CN107436925A - A kind of POI data search method and correlating method - Google Patents

A kind of POI data search method and correlating method Download PDF

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Publication number
CN107436925A
CN107436925A CN201710550828.8A CN201710550828A CN107436925A CN 107436925 A CN107436925 A CN 107436925A CN 201710550828 A CN201710550828 A CN 201710550828A CN 107436925 A CN107436925 A CN 107436925A
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similarity
resultant vector
component
mrow
poi data
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CN107436925B (en
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马骁驰
邓皓冉
吕中驰
王健盟
汤亚凡
范小曼
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Jiangsu Airui Electronic Technology Co ltd
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Hangzhou Gallop Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The present invention provides a kind of POI data search method and correlating method, to solve the problems, such as that POI data information lacks in the prior art.Comprise the following steps：POI data and supplementary data to be associated are associated；Obtain the longitude and latitude of Access Points；According to the longitude and latitude in the longitude and latitude and POI data of Access Points, the POI data in basic search domain is obtained；According to the longitude and latitude in the longitude and latitude and POI data of Access Points, the POI data in emphasis search domain and the supplementary data associated with the POI data in emphasis search domain are obtained；The POI data got and supplementary data are exported as retrieval result can obtain following advantageous effects by implementing the present invention：By the way that POI data and supplementary data are associated so that supplementary data can be acquired, and overcome the problem of POI data information lacks in the prior art.

Description

A kind of POI data search method and correlating method
Technical field
The present invention relates to power domain, and in particular to a kind of POI data search method and correlating method.
Background technology
Increasing user carrys out the relevant information of gain location by using electronic map query POI at present, has POI Database for POI retrieval data supporting is provided.Generally, each POI includes title, classification, latitude, longitude and neighbouring hotel The part basis information of retail shop etc. four, is mainly used in digital map navigation and positioning.
At present, the POI data storehouse renewal to database mainly passes through two ways：Mode one is gathered data on the spot, root The data factually adopted are updated to POI data storehouse；Mode two, will to obtain POI data from all kinds of websites on internet It is arranged to be preserved after unified form into database.Compared with mode one, mode two greatly improves database update efficiency, As the main method of POI data storehouse renewal.And with the continuous development of POI data processing mode, at present can be automatic The method (such as A of patent No. CN 103514199) of repeated POI data is identified, further improves and POI numbers is obtained by internet According to accuracy.But influenceed by POI source-informations are limited, the POI data that internet obtains is only comprising above institute The Back ground Information mentioned, the access to information demand of users can not be met.
POI data information is less at present mainly following two reasons：1st, the POI data that internet obtains only includes base Plinth information, lack such as picture, description details；2nd, POI data is stored in database in the form of character string, electronically The programs such as figure are called be required for scanning substantial amounts of data every time, when the information that single POI packets contain is larger, can have a strong impact on Efficiency is called in scanning, and then influences Consumer's Experience.
Therefore be badly in need of providing a kind of search method, the information that POI data lacks can be both provided, and do not influence to call efficiency.
The content of the invention
The present invention provides a kind of POI data search method and correlating method, is lacked with solving POI data information in the prior art The problem of few.
In order to realize the purpose, a kind of POI data search method of the present invention, comprise the following steps：
POI data and supplementary data to be associated are associated；
Obtain the longitude and latitude of Access Points；
According to the longitude and latitude in the longitude and latitude and POI data of Access Points, the POI data in basic search domain is obtained；
According to the longitude and latitude in the longitude and latitude and POI data of Access Points, obtain POI data in emphasis search domain and The supplementary data associated with the POI data in emphasis search domain；
The POI data got and supplementary data are exported as retrieval result；
Wherein, the basic search domain is border circular areas using Access Points position as the center of circle, the emphasis search domain For the sector region using Access Points position as the center of circle.
Preferably, it is described by POI data and supplementary data to be associated be associated including：
POI data is obtained, wherein, POI data includes title and longitude and latitude；
Supplementary data to be associated is obtained, wherein, supplementary data to be associated includes title and details；
POI data and supplement number to be associated are calculated according to the title of the title of POI data and supplementary data to be associated According to similarity；
The POI data that similarity is exceeded to predetermined threshold value is associated with corresponding supplementary data to be associated.
Preferably, the longitude and latitude in the longitude and latitude and POI data according to Access Points is obtained in basic search domain POI data includes：
Calculation of longitude ＆ latitude POI data in the longitude and latitude and POI data of Access Points represents position and Access Points position The distance between, the distance between position and Access Points position are represented according to POI data, POI data is obtained and represents position and inspection Distance is less than the POI data of pre-determined distance between rope point position.
Preferably, the radius of the sector region is less than or equal to the radius of border circular areas.
Preferably, the longitude and latitude in the longitude and latitude and POI data according to Access Points is obtained in emphasis search domain POI data and include with the supplementary data that the POI data in emphasis search domain associates：
Calculate azimuth of the POI data in basic search domain relative to Access Points position, according to POI data relative to The azimuth of Access Points position and emphasis search domain obtain emphasis relative to the azimuth angle scope of Access Points position and retrieved POI data in region, obtain what is associated in emphasis search domain with POI data according to the POI data in emphasis search domain Supplementary data.
Preferably, the title of the title according to POI data and supplementary data to be associated be calculated POI data with The similarity of supplementary data to be associated includes：
The title of the title to POI data and supplementary data to be associated carries out word segmentation processing respectively, obtains POI data names Claim set of segments vector U [U1,U2,…,Ui] and supplementary data name segment collection resultant vector V [V to be associated1,V2,…,Vj]；
Resultant vector U will be collected and collect all words in resultant vector V and merged, and the word for repeating only retains one It is individual, obtain two vectorial sum W [W1,W2,…,Wk]；
The similarity of each component and collection resultant vector U in W is calculated, if the component in W occurs in resultant vector U is collected, The similarity of the component and collection resultant vector U takes 1, if the component in W does not occur in resultant vector U is collected, the component and set Vectorial U similarity takes a, finally gives W with collecting resultant vector U similarity vector SimilarityU[C1,C2,…,Ck]；Wherein, SimilarityUIn CkFor the similarity of the k-th component in W and collection resultant vector U, a is the preset value between 0 to 1；
The similarity of each component and collection resultant vector V in W is calculated, if the component in W occurs in resultant vector V is collected, The similarity of the component and collection resultant vector V takes 1, if the component in W does not occur in resultant vector V is collected, the component and set Vectorial V similarity takes b, finally gives W with collecting resultant vector V similarity vector SimilarityV[D1,D2,…,Dk]；Wherein, SimilarityVIn DkFor the similarity of the k-th component in W and collection resultant vector V, b is the preset value between 0 to 1；
Collection resultant vector U is calculated according to equation below and collects resultant vector V similarity SimilarityUVAs POI numbers According to the similarity with supplementary data to be associated；
Wherein, SimilarityU 2For SimilarityUIn each component square sum；SimilarityV 2For SimilarityVIn each component square sum.
