CN107436925B - POI data retrieval method and association method - Google Patents

POI data retrieval method and association method Download PDF

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CN107436925B
CN107436925B CN201710550828.8A CN201710550828A CN107436925B CN 107436925 B CN107436925 B CN 107436925B CN 201710550828 A CN201710550828 A CN 201710550828A CN 107436925 B CN107436925 B CN 107436925B
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similarity
poi data
data
vector
component
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CN107436925A (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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    • 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 invention provides a POI data retrieval method and a related method, which are used for solving the problem of lack of POI data information in the prior art. The method comprises the following steps: associating the POI data with the supplementary data to be associated; acquiring longitude and latitude of a search point; acquiring POI data in a basic retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data; acquiring POI data in the key retrieval area and supplementary data associated with the POI data in the key retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data; the invention can obtain the following beneficial technical effects by taking the acquired POI data and the supplementary data as the retrieval result to output: by associating the POI data with the supplemental data, the supplemental data may be obtained, overcoming the lack of POI data information in the prior art.

Description

POI data retrieval method and association method
Technical Field
The invention relates to the field of electric power, in particular to a POI data retrieval method and an association method.
Background
At present, more and more users query POIs by using an electronic map to acquire relevant information of a place, and a database storing the POIs provides data support for POI retrieval. Typically, each POI contains four basic pieces of information, namely name, category, latitude and longitude, and nearby hotel shops, mainly for map navigation and positioning.
Currently, POI database updates to databases are mainly performed in two ways: the first mode is that data are collected in the field, and the POI database is updated according to the data collected in the field; and secondly, acquiring POI data from various websites on the Internet, arranging the POI data into a uniform format, and storing the POI data in a database. Compared with the first mode, the first mode greatly improves the database updating efficiency and becomes a main method for updating the POI database. With the continuous development of POI data processing methods, there is currently a method (for example, patent number CN 103514199A) capable of automatically identifying repeated POI data, so as to further improve accuracy of obtaining POI data through the internet. However, the POI source information is limited, and the POI data acquired by the internet only contains the basic information mentioned above, so that the information acquisition requirement of the vast users cannot be met.
The current POI data information is less mainly for the following two reasons: 1. the POI data acquired by the Internet only comprise basic information, and lack detailed information such as pictures, descriptions and the like; 2. the POI data is stored in the database in the form of character strings, programs such as an electronic map and the like need to scan a large amount of data every time, and when the information contained in the single POI data is large, the scanning calling efficiency is seriously affected, so that the user experience is affected.
Therefore, it is highly desirable to provide a retrieval method that can provide information lacking in POI data without affecting the calling efficiency.
Disclosure of Invention
The invention provides a POI data retrieval method and a related method, which are used for solving the problem of lack of POI data information in the prior art.
In order to achieve the purpose, the POI data retrieval method comprises the following steps:
associating the POI data with the supplementary data to be associated;
acquiring longitude and latitude of a search point;
acquiring POI data in a basic retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data;
acquiring POI data in the key retrieval area and supplementary data associated with the POI data in the key retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data;
the acquired POI data and the supplementary data are output as search results;
the basic search area is a circular area taking the position of a search point as a circle center, and the key search area is a sector area taking the position of the search point as the circle center.
Preferably, the associating the POI data with the supplementary data to be associated includes:
acquiring POI data, wherein the POI data comprise names and longitudes and latitudes;
acquiring supplementary data to be associated, wherein the supplementary data to be associated comprises a name and detailed information;
calculating to obtain the similarity of the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated;
and associating the POI data with the similarity exceeding a preset threshold value with corresponding supplementary data to be associated.
Preferably, the acquiring the POI data in the basic search area according to the longitude and latitude of the search point and the longitude and latitude of the POI data includes:
and calculating the distance between the POI data representing position and the position of the retrieval point according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data, and acquiring the POI data with the distance between the POI data representing position and the position of the retrieval point smaller than the preset distance according to the distance between the POI data representing position and the position of the retrieval point.
Preferably, the radius of the sector area is smaller than or equal to the radius of the circular area.
Preferably, the acquiring the POI data in the key retrieval area and the supplementary data associated with the POI data in the key retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude of the POI data includes:
and calculating the azimuth angle of the POI data in the basic search area relative to the position of the search point, obtaining the POI data in the key search area according to the azimuth angle of the POI data relative to the position of the search point and the azimuth angle range of the key search area relative to the position of the search point, and obtaining the supplementary data associated with the POI data in the key search area according to the POI data in the key search area.
