodiment of the present invention predicts modeling method schematic diagram；
Fig. 3 is vehicle trouble Forecasting Model System schematic diagram provided in an embodiment of the present invention；
Fig. 4 is electronic equipment schematic diagram provided in an embodiment of the present invention.
Specific embodiment
The embodiment of the present invention is described in detail with reference to the accompanying drawing.
It will be appreciated that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Base
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
Its embodiment, shall fall within the protection scope of the present invention.
According to shown in Fig. 1, this system first will a large amount of vehicle-mounted OBD data, maintenance data and weather conditions number
According to cloud is uploaded, modeling analysis is carried out to the vehicle trouble being likely to occur, obtains prediction model.The prediction model can will be current
Vehicle health predictive information reaches corporate client maintenance device, is safeguarded in advance for judging whether to vehicle, to avoid
There is significant trouble；Prediction model can also be pushed to individual client mobile phone A pp, even if in conjunction with the OBD data generated to vehicle
Health status provides instant prediction.Prediction model can be adjusted according to the new data of continuous renewal and client feedback beyond the clouds
It improves.
Referring to fig. 2, vehicle trouble provided in an embodiment of the present invention predicts modeling method, comprising:
Vehicle-mounted OBD data, maintenance data and weather condition data are uploaded cloud, to what is be likely to occur by S101
Vehicle trouble carries out modeling analysis, obtains prediction model；
Current vehicle health forecast information is reached corporate client maintenance device by prediction model, for sentencing by S102
It is disconnected whether vehicle to be safeguarded in advance, to avoid there is significant trouble；
Prediction model is pushed to individual client mobile phone A pp by S103, even if in conjunction with the OBD data generated to vehicle health
Situation provides instant prediction, and the prediction model can be adjusted according to the new data of continuous renewal and client feedback beyond the clouds
It improves；
S104 predicts vehicle trouble that the output of the prediction model includes: to timing by the prediction model
It is interior a possibility that certain failure, the possible cause that failure occurs occur.
Modeling process can comparatively large number of machine learning method, including decision tree, random forest, classification analysis, support to
Amount machine, neural network etc. are therefrom selected most accurately pre- in conjunction with some extremum determination methods and missing data fill method
Survey model.
The output of prediction model includes occurring a possibility that certain failure in given time；The possible cause that failure occurs.
Each characteristic variable can ensure that each characteristic variable is joined under same standard by standardized mode in data
With modeling.The standardisation process of feature is as follows:
Wherein X is the original data vector of arbitrary characteristics variable,For the average value of the initial data, σ is the original number
According to standard deviation, X ' be standardization after characteristic variable.
When characteristic variable number is very big, dimensionality reduction can be carried out to feature space by principal component analysis.Specific reduction process
It is as follows:
Variance matrix R is calculated based on the characteristic after standardization, singular value decomposition is carried out to the chief later
R=U ∑ Vt,
Wherein U and V is orthogonal matrix, and ∑ is diagonal matrix.The result of singular value decomposition can directly obtain feature space
Dimensionality reduction result:
F=X ' Vk,
Wherein VkIt is arranged for the preceding k of matrix V, F is transformed feature space, includes k feature.The general number is much small
In the feature quantity of initial data.
It is after dimensionality reduction as a result, can be used for the machine learning method of next step to select most accurately prediction model.As before
Described, using including decision tree, random forest, classification analysis, support vector machines, the machine learning methods such as neural network are built
Prediction model between vertical Y and F
Wherein Y is the observation of system, whether for recording system errors.
Referring to Fig. 3, vehicle trouble Forecasting Model System 30 provided in an embodiment of the present invention, comprising:
Vehicle-mounted OBD data, maintenance data and weather condition data are uploaded cloud, to possible by uploading module 31
The vehicle trouble of appearance carries out modeling analysis, obtains prediction model；
Current vehicle health forecast information is reached corporate client maintenance device by prediction model by judgment module 32,
For judging whether to safeguard vehicle in advance, to avoid there is significant trouble；
Prediction model is pushed to individual client mobile phone A pp by prediction module 33, even if in conjunction with the OBD data generated to vehicle
Health status provides instant prediction, the prediction model can beyond the clouds according to the new data of continuous renewal and client feedback into
Row adjustment improves；
Output module 34 predicts vehicle trouble that the output of the prediction model includes: by the prediction model
Occur a possibility that certain failure, the possible cause that failure occurs in given time.