Preferably, the title of the title according to POI data and supplementary data to be associated be calculated POI data with The similarity of supplementary data to be associated includes：
The title of the title to POI data and supplementary data to be associated carries out word segmentation processing respectively, obtains POI data names Claim set of segments vector U [U1,U2,…,Ui] and supplementary data name segment collection resultant vector V [V to be associated1,V2,…,Vj]；
Proper noun is obtained from proprietary name dictionary, collection resultant vector U and collection resultant vector V are carried out according to proprietary name Screening, proper noun and non-proprietary noun is made a distinction, and then obtain the proper noun collection resultant vector U of POI dataA[U1, U2,…,Ui] and supplementary data to be associated proper noun collection resultant vector VA[U1,U2,…,Uj] and POI data is non-proprietary Name term vector UB[U1,U2,…,Um-i] and supplementary data to be associated nonproprietary name term vector VB[U1,U2,…,Un-j]；
Will collection resultant vector UAWith collection resultant vector VAIn all words merge, and the word for repeating only retains One, obtain two vectorial sum WA[W1,W2,…,Wk]；
Will collection resultant vector UBWith collection resultant vector VBIn all words merge, and the word for repeating only retains One, thus obtain two vectorial sum WB[W1,W2,…,WL]；
Calculate WAIn each component and UAThe similarity of interior each component, if WAIn component collection resultant vector UAIn go out Existing, then the component is with collecting resultant vector UASimilarity take 1, if WAIn component collection resultant vector UAIn do not occur, then this point Amount and collection resultant vector UASimilarity take e, finally give WAWith collecting resultant vector UAProper noun similarity vector SimilarityUA [C1,C2,…,Ck], SimilarityUAIn CkFor WAIn k-th vector and collection resultant vector U similarity, e be 0 to 1 it Between preset value；
Calculate WAIn each component and VAThe similarity of interior each component, if WAIn component collection resultant vector VAIn go out Existing, then the component is with collecting resultant vector VASimilarity take 1, if WAIn component collection resultant vector VAIn do not occur, then this point Amount and collection resultant vector VASimilarity take e, finally give WAWith collecting resultant vector VAProper noun similarity vector SimilarityVA [D1,D2,…,Dk]；SimilarityVAIn CkFor WAIn k-th component and collection resultant vector U similarity, e be 0 to 1 it Between preset value；
Calculate WBIn each component and UBThe similarity of interior each component, if WBIn component collection resultant vector UAIn go out Existing, then the component is with collecting resultant vector UBSimilarity take 1, if WBIn component collection resultant vector UBIn do not occur, then this point Amount and collection resultant vector UBSimilarity take f, finally give WBWith collecting resultant vector UBProper noun similarity vector SimilarityUB [C1,C2,…,CL], SimilarityUBIn CLFor WBIn l-th component and collection resultant vector U similarity, f be 0 to 1 it Between preset value；
Calculate WBIn each component and VBThe similarity of interior each component, if WBIn component collection resultant vector VAIn go out Existing, then the component is with collecting resultant vector VBSimilarity take 1, if WBIn component collection resultant vector VBIn do not occur, then this point Amount and collection resultant vector VBSimilarity take f, finally give WBWith collecting resultant vector VBProper noun similarity vector SimilarityVB [D1,D2,…,DL], SimilarityVBIn DLFor WBIn l-th vector and collection resultant vector V similarity, f be 0 to 1 it Between preset value；
Collection resultant vector U is calculated according to equation below and collects resultant vector V similarity SimilarityUV, and made For POI data and the similarity of supplementary data to be associated；
Wherein, SimilarityUA 2For SimilarityUAIn each component square sum, SimilarityVA 2For SimilarityVAIn each component square sum, SimilarityUB 2For SimilarityUBIn each component square With SimilarityVB 2For SimilarityVBIn each component square sum, P and Q are weight coefficient, wherein P+Q=1,0< P<1,0<Q<1,P≠Q。
Preferably, the POI data that similarity is exceeded to predetermined threshold value is closed with corresponding supplementary data to be associated Connection includes：By similarity SimilarityUVCompared with default similarity threshold X, if SimilarityUV＞ X, then sentence It is fixed similar, POI data and the to be associated supplementary data similar to its are associated.
A kind of POI data correlating method, comprises the following steps：
POI data is obtained, wherein, each POI data includes title and longitude and latitude；
Supplementary data to be associated is obtained, wherein, each supplementary data to be associated includes title and details；
POI data and supplement number to be associated are calculated according to the title of the title of POI data and supplementary data to be associated According to similarity；
The POI data that similarity is exceeded to predetermined threshold value is associated with corresponding supplementary data to be associated.