Preferably, the calculating the similarity between the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated includes:
word segmentation is respectively carried out on the names of the POI data and the names of the complementary data to be associated to obtain a POI data name fragment set vector U [ U ] 1 ,U 2 ,…,U i ]And a complementary data name fragment set vector V [ V ] to be associated 1 ,V 2 ,…,V j ]；
Merging all words in the set vector U and the set vector V, and reserving only one word for repeated occurrence to obtain a sum W [ W ] of the two vectors 1 ,W 2 ,…,W k ]；
Calculating the Similarity between each component in W and the set vector U, taking 1 if the component in W appears in the set vector U, taking a if the component in W does not appear in the set vector U, and finally obtaining the Similarity between the component in W and the set vector U U [C 1 ,C 2 ,…,C k ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the Similarity is U C in (C) k A is a preset value between 0 and 1 for the similarity between the K-th component in W and the aggregate vector U;
calculating the Similarity between each component in W and the set vector V, if the component in W appears in the set vector V, taking 1 for the Similarity between the component and the set vector V, and if the component in W does not appear in the set vector V, taking b for the Similarity between the component and the set vector V, and finally obtaining the Similarity between the W and the set vector V V [D 1 ,D 2 ,…,D k ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the Similarity is V D in (2) k B is a preset value between 0 and 1 for the similarity between the K-th component in W and the set vector V;
the Similarity of the set vector U and the set vector V is calculated according to the following formula UV As POI numberAccording to the similarity with the supplementary data to be associated;
wherein the Similarity is U 2 Is of Similarity U The sum of the squares of each component in (a); similarity of Similarity V 2 Is of Similarity V The sum of the squares of each component in (c).
Preferably, the calculating the similarity between the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated includes:
word segmentation is respectively carried out on the names of the POI data and the names of the complementary data to be associated to obtain a POI data name fragment set vector U [ U ] 1 ,U 2 ,…,U i ]And a complementary data name fragment set vector V [ V ] to be associated 1 ,V 2 ,…,V j ]；
Acquiring proper nouns from the proper name word library, screening the collection vector U and the collection vector V according to proper names, distinguishing proper nouns from non-proper nouns, and further obtaining the proper noun collection vector U of POI data A [U 1 ,U 2 ,…,U i ]And proper noun set vector V of complementary data to be associated A [U 1 ,U 2 ,…,U j ]Non-proper noun vector U of POI data B [U 1 ,U 2 ,…,U m-i ]And non-proper noun vector V of complementary data to be associated B [U 1 ,U 2 ,…,U n-j ]；
Will aggregate vector U A And aggregate vector V A Merging all words in (1) and keeping only one for the repeated words to obtain the sum W of two vectors A [W 1 ,W 2 ,…,W k ]；
Will aggregate vector U B And aggregate vector V B Merging all words in (1) and keeping only one for the repeated word, thereby obtaining the sum W of the two vectors B [W 1 ,W 2 ,…,W L ]；
Calculation of W A Each component of (a) and U A Similarity of each component in if W A In the set vector U A If present, the component is associated with a set vector U A Taking 1 for the similarity of W A In the set vector U A If not, the component and aggregate vector U A E is taken to obtain W finally A And aggregate vector U A Similarity of noun Similarity vectors UA [C 1 ,C 2 ,…,C k ]，Similarity UA C in (C) k Is W A The similarity between the Kth vector and the set vector U, and e is a preset value between 0 and 1;
calculation of W A Each component of (2) is equal to V A Similarity of each component in if W A In the set vector V A If present, the component is associated with a set vector V A Taking 1 for the similarity of W A In the set vector V A If not, the component and aggregate vector V A E is taken to obtain W finally A And aggregate vector V A Similarity of noun Similarity vectors VA [D 1 ,D 2 ,…,D k ]；Similarity VA C in (C) k Is W A The similarity between the K-th component and the set vector U, and e is a preset value between 0 and 1;
calculation of W B Each component of (a) and U B Similarity of each component in if W B In the set vector U A If present, the component is associated with a set vector U B Taking 1 for the similarity of W B In the set vector U B If not, the component and aggregate vector U B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector U B Similarity of noun Similarity vectors UB [C 1 ,C 2 ,…,C L ]，Similarity UB C in (C) L Is W B The similarity between the L-th component of the set vector U and the set vector U, and f is a preset value between 0 and 1;
calculation of W B Each component of (2) is equal to V B Similarity of each component in if W B In the set vector V A If present, the component is associated with a set vector V B Taking 1 for the similarity of W B In the set vector V B If not, the component and aggregate vector V B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector V B Similarity of noun Similarity vectors VB [D 1 ,D 2 ,…,D L ]，Similarity VB D in (2) L Is W B The similarity between the L-th vector and the set vector V, and f is a preset value between 0 and 1;
the Similarity of the set vector U and the set vector V is calculated according to the following formula UV And taking the POI data as the similarity of the POI data and the supplementary data to be associated;
wherein the Similarity is UA 2 Is of Similarity UA Sum of squares of each component of (b) Similarity VA 2 Is of Similarity VA Sum of squares of each component of (b) Similarity UB 2 Is of Similarity UB Sum of squares of each component of (b) Similarity VB 2 Is of Similarity VB The sum of the squares of each component of (1), P and Q being weight coefficients, where p+q=1, 0<P<1,0<Q<1,P≠Q。
Preferably, associating the POI data with the similarity exceeding the preset threshold value with the corresponding supplementary data to be associated includes: similarity of Similarity UV Comparing with a preset Similarity threshold X, if Similarity UV And if the POI data is more than X, judging the POI data to be similar to the POI data, and associating the POI data with the complementary data to be associated similar to the POI data.
