CN107908724A - A kind of data model matching process, device, equipment and storage medium - Google Patents
A kind of data model matching process, device, equipment and storage medium Download PDFInfo
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- CN107908724A CN107908724A CN201711121199.3A CN201711121199A CN107908724A CN 107908724 A CN107908724 A CN 107908724A CN 201711121199 A CN201711121199 A CN 201711121199A CN 107908724 A CN107908724 A CN 107908724A
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
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Abstract
The embodiment of the invention discloses a kind of data model matching process, device, equipment and storage medium, wherein, this method includes：Model index relative is established to all preset models by preset rules；Label according to destination object is searched in the model index relative, obtains the pertinent model information of the destination object；Judged whether and the matched preset model of the destination object according to the pertinent model information.For the embodiment of the present invention during data model is matched, lookup number is only related with the label number of destination object, greatly improves the matching efficiency of data model, especially when model quantity is larger, can realize the matching to mass data fast accurate.
Description
Technical field
The present invention relates to field of computer technology, more particularly to a kind of data model matching process, device, equipment and storage
Medium.
Background technology
Information on big data epoch internets is more and more, and renewal frequency is getting faster, the production based on internet data
It is excessive that product can all face information, data, so as to cause to perplex to user and product developer, this problem can by label come
Alleviate.
Under label system, destination object is described with a series of label.At the same time according to business feature, one
The combination of series of labels is defined as a model, model is matched in turn with the label of destination object, when destination object
When label has met the tag combination of some model, we just say that the destination object has hit this model, at this time can be according to mould
Type characteristic carries out destination object specific business operation, for example precisely recommends.
But as the continuous increase of internet data, the continuous of business extend, the quantity of model also can be more and more,
When the quantity of model is larger, the label of destination object is matched in turn with model, efficiency will substantially reduce, Wu Fashi
The matching of existing destination object and model fast accurate.
The content of the invention
It is an object of the present invention to provide a kind of data model matching process, device, equipment and storage medium, greatly
The matching efficiency of data model is improved, especially when model quantity is larger, can be realized to mass data fast accurate
Matching.
For this purpose, the present invention uses following technical scheme：
In a first aspect, an embodiment of the present invention provides a kind of data model matching process, including：
Model index relative is established to all preset models by preset rules；
Label according to destination object is searched in the model index relative, obtains the correlation of the destination object
Model information；
Judged whether and the matched preset model of the destination object according to the pertinent model information.
Second aspect, an embodiment of the present invention provides a kind of data model coalignment, including：
Index establishes module, for establishing model index relative to all preset models by preset rules；
Label lookup module, is searched in the model index relative for the label according to destination object, is obtained
The pertinent model information of the destination object；
Model judgment module, it is matched with the destination object for being judged whether according to the pertinent model information
Preset model.
The third aspect, an embodiment of the present invention provides a kind of data model matching unit, including：
One or more processors；
Storage device, for storing one or more programs；
When one or more of programs are performed by one or more of processors so that one or more of processing
Device realizes the data model matching process as described in any embodiment of the present invention.
Fourth aspect, the embodiment of the present invention additionally provide a kind of computer-readable recording medium, are stored thereon with computer
Program, realizes the data model matching process as described in any embodiment of the present invention when which is executed by processor.
An embodiment of the present invention provides a kind of data model matching process, device, equipment and storage medium, by default
Model foundation model index relative, the label of destination object is searched in model index relative, and then judges whether to deposit
With the matched preset model of destination object.During data model is matched, the label of number and destination object is searched
Number is related, greatly improves the matched efficiency of data model, especially when model quantity is larger, can realize to magnanimity
The matching of data fast accurate.