Preferably, the title of the title according to POI data and supplementary data to be associated be calculated POI data with The similarity of supplementary data to be associated includes：
The title of the title to POI data and supplementary data to be associated carries out word segmentation processing respectively, obtains POI data names Claim set of segments vector U [U1,U2,…,Ui] and supplementary data name segment collection resultant vector V [V to be associated1,V2,…,Vj]；
Proper noun is obtained from proprietary name dictionary, collection resultant vector U and collection resultant vector V are carried out according to proprietary name Screening, proper noun and non-proprietary noun is made a distinction, and then obtain the proper noun collection resultant vector U of POI dataA[U1, U2,…,Ui] and supplementary data to be associated proper noun collection resultant vector VA[U1,U2,…,Uj] and POI data is non-proprietary Name term vector UB[U1,U2,…,Um-i] and supplementary data to be associated nonproprietary name term vector VB[U1,U2,…,Un-j]；
Will collection resultant vector UAWith collection resultant vector VAIn all words merge, and the word for repeating only retains One, obtain two vectorial sum WA[W1,W2,…,Wk]；
Will collection resultant vector UBWith collection resultant vector VBIn all words merge, and the word for repeating only retains One, thus obtain two vectorial sum WB[W1,W2,…,WL]；
Calculate WAIn each component and UAThe similarity of interior each component, if WAIn component collection resultant vector UAIn go out Existing, then the component is with collecting resultant vector UASimilarity take 1, if WAIn component collection resultant vector UAIn do not occur, then this point Amount and collection resultant vector UASimilarity take e, finally give WAWith collecting resultant vector UAProper noun similarity vector SimilarityUA [C1,C2,…,Ck], SimilarityUAIn CkFor WAIn k-th component and collection resultant vector U similarity, e be 0 to 1 it Between preset value；
Calculate WAIn each component and VAThe similarity of interior each component, if WAIn component collection resultant vector VAIn go out Existing, then the component is with collecting resultant vector VASimilarity take 1, if WAIn component collection resultant vector VAIn do not occur, then this point Amount and collection resultant vector VASimilarity take e, finally give WAWith collecting resultant vector VAProper noun similarity vector SimilarityVA [D1,D2,…,Dk]；SimilarityVAIn CkFor WAIn k-th component and collection resultant vector U similarity, e be 0 to 1 it Between preset value；
Calculate WBIn each component and UBThe similarity of interior each component, if WBIn component collection resultant vector UAIn go out Existing, then the component is with collecting resultant vector UBSimilarity take 1, if WBIn component collection resultant vector UBIn do not occur, then this point Amount and collection resultant vector UBSimilarity take f, finally give WBWith collecting resultant vector UBProper noun similarity vector SimilarityUB [C1,C2,…,CL], SimilarityUBIn CLFor WBIn l-th component and collection resultant vector U similarity, f be 0 to 1 it Between preset value；
Calculate WBIn each component and VBThe similarity of interior each component, if WBIn component collection resultant vector VAIn go out Existing, then the component is with collecting resultant vector VBSimilarity take 1, if WBIn component collection resultant vector VBIn do not occur, then this point Amount and collection resultant vector VBSimilarity take f, finally give WBWith collecting resultant vector VBProper noun similarity vector SimilarityVB [D1,D2,…,DL], SimilarityVBIn DLFor WBIn l-th vector and collection resultant vector V similarity, f be 0 to 1 it Between preset value；
Collection resultant vector U is calculated according to equation below and collects resultant vector V similarity SimilarityUV, and made For POI data and the similarity of supplementary data to be associated；
Wherein, SimilarityUA 2For SimilarityUAIn each component square sum, SimilarityVA 2For SimilarityVAIn each component square sum, SimilarityUB 2For SimilarityUBIn each component square With SimilarityVB 2For SimilarityVBIn each component square sum, P and Q are weight coefficient, wherein P+Q=1,0< P<1,0<Q<1,P≠Q
Following advantageous effects can be obtained by implementing the present invention：It is an advantage of the current invention that by by POI numbers Be associated according to supplementary data so that supplementary data can be acquired, overcome in the prior art POI data information lack The problem of；In retrieval, by distinguishing weight search domain and common search domain, POI data is obtained in normal areas, and in weight Point region obtains POI data and its supplementary data of association so that retrieval user can get in detail in emphasis search domain Data.
Brief description of the drawings
Fig. 1 is a kind of flow chart of POI data search method；
Fig. 2 is the flow chart of POI data correlating method.
Embodiment
For the ease of the understanding of those skilled in the art, the present invention is made further with reference to specific embodiment It is bright：
Embodiment 1：
As shown in figure 1, the present invention provides a kind of POI data search method, comprise the following steps：
Step S1：POI data and supplementary data to be associated are associated；
Step S2：Obtain the longitude and latitude of Access Points；
Step S3：According to the longitude and latitude in the longitude and latitude and POI data of Access Points, the POI in basic search domain is obtained Data；
Step S4：According to the longitude and latitude in the longitude and latitude and POI data of Access Points, the POI in emphasis search domain is obtained Data and the supplementary data associated with the POI data in emphasis search domain；
Step S5：The POI data got and supplementary data are exported as retrieval result；
Wherein, basic search domain is the border circular areas that Access Points position is the center of circle, and emphasis search domain is positioned at retrieval Point front and the sector region using Access Points position as the center of circle.
Step S2 starts to perform when retrieving user and being retrieved, and step 1 can perform in day part.
POI data and supplementary data are first associated by the present invention, when retrieval user is retrieved, obtain Access Points The longitude and latitude of position, i.e. Access Points, and will be search domain based on the border circular areas in the center of circle by Access Points position, and obtain The POI data of basic search domain, using the sector region that Access Points position is the center of circle as emphasis search domain, and obtain emphasis The POI data of search domain and its supplementary data of association.Supplementary data includes POI as the supplement of POI data in the present invention Details lacking in data, can be picture, detailed description etc.；The foundation of Access Points range of search in the present invention, can be with To retrieve the position location of user itself.It is an advantage of the current invention that by the way that POI data and supplementary data are associated, make Obtaining supplementary data can be acquired, and overcome the problem of POI data information lacks in the prior art；In retrieval, pass through differentiation Weight search domain and common search domain, POI data is obtained in normal areas, and POI data and its pass are obtained in key area The supplementary data of connection so that retrieval user can get detailed data in emphasis search domain, and Access Points are retrieval user When itself GRS is positioned, the present invention can overcome only retrieval emphasis search domain to cause master because of Access Points GRS position errors The problem of wanting information missing inspection.In order to reduce influence of the supplementary data to recall precision, common search domain can got The retrieval result of common search domain is first exported during POI data, is exported again after the supplementary data to be obtained to emphasis search domain The retrieval result of emphasis search domain.
Alternatively, by POI data and supplementary data to be associated be associated including：
POI data is obtained, wherein, POI data includes title and longitude and latitude；
Supplementary data to be associated is obtained, wherein, supplementary data to be associated includes title and details；
POI data and supplement number to be associated are calculated according to the title of the title of POI data and supplementary data to be associated According to similarity；
The POI data that similarity is exceeded to predetermined threshold value is associated with corresponding supplementary data to be associated.
It is associated by the similarity of the title in POI data and the title of supplementary data to be associated, realizes automatic close The purpose of connection；In the present invention, similarity can select identical, i.e. the title of POI data and supplementary data to be associated Title is identical, such as by the supplementary data to be associated on entitled West Lake jewel mountain and the POI data on entitled West Lake jewel mountain Association.