A POI data association method, comprising the steps of:
acquiring POI data, wherein each POI data comprises a name and longitude and latitude;
acquiring supplementary data to be associated, wherein each supplementary data to be associated comprises a name and detailed information;
calculating to obtain the similarity of the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated;
and associating the POI data with the similarity exceeding a preset threshold value with corresponding supplementary data to be associated.
Preferably, the calculating the similarity between the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated includes:
word segmentation is respectively carried out on the names of the POI data and the names of the complementary data to be associated to obtain a POI data name fragment set vector U [ U ] 1 ,U 2 ,…,U i ]And a complementary data name fragment set vector V [ V ] to be associated 1 ,V 2 ,…,V j ]；
Acquiring proper nouns from the proper name word library, screening the collection vector U and the collection vector V according to proper names, distinguishing proper nouns from non-proper nouns, and further obtaining the proper noun collection vector U of POI data A [U 1 ,U 2 ,…,U i ]And proper noun set vector V of complementary data to be associated A [U 1 ,U 2 ,…,U j ]Non-proper noun vector U of POI data B [U 1 ,U 2 ,…,U m-i ]And non-proper noun vector V of complementary data to be associated B [U 1 ,U 2 ,…,U n-j ]；
Will aggregate vector U A And aggregate vector V A Merging all words in (1) and keeping only one for the repeated words to obtain the sum W of two vectors A [W 1 ,W 2 ,…,W k ]；
Will aggregate vector U B And aggregate vector V B Merging all words in (1) and keeping only one for the repeated word, thereby obtaining the sum W of the two vectors B [W 1 ,W 2 ,…,W L ]；
Calculation of W A Each component of (a) and U A Similarity of each component in if W A In the set vector U A If present, the component is associated with a set vector U A Taking 1 for the similarity of W A In the set vector U A If not, the component and aggregate vector U A E is taken to obtain W finally A And aggregate vector U A Similarity of noun Similarity vectors UA [C 1 ,C 2 ,…,C k ]，Similarity UA C in (C) k Is W A The similarity between the K-th component and the set vector U, and e is a preset value between 0 and 1;
calculation of W A Each component of (2) is equal to V A Similarity of each component in if W A In the set vector V A If present, the component is associated with a set vector V A Taking 1 for the similarity of W A In the set vector V A If not, the component and aggregate vector V A E is taken to obtain W finally A And aggregate vector V A Similarity of noun Similarity vectors VA [D 1 ,D 2 ,…,D k ]；Similarity VA C in (C) k Is W A The similarity between the K-th component and the set vector U, and e is a preset value between 0 and 1;
calculation of W B Each component of (a) and U B Similarity of each component in if W B In the set vector U A If present, the component is associated with a set vector U B Taking 1 for the similarity of W B In the set vector U B If not, the component and aggregate vector U B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector U B Similarity of noun Similarity vectors UB [C 1 ,C 2 ,…,C L ]，Similarity UB C in (C) L Is W B The similarity between the L-th component of the set vector U and the set vector U, and f is a preset value between 0 and 1;
calculation of W B Each component of (2) is equal to V B Similarity of each component in if W B In the set vector V A If present, the component is associated with a set vector V B Taking 1 for the similarity of W B In the set vector V B If not, the component and aggregate vector V B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector V B Similarity of noun Similarity vectors VB [D 1 ,D 2 ,…,D L ]，Similarity VB D in (2) L Is W B The similarity between the L-th vector and the set vector V, and f is a preset value between 0 and 1;
the Similarity of the set vector U and the set vector V is calculated according to the following formula UV And taking the POI data as the similarity of the POI data and the supplementary data to be associated;
wherein the Similarity is UA 2 Is of Similarity UA Sum of squares of each component of (b) Similarity VA 2 Is of Similarity VA Sum of squares of each component of (b) Similarity UB 2 Is of Similarity UB Sum of squares of each component of (b) Similarity VB 2 Is of Similarity VB The sum of the squares of each component of (1), P and Q being weight coefficients, where p+q=1, 0<P<1,0<Q<1,P≠Q
The following beneficial technical effects can be achieved by implementing the invention: the invention has the advantages that the supplementary data can be acquired by correlating the POI data with the supplementary data, thereby overcoming the problem of lack of POI data information in the prior art; during retrieval, POI data are acquired in the common area by distinguishing the re-retrieval area from the common retrieval area, and POI data and associated supplementary data are acquired in the key area, so that a retrieval user can acquire detailed data in the key retrieval area.
Drawings
FIG. 1 is a flow chart of a POI data retrieval method;
fig. 2 is a flow chart of a POI data association method.
Detailed Description
The invention will be further described in conjunction with the following specific examples, which are intended to facilitate an understanding of those skilled in the art:
example 1:
as shown in fig. 1, the present invention provides a POI data retrieval method, which includes the following steps:
step S1: associating the POI data with the supplementary data to be associated;
step S2: acquiring longitude and latitude of a search point;
step S3: acquiring POI data in a basic retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data;
step S4: acquiring POI data in the key retrieval area and supplementary data associated with the POI data in the key retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data;
step S5: the acquired POI data and the supplementary data are output as search results;
the basic search area is a circular area taking the position of a search point as a circle center, and the key search area is a sector area which is positioned in front of the search point and takes the position of the search point as the circle center.