Brief description of the drawings
By reading the detailed description made to non-limiting example made with reference to the following drawings, of the invention is other
Feature, objects and advantages will become more apparent upon：
Fig. 1 is a kind of flow chart for data model matching process that the embodiment of the present invention one provides；
Fig. 2 is the flow of more new model index relative in a kind of data model matching process provided by Embodiment 2 of the present invention
Figure；
Fig. 3 is a kind of structure diagram for data model coalignment that the embodiment of the present invention three provides；
Fig. 4 is a kind of structure diagram for data model matching unit that the embodiment of the present invention four provides.
Embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is only used for explaining the present invention, rather than limitation of the invention.It also should be noted that for the ease of
Describe, part related to the present invention rather than full content are illustrate only in attached drawing.
Embodiment one
Fig. 1 is a kind of flow chart for data model matching process that the embodiment of the present invention one provides, and the present embodiment is applicable
The matched process of data model is carried out in destination object, this method can be matched by data model provided in an embodiment of the present invention and filled
Put/equipment/storage medium performs, which can be realized by the way of hardware and/or software, as shown in Figure 1, the data mould
Type matching process includes the following steps：
Step S101：Model index relative is established to all preset models by preset rules.
Wherein, preset model can be made of several labels, represent the object with certain feature, for example,
The preset model being made of label " 22 years old ", " student ", " University Of Tianjin " and " Financial organization ", represents 22 years old University Of Tianjin's gold
Melt the student of specialty.Model index relative is a kind of data store organisation created to accelerate data model rate matched,
Store the relation between label in preset model and model in the data store organisation, model index relative functions as
The catalogue of preset model, can be quickly found out the matched preset model of institute according to the label in catalogue, it is preferred that model index closes
System can be showed by the form of form.Preset rules are that what is set in advance establish preset model the side of model index relative
Method.Preset rules have many kinds, it is preferred that can establish model index relative to all preset models by following preset rules：
Tag entry and model identification entry are established, the correspondence of the mark for storing label and preset model；
The label of each preset model in all preset models is extracted successively, and nothing is repeatedly added under tag entry, and will
The mark of preset model belonging to label is added to corresponding model identification bar now, obtains model index relative.
Wherein, tag entry is for recording label all in preset model, and model identification entry is to be used to record mark
The model identification of the affiliated model of label in label mesh.Model identification is unique mark for distinguishing different preset models and setting up
Know, can be numbering, title of model etc..Model index relative can have a variety of forms, for example, form, database.It is excellent
Choosing, when the model index relative of foundation is the form of form, a row are record tag entries, by the every of all preset models
One label is without being repeatedly added under tag entry；Another row are model identification entries, by the preset model belonging to label
Mark is added to the corresponding model identification bar of label now.Preferably, a label can correspond to multiple model identifications, and one
A model identification can also correspond to multiple labels.
Exemplary, it is assumed that preset model has two, is model 1 and model 2 respectively, and wherein model 1 has three labels, point
Not Wei A, B, C, model 2 has four labels, is respectively B, D, E, F, model identification is represented with the numbering of model, with the shape of form
Formula establishes model index relative.As shown in table 1, model 1 and all label As, B, C, D, E, F in model 2 recorded successively
Under tag entry, the corresponding model of each label is then looked for, by the number record of corresponding model to model identification bar now, so that
Establish model index relative.For example, A is the label in model 1, the just middle record cast 1 now of the model identification bar behind A
Numbering；B is both the label in label and model 2 in model 1, and just the model identification bar behind B both records mould in now
The numbering of type 1 and the numbering of record cast 2.
1 model index relative table of table
| Tag entry | Model identification entry |
| A | 1 |
| B | 1,2 |
| C | 1 |
| D | 2 |
| E | 2 |
| F | 2 |
Step S102：Label according to destination object is searched in model index relative, obtains the phase of destination object
Close model information.
Wherein, the matching of data carries out Model Matching for real-time mass data, and the data of magnanimity can be divided into
Different labels one by one, and several different labels constitute destination object one by one.Preferably, target pair is formed
The label of elephant can be the essential information of destination object, for example, the age of a people, gender, work etc.；It can also be target pair
The behavioural information of elephant, for example, the webpage often browsed on the internet.The pertinent model information of destination object refers to destination object
Each label correlation model corresponding in model index relative and correlation model hit number, correlation model is should
The general name of the part labels of destination object or each preset model of whole tag hits.
