Summary of the invention
The main purpose of the present invention is to provide a kind of training data processing method, device, equipment and computer-readable depositStorage media, it is intended to which the line of text for solving directly to cut from handwritten text page image is not used to train handwritten word identification mouldThe technical issues of type.
To achieve the above object, the present invention provides a kind of training data processing method, the training data processing method packetInclude following steps:
Handwritten text page image sample is obtained, and the line of text to be extracted in the handwritten text page image sample is carried outIndividual character mark, obtains the markup information of each character in line of text to be extracted；
According to the markup information of each character, each character institute is defined from the handwritten text page image sampleThe rectangle frame region of category；
To in the handwritten text page image sample, the region in addition to the rectangle frame region defined is carried out at coveringReason；
According to the markup information of each character, institute is marked off from the handwritten text page image sample after covering treatmentRegion belonging to line of text to be extracted is stated, and is cut, the handwriting text lines figure for training handwritten word identification model is obtainedPicture.
Optionally, the markup information of each character includes upper left point coordinate, width value and the height value of each character,
The markup information according to each character defines each word from the handwritten text page image sampleSymbol belonging to rectangle frame region the step of include:
According to the upper left point coordinate, the width value and the height value of each character, each character is calculatedLower-right most point coordinate；
According to the upper left point coordinate of each character and the lower-right most point coordinate, rectangle belonging to each character is definedFrame region.
Optionally, the markup information according to each character, from the handwritten text page image sample after covering treatmentThe step of marking off region belonging to the line of text to be extracted, includes: in example
The upper left point coordinate of each character is compared, to be determined from the upper left point coordinate of each characterMinimum abscissa value and maximum ordinate value out；
The lower-right most point coordinate of each character is compared, with true from the lower-right most point coordinate value of each characterMake maximum abscissa value and minimum ordinate value；
According to the minimum abscissa value, the maximum ordinate value, the maximum abscissa value and the minimum vertical seatScale value determines region belonging to the line of text to be extracted, and marks off from the handwritten text page image sample after covering treatmentRegion belonging to the line of text to be extracted.
Optionally, the region in the handwritten text page image sample, in addition to the rectangle frame region definedCarry out covering treatment the step of include:
In the handwritten text page image sample, by the region in addition to the rectangle frame region defined, it is filled with instituteState the background colour of handwritten text page image sample.
In addition, to achieve the above object, it is described to write the present invention also provides a kind of construction method of handwritten word identification modelThe construction method of identification model the following steps are included:
A handwriting text lines image is chosen from default handwriting text lines image to be stored as base-line data, it is describedDefault handwriting text lines image is obtained by training data processing method as described above；
When detecting the instruction of trained handwritten word identification model, according to the scene of described instruction carrying to the baseline of storageData carry out the conversion process of different modes respectively, obtain several training datas；
According to obtained several training datas, the training set for training handwritten word identification model is constructed；
Trained handwritten word identification model is obtained using the training set training convolutional Recognition with Recurrent Neural Network model of building.
Optionally, the mode of the conversion process includes brightness regulation, rotation, translation, scaling, background colour change, inverseOne of processing and increase background are a variety of.
Optionally, the training set training convolutional Recognition with Recurrent Neural Network model using building obtains trained handwritten wordThe step of identification model includes:
The parameter of loop initialization neural network model；
The training set of building is loaded onto convolution loop neural network model, according to formulaObtain the forward direction output of convolution loop neural network model, wherein a (t, u) indicates t momentThe forward direction output of u-th of handwritten word,Indicate that t moment output is the probability in space, l'uIndicate the overall length of handwritten word and spaceDegree, a (t-1, i) indicate the forward direction output of i-th of handwritten word of t-1 moment；And
According to formulaObtain the backward output of convolution loop neural network model, wherein b(t, u) indicates the backward output of u-th of handwritten word of t moment,The probability that the expression t+1 moment exports as space, b (t+1,I) the backward output of i-th of handwritten word of t+1 moment is indicated；
The parameter that convolution loop neural network model is updated according to forward direction output and backward output, obtains trainedHandwritten word identification model.