Step S2 starts to be executed when the search user performs the search, and step 1 may be executed at each period.
When a search user searches, the position of a search point, namely the longitude and latitude of the search point, is acquired, a circular area taking the position of the search point as a circle center is taken as a basic search area, the POI data of the basic search area is acquired, a sector area taking the position of the search point as the circle center is taken as an important search area, and the POI data of the important search area and the associated supplementary data thereof are acquired. The supplement data serving as the supplement of the POI data comprises the detailed information lacking in the POI data, and can be pictures, detailed descriptions and the like; the invention can search the locating position of the user. The invention has the advantages that the supplementary data can be acquired by correlating the POI data with the supplementary data, thereby overcoming the problem of lack of POI data information in the prior art; during retrieval, POI data are acquired in a common area by distinguishing a re-retrieval area from a common retrieval area, and POI data and associated supplementary data thereof are acquired in a key area, so that a retrieval user can acquire detailed data in the key retrieval area, and when a retrieval point is the GRS positioning of the retrieval user, the invention can solve the problem that main information is missed due to the GRS positioning error of the retrieval point only when the key retrieval area is retrieved. In order to reduce the influence of the supplementary data on the retrieval efficiency, the retrieval result of the common retrieval area can be output when the POI data of the common retrieval area is acquired, and the retrieval result of the key retrieval area is output after the supplementary data of the key retrieval area is acquired.
As an alternative, associating POI data with supplemental data to be associated includes:
acquiring POI data, wherein the POI data comprise names and longitudes and latitudes;
acquiring supplementary data to be associated, wherein the supplementary data to be associated comprises a name and detailed information;
calculating to obtain the similarity of the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated;
and associating the POI data with the similarity exceeding a preset threshold value with corresponding supplementary data to be associated.
The automatic association is realized by associating the names in the POI data with the similarity of the names of the complementary data to be associated; in the invention, the similarity can be selected to be identical, namely the name of the POI data is identical to the name of the supplementary data to be associated, such as associating the supplementary data to be associated, which is named as the West lake Baoshi mountain, with the POI data, which is named as the West lake Baoshi mountain.
As an alternative, acquiring POI data in the basic search area according to the latitude and longitude of the search point and the latitude and longitude of the POI data includes:
and calculating the distance between the representative position of the POI data and the position of the retrieval point according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data, and acquiring the POI data of which the distance between the representative position and the position of the retrieval point is smaller than the preset distance according to the distance between the representative position of the POI data and the position of the retrieval point.
As a preferred solution, the radius of the sector area is smaller than or equal to the radius of the circular area. This arrangement can improve the retrieval efficiency; after POI data in the basic retrieval area, only POI data in the key retrieval area is selected from the POI data in the basic retrieval area, and supplementary data associated with the POI data in the key retrieval area is acquired.
As a preferable mode, the radius of the sector area is equal to the radius of the circular area, and acquiring the POI data in the key retrieval area and the supplementary data associated with the POI data in the key retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude of the POI data includes:
and calculating the azimuth angle of the POI data in the basic search area relative to the position of the search point, obtaining the POI data in the key search area according to the azimuth angle of the POI data relative to the position of the search point and the azimuth angle range of the key search area relative to the position of the search point, and obtaining the to-be-associated supplementary data associated with the POI data in the key search area according to the POI data in the key search area.
Calculating the azimuth of the POI data in the base display area relative to the location of the retrieving user may employ calculation methods in the prior art.
Optionally, calculating the azimuth of the POI data in the base display area relative to the retrieved user location comprises:
the azimuth θ of POI data relative to the retrieved user location is calculated by the following formula:
wherein the method comprises the steps ofIs to detect the latitude of the user's position +.>Is the latitude of the POI data and Δλ is the longitude difference of the two.
After the POI data in the basic retrieval area is obtained, the retrieval efficiency of the key retrieval area is greatly improved only by judging whether the azimuth angle of the POI data and the retrieval point position in the basic retrieval area is within the azimuth angle range of the key retrieval area and the retrieval point position.