Preferably, the label of destination object can be searched in index relative by following method, obtains target
The pertinent model information of object：
Search each label of destination object successively in model index relative；
If finding the label of destination object in model index relative, which is determined according to model index relative
The mark of corresponding preset model, and recorded；
The pertinent model information of destination object, wherein pertinent model information bag are determined according to the record content of destination object
Include：The correlation model of destination object and the record number of correlation model.
Wherein, the number searched in model index relative with the label number of destination object be it is consistent, it is exemplary,
Assuming that destination object is made of tetra- labels of A, B, H, E, model index relative is shown in the table 1 in step S101, from label A
Start to search this four labels in model index relative successively, when finding in model index relative there are during label A, by mould
It is 1 that type index relative, which can obtain the corresponding model identification of label A, the phase of destination object in the pertinent model information recorded at this time
It is 1 to close model, and the number that model 1 is hit is 1 time, then searches and whether there is label B in model index relative, is found in the presence of mark
After signing B, the correlation model of destination object is 1 and 2 in the pertinent model information of record, and at this time, model 1 has been hit 2 times, model 2
Hit 1 time, carried out the lookup of label H in the same way, found there is no label H in model index relative, at this time without
Record, directly searches next label E in model index relative, and the record of pertinent model information is carried out after finding.
Step S103：Judged whether and the matched preset model of destination object according to pertinent model information.
The correlation model of destination object and the record number of correlation model are contained in pertinent model information, can according to this
To judge to whether there is and the matched preset model of destination object.
Preferably, judged whether according to the pertinent model information recorded in step S102 matched pre- with destination object
If the method for model can be：
For each correlation model, the record number of correlation model is obtained from pertinent model information, and from advance
The label number of correlation model is obtained in the model label number information of storage；
Compare the record number of correlation model and the label number of correlation model；
If the record number of correlation model is equal to the label number of correlation model, it is determined that correlation model and destination object
Match somebody with somebody.
Wherein, each label in the destination object is record in the pertinent model information of a destination object in model
The affiliated preset model found in index relative, and the number of preset model hit.Obtain respectively every in correlation model
The number of one preset model hit, which represent how many label in this preset model and the label in destination object
It is identical, then obtain the label number (i.e. the label total number of the preset model) that this preset model prestores, will both into
Row compares, if the number that some preset model is hit in correlation model is consistent with the label number that the model prestores,
Then illustrate that the model is matched with destination object.Preferably, the storage form of the label number of preset model has very much, for example, table
Lattice, database, it is preferred that the label number of preset model can be stored in the form of two tuples, for example, model 1
It is respectively A, B, C to have three labels, then two tuples of model 1 are (1,3), wherein 1 (uses pattern number table for the mark of the model
Show), 3 be the label number of the model.
Exemplary, it is assumed that destination object is made of tetra- labels of A, B, C, D, and model index relative is in step S101
Table 1 shown in, two tuples of the model 1 that can be prestored by model index relative are (1,3), two tuples of model 2
For (2,4), it is by the obtained pertinent model informations of step S102：Correlation model is model 1 and model 2, wherein, model 1 is hit
3 times, model 2 hit 2 times.It can be seen that in pertinent model information, model 1 has been hit 3 times, and model 1 is exactly
It is made of 3 labels, therefore destination object is matched with model 1；Model 2 has been hit 2 times, but model 2 is made of 4 labels
, the number that model 2 is hit in pertinent model information is less than the label total number of model 2, and therefore, destination object and model 2 are not
Matching.
Further, the label number of destination object is more than or equal to and the label of the matched preset model of destination object
Number.
