CN109635965A - Bus scraps decision-making technique, device and readable storage medium storing program for executing - Google Patents
Bus scraps decision-making technique, device and readable storage medium storing program for executing Download PDFInfo
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- CN109635965A CN109635965A CN201811582826.8A CN201811582826A CN109635965A CN 109635965 A CN109635965 A CN 109635965A CN 201811582826 A CN201811582826 A CN 201811582826A CN 109635965 A CN109635965 A CN 109635965A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/30—Administration of product recycling or disposal
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W90/00—Enabling technologies or technologies with a potential or indirect contribution to greenhouse gas [GHG] emissions mitigation
Abstract
The embodiment of the present application provides a kind of bus and scraps decision-making technique, device and readable storage medium storing program for executing, by obtaining each respective history operation data of car for public affairs, and data processing is carried out to history operation data, characteristic is filtered out from the history operation data after data processing according to preset rules, then according to the respective characteristic training machine learning model of each car for public affairs, and the characteristic of the car for public affairs to be measured of input is predicted according to the machine learning model that training obtains, and the case where car for public affairs to be measured is with the presence or absence of vehicle scrapping is judged according to prediction result.Thus, by carrying out depth excavation to the respective history operation data of each car for public affairs, it can comprehensively be analyzed so that the progress of vehicle scrapping situation is objective, it is not only the daily operation management offer data supporting of car for public affairs, intuitive and scientific foundation is also provided for vehicle scrapping decision, the unnecessary financial expense of reduction saves financial budget for institutional settings.
Description
Technical field
This application involves field of computer technology, decision-making technique, device and readable are scrapped in particular to a kind of bus
Storage medium.
Background technique
Vehicle scrapping, which refers to, refers to an automobile safety check system according to as defined in the quantity of vehicle seat and use age during use
Degree, will be forced to scrap and cannot reuse reaching service life rear vehicle.Car for public affairs actually belongs to vehicle in use, still
In the management of car for public affairs actual operation, since car operation situation is changeable, situation complexity is scrapped, vehicle reaches certain damage
After situation, maintenance cost is persistently paid can occupy institutional settings in the budget of bus management aspect extremely.Under appropriate circumstances
Vehicle is carried out to scrap processing, vehicle subsequent maintenance expenditure can be reduced, achieve the purpose that vehicle economical operation interests are optimal.
For vehicle scrapping, it is existing scrap mode all and be consider whether vehicle reaches service life, if reach traveling
Mileage.Scrapping process is to apply step by step, whether leader's examination & approval are agreed to then determine to scrap.Such decision mode has subjective piece
The drawbacks of face, due to information asymmetry, vehicle user of service is familiar with vehicle condition relatively, but does not have directly decision to decision is scrapped
Effect.Leading body at a higher level possesses decision-making power but not fully aware of to vehicle actual state.So vehicle report is subjectively determined
Useless to breed some spoilage problems, to bus overall restructuring, work against corruption has deleterious effect.
Summary of the invention
In order to overcome above-mentioned deficiency in the prior art, a kind of bus of being designed to provide of the application scraps decision-making party
Method, device and readable storage medium storing program for executing, to solve or improve the above problem.
To achieve the goals above, the embodiment of the present application the technical solution adopted is as follows:
In a first aspect, the embodiment of the present application, which provides a kind of bus, scraps decision-making technique, it is applied to server, the method packet
It includes:
Obtain the respective history operation data of each car for public affairs；
Data processing is carried out to the history operation data, and runs number from the history after data processing according to preset rules
According to filtering out characteristic, the characteristic includes in the history operation data of the car for public affairs under target data field
Data and corresponding label value；
According to the respective characteristic training machine learning model of each car for public affairs, and the engineering obtained according to training
Model is practised to predict the characteristic of the car for public affairs to be measured of input；
The case where car for public affairs to be measured is with the presence or absence of vehicle scrapping is judged according to prediction result.
In a kind of possible embodiment, described the step of obtaining each car for public affairs respective history operation data,
Include:
The respective history operation data of each car for public affairs, the history operation are obtained from each car for public affairs server
Data include the data information under multiple data fields and each data field.