The calculating the similarity between the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated comprises the following steps:
word segmentation is carried out on the names of the POI data and the names of the complementary data to be associated respectively, so as to obtain a POI data name fragment set vector U [ U ] 1 ，U 2 ，…，U i ]And a complementary data name fragment set vector V [ V ] to be associated 1 ，V 2 ，…，V j ]；
Acquiring proper nouns from the proper name word library, screening the collection vector U and the collection vector V according to proper names, distinguishing proper nouns from non-proper nouns, and further obtaining the proper noun collection vector U of POI data A [U 1 ，U 2 ，…，U i ]And proper noun set vector V of complementary data to be associated A [U 1 ，U 2 ，…，U j ]Non-proper noun vector U of POI data B [U 1 ，U 2 ，…，U m-i ]And non-proper noun vector V of complementary data to be associated B [U 1 ，U 2 ，…，U n-j ]；
Will aggregate vector U A And aggregate vector V A Merging all words in (1) and keeping only one for the repeated words to obtain the sum W of two vectors A [W 1 ，W 2 ，…，W k ]；
Will aggregate vector U B And aggregate vector V B Merging all words in (1) and keeping only one for the repeated word, thereby obtaining the sum W of the two vectors B [W 1 ，W 2 ，…，W L ]；
Calculation of W A Each component of (a) and U A Similarity of each component in if W A In the set vector U A If present, the component is associated with a set vector U A Taking 1 for the similarity of W A In the set vector U A If not, the component and aggregate vector U A E is taken to obtain W finally A And aggregate vector U A Similarity of noun Similarity vectors UA [C 1 ,C 2 ,…,C k ]，Similarity UA C in (C) k Is W A The similarity between the K-th component and the set vector U, e is a preset value between 0 and 1, and can be set to be 0.9;
calculation of W A Each component of (2) is equal to V A Similarity of each component in if W A In the set vector V A If present, the component is associated with a set vector V A Taking 1 for the similarity of W A In the set vector V A If not, the component and aggregate vector V A E is taken to obtain W finally A And aggregate vector V A Similarity of noun Similarity vectors VA [D 1 ,D 2 ,…,D k ]；Similarity VA C in (C) k Is W A The similarity between the K-th component and the set vector U, e is a preset value between 0 and 1, and can be set to be 0.9;
calculation of W B Each component of (a) and U B Similarity of each component in if W B In the set vector U A If present, the component is associated with a set vector U B Taking 1 for the similarity of W B In the set vector U B If not, the component and aggregate vector U B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector U B Proper noun phase of the methodSimilarity vector UB [C 1 ,C 2 ,…,C L ]，Similarity UB C in (C) L Is W B The similarity between the L-th component of the set vector U and the set vector U, wherein f is a preset value between 0 and 1 and can be set to be 0.9;
calculation of W B Each component of (2) is equal to V B Similarity of each component in if W B In the set vector V A If present, the component is associated with a set vector V B Taking 1 for the similarity of W B In the set vector V B If not, the component and aggregate vector V B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector V B Similarity of noun Similarity vectors VB [D 1 ,D 2 ,…,D L ]，Similarity VB D in (2) L Is W B The similarity between the L-th vector and the set vector V, f is a preset value between 0 and 1, and can be set to be 0.9;
the Similarity of the set vector U and the set vector V is calculated according to the following formula UV And taking the POI data as the similarity of the POI data and the supplementary data to be associated;
wherein the Similarity is UA 2 Is of Similarity UA Sum of squares of each component of (b) Similarity VA 2 Is of Similarity VA Sum of squares of each component of (b) Similarity UB 2 Is of Similarity UB Sum of squares of each component of (b) Similarity VB 2 Is of Similarity VB The sum of the squares of each component of (1), P and Q being weight coefficients, where p+q=1, 0<P<1,0<Q<1,P≠Q。
Similarity UA ×Similarity VA ＝C 1 ×D 1 +C 2 ×D 2 +…+C k ×D K ；
Wherein C is 1 ，C 2 ，C k Refers to Similarity UA C in (C) 1 ，C 2 ，C k ；D 1 ，D 2 ，D k Refers to Similarity VA D in (2) 1 ，D 2 ，D k 。
Similarity UB ×Similarity VB ＝C 1 ×D 1 +C 2 ×D 2 +…+C L ×D L ；
Wherein C is 1 ，C 2 ，C L Refers to Similarity UB C in (C) 1 ，C 2 ，C L ；D 1 ，D 2 ，D L Refers to Similarity VB D in (2) 1 ，D 2 ，D L 。
Because POI data is an arrangement of real geographic information, the POI data is usually composed of specific proper nouns and subsequent modified suffixes, such as western lake precious stone mountains, wherein the western lake is a specific proper noun, most of the POI data are landmark buildings or classical, and the POI data appear more frequently in POI vocabulary entries; the suffix precious stone refers to the description of a certain POI, so that the judgment weights of the two POIs are different, and effective distinction is needed.
According to the method, the names of the POI data and the names of the complementary data to be associated are subjected to word segmentation, the proper names and the non-proper names are separated from each other in the word segmentation, the association degree is calculated according to the proper names and the non-proper name weight, the final association degree is obtained, and the finally obtained association degree is closer to the actual situation than the association degree obtained by a conventional association degree calculation method. In the present invention, the patent names may be obtained from a proprietary name library in the prior art.
Specifically, associating the POI data with the similarity exceeding the preset threshold value with the corresponding supplementary data to be associated includes: similarity of Similarity UV Comparing with a preset Similarity threshold X, if Similarity UV And if the similarity is larger than X, the POI data and the complementary data to be associated similar to the POI data are judged to be similar, wherein the similarity threshold X can be set to be 1, 0.9, 0.8 and the like. The similarity threshold X can be set according to specific application occasions。
Regarding the method for associating the POI data with the similar supplementary data to be associated, the supplementary data to be associated may be stored in a detailed information database, a corresponding primary key may be produced, and the primary key may be stored in the information of the POI data, so as to achieve association of the supplementary data to be associated with the POI data.