Wherein, it is being determined in step S103 to be equal to target pair with the matched preset model of destination object label number
The label number of elephant, might be less that the label number of destination object.For example, destination object is made of four labels, and
Label number with the matched preset model of destination object can be four or two.
A kind of data model matching process is present embodiments provided, will by establishing model index relative to preset model
The label of destination object is searched in model index relative, and then is judged whether and the matched default mould of destination object
Type.During data model is matched, lookup number is only related with the label number of destination object, greatly improves data
The matching efficiency of model, especially when model quantity is larger, can realize the matching to mass data fast accurate.
Further, after step s 103, if in the presence of with the matched preset model of destination object, according to destination object
The feature of matched preset model operates destination object.
Wherein, the matching for preset model being carried out to destination object is exactly for the spy after successful match according to preset model
Sign carries out relevant operation to destination object.Preferably, destination object can accurately be recommended.For example, destination object
Matched model has the characteristic that the news for often browsing financial class, can be given at this time according to this feature of the model
Destination object recommends the news messages of financial class.
Embodiment two
The present embodiment is on the basis of embodiment one, there is provided the more method of new model index relative, Fig. 2 are real for the present invention
The flow chart of more new model index relative in a kind of data model matching process of the offer of example two is applied, as shown in Fig. 2, including as follows
Step：
Step S201：Detect new model, extract the label of new model.
With the continuous increase of internet data, the continuous extension of business, the number of preset model can also increase therewith, no
Disconnected renewal, when preset model updates, corresponding model index relative will be also updated.Therefore, to detect whether in real time
There is new model, all labels in new model are extracted when there is new model.
Step S202：Judge whether find the label of new model in existing label under tag entry, if so, performing step
Rapid S203, performs step S204 if not.
Wherein, because although some models are new models, but the label in model is not necessarily new label, for example,
New model can extract several labels in existing model to form.Therefore, after all labels for extracting new model, first according to
The secondary label by new model is searched under the tag entry of model index relative, is seen in model index relative and be whether there is
The label.If so, performing step S203, step S204 is performed if not.
Step S203：The mark of new model is added to the corresponding model identification bar of the label now.
If having found the label in new model in model index relative, adding into row label again is not just had at this time
Add, need to only add the mark of new model now in the corresponding model identification bar of the label.
Step S204：The label is added under tag entry, and the mark of new model is added to corresponding model mark
Know bar now.
Wherein, if in model index relative, the label in new model has not been found, has illustrated the label for new mark
New label and model identification, are all added in model index relative, specifically, new label is added to model rope by label at this time
Draw under the tag entry of relation, the mark of new model is added in model identification bar corresponding with new label now.
The method of more new model index relative provided in this embodiment, whether the label by judging new model is label bar
Now existing label, if needing to add the mark of new model, if not by label model identification corresponding with label
All it is added in model index relative, model index relative is updated in real time, greatly improves the matching of data model
Efficiency, especially when model quantity is larger, can realize the matching to mass data fast accurate.
Embodiment three
Fig. 3 is a kind of structure diagram for data model coalignment that the embodiment of the present invention three provides, which can perform
The data model matching process that any embodiment of the present invention is provided, possesses the corresponding function module of execution method and beneficial to effect
Fruit.As shown in figure 3, the device includes：
Index establishes module 301, for establishing model index relative to all preset models by preset rules；
Label lookup module 302, is searched in model index relative for the label according to destination object, obtains mesh
Mark the pertinent model information of object；
Model judgment module 303, it is matched default with destination object for being judged whether according to pertinent model information
Model.
A kind of data model coalignment is present embodiments provided, will by establishing model index relative to preset model
The label of destination object is searched in model index relative, and then is judged whether and the matched default mould of destination object
Type.During data model is matched, lookup number is only related with the label number of destination object, greatly improves data
The matching efficiency of model, especially when model quantity is larger, can realize the matching to mass data fast accurate.
Wherein, the label number of destination object is more than or equal to the label number with the matched preset model of the destination object.