In a kind of possible embodiment, described the step of data processing is carried out to the history operation data, comprising:
By the exceptional value and missing values rejecting in the history operation data, the history after obtaining data processing runs number
According to.
It is described to be screened according to preset rules from the history operation data after data processing in a kind of possible embodiment
The step of characteristic out, comprising:
The respective target data field of each car for public affairs for needing to screen and history vehicle are obtained from the preset rules
Disposal options；
For each car for public affairs, the mesh is filtered out from the history operation data of the car for public affairs after data processing
The data under data field are marked, and construct corresponding label value according to the history vehicle disposal options of the car for public affairs, to obtain
The characteristic of each car for public affairs.
It is described to be learnt according to the respective characteristic training machine of each car for public affairs in a kind of possible embodiment
Before the step of model, the method also includes:
Using it is preceding to fill method to the number under the target data field in the characteristic for filtering out each car for public affairs
It is filled up according to missing values are carried out, and binary conversion treatment is carried out to the corresponding label value of each car for public affairs.
It is described to be learnt according to the respective characteristic training machine of each car for public affairs in a kind of possible embodiment
The step of model, comprising:
The characteristic of the first ratio in the respective characteristic of each car for public affairs is chosen as training characteristics data,
And the characteristic of the second ratio in the respective characteristic of each car for public affairs is chosen as test feature data；
According to the training characteristics data and the test feature data training machine learning model, the machine learning mould
Type uses one of decision-tree model, Random Forest model, supporting vector machine model；
The model parameter of the machine learning model is updated according to training result, and is returned according to the training characteristics data
The step of with the test feature data training machine learning model, when meeting training termination condition, output training is obtained
Machine learning model.
In a kind of possible embodiment, to be measured public affair of the machine learning model obtained according to training to input
The step of being predicted with the characteristic of vehicle, comprising:
The characteristic of the car for public affairs to be measured of input is predicted according to the machine learning model that training obtains, is obtained
The confidence level of the car for public affairs to be measured the case where there are vehicle scrappings；
The described the step of the case where car for public affairs to be measured is with the presence or absence of vehicle scrapping is judged according to prediction result, comprising:
Whether the confidence level for the case where judging the car for public affairs to be measured there are vehicle scrappings is greater than setting confidence level, if so,
The case where then determining the car for public affairs to be measured there are vehicle scrappings, otherwise determining the car for public affairs to be measured, there is no vehicle scrappings
Situation.
It is described to judge the car for public affairs to be measured with the presence or absence of vehicle according to prediction result in a kind of possible embodiment
After the step of the case where scrapping, the method also includes:
Vehicle scrapping decision-making foundation is generated according to judging result, and the vehicle scrapping decision-making foundation is sent to preparatory pass
The examination & approval terminal of connection.
Second aspect, the embodiment of the present application also provide a kind of bus and scrap decision making device, are applied to server, described device
Include:
Module is obtained, for obtaining the respective history operation data of each car for public affairs；
Data processing module, for carrying out data processing to the history operation data, and according to preset rules from data
Treated, and history operation data filters out characteristic, and the characteristic includes the history operation data of the car for public affairs
Data and corresponding label value under middle target data field；
Training prediction module, is used for according to the respective characteristic training machine learning model of each car for public affairs, and root
The characteristic of the car for public affairs to be measured of input is predicted according to the machine learning model that training obtains；
Judgment module, for judging the case where car for public affairs to be measured is with the presence or absence of vehicle scrapping according to prediction result.
The third aspect, the embodiment of the present application also provide a kind of readable storage medium storing program for executing, are stored thereon with computer program, described
Computer program, which is performed, realizes that above-mentioned bus scraps decision-making technique.
