Body-sensing prediction technique, device and terminalTechnical field
The present invention relates to automatic Pilot technical fields, and in particular to a kind of body-sensing prediction technique, device and terminal.
Background technique
Currently, the mode for measuring automatic driving vehicle body-sensing in automatic Pilot field is relatively simple.There are two types of common weighing apparatusesAmount method, one is simply according to the Parameters Calculations automatic driving vehicle body-sensing such as acceleration, another kind is to rely on people to driving automaticallyIt sails vehicle and carries out body-sensing marking.For example, the existing body-sensing balancing method of high-speed rail is typically based on the calculating of acceleration and rule threshold,But such methods are not suitable for scene complexity, require finer unmanned vehicle field of giving a mark.
Automatic driving vehicle is constantly updating in iterative process, and the ride experience of driver or passenger are increasingly heavierIt wants.Rely on the mode of people's marking only at present to measure body-sensing, but people's scoring process is cumbersome, it is at high cost.And with vehicle, gardenArea, other cities garden increase, people marking measurement mode no longer have feasibility.The measurement mode of people's marking can only obtainRough body-sensing evaluation, can not further analyze the reason of body-sensing improves or degenerates.
Summary of the invention
The embodiment of the present invention provides body-sensing prediction technique, device and terminal, at least to solve the above skill in the prior artArt problem.
In a first aspect, the embodiment of the invention provides a kind of body-sensing prediction techniques, comprising:
The first body-sensing achievement data in predetermined time period is divided into body-sensing training sample, the body-sensing training sampleIn include the corresponding relationship of body-sensing type and body-sensing score marked；
It is trained using the body-sensing training sample, obtains body-sensing scoring model；
The second body-sensing achievement data in predetermined time period is input in the body-sensing scoring model, obtains describedThe corresponding prediction body-sensing score of two body-sensing achievement datas.
In one embodiment, it is trained using each body-sensing training sample, obtains body-sensing scoring model, comprising:
Using including that leaning forward of having marked feels type and lean forward and feel the corresponding relationship of score in the body-sensing training sample, obtainScoring model is felt to leaning forward.
In one embodiment, it is trained using each body-sensing training sample, obtains body-sensing scoring model, comprising:
Using including the dynamic type of rolling marked and the corresponding relationship for shaking dynamic score in the body-sensing training sample, obtainTo the dynamic scoring model of rolling.
In one embodiment, it is trained using each body-sensing training sample, obtains body-sensing scoring model, comprising:
Using the corresponding relationship in the body-sensing training sample including the pause and transition in rhythm or melody sense type and pause and transition in rhythm or melody sense score that have marked, obtainTo pause and transition in rhythm or melody sense scoring model.
In one embodiment, it is trained using each body-sensing training sample, obtains body-sensing scoring model, comprising:
Utilize the always body-sensing and the by bus corresponding pass of body-sensing gross score by bus in the body-sensing training sample including having markedSystem, obtains body-sensing scoring model of always riding.
In one embodiment, the second body-sensing achievement data in predetermined time period is input to the body-sensing marking mouldIn type, the corresponding prediction body-sensing score of the second body-sensing achievement data is obtained, comprising:
Obtain the second body-sensing achievement data, the second body-sensing achievement data include acceleration, speed, position andBrake rate；
The second body-sensing achievement data is input in multiple body-sensing scoring models, is respectively obtained corresponding multipleBody-sensing score, wherein multiple body-sensing scoring models include lean forward feel scoring model, shake dynamic scoring model, pause and transition in rhythm or melody sense is beatenSub-model and body-sensing scoring model of always riding, multiple body-sensing scores include leaning forward feel score, shake dynamic score, pause and transition in rhythm or melody senseScore and by bus body-sensing gross score.
In one embodiment, further includes:
By multiple body-sensing score summations of the second body-sensing achievement data, prediction body-sensing gross score is obtained；
The prediction body-sensing gross score obtains prediction body-sensing average mark divided by the number of the body-sensing score.
