CN101794307A - Vehicle navigation POI (Point of Interest) search engine based on internetwork word segmentation idea - Google Patents
Vehicle navigation POI (Point of Interest) search engine based on internetwork word segmentation idea Download PDFInfo
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Abstract
The invention provides a vehicle navigation POI (Point of Interest) search engine based on an internetwork word segmentation idea, comprising the following steps of: (1) carrying out word segmentation treatment on a POI name in POI original data; (2) generating information index of the POI search engine according to a word segmentation result; and (3) inquiring the POI name according to the information index of the POI search engine. The invention can provide queries of characters and pinyin and provide the functions of prompting association when a query is input, previewing the number of search results, automatically correcting a misinput and inquiring the multiple times of inputs in combination.
Description
Technical field
The present invention relates to POI (Point of Interest, the point of interest) inquiry system of vehicle mounted guidance, this system is based on the word segmentation idea of internet search engine.
Background technology
Since based on network search in 1994 occurred, internet search engine had just obtained great development, and internet search engine has become very important network navigation service aid on the internet now.Internet search engine is collected resource and the information in the internet, finds new website and webpage, through grasping and analyzing, stores corresponding copies of information.On this basis, further information is understood, extracts, organized and handles, and for the user provides retrieval service, thereby play the purpose of information navigation.Internet search engine can provide literal and pinyin queries, the association's prompting during the inquiry input, and according to inquiry input preview Query Result quantity, functions such as prompting are corrected in the mistake input.
POI search has many similarities with internet search engine in the vehicle mounted guidance, and POI information also needs to understand, extract, organize and handle, and provides retrieval service efficiently for the user.Therefore we can be used in the notion of internet search engine in the POI search in the vehicle mounted guidance fully, and this is our said POI search engine just.And the POI query function of present vehicle mounted guidance is comparatively single, can not meet consumers' demand.
Summary of the invention
Technical matters to be solved by this invention is: a kind of vehicle navigation POI (Point of Interest) search engine based on internetwork word segmentation idea is provided, the present invention can provide literal and pinyin queries, and provide the association when inquiring about input to point out, the preview of Query Result quantity, the mistake input is corrected automatically, repeatedly imports the function of query composition.
The technical solution adopted for the present invention to solve the technical problems is: the vehicle navigation POI (Point of Interest) search engine based on internetwork word segmentation idea may further comprise the steps: 1) the POI name in the former data of POI is called word segmentation processing; 2) generate POI search engine information index according to word segmentation result; 3) carry out the POI name query according to POI search engine information index.
The invention has the beneficial effects as follows: by dissimilar inputs, comprise Chinese, English, numeral, the POI search engine all can be finished the demonstration of result of association and Query Result within a short period of time; The user can predict the situation of Query Result in advance by the result of association, and whether whether input have Query Result, Query Result quantity are what, need to continue to import just to inquire own required result, makes the user reduce unnecessary inquiry input; If the user imports the abbreviation of POI title, the POI search engine also can accurately inquire the result; If the user inquiring input error, the POI search engine can be according to the information of the automatic prompting right of partials; If the user imports a plurality of Query Informations, the POI search engine can make Query Result more accurate according to the Query Information query composition.
Description of drawings
Fig. 1 is a POI name data participle schematic flow sheet.
Fig. 2 generates POI search engine information index schematic flow sheet.
Fig. 3 is a POI search engine information index composition diagram.
Fig. 4 is a POI search engine inquiry basic procedure synoptic diagram.
Fig. 5 is a keyword word segmentation processing process flow diagram.
Embodiment
Having used for reference internet search engine and Chinese and English participle technique based on the vehicle navigation POI (Point of Interest) search engine (hereinafter to be referred as the POI search engine) of internetwork word segmentation idea realizes forming.Though the POI search engine has been used for reference internet search engine and Chinese and English participle technique, but many different places are arranged.At first the former data of POI are all come as for data manufacturer, so can't have the ability of real-time extracting and analysis, data are that disposable generation is not to dynamically update; Next is that the POI search engine operates on the embedded device, and internet search engine then is to operate on the large server, and therefore the POI search engine can not show a candle to internet search engine on machine performance; The POI search engine data source is the data file that leaves on the navigating instrument in addition, rather than the picture internet search engine uses large database.Generally speaking the POI search engine is to be used for embedded device, more restrictive condition is arranged, but because data volume and so huge unlike internet search engine can not have big gap with internet search engine so the POI search engine is search efficiency or user experience.