In terms of existing technologies, the application has the advantages that
The embodiment of the present application provides a kind of bus and scraps decision-making technique, device and readable storage medium storing program for executing, each by obtaining
The respective history operation data of car for public affairs, and data processing is carried out to history operation data, according to preset rules from data
History operation data after reason filters out characteristic, then according to the respective characteristic training machine of each car for public affairs
Model is practised, and the characteristic of the car for public affairs to be measured of input is predicted according to the machine learning model that training obtains, and
The case where car for public affairs to be measured is with the presence or absence of vehicle scrapping is judged according to prediction result.As a result, by each car for public affairs
Respective history operation data carries out depth excavation, can be carried out with vehicle scrapping situation it is objective comprehensively analyze, not only public affair
Data supporting is provided with the daily operation management of vehicle, intuitive and scientific foundation is also provided for vehicle scrapping decision, reduction is not
Necessary financial expense saves financial budget for institutional settings.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is the flow diagram that bus provided by the embodiments of the present application scraps decision-making technique；
Fig. 2 is a kind of functional block diagram that bus provided by the embodiments of the present application scraps decision making device；
Fig. 3 is another functional block diagram that bus provided by the embodiments of the present application scraps decision making device；
Fig. 4 is the structural representation of the server provided by the embodiments of the present application that decision-making technique is scrapped for realizing above-mentioned bus
Block diagram.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiment is some embodiments of the present application, instead of all the embodiments.Usually herein
The component of the embodiment of the present application described and illustrated in place's attached drawing can be arranged and be designed with a variety of different configurations.
Therefore, the detailed description of the embodiments herein provided in the accompanying drawings is not intended to limit below claimed
Scope of the present application, but be merely representative of the selected embodiment of the application.Based on the embodiment in the application, this field is common
Technical staff's all other embodiment obtained without creative labor belongs to the application protection
Range.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.
Referring to Fig. 1, scrapping a kind of flow diagram of decision-making technique for bus provided by the embodiments of the present application, should say
Bright, it is limitation with Fig. 1 and specific order below that bus provided by the embodiments of the present application, which scraps decision-making technique not,.This method
Detailed process it is as follows:
Step S210 obtains the respective history operation data of each car for public affairs.
In a kind of possible embodiment, each car for public affairs can be during history be runed in real time or every pre-
It, on this basis, can be from each if respective history operation data is sent in associated car for public affairs server by the period
The respective history operation data of each car for public affairs is obtained in a car for public affairs server.
Wherein, the history operation data includes the data information under multiple data fields and each data field.Example
Such as, it is assumed that the data field in history operation data is successively [c1, c2, c3..., cn], amount to n field, entire history
Operation data is as follows:
[[c11, c12, c13..., c1n]
[c21, c22, c23..., c2n]
[c31, c32, c33..., c3n]
......
[cm1, cm2, cm3..., cmn]]
Wherein, c12Second data field being meant that in the first data, entire history operation data may include m item
Data, every data have n data field.
Step S220 carries out data processing to the history operation data, and according to preset rules after data processing
History operation data filters out characteristic.
In the present embodiment, it is necessary first to by the exceptional value and missing values rejecting in the history operation data, obtain data
Treated history operation data.
Then, it is obtained from the preset rules and needs the respective target data field of each car for public affairs screened and go through
History vehicle disposal options, and it is directed to each car for public affairs, it is sieved from the history operation data of the car for public affairs after data processing
The data under the target data field are selected, and construct corresponding label according to the history vehicle disposal options of the car for public affairs
Value, to obtain the characteristic of each car for public affairs.The characteristic includes the history operation number of the car for public affairs as a result,
According under middle target data field data and corresponding label value.
For example, in construction feature data, can choose the vehicle purchase date, current driving mileage, maintenance time last time,
Last time maintenance cost etc., and construct when time maintenance average price, whether overhaul, in season average maintenance number etc. be used as feature
Value.Label value can be history vehicle disposal options, can use 0,1 coded representation, and 1 indicates to scrap, and 0 indicates to continue to make after repairing
With.
Step S230, according to the respective characteristic training machine learning model of each car for public affairs, and according to trained
To machine learning model the characteristic of the car for public affairs to be measured of input is predicted.