Alternatively, the longitude and latitude in the longitude and latitude and POI data of Access Points obtains basis retrieval area POI data in domain includes：
Calculation of longitude ＆ latitude POI data in the longitude and latitude and POI data of Access Points represents position and Access Points position The distance between, the distance between position and Access Points position are represented according to POI data, acquisition represents position and Access Points position Between distance be less than pre-determined distance POI data.
As a kind of preferred scheme, the radius of sector region is less than or equal to the radius of border circular areas.So setting can be with Improve recall precision；After POI data in basic search domain, it need to only be selected from the POI data of basic search domain POI data in emphasis search domain, and obtain the supplementary data associated by the POI data in emphasis search domain.
As a kind of preferred scheme, the radius of sector region is equal to the radius of border circular areas, according to the longitude and latitude of Access Points Closed with the POI data in the longitude and latitude acquisition emphasis search domain in POI data and with the POI data in emphasis search domain The supplementary data of connection includes：
Calculate azimuth of the POI data in basic search domain relative to Access Points position, according to POI data relative to The azimuth of Access Points position and emphasis search domain obtain emphasis relative to the azimuth angle scope of Access Points position and retrieved POI data in region, obtain what is associated in emphasis search domain with POI data according to the POI data in emphasis search domain Supplementary data to be associated.
The POI data calculated in the domain of basic display area can use in the prior art relative to the azimuth of retrieval customer location Computational methods.
Optionally, the POI data calculated in the domain of basic display area includes relative to the azimuth of retrieval customer location：
POI data is calculated relative to the azimuth angle theta of retrieval customer location by equation below：
WhereinIt is the latitude for detecting customer location,It is the latitude of POI data, Δ λ is both longitude differences.
, only need to be according to the POI data in basic search domain and retrieval after the POI data in obtaining basic search domain The azimuth of point position substantially increases weight whether in the azimuth angular region of emphasis search domain Yu Access Points position The recall precision in point retrieval region.
POI data and supplement number to be associated are calculated according to the title of the title of POI data and supplementary data to be associated According to similarity include：
The title of the title of POI data and supplementary data to be associated carries out word segmentation processing respectively, obtains POI data title piece Section collection resultant vector U [U1, U2..., Ui] and supplementary data name segment collection resultant vector V [V to be associated1, V2..., Vj]；
Proper noun is obtained from proprietary name dictionary, collection resultant vector U and collection resultant vector V are carried out according to proprietary name Screening, proper noun and non-proprietary noun is made a distinction, and then obtain the proper noun collection resultant vector U of POI dataA[U1, U2..., Ui] and supplementary data to be associated proper noun collection resultant vector VA[U1, U2..., Uj] and POI data is non-proprietary Name term vector UB[U1, U2..., Um-i] and supplementary data to be associated nonproprietary name term vector VB[U1, U2..., Un-j]；
Will collection resultant vector UAWith collection resultant vector VAIn all words merge, and the word for repeating only retains One, obtain two vectorial sum WA[W1, W2..., Wk]；
Will collection resultant vector UBWith collection resultant vector VBIn all words merge, and the word for repeating only retains One, thus obtain two vectorial sum WB[W1, W2..., WL]；
Calculate WAIn each component and UAThe similarity of interior each component, if WAIn component collection resultant vector UAIn go out Existing, then the component is with collecting resultant vector UASimilarity take 1, if WAIn component collection resultant vector UAIn do not occur, then this point Amount and collection resultant vector UASimilarity take e, finally give WAWith collecting resultant vector UAProper noun similarity vector SimilarityUA [C1,C2,…,Ck], SimilarityUAIn CkFor WAIn k-th component and collection resultant vector U similarity, e be 0 to 1 it Between preset value, could be arranged to 0.9；
Calculate WAIn each component and VAThe similarity of interior each component, if WAIn component collection resultant vector VAIn go out Existing, then the component is with collecting resultant vector VASimilarity take 1, if WAIn component collection resultant vector VAIn do not occur, then this point Amount and collection resultant vector VASimilarity take e, finally give WAWith collecting resultant vector VAProper noun similarity vector SimilarityVA [D1,D2,…,Dk]；SimilarityVAIn CkFor WAIn k-th component and collection resultant vector U similarity, e be 0 to 1 it Between preset value, could be arranged to 0.9；
Calculate WBIn each component and UBThe similarity of interior each component, if WBIn component collection resultant vector UAIn go out Existing, then the component is with collecting resultant vector UBSimilarity take 1, if WBIn component collection resultant vector UBIn do not occur, then this point Amount and collection resultant vector UBSimilarity take f, finally give WBWith collecting resultant vector UBProper noun similarity vector SimilarityUB [C1,C2,…,CL], SimilarityUBIn CLFor WBIn l-th component and collection resultant vector U similarity, f be 0 to 1 it Between preset value, could be arranged to 0.9；
Calculate WBIn each component and VBThe similarity of interior each component, if WBIn component collection resultant vector VAIn go out Existing, then the component is with collecting resultant vector VBSimilarity take 1, if WBIn component collection resultant vector VBIn do not occur, then this point Amount and collection resultant vector VBSimilarity take f, finally give WBWith collecting resultant vector VBProper noun similarity vector SimilarityVB [D1,D2,…,DL], SimilarityVBIn DLFor WBIn l-th vector and collection resultant vector V similarity, f be 0 to 1 it Between preset value, could be arranged to 0.9；
Collection resultant vector U is calculated according to equation below and collects resultant vector V similarity SimilarityUV, and made For POI data and the similarity of supplementary data to be associated；
Wherein, SimilarityUA 2For SimilarityUAIn each component square sum, SimilarityVA 2For SimilarityVAIn each component square sum, SimilarityUB 2For SimilarityUBIn each component square With SimilarityVB 2For SimilarityVBIn each component square sum, P and Q are weight coefficient, wherein P+Q=1,0< P<1,0<Q<1,P≠Q。
SimilarityUA×SimilarityVA=C1×D1+C2×D2+…+Ck×DK；
Wherein, C1, C2, CkRefer to SimilarityUAIn C1, C2, Ck；D1, D2, DkRefer to SimilarityVAIn D1, D2, Dk。
SimilarityUB×SimilarityVB=C1×D1+C2×D2+…+CL×DL；
Wherein, C1, C2, CLRefer to SimilarityUBIn C1, C2, CL；D1, D2, DLRefer to SimilarityVBIn D1, D2, DL。
POI data due to be real geography information arrangement, generally by specific proper noun and follow-up qualifier Sew composition, such as West Lake jewel mountain, the wherein West Lake is specific proper noun, mostly landmark or classics, is gone out in POI entries It is now more frequent；Certain POI description is refered in particular on suffix jewel mountain, and therefore, both judgement weighteds for POI are, it is necessary to effectively Distinguish.