The present invention also provides a kind of body-sensing prediction meanss, comprising:
Body-sensing training sample obtains module, for the first body-sensing achievement data in predetermined time period to be divided into body-sensingTraining sample includes the corresponding relationship of the body-sensing type and body-sensing score that have marked in the body-sensing training sample；
Body-sensing scoring model training module obtains body-sensing marking mould for being trained using the body-sensing training sampleType；
Body-sensing Score on Prediction module, for the second body-sensing achievement data in predetermined time period to be input to the body-sensingIn scoring model, the corresponding prediction body-sensing score of the second body-sensing achievement data is obtained.
In one embodiment, the body-sensing scoring model training module includes:
Lean forward and feel scoring model training unit, for using include in the body-sensing training sample marked lean forward and feel classIt type and leans forward and feels the corresponding relationship of score, obtain leaning forward and feel scoring model.
In one embodiment, the body-sensing scoring model training module includes:
Dynamic scoring model training unit is shaken, for utilizing the rolling innervation class in the body-sensing training sample including having markedType and the corresponding relationship for shaking dynamic score, obtain shaking dynamic scoring model.
In one embodiment, the body-sensing scoring model training module includes:
Pause and transition in rhythm or melody sense scoring model training unit, for utilizing the pause and transition in rhythm or melody sense class in the body-sensing training sample including having markedThe corresponding relationship of type and pause and transition in rhythm or melody sense score obtains pause and transition in rhythm or melody sense scoring model.
In one embodiment, the body-sensing scoring model training module includes:
Total body-sensing scoring model training unit by bus, for always multiplying using in the body-sensing training sample including what is markedCar body sense and the by bus corresponding relationship of body-sensing gross score obtain body-sensing scoring model of always riding.
In one embodiment, the body-sensing Score on Prediction module includes:
Second body-sensing achievement data acquiring unit, for obtaining the second body-sensing achievement data, second body-sensing refers toMarking data includes acceleration, speed, position and brake rate；
Body-sensing Score on Prediction unit, for the second body-sensing achievement data to be input to multiple body-sensing scoring modelsIn, respectively obtain corresponding multiple body-sensing scores, wherein multiple body-sensing scoring models include leaning forward feel scoring model, shakeDynamic scoring model, pause and transition in rhythm or melody sense scoring model and body-sensing scoring model of always riding, multiple body-sensing scores include sense of leaning forwardScore shakes dynamic score, pause and transition in rhythm or melody sense score and body-sensing gross score by bus.
In one embodiment, further includes:
Body-sensing average mark computing module is predicted, for by multiple body-sensing scores of the second body-sensing achievement dataSummation, obtains prediction body-sensing gross score, and the prediction body-sensing gross score obtains prediction body-sensing divided by the number of the body-sensing scoreAverage mark.
The third aspect, the embodiment of the invention provides a kind of body-sensing predict terminal, the function can by hardware realization,Corresponding software realization can also be executed by hardware.The hardware or software include one or more corresponding with above-mentioned functionModule.
In a possible design, body-sensing predicts to include processor and memory, the memory in the structure of terminalFor storing the program for supporting body-sensing prediction terminal to execute body-sensing prediction technique in above-mentioned first aspect, the processor is configuredFor for executing the program stored in the memory.The body-sensing prediction terminal can also include communication interface, be used for body-sensingPredict terminal and other equipment or communication.
Fourth aspect, the embodiment of the invention provides a kind of computer readable storage mediums, for storing body-sensing prediction dressSet computer software instructions used comprising for executing body-sensing prediction technique in above-mentioned first aspect be body-sensing prediction meanssRelated program.
A technical solution in above-mentioned technical proposal has the following advantages that or the utility model has the advantages that passes through what extraction manually markedBody-sensing training sample, training obtain body-sensing scoring model, feel scoring model for example, leaning forward, shake dynamic scoring model or pause and transition in rhythm or melodyFeel scoring model.Body-sensing achievement data in predetermined time period is input in body-sensing scoring model and is predicted, is obtained pre-If leaning forward in time span feels score, shakes dynamic score and pause and transition in rhythm or melody sense score.Without manually giving a mark, prediction body-sensing point is improvedSeveral efficiency is close with the result manually given a mark.