The present invention is a kind of POI inquiry system of vehicle mounted guidance, and it is based on the word segmentation idea of internet search engine, the vehicle navigation POI (Point of Interest) search engine of realizing on embedded device.The POI search engine uses POI search engine information index, provides input association, Query Result preview, mistake input to correct, repeatedly import the query composition function.By dissimilar inputs, comprise Chinese, English, numeral, the POI search engine all can be finished the demonstration of result of association and Query Result within a short period of time; The user can predict the situation of Query Result in advance by the result of association, and whether whether input have Query Result, Query Result quantity are what, need to continue to import just to inquire own required result, makes the user reduce unnecessary inquiry input; If the user imports the abbreviation of POI title, the POI search engine also can accurately inquire the result; If the user inquiring input error, the POI search engine can be according to the information of the automatic prompting right of partials; If the user imports a plurality of Query Informations, the POI search engine can make Query Result more accurate according to the Query Information query composition.
Below in conjunction with accompanying drawing in detail the present invention is described in detail.
In Fig. 1, at first step S101 goes out the title of POI from the former extracting data of POI, and step S102 saves as text with the POI title that extracts then, uses when being convenient to follow-up generation POI search engine information index.Step S103 is called word segmentation processing with the POI name, according to algorithm the Chinese of POI title is divided into speech smaller or equal to 4 words; Numeral and English word are cut apart as a whole separately; And the symbol in the removal POI title.4 words that final step S104 generates after with POI title word segmentation processing save as word segmentation result with interior Chinese keyword, digital keyword and English keyword.
In Fig. 2, according to the word segmentation result of step S104 among Fig. 1, step S201 generates initial with the keyword in the word segmentation result, and wherein the initial of Chinese keyword is a first letter of pinyin; The initial of numeral keyword is itself; The initial of English keyword is its lowercase.So the character range of keyword initial is 0-9 and a-z, totally 36 kinds of characters.Step S202 is the initial inverted index that word segmentation result is set up character 0-9 and a-z.Step S203 sets up the keyword inverted index of word segmentation result for the POI title, searches the POI record that contains keyword according to the keyword of word segmentation result in the POI title.Step S204 is saved in address and the record quantity that the POI of word segmentation result keyword lookup is recorded in the data in the inverted index of this keyword, generates POI search engine information index at last.
In Fig. 3, shown that POI search engine information index has 6 ingredients, is respectively: management department's data block, initial management data block, initial index data piece, keyword data piece, matching result index data piece and solid data piece.The structure of each ingredient is as follows:
1) management department's data block: manage whole data structure, write down the first address of each data block, conveniently read the data of each data block, its structure sees Table 1, and (unification of data length unit is byte in the following form, and DWORD is a double word, and WORD is an individual character, BYTE is a byte, BYTE[] be byte arrays).
Table 1 management department block data structure
| Sequence number | Skew | Data length | Data type | Field |
| ??1 | ??0 | ??4 | ??DWORD | Initial management data block first address |
| ??2 | ??4 | ??4 | ??DWORD | Initial index data piece first address |
| ??3 | ??8 | ??4 | ??DWORD | Keyword data piece first address |
| ??4 | ??12 | ??4 | ??DWORD | POI matching result index data piece first address |
| ??5 | ??16 | ??4 | ??DWORD | POI solid data piece first address |
2) initial management data block: management initial data message, comprise that the lowercase of English keyword makes up and digital keyword itself, convenience to keyword, reduces the resource amount of being written into according to a letter or number quick indexing, and its structure sees Table 2.
Table 2 initial management data block structure
| Sequence number | Skew | Data length | Data type | Field |
| ??1 | ??0 | ??4 | ??DWORD | First letter or number of initial combination |
| ??2 | ??4 | ??4 | ??DWORD | The offset address of initial index |
3) initial index data piece: indexing key words, can search corresponding keyword fast by initial, reduce the resource amount of being written into, its structure sees Table 3.