Example 2:
the difference from embodiment 1 is a method of calculating the similarity of the POI data and the supplementary data to be associated from the name of the POI data and the name of the supplementary data to be associated.
In this embodiment, calculating the similarity between the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated includes:
word segmentation is respectively carried out on the names of the POI data and the names of the complementary data to be associated to obtain a POI data name fragment set vector U [ U ] 1 ,U 2 ,…,U i ]And a complementary data name fragment set vector V [ V ] to be associated 1 ,V 2 ,…,V j ]；
Merging all words in the set vector U and the set vector V, and reserving only one word for repeated occurrence to obtain a sum W [ W ] of the two vectors 1 ,W 2 ,…,W k ]；
Calculating the Similarity between each component in W and the set vector U, taking 1 if the component in W appears in the set vector U, taking a if the component in W does not appear in the set vector U, and finally obtaining the Similarity between the component in W and the set vector U U [C 1 ,C 2 ,…,C k ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the Similarity is U C in (C) k A is a preset value between 0 and 1 for the similarity between the K-th component in W and the aggregate vector U;
calculating the similarity of each component in W and the set vector V, taking 1 if the component in W appears in the set vector V, and taking 1 if the component in W does not appear in the set vector VThe Similarity of the quantity V is b, and finally the Similarity vector Similarity of the W and the aggregate vector V is obtained V [D 1 ,D 2 ,…,D k ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the Similarity is V D in (2) k B is a preset value between 0 and 1 for the similarity between the K-th component in W and the set vector V;
the Similarity of the set vector U and the set vector V is calculated according to the following formula UV As the similarity of POI data and the supplementary data to be associated;
wherein the Similarity is U 2 Is of Similarity U The sum of the squares of each component in (a); i.e. Similarity U Is then added.
Wherein the Similarity is V 2 Is of Similarity V The sum of the squares of each component in (a); i.e. Similarity U The squares of each element are then added.
Similarity U ×Similarity V ＝C 1 ×D 1 +C 2 ×D 2 +…+C L ×D L ；
Wherein C is 1 ，C 2 ，C k Refers to Similarity U C in (C) 1 ，C 2 ，C k ；D 1 ，D 2 ，D k Refers to Similarity V D in (2) 1 ，D 2 ，D k 。
Example 3:
as shown in fig. 2, a POI data association method includes the following steps;
step S01: acquiring POI data, wherein each POI data comprises a name and longitude and latitude;
step S02: acquiring supplementary data to be associated, wherein each supplementary data to be associated comprises a name and detailed information;
step S03: calculating to obtain the similarity of the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated;
step S04: and associating the POI data with the similarity exceeding a preset threshold value with corresponding supplementary data to be associated.
The calculating the similarity between the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated comprises:
word segmentation is respectively carried out on the names of the POI data and the names of the complementary data to be associated to obtain a POI data name fragment set vector U [ U ] 1 ,U 2 ,…,U i ]And a complementary data name fragment set vector V [ V ] to be associated 1 ,V 2 ,…,V j ]；
Acquiring proper nouns from the proper name word library, screening the collection vector U and the collection vector V according to proper names, distinguishing proper nouns from non-proper nouns, and further obtaining the proper noun collection vector U of POI data A [U 1 ,U 2 ,…,U i ]And proper noun set vector V of complementary data to be associated A [U 1 ,U 2 ,…,U j ]Non-proper noun vector U of POI data B [U 1 ,U 2 ,…,U m-i ]And non-proper noun vector V of complementary data to be associated B [U 1 ,U 2 ,…,U n-j ]；
Will aggregate vector U A And aggregate vector V A Merging all words in (1) and keeping only one for the repeated words to obtain the sum W of two vectors A [W 1 ,W 2 ,…,W k ]；
Will aggregate vector U B And aggregate vector V B Merging all words in (1) and keeping only one for the repeated word, thereby obtaining the sum W of the two vectors B [W 1 ,W 2 ,…,W L ]；
Calculation of W A Each component of (a) and U A Similarity of each component in if W A In the set vector U A If present, the component is associated with a set vector U A Taking 1 for the similarity of W A In the set vector U A If not, the component and aggregate vector U A E is taken to obtain W finally A And aggregate vector U A Similarity of noun Similarity vectors UA [C 1 ,C 2 ,…,C k ]，Similarity UA C in (C) k Is W A The similarity between the K-th component and the set vector U, and e is a preset value between 0 and 1;
calculation of W A Each component of (2) is equal to V A Similarity of each component in if W A In the set vector V A If present, the component is associated with a set vector V A Taking 1 for the similarity of W A In the set vector V A If not, the component and aggregate vector V A E is taken to obtain W finally A And aggregate vector V A Similarity of noun Similarity vectors VA [D 1 ,D 2 ,…,D k ]；Similarity VA C in (C) k Is W A The similarity between the K-th component and the set vector U, and e is a preset value between 0 and 1;
calculation of W B Each component of (a) and U B Similarity of each component in if W B In the set vector U A If present, the component is associated with a set vector U B Taking 1 for the similarity of W B In the set vector U B If not, the component and aggregate vector U B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector U B Similarity of noun Similarity vectors UB [C 1 ,C 2 ,…,C L ]，Similarity UB C in (C) L Is W B The similarity between the L-th component of the set vector U and the set vector U, and f is a preset value between 0 and 1;