Vehicle trouble Forecasting Model System 30 can be run in the electronic device, as shown in figure 4, electronic equipment 400 can be with
It, can be according to being stored in read-only memory (ROM) including processing unit (such as central processing unit, graphics processor etc.) 401
Program in 402 executes various suitable from the program that storage device 408 is loaded into random access storage device (RAM) 403
When movement and processing.In RAM 403, also it is stored with electronic equipment 400 and operates required various programs and data.Processing dress
401, ROM 402 and RAM 403 is set to be connected with each other by bus 404.Input/output (I/O) interface 405 is also connected to bus
404。
In general, following device can connect to I/O interface 405: including such as touch screen, touch tablet, keyboard, mouse, figure
As the input unit 406 of sensor, microphone, accelerometer, gyroscope etc.；Including such as liquid crystal display (LCD), loudspeaking
The output device 407 of device, vibrator etc.；Storage device 408 including such as tape, hard disk etc.；And communication device 409.It is logical
T unit 409 can permit electronic equipment 400 and wirelessly or non-wirelessly be communicated with other equipment to exchange data.Although Fig. 4 shows
The electronic equipment 400 with various devices is gone out, it should be understood that being not required for implementing or having all dresses shown
It sets.It can alternatively implement or have more or fewer devices.
Particularly, in accordance with an embodiment of the present disclosure, it may be implemented as computer above with reference to the process of flow chart description
Software program.For example, embodiment of the disclosure includes a kind of computer program product comprising be carried on computer-readable medium
On computer program, which includes the program code for method shown in execution flow chart.In such reality
It applies in example, which can be downloaded and installed from network by communication device 409, or from storage device 408
It is mounted, or is mounted from ROM 402.When the computer program is executed by processing unit 401, the embodiment of the present disclosure is executed
Method in the above-mentioned function that limits.
It should be noted that the above-mentioned computer-readable medium of the disclosure can be computer-readable signal media or meter
Calculation machine readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example can be --- but not
Be limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above combination.Meter
The more specific example of calculation machine readable storage medium storing program for executing can include but is not limited to: have the electrical connection, just of one or more conducting wires
Taking formula computer disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type may be programmed read-only storage
Device (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device,
Or above-mentioned any appropriate combination.In the disclosure, computer readable storage medium can be it is any include or storage journey
The tangible medium of sequence, the program can be commanded execution system, device or device use or in connection.And at this
In open, computer-readable signal media may include in a base band or as the data-signal that carrier wave a part is propagated,
In carry computer-readable program code.The data-signal of this propagation can take various forms, including but not limited to
Electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be computer-readable and deposit
Any computer-readable medium other than storage media, the computer-readable signal media can send, propagate or transmit and be used for
By the use of instruction execution system, device or device or program in connection.Include on computer-readable medium
Program code can transmit with any suitable medium, including but not limited to: electric wire, optical cable, RF (radio frequency) etc. are above-mentioned
Any appropriate combination.
Above-mentioned computer-readable medium can be included in above-mentioned electronic equipment；It is also possible to individualism, and not
It is fitted into the electronic equipment.
Above-mentioned computer-readable medium carries one or more program, when said one or multiple programs are by the electricity
When sub- equipment executes, so that the electronic equipment: obtaining at least two internet protocol addresses；Send to Node evaluation equipment includes institute
State the Node evaluation request of at least two internet protocol addresses, wherein the Node evaluation equipment is internet from described at least two
In protocol address, chooses internet protocol address and return；Receive the internet protocol address that the Node evaluation equipment returns；Its
In, the fringe node in acquired internet protocol address instruction content distributing network.