In addition, to achieve the above object, the present invention also provides training data processing unit, the training data processing unitInclude:
Individual character labeling module, for obtaining handwritten text page image sample, and in the handwritten text page image sampleLine of text to be extracted carry out individual character mark, obtain the markup information of each character in line of text to be extracted；
Module is defined, for the markup information according to each character, the circle from the handwritten text page image sampleMake rectangle frame region belonging to each character；
Overlay module, for the area in the handwritten text page image sample, in addition to the rectangle frame region definedDomain carries out covering treatment；
Division module, for the markup information according to each character, from the handwritten text page image after covering treatmentRegion belonging to the line of text to be extracted is marked off in sample, and is cut, and is obtained for training handwritten word identification modelHandwriting text lines image.
In addition, to achieve the above object, the present invention also provides a kind of training data processing equipment, the training data processingEquipment includes processor, memory and is stored on the memory and at the training data that can be executed by the processorProgram is managed, wherein realizing such as above-mentioned training data processing side when the training data processing routine is executed by the processorThe step of method.
In addition, to achieve the above object, it is described computer-readable the present invention also provides a kind of computer readable storage mediumTraining data processing routine is stored on storage medium, wherein realizing when the training data processing routine is executed by processorSuch as the step of above-mentioned training data processing method.
The present invention provides a kind of training data processing method, obtains handwritten text page image sample, and to the hand-written textLine of text to be extracted in this page of image sample carries out individual character mark, obtains the mark letter of each character in line of text to be extractedBreath；According to the markup information of each character, defined belonging to each character from the handwritten text page image sampleRectangle frame region；To in the handwritten text page image sample, the region in addition to the rectangle frame region defined is coveredProcessing；According to the markup information of each character, marked off from the handwritten text page image sample after covering treatment describedRegion belonging to line of text to be extracted, and cut, obtain the handwriting text lines image for training handwritten word identification model.The present invention is by carrying out region segmentation and covering treatment to handwriting text lines image sample, to mark off line of text institute to be extractedThe region of category, then cut, compared to the mode directly cut, the obtained handwriting text lines image of the present invention, not byThe phenomenon that influence of uplink and downlink adjacent thereto will not be mingled with the character of uplink and downlink adjacent thereto, and also there is no character missings,The accuracy of handwriting text lines image is effectively increased, suitable for training handwritten word identification model.
Specific embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
The present embodiments relate to training data processing method be mainly used in training data processing equipment, the training numberIt can be that personal computer (personal computer, PC), server etc. are having data processing function to be set according to processing equipmentIt is standby.
Referring to Fig.1, Fig. 1 is the hardware configuration signal of training data processing equipment involved in the embodiment of the present inventionFigure.In the embodiment of the present invention, training data processing equipment may include (such as the central processing unit Central of processor 1001Processing Unit, CPU), communication bus 1002, user interface 1003, network interface 1004, memory 1005.Wherein,Communication bus 1002 is for realizing the connection communication between these components；User interface 1003 may include display screen(Display), input unit such as keyboard (Keyboard)；Network interface 1004 optionally may include that the wired of standard connectsMouth, wireless interface (such as Wireless Fidelity WIreless-FIdelity, WI-FI interface)；Memory 1005 can be high speed and deposit at randomAccess to memory (random access memory, RAM), is also possible to stable memory (non-volatile memory),Such as magnetic disk storage, memory 1005 optionally can also be the storage device independently of aforementioned processor 1001.This fieldTechnical staff is appreciated that hardware configuration shown in Fig. 1 and does not constitute a limitation of the invention, and may include more than illustratingOr less component, perhaps combine certain components or different component layouts.
With continued reference to Fig. 1, the memory 1005 in Fig. 1 as a kind of computer storage medium may include operating system,Network communication module and training data processing routine.In Fig. 1, processor 1001, which can call, to be stored in memory 1005Training data processing routine, and the training data processing method of various embodiments of the present invention offer is provided.
The embodiment of the invention provides a kind of training data processing methods.
It is the flow diagram of training data processing method first embodiment of the present invention referring to Fig. 2, Fig. 2.