Above-mentioned general introduction is merely to illustrate that the purpose of book, it is not intended to be limited in any way.Except foregoing descriptionSchematical aspect, except embodiment and feature, by reference to attached drawing and the following detailed description, the present invention is furtherAspect, embodiment and feature, which will be, to be readily apparent that.
Detailed description of the invention
In the accompanying drawings, unless specified otherwise herein, otherwise indicate the same or similar through the identical appended drawing reference of multiple attached drawingsComponent or element.What these attached drawings were not necessarily to scale.It should be understood that these attached drawings depict only according to the present inventionDisclosed some embodiments, and should not serve to limit the scope of the present invention.
Fig. 1 is a kind of body-sensing prediction technique flow chart provided in an embodiment of the present invention；
Fig. 2 is body-sensing item table corresponding with the data of body-sensing score in predetermined time period provided in an embodiment of the present invention；
Fig. 3 is another body-sensing prediction technique flow chart provided in an embodiment of the present invention；
Fig. 4 is another body-sensing prediction technique flow chart provided in an embodiment of the present invention；
Fig. 5 is body-sensing prediction technique schematic diagram provided in an embodiment of the present invention；
Fig. 6 is a kind of body-sensing prediction meanss block diagram provided in an embodiment of the present invention；
Fig. 7 is another body-sensing prediction meanss block diagram provided in an embodiment of the present invention；
Fig. 8 is that a kind of body-sensing provided in an embodiment of the present invention predicts terminal schematic diagram.
Specific embodiment
Hereinafter, certain exemplary embodiments are simply just described.As one skilled in the art will recognize thatLike that, without departing from the spirit or scope of the present invention, described embodiment can be modified by various different modes.Therefore, attached drawing and description are considered essentially illustrative rather than restrictive.
Embodiment one
In a specific embodiment, as shown in Figure 1, a kind of body-sensing prediction technique flow chart provided, the methodInclude:
Step S10: the first body-sensing achievement data in predetermined time period is divided into body-sensing training sample, body-sensing trainingIt include the corresponding relationship of the body-sensing type and body-sensing score that have marked in sample.
Step S20: being trained using body-sensing training sample, obtains body-sensing scoring model.
Step S30: the second body-sensing achievement data in predetermined time period is input in body-sensing scoring model, obtainsThe corresponding prediction body-sensing score of two body-sensing achievement datas.
In a kind of example, any one predetermined time period is intercepted in one section of unmanned vehicle running time, for example, oneIn it unmanned vehicle driving process, any 20s is intercepted.As in 20s in predetermined time period, the first body-sensing index number is extractedAccording to.Since in the process of moving, body-sensing is related to speed, acceleration, position, brake rate etc., so, such as by predetermined time periodThe indexs source datas such as speed, acceleration, position, brake rate in some 20s of interception are as the first body-sensing achievement data.It willPredetermined time period is divided into multiple periods, and the first body-sensing achievement data can be divided into multiple bodies according to multiple periodsFeel training sample, each body-sensing training sample includes the body-sensing type and body-sensing score by manually marking.As Fig. 2 it is default whenBetween in length shown in body-sensing type table corresponding with the data of body-sensing score: predetermined time period 20s be continuously cut into 2s,Seven periods of 3s, 4s, 3s, 4s, 2s, 2s.Certainly, predetermined time period can also be 30s, 40s etc., according to the actual situation intoThe adjustment of row adaptability, in the protection scope of present embodiment.At the beginning of recording each period and the end time,Each period is corresponding with sense of leaning forward, pause and transition in rhythm or melody sense and shakes innervation/centrifugation sense one of them and each single item body-sensing corresponding oneA body-sensing score.Body-sensing score is bigger, and body-sensing is stronger, for example, sense of leaning forward is 1 point in first 2s, it is corresponding be compared withFor gentle curve；In the 4th 6s, shaking innervation is 6 points, and corresponding is steep curve.Wherein, in predetermined time periodInterior, body-sensing score can be used as the label of body-sensing type.Body-sensing training sample includes the period divided, corresponding with the periodBody-sensing score and body-sensing type are stored in jointly in a file, for example, can intercept in one day driving process multipleThe corresponding file of 20s, each 20s.