Alternatively, above-mentioned computer-readable medium carries one or more program, when said one or multiple programs
When being executed by the electronic equipment, so that the electronic equipment: receiving the Node evaluation including at least two internet protocol addresses and request；
From at least two internet protocol address, internet protocol address is chosen；Return to the internet protocol address selected；Wherein,
The fringe node in internet protocol address instruction content distributing network received.
The calculating of the operation for executing the disclosure can be write with one or more programming languages or combinations thereof
Machine program code, above procedure design language include object oriented program language-such as Java, Smalltalk, C+
+, it further include conventional procedural programming language-such as " C " language or similar programming language.Program code can
Fully to execute, partly execute on the user computer on the user computer, be executed as an independent software package,
Part executes on the remote computer or executes on a remote computer or server completely on the user computer for part.
In situations involving remote computers, remote computer can pass through the network of any kind --- including local area network (LAN)
Or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as utilize Internet service
Provider is connected by internet).
Flow chart and block diagram in attached drawing are illustrated according to the system of the various embodiments of the disclosure, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of the module, program segment or code include one or more use
The executable instruction of the logic function as defined in realizing.It should also be noted that in some implementations as replacements, being marked in box
The function of note can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are actually
It can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it to infuse
Meaning, the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart can be with holding
The dedicated hardware based system of functions or operations as defined in row is realized, or can use specialized hardware and computer instruction
Combination realize.
Being described in unit involved in the embodiment of the present disclosure can be realized by way of software, can also be by hard
The mode of part is realized.Wherein, the title of unit does not constitute the restriction to the unit itself under certain conditions, for example, the
One acquiring unit is also described as " obtaining the unit of at least two internet protocol addresses ".
It should be appreciated that each section of the invention can be realized with hardware, software, firmware or their combination.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by those familiar with the art, all answers
It is included within the scope of the present invention.Therefore, protection scope of the present invention should be subject to the protection scope in claims.
Claims (10)
1. a kind of vehicle trouble predicts modeling method characterized by comprising
By vehicle-mounted OBD data, maintenance data and weather condition data upload cloud, to the vehicle trouble being likely to occur into
Row modeling analysis, obtains prediction model；
Current vehicle health forecast information is reached into corporate client maintenance device by prediction model, for judging whether to vehicle
It is safeguarded in advance, to avoid there is significant trouble；
Prediction model is pushed into individual client mobile phone A pp, even if being to the offer of vehicle health situation in conjunction with the OBD data generated
When predict, the prediction model can be adjusted improvement according to the new data of continuous renewal and client feedback beyond the clouds；
Vehicle trouble is predicted by the prediction model, the output of the prediction model includes: to occur in given time
A possibility that certain failure, the possible cause that failure occurs.
2. according to the method described in claim 1, it is characterized by:
Each characteristic variable ensures that each characteristic variable participates in modeling under same standard by standardized mode in data,
The standardisation process of feature is as follows:
Wherein X is the original data vector of arbitrary characteristics variable,For the average value of the initial data, σ is the initial data
Standard deviation, X ' are the characteristic variable after standardization.
3. according to the method described in claim 2, it is characterized by:
When characteristic variable number is very big, dimensionality reduction is carried out to feature space by principal component analysis.
4. according to the method described in claim 3, it is characterized in that, the specific reduction process includes:
Variance matrix R is calculated based on the characteristic after standardization, singular value decomposition is carried out to the chief later
R=U ∑ Vt
Wherein U and V is orthogonal matrix, and Σ is diagonal matrix.The result of singular value decomposition can directly obtain the dimensionality reduction of feature space
As a result:
F=X ' Vk
Wherein VkIt is arranged for the preceding k of matrix V, it includes k feature that F, which is transformed feature space, which is far smaller than original number
According to feature quantity.
5. according to the method described in claim 4, it is characterized in that, the method also includes:
It is after dimensionality reduction as a result, selecting most accurately prediction model for the machine learning method of next step；
Using including decision tree, random forest, classification analysis, support vector machines, the machine learning methods such as neural network establish Y
Prediction model between F
Wherein Y is the observation of system, whether for recording system errors.