calculation of W B Each component of (2) is equal to V B Similarity of each component in if W B In the set vector V A If present, the component is associated with a set vector V B Taking 1 for the similarity of W B In the set vector V B If not, the component and aggregate vector V B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector V B Similarity of noun Similarity vectors VB [D 1 ,D 2 ,…,D L ]，Similarity VB D in (2) L Is W B The similarity between the L-th vector and the set vector V, and f is a preset value between 0 and 1;
the Similarity of the set vector U and the set vector V is calculated according to the following formula UV And taking the POI data as the similarity of the POI data and the supplementary data to be associated;
wherein the Similarity is UA 2 Is of Similarity UA Sum of squares of each component of (b) Similarity VA 2 Is of Similarity VA Sum of squares of each component of (b) Similarity UB 2 Is of Similarity UB Sum of squares of each component of (b) Similarity VB 2 Is of Similarity VB The sum of the squares of each component of (1), P and Q being weight coefficients, where p+q=1, 0<P<1,0<Q<1,P≠Q。
Similarity UA ×Similarity VA ＝C 1 ×D 1 +C 2 ×D 2 +…+C k ×D k ；
Wherein C is 1 ，C 2 ，C k Refers to Similarity UA C in (C) 1 ，C 2 ，C k ；D 1 ，D 2 ，D k Refers to Similarity VA D in (2) 1 ，D 2 ，D k 。
Similarity UB ×Similarity VB ＝C 1 ×D 1 +C 2 ×D 2 +…+C L ×D L ；
Wherein C is 1 ，C 2 ，C L Refers to Similarity UB C in (C) 1 ，C 2 ，C L ；D 1 ，D 2 ，D L Refers to Similarity VB D in (2) 1 ，D 2 ，D L 。
The principle and effects of the above method, which is a related method section in embodiment 1, are described in embodiment 1, and this embodiment is not repeated.
The above embodiments are merely examples of the present invention, but the technical features of the present invention are not limited thereto, and any changes or modifications made by those skilled in the art within the scope of the present invention are included in the scope of the present invention.

Claims (6)

1. A POI data retrieval method, comprising the steps of:
associating the POI data with the supplementary data to be associated;
acquiring longitude and latitude of a search point;
acquiring POI data in a basic retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data;
acquiring POI data in the key retrieval area and supplementary data associated with the POI data in the key retrieval area according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data;
the acquired POI data and the supplementary data are output as search results;
the basic search area is a circular area taking the position of a search point as a circle center, and the key search area is a sector area taking the position of the search point as the circle center;
the associating the POI data with the supplemental data to be associated includes:
acquiring POI data, wherein the POI data comprise names and longitudes and latitudes;
acquiring supplementary data to be associated, wherein the supplementary data to be associated comprises a name and detailed information;
calculating to obtain the similarity of the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated;
associating the POI data with the similarity exceeding a preset threshold value with corresponding supplementary data to be associated;
the calculating the similarity between the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated comprises:
word segmentation is respectively carried out on the names of the POI data and the names of the complementary data to be associated to obtain a POI data name fragment set vector U [ U ] 1 ,U 2 ,…,U i ]And a complementary data name fragment set vector V [ V ] to be associated 1 ,V 2 ,…,V j ]；
Merging all words in the set vector U and the set vector V, and reserving only one word for repeated occurrence to obtain a sum W [ W ] of the two vectors 1 ,W 2 ,…,W k ]；
Calculating the Similarity between each component in W and the set vector U, taking 1 if the component in W appears in the set vector U, taking a if the component in W does not appear in the set vector U, and finally obtaining the Similarity between the component in W and the set vector U U [C 1 ,C 2 ,…,C k ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the Similarity is U C in (C) k A is a preset value between 0 and 1 for the similarity between the K-th component in W and the aggregate vector U;
calculating the Similarity between each component in W and the set vector V, if the component in W appears in the set vector V, taking 1 for the Similarity between the component and the set vector V, and if the component in W does not appear in the set vector V, taking b for the Similarity between the component and the set vector V, and finally obtaining the Similarity between the W and the set vector V V [D 1 ,D 2 ,…,D k ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the Similarity is V D in (2) k B is a preset value between 0 and 1 for the similarity between the K-th component in W and the set vector V;
the Similarity of the set vector U and the set vector V is calculated according to the following formula UV As the similarity of POI data and the supplementary data to be associated;
wherein the Similarity is U 2 Is of Similarity U The sum of the squares of each component in (a); similarity of Similarity V 2 Is of Similarity V The sum of the squares of each component in (c).
2. The POI data retrieval method as defined in claim 1, wherein the obtaining the POI data in the basic retrieval area according to the latitude and longitude of the retrieval point and the latitude and longitude of the POI data comprises:
and calculating the distance between the POI data representing position and the position of the retrieval point according to the longitude and latitude of the retrieval point and the longitude and latitude in the POI data, and acquiring the POI data with the distance between the POI data representing position and the position of the retrieval point smaller than the preset distance according to the distance between the POI data representing position and the position of the retrieval point.