In the present embodiment, before the training machine learning model, the present embodiment can also be using preceding to fill method pair
The data progress missing values filtered out under the target data field in the characteristic of each car for public affairs are filled up, and to each public affairs
Business carries out binary conversion treatment with the corresponding label value of vehicle.
Then, the characteristic of the first ratio in the respective characteristic of each car for public affairs is chosen as training characteristics
Data, and the characteristic of the second ratio in the respective characteristic of each car for public affairs is chosen as test feature data.
For example, 70% characteristic that can be chosen in the respective characteristic of each car for public affairs is used as training characteristics number
According to choosing 30% characteristic in the respective characteristic of each car for public affairs as test feature data.
It then, can be according to the training characteristics data and the test feature data training machine learning model
Machine learning model uses one of decision-tree model, Random Forest model, supporting vector machine model, or can also use
Other any feasible machine learning models.
Then, the model parameter of the machine learning model is updated according to training result, and is returned special according to the training
The step of levying data and the test feature data training machine learning model, when meeting training termination condition, output instruction
The machine learning model got.
Optionally, above-mentioned trained termination condition can be but not limited to: training the number of iterations reaches given threshold etc..
It on the basis of the above, can be according to the machine learning model that training obtains to the feature of the car for public affairs to be measured of input
The confidence level for the case where data are predicted, obtain the car for public affairs to be measured there are vehicle scrappings.Wherein, confidence level is higher, wave
This car for public affairs to be measured is bigger a possibility that there are vehicle scrappings.
Step S240 judges the case where car for public affairs to be measured is with the presence or absence of vehicle scrapping according to prediction result.
In the present embodiment, it can be determined that whether the confidence level of the car for public affairs to be measured the case where there are vehicle scrappings, which is greater than, sets
Fixation reliability, if so, the case where determining the car for public affairs to be measured there are vehicle scrappings, otherwise determines the car for public affairs to be measured not
The case where there are vehicle scrappings.For example, it is assumed that confidence level is 8, if the car for public affairs to be measured the case where there are vehicle scrappings sets
Reliability is 7, then the case where vehicle scrapping is not present in the car for public affairs to be measured is determined, if there are vehicle scrappings for the car for public affairs to be measured
The case where confidence level be 9, then the case where determining the car for public affairs to be measured there are vehicle scrappings.
Bus provided in this embodiment scraps decision-making technique as a result, and it is excessive to overcome prior art Subjective Factors
Disadvantage, propose it is more scientific effectively, it is easy accurately scrap means, by the respective history operation data of each car for public affairs into
Row depth is excavated, and can comprehensively be analyzed so that the progress of vehicle scrapping situation is objective, the daily operation management of only car for public affairs does not mention
For data supporting, intuitive and scientific foundation is also provided for vehicle scrapping decision, the unnecessary financial expense of reduction is organ
Unit saves financial budget.
Further, referring to Fig. 2, bus provided in this embodiment scraps decision-making technique also after step S240
It may comprise steps of:
Step S250 generates vehicle scrapping decision-making foundation according to judging result, and the vehicle scrapping decision-making foundation is sent out
Give preparatory associated examination & approval terminal.
In the present embodiment, according to above-mentioned judging result, if it is determined that the car for public affairs to be measured the case where there are vehicle scrappings, then
Generate the approval information for scrap processing, and using the approval information and history operation data as vehicle scrapping decision according to
According to being sent to preparatory associated examination & approval terminal.If it is determined that the case where vehicle scrapping is not present in the car for public affairs to be measured, then generating needs
The approval information to be repaired, and the approval information is sent to preparatory associated examination & approval terminal.
Further, referring to Fig. 3, the embodiment of the present application, which also provides a kind of bus, scraps decision making device 200, the bus report
Useless decision making device 200 may include:
Module 210 is obtained, for obtaining the respective history operation data of each car for public affairs.
Data processing module 220, for carrying out data processing to the history operation data, and according to preset rules from number
According to treated, history operation data filters out characteristic, and the characteristic includes the history operation number of the car for public affairs
According under middle target data field data and corresponding label value.