The body-sensing scoring model that training obtains may include leaning forward feel scoring model, shake dynamic scoring model and pause and transition in rhythm or melody senseScoring model etc..Certainly, the scoring model of including but not limited to above-mentioned three types, can also be other types, in this realityIt applies in the protection scope of mode.Finally, the second body-sensing achievement data in predetermined time period is input to the body that training obtainsFeel in scoring model, obtains the corresponding prediction body-sensing score of the second body-sensing achievement data.For example, leaning forward in predetermined time periodFeel score, shake dynamic score and pause and transition in rhythm or melody sense score etc..Without manually giving a mark, the efficiency of prediction body-sensing score is improved, and it is artificialThe result of marking is close.
In one embodiment, as shown in figure 3, step S20 includes:
Step S201: using including that leaning forward of having marked feels type and leaning forward and feel the corresponding of score and close in body-sensing training sampleSystem, obtains leaning forward and feels scoring model.
In one embodiment, as shown in figure 3, step S20 includes:
Step S202: corresponding the closing in body-sensing training sample including the dynamic type of the rolling and dynamic score of rolling marked is utilizedSystem, obtains shaking dynamic scoring model.
In one embodiment, as shown in figure 3, step S20 includes:
Step S203: the corresponding pass in body-sensing training sample including the pause and transition in rhythm or melody sense type marked and pause and transition in rhythm or melody sense score is utilizedSystem, obtains pause and transition in rhythm or melody sense scoring model.
Before being trained using body-sensing training sample, the data significantly more than threshold value are filtered out.And the first body-sensing is referred toIt marks data and carries out calculation processing, maximum value, minimum, mean value, variance and the change rate for obtaining the first body-sensing achievement data will be madeIt is characterized value.For example, the features such as acceleration maximum value, acceleration minimum value, speed mean value, velocity variance and percentage speed variationValue.The big characteristic value of influence degree is screened by decision Tree algorithms.Characteristic value is input to decision tree, neural network, integrated mouldType etc., training obtain body-sensing scoring model.Feel scoring model for example, leaning forward, shake dynamic scoring model and pause and transition in rhythm or melody sense marking mouldType.
In one embodiment, as shown in figure 3, step S20 includes:
Step S204: the always body-sensing and by bus pair of body-sensing gross score by bus in body-sensing training sample including having marked are utilizedIt should be related to, obtain body-sensing scoring model of always riding.
It can also include the body-sensing of always riding marked in body-sensing training sample, always body-sensing refers to predetermined time period by busInterior, it is to experience gross score by bus according to always the score provided is experienced by bus that the total of user experiences by bus.For example, implementing aboveIn example, when predetermined time period is 20s, the experience gross score by bus in 20s is provided, is stored in file.It can take work as within one dayIn 10 minutes, be cut into multiple 20s, corresponding one of each 20s experience gross score by bus is deposited in one file.
In one embodiment, as shown in figure 3, step S30 includes:
Step S301: obtain the second body-sensing achievement data, the second body-sensing achievement data include acceleration, speed, position withAnd brake rate；
Step S302: the second body-sensing achievement data is input in multiple body-sensing scoring models, is respectively obtained corresponding moreA body-sensing score, wherein multiple body-sensing scoring models include leaning forward feel scoring model, shake dynamic scoring model, pause and transition in rhythm or melody sense markingModel and always body-sensing scoring model by bus, multiple body-sensing scores include lean forward feel score, shake dynamic score, pause and transition in rhythm or melody sense score withAnd body-sensing gross score by bus.
In a kind of example, as shown in figure 5, data i.e. the second body-sensing achievement data that triggering inquiry daily is newly-increased, simultaneouslyThe second body-sensing achievement data in memory is executed into distributed task scheduling, is input in each body-sensing scoring model, is given a markAs a result.Downloading marking result, which such as leans forward, to be felt score, shakes dynamic score and pause and transition in rhythm or melody sense score, then downloads body-sensing gross score by bus,These data are deposited in the database, are saved in predetermined time period in database, body-sensing of certain vehicle in some region refers toMark the corresponding relationship of data, body-sensing type and body-sensing score.