The present invention carries out word segmentation processing by the title of the title to POI data and supplementary data to be associated, and will participle Processing carries out proprietary name and separated with nonproprietary name, according to proprietary name and nonproprietary name fraction again degree of being associated Calculate, and draw the final degree of association, the degree of association that the degree of association finally drawn is drawn than the calculation of relationship degree method of routine is more Close to actual conditions.In the present invention, obtained in the proprietary name storehouse that patent name can be from the prior art.
Specifically, the POI data that similarity is exceeded to predetermined threshold value is associated bag with corresponding supplementary data to be associated Include：By similarity SimilarityUVCompared with default similarity threshold X, if SimilarityUV＞ X, then judge phase Seemingly, POI data and the to be associated supplementary data similar to its are associated, herein, similarity threshold X could be arranged to 1, 0.9th, 0.8 etc..Similarity threshold X can be configured according to specific application occasion.
Method on POI data and the to be associated supplementary data similar to its are associated, can be by benefit to be associated Evidence of making up the number is stored in detail information data storehouse, and produces a corresponding major key, and the major key is stored in the letter of POI data In breath, associating for supplementary data to be associated and POI data is realized.
Embodiment 2：
Difference with embodiment 1 is, is calculated according to the title of the title of POI data and supplementary data to be associated The method of POI data and the similarity of supplementary data to be associated.
In the present embodiment, POI data is calculated with treating according to the title of the title of POI data and supplementary data to be associated The similarity of association supplementary data includes：
The title of the title to POI data and supplementary data to be associated carries out word segmentation processing respectively, obtains POI data names Claim set of segments vector U [U1,U2,…,Ui] and supplementary data name segment collection resultant vector V [V to be associated1,V2,…,Vj]；
Resultant vector U will be collected and collect all words in resultant vector V and merged, and the word for repeating only retains one It is individual, obtain two vectorial sum W [W1,W2,…,Wk]；
The similarity of each component and collection resultant vector U in W is calculated, if the component in W occurs in resultant vector U is collected, The similarity of the component and collection resultant vector U takes 1, if the component in W does not occur in resultant vector U is collected, the component and set Vectorial U similarity takes a, finally gives W with collecting resultant vector U similarity vector SimilarityU[C1,C2,…,Ck]；Wherein, SimilarityUIn CkFor the similarity of the k-th component in W and collection resultant vector U, a is the preset value between 0 to 1；
The similarity of each component and collection resultant vector V in W is calculated, if the component in W occurs in resultant vector V is collected, The similarity of the component and collection resultant vector V takes 1, if the component in W does not occur in resultant vector V is collected, the component and set Vectorial V similarity takes b, finally gives W with collecting resultant vector V similarity vector SimilarityV[D1,D2,…,Dk]；Wherein, SimilarityVIn DkFor the similarity of the k-th component in W and collection resultant vector V, b is the preset value between 0 to 1；
Collection resultant vector U is calculated according to equation below and collects resultant vector V similarity SimilarityUVAs POI numbers According to the similarity with supplementary data to be associated；
Wherein SimilarityU 2It is for SimilarityUIn each component square sum；That is SimilarityUIn it is every Square of individual element, is then added.
Wherein SimilarityV 2It is for SimilarityVIn each component square sum；That is SimilarityUEach Square of element, is then added.
SimilarityU×SimilarityV=C1×D1+C2×D2+…+CL×DL；
Wherein, C1, C2, CkRefer to SimilarityUIn C1, C2, Ck；D1, D2, DkRefer to SimilarityVIn D1, D2, Dk。
Embodiment 3：
As shown in Fig. 2 a kind of POI data correlating method, comprises the following steps；
Step S01：POI data is obtained, wherein, each POI data includes title and longitude and latitude；
Step S02：Supplementary data to be associated is obtained, wherein, each supplementary data to be associated is believed comprising title and in detail Breath；
Step S03：POI data is calculated according to the title of the title of POI data and supplementary data to be associated to close with waiting Join the similarity of supplementary data；
Step S04：The POI data that similarity is exceeded to predetermined threshold value is associated with corresponding supplementary data to be associated.
Wherein, POI data and benefit to be associated are calculated according to the title of the title of POI data and supplementary data to be associated The similarity for evidence of making up the number includes：
The title of the title to POI data and supplementary data to be associated carries out word segmentation processing respectively, obtains POI data names Claim set of segments vector U [U1,U2,…,Ui] and supplementary data name segment collection resultant vector V [V to be associated1,V2,…,Vj]；
Proper noun is obtained from proprietary name dictionary, collection resultant vector U and collection resultant vector V are carried out according to proprietary name Screening, proper noun and non-proprietary noun is made a distinction, and then obtain the proper noun collection resultant vector U of POI dataA[U1, U2,…,Ui] and supplementary data to be associated proper noun collection resultant vector VA[U1,U2,…,Uj] and POI data is non-proprietary Name term vector UB[U1,U2,…,Um-i] and supplementary data to be associated nonproprietary name term vector VB[U1,U2,…,Un-j]；
Will collection resultant vector UAWith collection resultant vector VAIn all words merge, and the word for repeating only retains One, obtain two vectorial sum WA[W1,W2,…,Wk]；
Will collection resultant vector UBWith collection resultant vector VBIn all words merge, and the word for repeating only retains One, thus obtain two vectorial sum WB[W1,W2,…,WL]；
Calculate WAIn each component and UAThe similarity of interior each component, if WAIn component collection resultant vector UAIn go out Existing, then the component is with collecting resultant vector UASimilarity take 1, if WAIn component collection resultant vector UAIn do not occur, then this point Amount and collection resultant vector UASimilarity take e, finally give WAWith collecting resultant vector UAProper noun similarity vector SimilarityUA [C1,C2,…,Ck], SimilarityUAIn CkFor WAIn k-th component and collection resultant vector U similarity, e be 0 to 1 it Between preset value；
Calculate WAIn each component and VAThe similarity of interior each component, if WAIn component collection resultant vector VAIn go out Existing, then the component is with collecting resultant vector VASimilarity take 1, if WAIn component collection resultant vector VAIn do not occur, then this point Amount and collection resultant vector VASimilarity take e, finally give WAWith collecting resultant vector VAProper noun similarity vector SimilarityVA [D1,D2,…,Dk]；SimilarityVAIn CkFor WAIn k-th component and collection resultant vector U similarity, e be 0 to 1 it Between preset value；
Calculate WBIn each component and UBThe similarity of interior each component, if WBIn component collection resultant vector UAIn go out Existing, then the component is with collecting resultant vector UBSimilarity take 1, if WBIn component collection resultant vector UBIn do not occur, then this point Amount and collection resultant vector UBSimilarity take f, finally give WBWith collecting resultant vector UBProper noun similarity vector SimilarityUB [C1,C2,…,CL], SimilarityUBIn CLFor WBIn l-th component and collection resultant vector U similarity, f be 0 to 1 it Between preset value；