Further, above-mentioned index is established module 301 and is included：
Entry establishes unit, for establishing tag entry and model identification entry, for storing label and preset model
The correspondence of mark；
Index relative establishes unit, for extracting the label of each preset model in all preset models successively, without repeatedly
It is added under tag entry, and the mark of the preset model belonging to label is added to corresponding model identification bar now, obtains
Model index relative.
Further, above-mentioned label lookup module 302 includes：
Label lookup unit, for searching each label of destination object successively in model index relative；
Information recording unit, in the case of finding the label of destination object in model index relative, according to mould
Type index relative determines the mark of the corresponding preset model of the label, and is recorded；
Information determination unit, for determining the pertinent model information of destination object according to the record content of destination object, its
Middle pertinent model information includes：The correlation model of destination object and the record number of correlation model.
Further, above-mentioned model judgment module 303 includes：
Information acquisition unit, for for each correlation model, the note of correlation model to be obtained from pertinent model information
Number is recorded, and the label number of correlation model is obtained from the model label number information prestored；
Information comparing unit, the label number for the record number and the correlation model of the correlation model；
Determination unit is matched, in the case of being equal to the label number of correlation model in the record number of correlation model,
Determine that correlation model is matched with destination object.
In order to carry out relevant operation to destination object according to the feature of matched model, above device further includes：
Object Operations module, for exist preset model matched with destination object when, according to matched with destination object
The feature of preset model operates destination object.
Further, as the increase of data volume, the extension of business, preset model can also be updated in real time, when having
When new model occurs, above device further includes：
Tag extraction module, during for detecting new model, extracts the label of new model；
Index upgrade module, when finding the label of new model in label for having under tag entry, by new model
Mark be added to the corresponding model identification bar of the label now；New model is not found in existing label under tag entry
During label, which is added under tag entry, and the mark of new model is added to corresponding model identification bar now.
It is worth noting that, in the embodiment of above-mentioned data model coalignment, included unit and module are only
Divided according to function logic, but be not limited to above-mentioned division, as long as corresponding function can be realized；Example
Such as, which can only include acquisition module and control module, and acquisition module realizes the extraction of information in correlation model；Control mould
Block, which is realized, establishes the relevant functions such as index, lookup, judgement, record, renewal.In addition, the specific name of each functional unit also only
It is the protection domain being not intended to limit the invention for the ease of mutually distinguishing.
Example IV
Fig. 4 is a kind of structure diagram for data model matching unit that the embodiment of the present invention four provides.Fig. 4 shows suitable
In for realizing the block diagram of the example devices 12 of embodiment of the present invention.The equipment 12 that Fig. 4 is shown is only an example, no
The function and use scope for tackling the embodiment of the present invention bring any restrictions.As shown in figure 4, the equipment 12 is with universal computing device
Form performance.The component of the equipment 12 can include but is not limited to：One or more processor or processing unit 16, are
System memory 28, the bus 18 of connection different system component (including system storage 28 and processing unit 16).
Bus 18 represents the one or more in a few class bus structures, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.Lift
For example, these architectures include but not limited to industry standard architecture (ISA) bus, microchannel architecture (MAC)
Bus, enhanced isa bus, Video Electronics Standards Association (VESA) local bus and periphery component interconnection (PCI) bus.
Equipment 12 typically comprises various computing systems computer-readable recording medium.These media can be it is any can be by equipment 12
The usable medium of access, including volatile and non-volatile medium, moveable and immovable medium.
System storage 28 can include the computer system readable media of form of volatile memory, such as arbitrary access
Memory (RAM) 30 and/or cache memory 32.Equipment 12 may further include it is other it is removable/nonremovable,
Volatile/non-volatile computer system storage medium.Only as an example, storage system 34 can be used for reading and writing irremovable
, non-volatile magnetic media (Fig. 4 do not show, commonly referred to as " hard disk drive ").Although not shown in Fig. 4, use can be provided
In the disc driver to moving non-volatile magnetic disk (such as " floppy disk ") read-write, and to moving anonvolatile optical disk
The CD drive of (such as CD-ROM, DVD-ROM or other optical mediums) read-write.In these cases, each driver can
To be connected by one or more data media interfaces with bus 18.Memory 28 can include at least one program product,
The program product has one group of (for example, at least one) program module, these program modules are configured to perform each implementation of the invention
The function of example.