In one embodiment, as shown in figure 4, the method also includes:
Step S40: multiple body-sensing scores of the second body-sensing achievement data are summed, and obtain prediction body-sensing gross score, predictionBody-sensing gross score obtains prediction body-sensing average mark divided by the number of body-sensing score.
In a kind of example, multiple 20s can be intercepted.Corresponding lean forward of multiple 20s is felt into score summation, the dynamic score of rollingSummation and the summation of pause and transition in rhythm or melody sense score.Then, will lean forward feel the sum of score, shake dynamic score it, the sum of pause and transition in rhythm or melody sense score andThe sum of body-sensing gross score is respectively divided by corresponding multiple periods by bus, obtain leaning forward feel average mark, shake dynamic average mark,Pause and transition in rhythm or melody sense average mark and by bus body-sensing average mark.
Embodiment two
In a kind of specific embodiment, as shown in fig. 6, body-sensing prediction meanss include:
Body-sensing training sample obtains module 10, more for the first body-sensing achievement data in predetermined time period to be divided intoA body-sensing training sample includes the corresponding relationship of the body-sensing type and body-sensing score that have marked in body-sensing training sample；
Body-sensing scoring model training module 20 obtains body-sensing marking mould for being trained using each body-sensing training sampleType；
Body-sensing Score on Prediction module 30 is beaten for the second body-sensing achievement data in predetermined time period to be input to body-sensingIn sub-model, the corresponding prediction body-sensing score of the second body-sensing achievement data is obtained.
In one embodiment, as shown in fig. 7, body-sensing scoring model training module 20 includes:
It leans forward and feels scoring model training unit 201, for leaning forward using in the body-sensing training sample including what is markedFeel type and lean forward and feel the corresponding relationship of score, obtains leaning forward and feel scoring model.
In one embodiment, as shown in fig. 7, body-sensing scoring model training module 20 includes:
Dynamic scoring model training unit 202 is shaken, for utilizing the rolling innervation class in body-sensing training sample including having markedType and the corresponding relationship for shaking dynamic score, obtain shaking dynamic scoring model.
In one embodiment, as shown in fig. 7, body-sensing scoring model training module 20 includes:
Pause and transition in rhythm or melody sense scoring model training unit 203, for utilizing the pause and transition in rhythm or melody sense class in body-sensing training sample including having markedType and the corresponding relationship for shaking dynamic score, obtain pause and transition in rhythm or melody sense scoring model.
In one embodiment, as shown in fig. 7, body-sensing scoring model training module 20 includes:
Always body-sensing scoring model training unit 204 by bus, for using including having marked in the body-sensing training sampleAlways body-sensing and the by bus corresponding relationship of body-sensing gross score by bus, obtain body-sensing scoring model of always riding.
In one embodiment, as shown in fig. 7, body-sensing Score on Prediction module 30 includes:
Second body-sensing achievement data acquiring unit 301, for obtaining the second body-sensing achievement data, the second body-sensing indexData include acceleration, speed, position and brake rate；
Body-sensing Score on Prediction unit 302, for the second body-sensing achievement data to be input in multiple body-sensing scoring models, pointCorresponding multiple body-sensing scores are not obtained, wherein multiple body-sensing scoring models include leaning forward feel scoring model, shake dynamic marking mouldType, pause and transition in rhythm or melody sense scoring model and body-sensing scoring model of always riding, multiple body-sensing scores include leaning forward feel score, shake dynamic pointSeveral, pause and transition in rhythm or melody sense score and by bus body-sensing gross score.
In one embodiment, as shown in fig. 7, described device further include:
Predict body-sensing average mark computing module 40, for multiple body-sensing scores of the second body-sensing achievement data to be summed,Prediction body-sensing gross score is obtained, prediction body-sensing gross score obtains prediction body-sensing average mark divided by the number of body-sensing score.