Calculate WBIn each component and VBThe similarity of interior each component, if WBIn component collection resultant vector VAIn go out Existing, then the component is with collecting resultant vector VBSimilarity take 1, if WBIn component collection resultant vector VBIn do not occur, then this point Amount and collection resultant vector VBSimilarity take f, finally give WBWith collecting resultant vector VBProper noun similarity vector SimilarityVB [D1,D2,…,DL], SimilarityVBIn DLFor WBIn l-th vector and collection resultant vector V similarity, f be 0 to 1 it Between preset value；
Collection resultant vector U is calculated according to equation below and collects resultant vector V similarity SimilarityUV, and made For POI data and the similarity of supplementary data to be associated；
Wherein, SimilarityUA 2For SimilarityUAIn each component square sum, SimilarityVA 2For SimilarityVAIn each component square sum, SimilarityUB 2For SimilarityUBIn each component square With SimilarityVB 2For SimilarityVBIn each component square sum, P and Q are weight coefficient, wherein P+Q=1,0< P<1,0<Q<1,P≠Q。
SimilarityUA×SimilarityVA=C1×D1+C2×D2+…+Ck×Dk；
Wherein, C1, C2, CkRefer to SimilarityUAIn C1, C2, Ck；D1, D2, DkRefer to SimilarityVAIn D1, D2, Dk。
SimilarityUB×SimilarityVB=C1×D1+C2×D2+…+CL×DL；
Wherein, C1, C2, CLRefer to SimilarityUBIn C1, C2, CL；D1, D2, DLRefer to SimilarityVBIn D1, D2, DL。
The above method, as the correlating method part in embodiment 1, its principle and effect described in embodiment 1, The present embodiment is no longer repeated.
The specific embodiment of the present invention is these are only, but the technical characteristic of the present invention is not limited thereto, any this area Technical staff in the field of the invention, the change made or modification are all covered among the scope of the claims of the present invention.

Claims (10)

1. a kind of POI data search method, it is characterised in that comprise the following steps：
POI data and supplementary data to be associated are associated；
Obtain the longitude and latitude of Access Points；
According to the longitude and latitude in the longitude and latitude and POI data of Access Points, the POI data in basic search domain is obtained；
According to the longitude and latitude in the longitude and latitude and POI data of Access Points, obtain POI data in emphasis search domain and with again The supplementary data of POI data association in point retrieval region；
The POI data got and supplementary data are exported as retrieval result；
Wherein, the basic search domain is border circular areas using Access Points position as the center of circle, the emphasis search domain be with Access Points position is the sector region in the center of circle.
2. a kind of POI data search method as claimed in claim 1, it is characterised in that described by POI data and to be associated Supplementary data be associated including：
POI data is obtained, wherein, POI data includes title and longitude and latitude；
Supplementary data to be associated is obtained, wherein, supplementary data to be associated includes title and details；
POI data and supplementary data to be associated are calculated according to the title of the title of POI data and supplementary data to be associated Similarity；
The POI data that similarity is exceeded to predetermined threshold value is associated with corresponding supplementary data to be associated.
A kind of 3. POI data search method as claimed in claim 1, it is characterised in that the longitude and latitude according to Access Points Obtaining the POI data in basic search domain with the longitude and latitude in POI data includes：
Calculation of longitude ＆ latitude POI data in the longitude and latitude and POI data of Access Points is represented between position and Access Points position Distance, the distance between position and Access Points position are represented according to POI data, POI data is obtained and represents position and Access Points Distance is less than the POI data of pre-determined distance between position.
4. a kind of POI data search method as claimed in claim 1, it is characterised in that the radius of the sector region is less than Or the radius equal to border circular areas.
A kind of 5. POI data search method as claimed in claim 4, it is characterised in that the longitude and latitude according to Access Points Closed with the POI data in the longitude and latitude acquisition emphasis search domain in POI data and with the POI data in emphasis search domain The supplementary data of connection includes：
Azimuth of the POI data in basic search domain relative to Access Points position is calculated, according to POI data relative to retrieval The azimuth of point position and emphasis search domain obtain emphasis search domain relative to the azimuth angle scope of Access Points position Interior POI data, the supplement associated in emphasis search domain with POI data is obtained according to the POI data in emphasis search domain Data.
A kind of 6. POI data search method as claimed in claim 1, it is characterised in that the title according to POI data with The similarity that POI data and supplementary data to be associated is calculated in the title of supplementary data to be associated includes：
The title of the title to POI data and supplementary data to be associated carries out word segmentation processing respectively, obtains POI data name segment Collect resultant vector U [U1,U2,…,Ui] and supplementary data name segment collection resultant vector V [V to be associated1,V2,…,Vj]；
Resultant vector U will be collected and collect all words in resultant vector V and merged, and the word for repeating only retains one, Obtain two vectorial sum W [W1,W2,…,Wk]；
Calculate the similarity of each component and collection resultant vector U in W, if the component in W occurs in resultant vector U is collected, this point Amount and collection resultant vector U similarity take 1, if the component in W does not occur in resultant vector U is collected, the component and collection resultant vector U Similarity take a, finally give W with collecting resultant vector U similarity vector SimilarityU[C1,C2,…,Ck]；Wherein, SimilarityUIn CkFor the similarity of the k-th component in W and collection resultant vector U, a is the preset value between 0 to 1；
Calculate the similarity of each component and collection resultant vector V in W, if the component in W occurs in resultant vector V is collected, this point Amount and collection resultant vector V similarity take 1, if the component in W does not occur in resultant vector V is collected, the component and collection resultant vector V Similarity take b, finally give W with collecting resultant vector V similarity vector SimilarityV[D1,D2,…,Dk]；Wherein, SimilarityVIn DkFor the similarity of the k-th component in W and collection resultant vector V, b is the preset value between 0 to 1；
Collection resultant vector U is calculated according to equation below and collects resultant vector V similarity SimilarityUVAs POI data with The similarity of supplementary data to be associated；
<mrow> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>V</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>Similarity</mi> <mi>U</mi> </msub> <mo>&amp;times;</mo> <msub> <mi>Similarity</mi> <mi>V</mi> </msub> </mrow> <msqrt> <mrow> <mo>(</mo> <msup> <msub> <mi>Similarity</mi> <mi>U</mi> </msub> <mn>2</mn> </msup> <mo>&amp;times;</mo> <msup> <msub> <mi>Similarity</mi> <mi>V</mi> </msub> <mn>2</mn> </msup> <mo>)</mo> <mo>/</mo> <mn>2</mn> </mrow> </msqrt> </mfrac> </mrow>
Wherein, SimilarityU 2For SimilarityUIn each component square sum；SimilarityV 2For SimilarityVIn each component square sum.