Program/utility 40 with one group of (at least one) program module 42, can be stored in such as memory 28
In, such program module 42 include but not limited to operating system, one or more application program, other program modules and
Routine data, may include the realization of network environment in each or certain combination in these examples.Program module 42 is usual
Perform the function and/or method in embodiment described in the invention.
Equipment 12 can also communicate with one or more external equipments 14 (such as keyboard, sensing equipment, display 24 etc.),
It can also enable a user to the equipment communication interacted with the equipment with one or more, and/or with enabling the equipment 12 and one
Any equipment (such as network interface card, modem etc.) communication that a or a number of other computing devices communicate.This communication
It can be carried out by input/output (I/O) interface 22.Also, equipment 12 can also by network adapter 20 and one or
Multiple networks (such as LAN (LAN), wide area network (WAN) and/or public network, such as internet) communication.As shown in figure 4,
Network adapter 20 is communicated by bus 18 with other modules of equipment 12.It should be understood that although not shown in the drawings, it can combine
Equipment 12 uses other hardware and/or software module, includes but not limited to：Microcode, device driver, redundant processing unit,
External disk drive array, RAID system, tape drive and data backup storage system etc..
Processing unit 16 is stored in program in system storage 28 by operation, thus perform various functions application and
Data processing, such as realize the data model matching process that the embodiment of the present invention is provided.
Embodiment five
The embodiment of the present invention five additionally provides a kind of computer-readable recording medium, is stored thereon with computer program, should
Program can realize data model matching process any in above-described embodiment when being executed by processor.
The computer-readable storage medium of the embodiment of the present invention, can use any of one or more computer-readable media
Combination.Computer-readable medium can be computer-readable signal media or computer-readable recording medium.It is computer-readable
Storage medium can be for example but not limited to：Electricity, magnetic, optical, electromagnetic, system, device or the device of infrared ray or semiconductor, or
Combination more than person is any.The more specifically example (non exhaustive list) of computer-readable recording medium includes：With one
Or the electrical connections of multiple conducting wires, portable computer diskette, hard disk, random access memory (RAM), read-only storage (ROM),
Erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only storage (CD-ROM), light
Memory device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer-readable recording medium can
Be it is any include or the tangible medium of storage program, the program can be commanded execution system, device or device use or
Person is in connection.
Computer-readable signal media can include in a base band or as carrier wave a part propagation data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium beyond storage medium is read, which, which can send, propagates or transmit, is used for
By instruction execution system, device either device use or program in connection.
The program code included on computer-readable medium can be transmitted with any appropriate medium, be included but not limited to：
Wirelessly, electric wire, optical cable, RF etc., or above-mentioned any appropriate combination.
It can be write with one or more programming languages or its combination for performing the computer that operates of the present invention
Program code, described program design language include object oriented program language, such as Java, Smalltalk, C++, also
Including conventional procedural programming language-such as " C " language or similar programming language.Program code can be complete
Perform, partly performed on the user computer on the user computer entirely, the software kit independent as one performs, part
Part performs or is performed completely on remote computer or server on the remote computer on the user computer.Relating to
And in the situation of remote computer, remote computer can pass through the network of any kind, including LAN (LAN) or wide area network
(WAN), subscriber computer is connected to, or, it may be connected to outer computer (such as led to using ISP
Cross Internet connection).
Above-described embodiment sequence number is for illustration only, does not represent the quality of embodiment.