Training prediction module 230, is used for according to the respective characteristic training machine learning model of each car for public affairs, and
The characteristic of the car for public affairs to be measured of input is predicted according to the machine learning model that training obtains.
Judgment module 240, for judging the case where car for public affairs to be measured is with the presence or absence of vehicle scrapping according to prediction result.
It is understood that the concrete operation method of each functional module in the present embodiment can refer to above method embodiment
The detailed description of middle corresponding steps, it is no longer repeated herein.
Further, referring to Fig. 4, for the service provided by the embodiments of the present application for scrapping decision-making technique for above-mentioned bus
The structural schematic block diagram of device 100.In the present embodiment, the server 100 can be made general bus architecture knot by bus 110
Structure is realized.According to the concrete application of server 100 and overall design constraints condition, bus 110 may include any number of
Interconnection bus and bridge joint.Together by various circuit connections, these circuits include processor 120, storage medium 130 to bus 110
With bus interface 140.Optionally, bus interface 140 can be used, and network adapter 150 is equal via bus 110 in server 100
Connection.Network adapter 150 can be used for realizing the signal processing function of physical layer in server 100, and be penetrated by antenna realization
Frequency signal sends and receives.User interface 160 can connect external equipment, such as: keyboard, display, mouse or manipulation
Bar etc..Bus 110 can also connect various other circuits, such as timing source, peripheral equipment, voltage regulator or power management electricity
Road etc., these circuits are known in the art, therefore are no longer described in detail.
It can replace, server 100 may also be configured to generic processing system, such as be commonly referred to as chip, the general procedure
System includes: to provide the one or more microprocessors of processing function, and provide at least part of outer of storage medium 130
Portion's memory, it is all these all to be linked together by external bus architecture and other support circuits.
Alternatively, following realize can be used in server 100: having processor 120, bus interface 140, Yong Hujie
The ASIC (specific integrated circuit) of mouth 160；And it is integrated at least part of the storage medium 130 in one single chip, alternatively,
Following realize: one or more FPGA (field programmable gate array), PLD (programmable logic device can be used in server 100
Part), controller, state machine, gate logic, discrete hardware components, any other suitable circuit or to be able to carry out the application logical
Any combination of the circuit of various functions described in.
Wherein, processor 120 is responsible for management bus 110 and general processing (is stored on storage medium 130 including executing
Software).One or more general processors and/or application specific processor can be used to realize in processor 120.Processor 120
Example includes microprocessor, microcontroller, dsp processor and the other circuits for being able to carry out software.It should be by software broadly
It is construed to indicate instruction, data or any combination thereof, regardless of being called it as software, firmware, middleware, microcode, hard
Part description language or other.
Storage medium 130 is illustrated as separating with processor 120 in Fig. 4, however, those skilled in the art be easy to it is bright
White, storage medium 130 or its arbitrary portion can be located at except server 100.For example, storage medium 130 may include passing
Defeated line, the carrier waveform modulated with data, and/or the computer product that separates with radio node, these media can be by
Processor 120 is accessed by bus interface 140.Alternatively, storage medium 130 or its arbitrary portion are desirably integrated into processing
In device 120, for example, it may be cache and/or general register.
Above-described embodiment can be performed in the processor 120, specifically, can store in the storage medium 130 described
Bus scraps decision making device 200, and the processor 120 can be used for executing the bus and scrap decision making device 200.
Further, the embodiment of the present application also provides a kind of nonvolatile computer storage media, the computer is deposited
Storage media is stored with computer executable instructions, which can be performed the public affairs in above-mentioned any means embodiment
Vehicle scraps decision-making technique.
In conclusion the embodiment of the present application, which provides a kind of bus, scraps decision-making technique, device and readable storage medium storing program for executing, pass through
The respective history operation data of each car for public affairs is obtained, and data processing is carried out to history operation data, according to preset rules
Characteristic is filtered out from the history operation data after data processing, is then instructed according to the respective characteristic of each car for public affairs
Practice machine learning model, and the machine learning model obtained according to training carries out the characteristic of the car for public affairs to be measured of input
Prediction, and the case where car for public affairs to be measured is with the presence or absence of vehicle scrapping is judged according to prediction result.As a result, by each public affairs
Business carries out depth excavation with the respective history operation data of vehicle, can comprehensively analyze so that the progress of vehicle scrapping situation is objective, not only
Data supporting is provided for the daily operation management of car for public affairs, intuitive and scientific foundation is also provided for vehicle scrapping decision, is contracted
The unnecessary financial expense subtracted saves financial budget for institutional settings.