3. A POI data retrieval method as defined in claim 1, wherein the radius of said sector area is less than or equal to the radius of the circular area.
4. The POI data retrieval method of claim 3, wherein the obtaining the POI data in the key retrieval area and the supplementary data associated with the POI data in the key retrieval area according to the latitude and longitude of the retrieval point and the latitude and longitude of the POI data comprises:
and calculating the azimuth angle of the POI data in the basic search area relative to the position of the search point, obtaining the POI data in the key search area according to the azimuth angle of the POI data relative to the position of the search point and the azimuth angle range of the key search area relative to the position of the search point, and obtaining the supplementary data associated with the POI data in the key search area according to the POI data in the key search area.
5. The method for retrieving POI data according to claim 1, wherein said calculating the similarity between the POI data and the supplementary data to be associated according to the name of the POI data and the name of the supplementary data to be associated comprises:
word segmentation is respectively carried out on the names of the POI data and the names of the complementary data to be associated to obtain a POI data name fragment set vector U [ U ] 1 ,U 2 ,…,U i ]And a complementary data name fragment set vector V [ V ] to be associated 1 ,V 2 ,…,V j ]；
Acquiring proper nouns from the proper name word library, screening the collection vector U and the collection vector V according to proper names, distinguishing proper nouns from non-proper nouns, and further obtaining the proper noun collection vector U of POI data A [U 1 ,U 2 ,…,U i ]And proper noun set vector V of complementary data to be associated A [U 1 ,U 2 ,…,U j ]Non-proper noun vector U of POI data B [U 1 ,U 2 ,…,U m-i ]And non-proper noun vector V of complementary data to be associated B [U 1 ,U 2 ,…,U n-j ]；
Will aggregate vector U A And aggregate vector V A Merging all words in (1) and keeping only one for the repeated words to obtain the sum W of two vectors A [W 1 ,W 2 ,…,W k ]；
Will aggregate vector U B And aggregate vector V B Merging all words in (1) and keeping only one for the repeated word, thereby obtaining the sum W of the two vectors B [W 1 ,W 2 ,…,W L ]；
Calculation of W A Each component of (a) and U A Similarity of each component in if W A In the set vector U A If present, the component is associated with a set vector U A Taking 1 for the similarity of W A In the set vector U A If not, the component and aggregate vector U A E is taken to obtain W finally A And aggregate vector U A Is specially used for (1)Similarity of noun Similarity vectors UA [C 1 ,C 2 ,…,C k ]，Similarity UA C in (C) k Is W A The similarity between the K-th component and the set vector U, and e is a preset value between 0 and 1;
calculation of W A Each component of (2) is equal to V A Similarity of each component in if W A In the set vector V A If present, the component is associated with a set vector V A Taking 1 for the similarity of W A In the set vector V A If not, the component and aggregate vector V A E is taken to obtain W finally A And aggregate vector V A Similarity of noun Similarity vectors VA [D 1 ,D 2 ,…,D k ]；Similarity VA D in (2) k Is W A The similarity between the K-th component and the set vector V, and e is a preset value between 0 and 1;
calculation of W B Each component of (a) and U B Similarity of each component in if W B In the set vector U A If present, the component is associated with a set vector U B Taking 1 for the similarity of W B In the set vector U B If not, the component and aggregate vector U B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector U B Similarity of noun Similarity vectors UB [C 1 ,C 2 ,…,C L ]，Similarity UB C in (C) L Is W B The similarity between the L-th component of the set vector U and the set vector U, and f is a preset value between 0 and 1;
calculation of W B Each component of (2) is equal to V B Similarity of each component in if W B In the set vector V A If present, the component is associated with a set vector V B Taking 1 for the similarity of W B In the set vector V B If not, the component and aggregate vector V B F is taken out of the similarity of (2) to finally obtain W B And aggregate vector V B Similarity of noun Similarity vectors VB [D 1 ,D 2 ,…,D L ]，Similarity VB D in (2) L Is W B The similarity between the L-th vector and the set vector V, and f is a preset value between 0 and 1;
the Similarity of the set vector U and the set vector V is calculated according to the following formula UV And taking the POI data as the similarity of the POI data and the supplementary data to be associated;
wherein the Similarity is UA 2 Is of Similarity UA Sum of squares of each component of (b) Similarity VA 2 Is of Similarity VA Sum of squares of each component of (b) Similarity UB 2 Is of Similarity UB Sum of squares of each component of (b) Similarity VB 2 Is of Similarity VB The sum of the squares of each component of (1), P and Q being weight coefficients, where p+q=1, 0<P<1,0<Q<1,P≠Q。
6. The POI data retrieval method as defined in claim 5, wherein associating the POI data having the similarity exceeding the preset threshold with the corresponding supplementary data to be associated comprises: similarity of Similarity UV Comparing with a preset Similarity threshold X, if Similarity UV >And X, judging similarity, and associating the POI data with the similar supplementary data to be associated.
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