A kind of 7. POI data search method as claimed in claim 1, it is characterised in that the title according to POI data with The similarity that POI data and supplementary data to be associated is calculated in the title of supplementary data to be associated includes：
The title of the title to POI data and supplementary data to be associated carries out word segmentation processing respectively, obtains POI data name segment Collect resultant vector U [U1,U2,…,Ui] and supplementary data name segment collection resultant vector V [V to be associated1,V2,…,Vj]；
Proper noun is obtained from proprietary name dictionary, collection resultant vector U and collection resultant vector V are screened according to proprietary name, Proper noun and non-proprietary noun are made a distinction, and then obtain the proper noun collection resultant vector U of POI dataA[U1,U2,…, Ui] and supplementary data to be associated proper noun collection resultant vector VA[U1,U2,…,Uj] and POI data non-proprietary noun to Measure UB[U1,U2,…,Um-i] and supplementary data to be associated nonproprietary name term vector VB[U1,U2,…,Un-j]；
Will collection resultant vector UAWith collection resultant vector VAIn all words merge, and the word for repeating only retains one, Obtain two vectorial sum WA[W1,W2,…,Wk]；
Will collection resultant vector UBWith collection resultant vector VBIn all words merge, and the word for repeating only retains one, Thus two vectorial sum W are obtainedB[W1,W2,…,WL]；
Calculate WAIn each component and UAThe similarity of interior each component, if WAIn component collection resultant vector UAMiddle appearance, then The component and collection resultant vector UASimilarity take 1, if WAIn component collection resultant vector UAIn do not occur, then the component with collection Resultant vector UASimilarity take e, finally give WAWith collecting resultant vector UAProper noun similarity vector SimilarityUA[C1, C2,…,Ck], SimilarityUAIn CkFor WAIn k-th component and collection resultant vector U similarity, between e is 0 to 1 Preset value；
Calculate WAIn each component and VAThe similarity of interior each component, if WAIn component collection resultant vector VAMiddle appearance, then The component and collection resultant vector VASimilarity take 1, if WAIn component collection resultant vector VAIn do not occur, then the component with collection Resultant vector VASimilarity take e, finally give WAWith collecting resultant vector VAProper noun similarity vector SimilarityVA[D1, D2,…,Dk]；SimilarityVAIn CkFor WAIn k-th component and collection resultant vector U similarity, between e is 0 to 1 Preset value；
Calculate WBIn each component and UBThe similarity of interior each component, if WBIn component collection resultant vector UAMiddle appearance, then The component and collection resultant vector UBSimilarity take 1, if WBIn component collection resultant vector UBIn do not occur, then the component with collection Resultant vector UBSimilarity take f, finally give WBWith collecting resultant vector UBProper noun similarity vector SimilarityUB[C1, C2,…,CL], SimilarityUBIn CLFor WBIn l-th component and collection resultant vector U similarity, between f is 0 to 1 Preset value；
Calculate WBIn each component and VBThe similarity of interior each component, if WBIn component collection resultant vector VAMiddle appearance, then The component and collection resultant vector VBSimilarity take 1, if WBIn component collection resultant vector VBIn do not occur, then the component with collection Resultant vector VBSimilarity take f, finally give WBWith collecting resultant vector VBProper noun similarity vector SimilarityVB[D1, D2,…,DL], SimilarityVBIn DLFor WBIn l-th vector and collection resultant vector V similarity, between f is 0 to 1 Preset value；
Collection resultant vector U is calculated according to equation below and collects resultant vector V similarity SimilarityUV, and as POI The similarity of data and supplementary data to be associated；
<mrow> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>V</mi> </mrow> </msub> <mo>=</mo> <mi>P</mi> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <mfrac> <mrow> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>A</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>Similarity</mi> <mrow> <mi>V</mi> <mi>A</mi> </mrow> </msub> </mrow> <msqrt> <mrow> <mo>(</mo> <msup> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>A</mi> </mrow> </msub> <mn>2</mn> </msup> <mo>&amp;times;</mo> <msup> <msub> <mi>Similarity</mi> <mrow> <mi>V</mi> <mi>A</mi> </mrow> </msub> <mn>2</mn> </msup> <mo>)</mo> <mo>/</mo> <mn>2</mn> </mrow> </msqrt> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mi>Q</mi> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <mfrac> <mrow> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>B</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>Similarity</mi> <mrow> <mi>V</mi> <mi>B</mi> </mrow> </msub> </mrow> <msqrt> <mrow> <mo>(</mo> <msup> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>B</mi> </mrow> </msub> <mn>2</mn> </msup> <mo>&amp;times;</mo> <msup> <msub> <mi>Similarity</mi> <mrow> <mi>V</mi> <mi>B</mi> </mrow> </msub> <mn>2</mn> </msup> <mo>)</mo> <mo>/</mo> <mn>2</mn> </mrow> </msqrt> </mfrac> <mo>)</mo> </mrow> </mrow>
Wherein, SimilarityUA 2For SimilarityUAIn each component square sum, SimilarityVA 2For SimilarityVAIn each component square sum, SimilarityUB 2For SimilarityUBIn each component square With SimilarityVB 2For SimilarityVBIn each component square sum, P and Q are weight coefficient, wherein P+Q=1,0< P<1,0<Q<1,P≠Q。
8. POI data search method as claimed in claims 6 or 7, it is characterised in that described that similarity is exceeded into predetermined threshold value POI data and corresponding supplementary data to be associated be associated including：By similarity SimilarityUVWith default similarity Threshold X compares, if SimilarityUV＞ X, then judge it is similar, by POI data and the to be associated supplementary data similar to its It is associated.