In embodiment provided herein, it should be understood that disclosed device and method, it can also be by other
Mode realize.Device and method embodiment described above is only schematical, for example, flow chart and frame in attached drawing
Figure shows the system frame in the cards of the system of multiple embodiments according to the application, method and computer program product
Structure, function and operation.In this regard, each box in flowchart or block diagram can represent a module, section or code
A part, a part of the module, section or code includes one or more for implementing the specified logical function
Executable instruction.It should also be noted that function marked in the box can also be with not in some implementations as replacement
It is same as the sequence marked in attached drawing generation.For example, two continuous boxes can actually be basically executed in parallel, they have
When can also execute in the opposite order, this depends on the function involved.It is also noted that in block diagram and or flow chart
Each box and the box in block diagram and or flow chart combination, can function or movement as defined in executing it is dedicated
Hardware based system realize, or can realize using a combination of dedicated hardware and computer instructions.
In addition, each functional module in each embodiment of the application can integrate one independent portion of formation together
Point, it is also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
It can replace, can be realized wholly or partly by software, hardware, firmware or any combination thereof.When
When using software realization, can entirely or partly it realize in the form of a computer program product.The computer program product
Including one or more computer instructions.It is all or part of when loading on computers and executing the computer program instructions
Ground is generated according to process or function described in the embodiment of the present application.The computer can be general purpose computer, special purpose computer,
Computer network or other programmable devices.The computer instruction may be stored in a computer readable storage medium, or
Person is transmitted from a computer readable storage medium to another computer readable storage medium, for example, the computer instruction
Wired (such as coaxial cable, optical fiber, digital subscriber can be passed through from a web-site, computer, server or data center
Line (DSL)) or wireless (such as infrared, wireless, microwave etc.) mode to another web-site, computer, server or data
It is transmitted at center.The computer readable storage medium can be any usable medium that computer can access and either wrap
The data storage devices such as electronic equipment, server, the data center integrated containing one or more usable mediums.The usable medium
It can be magnetic medium, (for example, floppy disk, hard disk, tape), optical medium (for example, DVD) or semiconductor medium (such as solid-state
Hard disk Solid State Disk (SSD)) etc..
It should be noted that, in this document, term " including ", " including " or its any other variant are intended to non-row
Its property includes, so that the process, method, article or equipment for including a series of elements not only includes those elements, and
And further include the other elements being not explicitly listed, or further include for this process, method, article or equipment institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence " including one ... ", it is not excluded that including institute
State in the process, method, article or equipment of element that there is also other identical elements.
It is obvious to a person skilled in the art that the application is not limited to the details of above-mentioned exemplary embodiment, Er Qie
In the case where without departing substantially from spirit herein or essential characteristic, the application can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and scope of the present application is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included in the application.Any reference signs in the claims should not be construed as limiting the involved claims.
Claims (10)
1. a kind of bus scraps decision-making technique, which is characterized in that be applied to server, which comprises
Obtain the respective history operation data of each car for public affairs；
Data processing is carried out to the history operation data, and is sieved according to preset rules from the history operation data after data processing
Characteristic is selected, the characteristic includes the data in the history operation data of the car for public affairs under target data field
And corresponding label value；
According to the respective characteristic training machine learning model of each car for public affairs, and the machine learning mould obtained according to training
Type predicts the characteristic of the car for public affairs to be measured of input；
The case where car for public affairs to be measured is with the presence or absence of vehicle scrapping is judged according to prediction result.
2. bus according to claim 1 scraps decision-making technique, which is characterized in that described to obtain each car for public affairs respectively
History operation data the step of, comprising:
The respective history operation data of each car for public affairs, the history operation data are obtained from each car for public affairs server
Including the data information under multiple data fields and each data field.