Will be appreciated by those skilled in the art that above-mentioned each module of the invention or each step can use general meter
Device is calculated to realize, they can be concentrated on single computing device, or are distributed in the network that multiple computing devices are formed
On, alternatively, they can be realized with the program code that computer installation can perform, so as to be stored in storage
Performed in device by computing device, they are either fabricated to each integrated circuit modules respectively or will be more in them
A module or step are fabricated to single integrated circuit module to realize.In this way, the present invention be not restricted to any specific hardware and
The combination of software.
Each embodiment in this specification is described by the way of progressive, what each embodiment stressed be with
The difference of other embodiment, the same or similar part between each embodiment mutually referring to.
The foregoing is merely the preferred embodiment of the present invention, is not intended to limit the invention, for those skilled in the art
For, the present invention can have various modifications and changes.All any modifications made within spirit and principles of the present invention, be equal
Replace, improve etc., it should all be included in the protection scope of the present invention.
Claims (10)
- A kind of 1. data model matching process, it is characterised in that including：Model index relative is established to all preset models by preset rules；Label according to destination object is searched in the model index relative, obtains the correlation model of the destination object Information；Judged whether and the matched preset model of the destination object according to the pertinent model information.
- 2. according to the method described in claim 1, it is characterized in that, by preset rules all preset models are established with model index Relation, including：Tag entry and model identification entry are established, the correspondence of the mark for storing label and preset model；The label of each preset model in all preset models is extracted successively, nothing is repeatedly added under the tag entry, And the mark of the preset model belonging to the label is added to corresponding model identification bar now, obtain the model index and close System.
- 3. according to the method described in claim 1, it is characterized in that, the label according to destination object is in the model index relative In searched, obtain the pertinent model information of the destination object, including：Search each label of the destination object successively in the model index relative；It is true according to the model index relative if finding the label of the destination object in the model index relative The mark of the corresponding preset model of the fixed label, and recorded；The pertinent model information of the destination object is determined according to the record content of the destination object, wherein the correlation model Information includes：The record number of the correlation model of the destination object and the correlation model.
- 4. according to the method described in claim 1, it is characterized in that, judged whether according to the pertinent model information and institute The matched preset model of destination object is stated, including：For each correlation model, the record number of the correlation model, Yi Jicong are obtained from the pertinent model information The label number of the correlation model is obtained in the model label number information prestored；Compare the label number of the record number and the correlation model of the correlation model；If the record number of the correlation model is equal to the label number of the correlation model, it is determined that the correlation model and institute State destination object matching.
- 5. according to the method described in claim 1, it is characterised in that the label number of the destination object is more than or equal to and the mesh Mark the label number of the preset model of object matching.
- 6. according to the method described in claim 1, it is characterized in that, according to the pertinent model information judge whether with After the matched preset model of destination object, the method further includes：If in the presence of with the matched preset model of the destination object, according to the feature with the matched preset model of the destination object The destination object is operated.
- 7. according to the method described in claim 2, it is characterized in that, model rope is being established to all preset models by preset rules After drawing relation, the method further includes：Detect new model, extract the label of the new model；If the label of the new model is found in existing label under the tag entry, by the mark of the new model It is added to the corresponding model identification bar of the label now；If not finding the label of the new model in existing label under the tag entry, which is added to institute State under tag entry, and the mark of the new model is added to corresponding model identification bar now.
- A kind of 8. data model coalignment, it is characterised in that including：Index establishes module, for establishing model index relative to all preset models by preset rules；Label lookup module, is searched for the label according to destination object in the model index relative, is obtained described The pertinent model information of destination object；Model judgment module, it is matched default with the destination object for being judged whether according to the pertinent model information Model.
- 9. a kind of data model matching unit, it is characterised in that the equipment includes：One or more processors；Storage device, for storing one or more programs；When one or more of programs are performed by one or more of processors so that one or more of processors are real The now data model matching process as described in any in claim 1-7.
- 10. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the program is by processor The data model matching process as described in any in claim 1-7 is realized during execution.
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