In the present embodiment, the training data processing method the following steps are included:
Step S10 obtains handwritten text page image sample, and to the text to be extracted in the handwritten text page image sampleCurrent row carries out individual character mark, obtains the markup information of each character in line of text to be extracted；
Training data processing method in the present embodiment can be by the equipment having data processing function such as PC or serverIt realizes, the present embodiment is illustrated by taking server as an example.In the present embodiment, it need to be pre-configured with line of text extraction in the serverTool, line of text extracting tool are extracted from handwritten text page image sample mainly for the treatment of handwritten text page image sampleOut for training the handwriting text lines image of handwritten word identification model.
Firstly, server obtains handwritten text page image sample, then by line of text extracting tool to hand-written page of textLine of text to be extracted in image sample carries out individual character mark, and individual character mark includes that classification annotation and position mark.Wherein, classifyMark is which word each character is in mark line of text to be extracted, by every in the available line of text to be extracted of classification annotationThe label word of one character；Position mark is the upper left point coordinate of each character and its width value and height in mark line of text to be extractedAngle value, by position mark the upper left point coordinate (xi, yi) of each character in available line of text to be extracted, width value wi andUpper left point coordinate, width value and height value are defined as position by height value hi (i indicates i-th of character in line of text to be extracted)Confidence breath.So, by hand-written page of text image sample line of text to be extracted carry out individual character mark, can obtain toExtract the markup information (including label word and location information) of each character in line of text.
Step S20 is defined every according to the markup information of each character from the handwritten text page image sampleRectangle frame region belonging to one character；
Later, according to the markup information of character each in line of text to be extracted, defined from handwritten text page sample toExtract rectangle frame region belonging to each character in line of text.That is, being sat according to the upper left point of character each in line of text to be extracted(xi, yi), width value wi and height value hi are marked, the lower-right most point coordinate for obtaining each character in line of text to be extracted is calculated separately(Xi, Yi), wherein Xi=xi+wi, Yi=yi+hi.In this way, can be according to the upper left of character each in line of text to be extractedPoint coordinate (xi, yi) and lower-right most point coordinate (Xi, Yi), define rectangle frame region belonging to each character, effect can joinAccording to the example of Fig. 3.
Step S30, in the handwritten text page image sample, the region in addition to the rectangle frame region defined is carried outCovering treatment；
After defining rectangle frame region belonging to each character, in handwritten text page image sample, defined to removingRegion except rectangle frame region out carries out covering treatment.Specifically, boundary will can be removed in handwritten text page image sampleArea filling except the rectangle frame region made is the background colour of handwritten text page image sample (except the rectangle frame area definedThe text in region except domain is also capped), for example in the example of Fig. 3, the background colour of handwritten text page image sample is whiteColor, then can be white by the area filling in addition to the rectangle frame region defined, as shown in Figure 4.
Step S40, according to the markup information of each character, from the handwritten text page image sample after covering treatmentRegion belonging to the line of text to be extracted is marked off, and is cut, is obtained for training the hand-written of handwritten word identification modelLine of text image.
Later, according to the markup information of character each in line of text to be extracted, from the handwritten text page figure after covering treatmentRegion belonging to line of text to be extracted is marked off in decent example.Specifically, by the upper left point of character each in line of text to be extractedCoordinate (xi, yi) is compared, and determines the maximum value ymax in minimum value xmin, yi in xi, by the bottom right of each characterPoint coordinate (Xi, Yi) be compared, determine the minimum value Ymin in maximum value Xmax, Yi in Xi, then according to xmin,Ymax, Xmax and Ymin tetra- values determine the cut-off rule of line of text to be extracted, can determine that text to be extracted according to the cut-off ruleRectangle frame region belonging to current row, cuts rectangle frame region belonging to line of text to be extracted, can be obtained for trainingThe handwriting text lines image of handwritten word identification model, effect can refer to the example of Fig. 5, from figure 5 it can be seen that by above-mentionedThe handwriting text lines image that mode obtains, is not influenced by uplink and downlink adjacent thereto, is not also mingled with adjacent thereto upperThe phenomenon that character of downlink, also there is no character missings, effectively increase the accuracy of handwriting text lines image.