3. bus according to claim 1 scraps decision-making technique, which is characterized in that it is described to the history operation data into
The step of row data processing, comprising:
History operation data by the exceptional value and missing values rejecting in the history operation data, after obtaining data processing.
4. bus according to claim 1 scraps decision-making technique, which is characterized in that it is described according to preset rules from data
The step of history operation data after reason filters out characteristic, comprising:
From being obtained in the preset rules from needing the respective target data field of each car for public affairs screened and history vehicle
Set mode；
For each car for public affairs, the number of targets is filtered out from the history operation data of the car for public affairs after data processing
Corresponding label value is constructed according to the data under field, and according to the history vehicle disposal options of the car for public affairs, it is each to obtain
The characteristic of car for public affairs.
5. bus according to claim 1 scraps decision-making technique, which is characterized in that it is described according to each car for public affairs respectively
Characteristic training machine learning model the step of before, the method also includes:
Using it is preceding to fill method to the data under the target data field in the characteristic for filtering out each car for public affairs into
Row missing values are filled up, and carry out binary conversion treatment to the corresponding label value of each car for public affairs.
6. bus according to claim 1 scraps decision-making technique, which is characterized in that it is described according to each car for public affairs respectively
Characteristic training machine learning model the step of, comprising:
The characteristic of the first ratio in the respective characteristic of each car for public affairs is chosen as training characteristics data, and is selected
Take the characteristic of the second ratio in the respective characteristic of each car for public affairs as test feature data；
According to the training characteristics data and the test feature data training machine learning model, the machine learning model is adopted
With one of decision-tree model, Random Forest model, supporting vector machine model；
The model parameter of the machine learning model is updated according to training result, and is returned according to the training characteristics data and institute
The step of stating test feature data training machine learning model, when meeting training termination condition, obtained machine is trained in output
Device learning model.
7. bus according to claim 1 scraps decision-making technique, which is characterized in that the engineering obtained according to training
Practise the step of model predicts the characteristic of the car for public affairs to be measured of input, comprising:
The characteristic of the car for public affairs to be measured of input is predicted according to training obtained machine learning model, obtain this to
Survey the confidence level of car for public affairs the case where there are vehicle scrappings；
The described the step of the case where car for public affairs to be measured is with the presence or absence of vehicle scrapping is judged according to prediction result, comprising:
Whether the confidence level for the case where judging the car for public affairs to be measured there are vehicle scrappings is greater than setting confidence level, if so, sentencing
The fixed car for public affairs to be measured the case where there are vehicle scrappings, otherwise determine that the feelings of vehicle scrapping are not present in the car for public affairs to be measured
Condition.
8. bus according to claim 1 scraps decision-making technique, which is characterized in that it is described should be to according to prediction result judgement
After the step of surveying the case where car for public affairs whether there is vehicle scrapping, the method also includes:
According to judging result generate vehicle scrapping decision-making foundation, and by the vehicle scrapping decision-making foundation be sent in advance it is associated
Examine terminal.
9. a kind of bus scraps decision making device, which is characterized in that be applied to server, described device includes:
Module is obtained, for obtaining the respective history operation data of each car for public affairs；
Data processing module, for carrying out data processing to the history operation data, and according to preset rules from data processing
History operation data afterwards filters out characteristic, and the characteristic includes mesh in the history operation data of the car for public affairs
Mark the data under data field and corresponding label value；
Training prediction module, is used for according to the respective characteristic training machine learning model of each car for public affairs, and according to instruction
The machine learning model got predicts the characteristic of the car for public affairs to be measured of input；
Judgment module, for judging the case where car for public affairs to be measured is with the presence or absence of vehicle scrapping according to prediction result.
10. a kind of readable storage medium storing program for executing, which is characterized in that be stored with computer program, the meter in the readable storage medium storing program for executing
Calculation machine program, which is performed, realizes that bus described in any one of claim 1-8 scraps decision-making technique.
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