6. a kind of vehicle trouble Forecasting Model System characterized by comprising
Vehicle-mounted OBD data, maintenance data and weather condition data are uploaded cloud, to what is be likely to occur by uploading module
Vehicle trouble carries out modeling analysis, obtains prediction model；
Current vehicle health forecast information is reached corporate client maintenance device by prediction model, for sentencing by judgment module
It is disconnected whether vehicle to be safeguarded in advance, to avoid there is significant trouble；
Prediction model is pushed to individual client mobile phone A pp by prediction module, even if in conjunction with the OBD data generated to vehicle health
Situation provides instant prediction, and the prediction model can be adjusted according to the new data of continuous renewal and client feedback beyond the clouds
It improves；
Output module predicts vehicle trouble that the output of the prediction model includes: to timing by the prediction model
It is interior a possibility that certain failure, the possible cause that failure occurs occur.
7. system according to claim 6, it is characterised in that:
Each characteristic variable ensures that each characteristic variable participates in modeling under same standard by standardized mode in data,
The standardisation process of feature is as follows:
Wherein X is the original data vector of arbitrary characteristics variable,For the average value of the initial data, σ is the initial data
Standard deviation, X ' are the characteristic variable after standardization.
8. system according to claim 7, it is characterised in that:
When characteristic variable number is very big, dimensionality reduction is carried out to feature space by principal component analysis.
9. system according to claim 8, which is characterized in that the specific reduction process includes:
Variance matrix R is calculated based on the characteristic after standardization, singular value decomposition is carried out to the chief later
R=U ∑ Vt
Wherein U and V is orthogonal matrix, and Σ is diagonal matrix.The result of singular value decomposition can directly obtain the dimensionality reduction of feature space
As a result:
F=X ' Vk
Wherein VkIt is arranged for the preceding k of matrix V, it includes k feature that F, which is transformed feature space, which is far smaller than original number
According to feature quantity.
10. system according to claim 9, which is characterized in that the system is also used to:
It is after dimensionality reduction as a result, selecting most accurately prediction model for the machine learning method of next step；
Using including decision tree, random forest, classification analysis, support vector machines, the machine learning methods such as neural network establish Y
Prediction model between F
Wherein Y is the observation of system, whether for recording system errors.
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| CN109933004A (en) * | 2019-03-27 | 2019-06-25 | 苏芯物联技术(南京)有限公司 | The machine failure diagnosis and prediction method and system cooperateed with based on edge calculations and cloud |
| CN109933004B (en) * | 2019-03-27 | 2022-05-24 | 苏芯物联技术(南京)有限公司 | Machine tool fault diagnosis and prediction method and system based on edge computing and cloud cooperation |
| CN110414837A (en) * | 2019-07-29 | 2019-11-05 | 上海乂学教育科技有限公司 | Based on mistake because of the man-machine interactive system of analysis |
| CN110990519A (en) * | 2019-11-08 | 2020-04-10 | 深圳市轱辘汽车维修技术有限公司 | Vehicle fault diagnosis method and device, electronic equipment and storage medium |
| CN110942096A (en) * | 2019-11-28 | 2020-03-31 | 新奥数能科技有限公司 | Fault detection method and device for refrigerant charge amount of multi-split air conditioner and electronic equipment |
| CN112734094A (en) * | 2020-12-30 | 2021-04-30 | 中南大学 | Smart city intelligent rail vehicle fault gene prediction method and system |
| CN112734094B (en) * | 2020-12-30 | 2023-11-24 | 中南大学 | Intelligent city intelligent rail vehicle fault gene prediction method and system |
| CN116258482A (en) * | 2023-05-16 | 2023-06-13 | 盐城数融智升科技有限公司 | Method for automatically selecting maintenance scheme, server and electronic equipment |
| CN116258482B (en) * | 2023-05-16 | 2023-07-18 | 盐城数融智升科技有限公司 | Method for automatically selecting maintenance scheme, server and electronic equipment |
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