Embodiment three
The embodiment of the invention provides a kind of body-sensings to predict terminal, as shown in Figure 8, comprising:
Memory 400 and processor 500 are stored with the computer journey that can be run on processor 500 in memory 400Sequence.Processor 500 realizes the body-sensing prediction technique in above-described embodiment when executing the computer program.Memory 400 and placeThe quantity for managing device 500 can be one or more.
Communication interface 600 is communicated for memory 400 and processor 500 with outside.
Memory 400 may include high speed RAM memory, it is also possible to further include nonvolatile memory (non-Volatile memory), a for example, at least magnetic disk storage.
If memory 400, processor 500 and the independent realization of communication interface 600, memory 400, processor 500And communication interface 600 can be connected with each other by bus and complete mutual communication.The bus can be industrial standardArchitecture (ISA, Industry Standard Architecture) bus, external equipment interconnection (PCI, PeripheralComponent) bus or extended industry-standard architecture (EISA, Extended Industry StandardComponent) bus etc..The bus can be divided into address bus, data/address bus, control bus etc..For convenient for expression, Fig. 8In only indicated with a thick line, it is not intended that an only bus or a type of bus.
Optionally, in specific implementation, if memory 400, processor 500 and communication interface 600 are integrated in one pieceOn chip, then memory 400, processor 500 and communication interface 600 can complete mutual communication by internal interface.
Example IV
A kind of computer readable storage medium is stored with computer program, realization when described program is executed by processorEmbodiment one include it is any as described in body-sensing prediction technique.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically showThe description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or examplePoint is included at least one embodiment or example of the invention.Moreover, particular features, structures, materials, or characteristics describedIt may be combined in any suitable manner in any one or more of the embodiments or examples.In addition, without conflicting with each other, thisThe technical staff in field can be by the spy of different embodiments or examples described in this specification and different embodiments or examplesSign is combined.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importanceOr implicitly indicate the quantity of indicated technical characteristic." first " is defined as a result, the feature of " second " can be expressed or hiddenIt include at least one this feature containing ground.In the description of the present invention, the meaning of " plurality " is two or more, unless otherwiseClear specific restriction.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includesIt is one or more for realizing specific logical function or process the step of executable instruction code module, segment or portionPoint, and the range of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discussed suitableSequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, Lai Zhihang function, this should be of the inventionEmbodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered useIn the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, forInstruction execution system, device or equipment (such as computer based system, including the system of processor or other can be held from instructionThe instruction fetch of row system, device or equipment and the system executed instruction) it uses, or combine these instruction execution systems, device or setIt is standby and use.For the purpose of this specification, " computer-readable medium ", which can be, any may include, stores, communicates, propagates or passDefeated program is for instruction execution system, device or equipment or the dress used in conjunction with these instruction execution systems, device or equipmentIt sets.The more specific example (non-exhaustive list) of computer-readable medium include the following: there is the electricity of one or more wiringsInterconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable read-only memory(CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other suitable JieMatter, because can then be edited, be interpreted or when necessary with other for example by carrying out optical scanner to paper or other mediaSuitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the invention can be realized with hardware, software, firmware or their combination.Above-mentionedIn embodiment, software that multiple steps or method can be executed in memory and by suitable instruction execution system with storageOr firmware is realized.It, and in another embodiment, can be under well known in the art for example, if realized with hardwareAny one of column technology or their combination are realized: having a logic gates for realizing logic function to data-signalDiscrete logic, with suitable combinational logic gate circuit specific integrated circuit, programmable gate array (PGA), sceneProgrammable gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carriesIt suddenly is that relevant hardware can be instructed to complete by program, the program can store in a kind of computer-readable storage mediumIn matter, which when being executed, includes the steps that one or a combination set of embodiment of the method.
It, can also be in addition, each functional unit in each embodiment of the present invention can integrate in a processing moduleIt is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mouldBlock both can take the form of hardware realization, can also be realized in the form of software function module.The integrated module is such asFruit is realized and when sold or used as an independent product in the form of software function module, also can store in a computerIn readable storage medium storing program for executing.The storage medium can be read-only memory, disk or CD etc..
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, anyThose familiar with the art in the technical scope disclosed by the present invention, can readily occur in its various change or replacement,These should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with the guarantor of the claimIt protects subject to range.