The present embodiment provides a kind of training data processing method, handwritten text page image sample is obtained, and to described hand-writtenLine of text to be extracted in page of text image sample carries out individual character mark, obtains the mark letter of each character in line of text to be extractedBreath；According to the markup information of each character, defined belonging to each character from the handwritten text page image sampleRectangle frame region；To in the handwritten text page image sample, the region in addition to the rectangle frame region defined is coveredProcessing；According to the markup information of each character, marked off from the handwritten text page image sample after covering treatment describedRegion belonging to line of text to be extracted, and cut, obtain the handwriting text lines image for training handwritten word identification model.The present embodiment is by carrying out region segmentation and covering treatment to handwriting text lines image sample, to mark off line of text to be extractedAffiliated region, then cut, compared to the mode directly cut, the obtained handwriting text lines image of the present embodiment does not haveHaving is influenced by uplink and downlink adjacent thereto, and the character of uplink and downlink adjacent thereto will not be mingled with, and also there is no character missingsPhenomenon effectively increases the accuracy of handwriting text lines image, suitable for training handwritten word identification model.
In addition, the embodiment of the present invention also provides a kind of training data processing unit.
Referring to figure, Fig. 6 is the functional block diagram of training data processing unit first embodiment of the present invention.
In the present embodiment, the training data processing unit includes:
Individual character labeling module 10, for obtaining handwritten text page image sample, and to the handwritten text page image sampleIn line of text to be extracted carry out individual character mark, obtain the markup information of each character in line of text to be extracted；
Module 20 is defined, for the markup information according to each character, from the handwritten text page image sampleDefine rectangle frame region belonging to each character；
Overlay module 30, for in the handwritten text page image sample, in addition to the rectangle frame region definedRegion carries out covering treatment；
Division module 40, for the markup information according to each character, from the handwritten text page figure after covering treatmentRegion belonging to the line of text to be extracted is marked off in decent example, and is cut, and is obtained for training handwritten word to identify mouldThe handwriting text lines image of type.
Wherein, each virtual functions module of above-mentioned training data processing unit is stored in the processing of training data shown in Fig. 1 and setsIt is functional for realizing the institute of training data processing routine in standby memory 1005；When each module is executed by processor 1001,Compared to the mode directly cut, the obtained handwriting text lines image of the present embodiment, not by uplink and downlink adjacent theretoThe phenomenon that influencing, the character of uplink and downlink adjacent thereto will not be mingled with, also lacking there is no character, effectively increase handwritten textThe accuracy of row image, suitable for training handwritten word identification model.
Further, the module 20 that defines includes:
Computing unit is calculated for the upper left point coordinate, the width value and the height value according to each characterObtain the lower-right most point coordinate of each character；
Unit is defined, for defining each word according to the upper left point coordinate of a character and the lower-right most point coordinateRectangle frame region belonging to symbol.
Further, the division module 40 includes:
First determination unit, for the upper left point coordinate of each character to be compared, with from the institute of each characterIt states in the point coordinate of upper left and determines minimum abscissa value and maximum ordinate value；
Second determination unit, for the lower-right most point coordinate of each character to be compared, with from the institute of each characterIt states and determines maximum abscissa value and minimum ordinate value in lower-right most point coordinate value；
Division unit, for according to the minimum abscissa value, the maximum ordinate value, the maximum abscissa value andThe minimum ordinate value determines region belonging to the line of text to be extracted, and from the handwritten text page image after covering treatmentRegion belonging to the line of text to be extracted is marked off in sample.
Further, the overlay module 30 further include:
Fills unit is used in the handwritten text page image sample, will be in addition to the rectangle frame region definedRegion is filled with the background colour of the handwritten text page image sample.
Wherein, the function of modules is realized and above-mentioned training data processing method reality in above-mentioned training data processing unitIt is corresponding to apply each step in example, function and realization process no longer repeat one by one here.
In addition, the embodiment of the present invention also provides a kind of computer readable storage medium.
Training data processing routine is stored on computer readable storage medium of the present invention, wherein the training data is handledWhen program is executed by processor, realize such as the step of above-mentioned training data processing method.
Wherein, training data processing routine, which is performed realized method, can refer to training data processing method of the present inventionEach embodiment, details are not described herein again.
The present embodiments relate to the construction method of handwritten word identification model be mainly used in handwritten word identification modelEquipment is constructed, the building equipment of the handwritten word identification model can be personal computer (personal computer, PC), clothesThe equipment having data processing function such as business device.