9. a kind of POI data correlating method, it is characterised in that comprise the following steps：
POI data is obtained, wherein, each POI data includes title and longitude and latitude；
Supplementary data to be associated is obtained, wherein, each supplementary data to be associated includes title and details；
POI data and supplementary data to be associated are calculated according to the title of the title of POI data and supplementary data to be associated Similarity；
The POI data that similarity is exceeded to predetermined threshold value is associated with corresponding supplementary data to be associated.
A kind of 10. POI data correlating method as claimed in claim 9, it is characterised in that the title according to POI data The similarity of POI data and supplementary data to be associated is calculated with the title of supplementary data to be associated to be included：
The title of the title to POI data and supplementary data to be associated carries out word segmentation processing respectively, obtains POI data name segment Collect resultant vector U [U1,U2,…,Ui] and supplementary data name segment collection resultant vector V [V to be associated1,V2,…,Vj]；
Proper noun is obtained from proprietary name dictionary, collection resultant vector U and collection resultant vector V are screened according to proprietary name, Proper noun and non-proprietary noun are made a distinction, and then obtain the proper noun collection resultant vector U of POI dataA[U1,U2,…, Ui] and supplementary data to be associated proper noun collection resultant vector VA[U1,U2,…,Uj] and POI data non-proprietary noun to Measure UB[U1,U2,…,Um-i] and supplementary data to be associated nonproprietary name term vector VB[U1,U2,…,Un-j]；
Will collection resultant vector UAWith collection resultant vector VAIn all words merge, and the word for repeating only retains one, Obtain two vectorial sum WA[W1,W2,…,Wk]；
Will collection resultant vector UBWith collection resultant vector VBIn all words merge, and the word for repeating only retains one, Thus two vectorial sum W are obtainedB[W1,W2,…,WL]；
Calculate WAIn each component and UAThe similarity of interior each component, if WAIn component collection resultant vector UAMiddle appearance, then The component and collection resultant vector UASimilarity take 1, if WAIn component collection resultant vector UAIn do not occur, then the component with collection Resultant vector UASimilarity take e, finally give WAWith collecting resultant vector UAProper noun similarity vector SimilarityUA[C1, C2,…,Ck], SimilarityUAIn CkFor WAIn k-th component and collection resultant vector U similarity, between e is 0 to 1 Preset value；
Calculate WAIn each component and VAThe similarity of interior each component, if WAIn component collection resultant vector VAMiddle appearance, then The component and collection resultant vector VASimilarity take 1, if WAIn component collection resultant vector VAIn do not occur, then the component with collection Resultant vector VASimilarity take e, finally give WAWith collecting resultant vector VAProper noun similarity vector SimilarityVA[D1, D2,…,Dk]；SimilarityVAIn CkFor WAIn k-th component and collection resultant vector U similarity, between e is 0 to 1 Preset value；
Calculate WBIn each component and UBThe similarity of interior each component, if WBIn component collection resultant vector UAMiddle appearance, then The component and collection resultant vector UBSimilarity take 1, if WBIn component collection resultant vector UBIn do not occur, then the component with collection Resultant vector UBSimilarity take f, finally give WBWith collecting resultant vector UBProper noun similarity vector SimilarityUB[C1, C2,…,CL], SimilarityUBIn CLFor WBIn l-th component and collection resultant vector U similarity, between f is 0 to 1 Preset value；
Calculate WBIn each component and VBThe similarity of interior each component, if WBIn component collection resultant vector VAMiddle appearance, then The component and collection resultant vector VBSimilarity take 1, if WBIn component collection resultant vector VBIn do not occur, then the component with collection Resultant vector VBSimilarity take f, finally give WBWith collecting resultant vector VBProper noun similarity vector SimilarityVB[D1, D2,…,DL], SimilarityVBIn DLFor WBIn l-th vector and collection resultant vector V similarity, between f is 0 to 1 Preset value；
Collection resultant vector U is calculated according to equation below and collects resultant vector V similarity SimilarityUV, and as POI The similarity of data and supplementary data to be associated；
<mrow> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>V</mi> </mrow> </msub> <mo>=</mo> <mi>P</mi> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <mfrac> <mrow> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>A</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>Similarity</mi> <mrow> <mi>V</mi> <mi>A</mi> </mrow> </msub> </mrow> <msqrt> <mrow> <mo>(</mo> <msup> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>A</mi> </mrow> </msub> <mn>2</mn> </msup> <mo>&amp;times;</mo> <msup> <msub> <mi>Similarity</mi> <mrow> <mi>V</mi> <mi>A</mi> </mrow> </msub> <mn>2</mn> </msup> <mo>)</mo> <mo>/</mo> <mn>2</mn> </mrow> </msqrt> </mfrac> <mo>)</mo> </mrow> <mo>+</mo> <mi>Q</mi> <mo>&amp;CenterDot;</mo> <mrow> <mo>(</mo> <mfrac> <mrow> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>B</mi> </mrow> </msub> <mo>&amp;times;</mo> <msub> <mi>Similarity</mi> <mrow> <mi>V</mi> <mi>B</mi> </mrow> </msub> </mrow> <msqrt> <mrow> <mo>(</mo> <msup> <msub> <mi>Similarity</mi> <mrow> <mi>U</mi> <mi>B</mi> </mrow> </msub> <mn>2</mn> </msup> <mo>&amp;times;</mo> <msup> <msub> <mi>Similarity</mi> <mrow> <mi>V</mi> <mi>B</mi> </mrow> </msub> <mn>2</mn> </msup> <mo>)</mo> <mo>/</mo> <mn>2</mn> </mrow> </msqrt> </mfrac> <mo>)</mo> </mrow> </mrow>
Wherein, SimilarityUA 2For SimilarityUAIn each component square sum, SimilarityVA 2For SimilarityVAIn each component square sum, SimilarityUB 2For SimilarityUBIn each component square With SimilarityVB 2For SimilarityVBIn each component square sum, P and Q are weight coefficient, wherein P+Q=1,0< P<1,0<Q<1,P≠Q。
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