The hardware configuration of the building equipment of handwritten word identification model involved in the embodiment of the present invention may include placeIt manages device (such as central processing unit Central Processing Unit, CPU), communication bus, user interface, network interface is depositedReservoir.Wherein, communication bus is for realizing the connection communication between these components；User interface may include display screen(Display), input unit such as keyboard (Keyboard)；Network interface optionally may include the wireline interface of standard, nothingLine interface (such as Wireless Fidelity WIreless-FIdelity, WI-FI interface)；Memory can be high-speed random access memory(random access memory, RAM) is also possible to stable memory (non-volatile memory), such as diskMemory, memory optionally can also be the storage device independently of aforementioned processor.It will be understood by those skilled in the art thatAbove-mentioned hardware configuration does not constitute a limitation of the invention simultaneously, may include components more more or fewer than diagram, or combine certainA little components or different component layouts.
A kind of memory as computer storage medium may include operating system, network communication module and handwritten wordThe construction procedures of identification model.Processor can call the construction procedures of the handwritten word identification model stored in memory, and holdThe construction method for the handwritten word identification model that row various embodiments of the present invention provide.
Further, propose that the first of the construction method of handwritten word identification model of the present invention implements based on first embodimentExample.
It is the flow diagram of the construction method first embodiment of handwritten word identification model of the present invention referring to Fig. 7, Fig. 7.
In the present embodiment, the handwritten word identification model construction method the following steps are included:
Step S50 chooses a handwriting text lines image from default handwriting text lines image and carries out as base-line dataStorage；
After obtaining several handwriting text lines images by first embodiment, in the present embodiment, in order to be not take up serverMemory space, only arbitrarily choose a handwriting text lines image from obtained handwriting text lines image and deposited as base-line dataIt is stored in the storage system of server.
Step S60, when detecting the instruction of trained handwritten word identification model, the scene carried according to described instruction is to depositingThe base-line data of storage carries out the conversion process of different modes respectively, obtains several training datas；
Since in practice, trained handwritten word identification model needs to identify the handwritten word line of text figure under different scenesPicture, then for training the training sample of handwritten word identification model just to need comprising the handwritten word line of text image under different scenes.In the present embodiment, when server detects the instruction of trained handwritten word identification model, then according to the field carried in the instructionScape carries out the conversion process of different modes to base-line data respectively, to construct training set on the basis of base-line data, meetsThe demand of training handwritten word identification model.Specifically, when server detects the instruction of trained handwritten word identification model, then rootAccording to the scene carried in the instruction, correspondingly, base-line data is carried out respectively within the storage system brightness regulation, rotation, translation,One of modes such as scaling, the processing of background colour change, inverse and increase background or a variety of processing, such as the base-line data of Fig. 8Example can carry out the processing that brightness is dimmed plus scaled to it, obtain first part of training data, can also carry out brightness tune to itThe processing that dark padding translates downwards, obtains second part of training data, can also be converted its background colour such as will be whiteIt is transformed to green and blue respectively, obtains third part training data and the 4th part of training data, it can also be carried out at inverseReason, for example the color of character is adjusted to white, background color tone as black (effect can refer to Fig. 9), obtain the 5th part of trained numberAccording to, etc., in this way, obtaining several training datas.
Step S70 constructs the training set for training handwritten word identification model according to obtained several training datas.
Later, training set can be formed according to obtained several training datas.
Step S80 obtains trained handwritten word using the training set training convolutional Recognition with Recurrent Neural Network model of building and knowsOther model.
Further, using the training set training handwritten word identification model of building, specifically, handwritten word identification model is volumeProduct Recognition with Recurrent Neural Network model-CRNN (Convolutional-Recurrent Neural Networks) model, first initiallyChange the parameter of convolution loop neural network model, wherein the parameter includes weighted value and weighting value, then by the training set of buildingIt is loaded onto convolution loop neural network model and is trained, obtain the forward direction output of convolution loop neural network model and backward(forward direction exports the probability for referring to u-th of the handwritten word exported sequentially in time, and backward output is defeated according to time opposite sequence for outputThe probability of u-th of handwritten word out), it can be according to formulaObtain convolution loop neural network mouldThe forward direction of type exports, wherein and a (t, u) indicates the forward direction output of u-th of handwritten word of t moment,Indicate that t moment output isThe probability in space, l'uIndicate that the total length of handwritten word and space, a (t-1, i) indicate that the forward direction of i-th of handwritten word of t-1 moment is defeatedOut；And according to formulaObtain the backward output of convolution loop neural network model, wherein b(t, u) indicates the backward output of u-th of handwritten word of t moment,The probability that the expression t+1 moment exports as space, b (t+1,I) it indicates the backward output of i-th of handwritten word of t+1 moment, later, calculates target output, base to output and backward output based on precedingBuilding loss function is exported in the target, further according to the loss function, using the backpropagation based on continuous time sorting algorithmAlgorithm updates parameter, to obtain trained handwritten word identification model.
The present embodiment from several handwriting text lines images by choosing a handwriting text lines image as base-line dataIt is stored in the storage system of server, then base-line data is carried out according to the actual scene of training handwritten word identification model eachThe conversion process of kind different modes, can meet the needs of trained handwritten word identification model, in this way, just not needing in serverA large amount of training data is stored in advance in storage system, memory space is greatly saved, while saving a large amount of training numbers of maintenanceAccording to required cost.
In addition, the embodiment of the present invention also provides a kind of construction device of handwritten word identification model.
In the present embodiment, the construction device device of the handwritten word identification model includes:
Memory module, for choosing a handwriting text lines image from default handwriting text lines image as base-line dataIt is stored, the default handwriting text lines image is obtained by training data processing method as described above；
Conversion process module, for being carried according to described instruction when detecting the instruction of trained handwritten word identification modelScene carry out the conversion process of different modes respectively to the base-line data of storage, obtain several training datas；
Module is constructed, for constructing the training for training handwritten word identification model according to obtained several training datasCollection；
Training module, it is trained hand-written for being obtained using the training set training convolutional Recognition with Recurrent Neural Network model of buildingWord identification model.
Wherein, each virtual functions module of the construction device of above-mentioned handwritten word identification model is stored in handwritten word shown in Fig. 1In the memory 1005 of the building equipment of identification model, the institute for realizing the construction procedures of handwritten word identification model is functional；When each module is executed by processor 1001, can meet the needs of trained handwritten word identification model.
Further, the training module includes:
Initialization unit, for initializing the parameter of convolution loop neural network model；
Forward direction exports acquiring unit, for the training set of building to be loaded onto convolution loop neural network model, according toFormulaObtain the forward direction output of convolution loop neural network model, wherein a (t, u) indicates tThe forward direction of u-th of handwritten word of moment exports,Indicate that t moment output is the probability in space, l'uIndicate handwritten word and spaceTotal length, a (t-1, i) indicate the forward direction output of i-th of handwritten word of t-1 moment；And
Backward output acquiring unit, for according to formulaObtain convolution loop neural networkThe backward output of model, wherein b (t, u) indicates the backward output of u-th of handwritten word of t moment,Indicate that the t+1 moment is defeatedIt is out the probability in space, b (t+1, i) indicates the backward output of i-th of handwritten word of t+1 moment；
Updating unit, for updating the ginseng of convolution loop neural network model according to forward direction output and backward outputNumber, obtains trained handwritten word identification model.
In addition, the embodiment of the present invention also provides a kind of computer readable storage medium.
The construction procedures of handwritten word identification model are stored on computer readable storage medium of the present invention, wherein described hand-writtenWhen the construction procedures of word identification model are executed by processor, the step of the construction method such as above-mentioned handwritten word identification model is realizedSuddenly.
Wherein, the construction procedures of handwritten word identification model, which are performed realized method, can refer to handwritten word knowledge of the present inventionEach embodiment of the construction method of other model, details are not described herein again.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-rowHis property includes, so that the process, method, article or the system that include a series of elements not only include those elements, andAnd further include other elements that are not explicitly listed, or further include for this process, method, article or system institute it is intrinsicElement.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to doThere is also other identical elements in the process, method of element, article or system.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment sideMethod can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many casesThe former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior artThe part contributed out can be embodied in the form of software products, which is stored in one as described aboveIn storage medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that terminal device (it can be mobile phone,Computer, server, air conditioner or network equipment etc.) execute method described in each embodiment of the present invention.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hairEquivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skillsArt field, is included within the scope of the present invention.