Table 3 initial index data block structure
| Sequence number | Skew | Data length | Data type | Field |
| ??1 | ??0 | ??2 | ??WORD | The initial number of combinations n of this beginning of letter 1 |
| ??2 | ??2 | ??1 | ??BYTE | The literal number i of the initial combination 1 of this beginning of letter 1 |
| ??3 | ??3 | ??1*i 1 | ??BYTE[] | The BYTE array of initial cypher |
| ??4 | ??3+i | ??2 | ??WORD | Corresponding keyword number m 1 |
| ??5 | ??5+i | ??4 | ??DWORD | The offset address of keyword 1 |
4) keyword data piece: preserve keyword, and as the dictionary of POI inquiry input participle and the result of input association, its structure sees Table 4.
Table 4 keyword data block structure
| Sequence number | Skew | Data length | Data type | Field |
| ??1 | ??0 | ??1 | ??BYTE | The number i of keyword 1 literal 2 |
| ??2 | ??1 | ??2*i 2 | ??WORD[] | The WORD array of keyword 1 literal |
| ??3 | ??1+2*i 2 | ??4 | ??DWORD | The offset address of keyword 1 matching result index |
5) POI matching result index data piece: index POI result, can find quantity and the offset address that keyword can match POI result fast by this index, its structure sees Table 5.
Table 5 POI matching result index data block structure
| Sequence number | Skew | Data length | Data type | Field |
| ??1 | ??0 | ??4 | ??DWORD | Coupling POI is quantity n as a result 2 |
| ??2 | ??4 | ??4 | ??DWORD | Coupling POI result's 1 offset address |
6) POI solid data piece (only being referred to as volume data in fact with the POI name herein): preserve the real solid data of POI, its structure sees Table 6.
Table 6 POI solid data block structure
| Sequence number | Skew | Data length | Data type | Field |
| ??1 | ??0 | ??1 | ??BYTE | POI writes down the number i of 1 title literal 3 |
| ??2 | ??1 | ??2*i 3 | ??WORD[] | POI writes down the WORD array of 1 title |
After POI search engine information index generated successfully, the POI search engine just can provide POI query function for the user according to POI search engine information index.At first the user must import the relevant information that will inquire about the POI title, and when the user inquiring input stopped, the POI inquiry had just begun.User's inquiry input is generally a speech or a word, comprises Chinese character, letter and number, is referred to as searching keyword here.After the user has imported keyword, from the search engine information index, find the result of association of keyword and the result of this association can mate POI result's record strip number by the POI search engine.The user selects the result of association of keyword, and the record that the POI search engine will be associated the POI result that the result can match shows.This process is exactly the basic procedure of POI search inquiry, comprises that user inquiring request input, keyword conversion process, keyword word segmentation processing, keyword index coupling, key word association result demonstration and Query Result show.Entire process flow process such as Fig. 4, treatment step are as follows successively:
S301) user inquiring request input: phrase or literal that keyboard button that the user provides by navigational system or handwriting functions input will be inquired about allow the character types of input Chinese character, letter and number to be arranged, alphabetical case insensitive.After the user imports or remove last time input first again under the situation of input, the user is input as a literal.
S302) keyword conversion process: the keyword of user's input is converted to corresponding initial.When keyword is Chinese, convert first letter of pinyin to; When keyword is English, convert the lowercase combination to; When keyword is numeral, be itself.
S303) keyword word segmentation processing: when the number of user's input characters surpasses 4 (keyword maximum lengths), just need carry out word segmentation processing to user's input.The entire process flow process is seen shown in Figure 5, and idiographic flow is as follows:
A) the user inputs character string is saved as target string S1, the character string sequence container Vector＜string of word segmentation result is preserved in initialization〉VetStr, and maximum participle length M axLen is set is 4.
B) if the length of S1 surpasses MaxLen, forward step C to.Otherwise S1 is saved in word segmentation result VetStr, and forwards step J to.
C) if S1 is empty, forwards step J to, otherwise forward step D to.
D), take out candidate character strings W, and the length of W is not more than MaxLen from the S1 left side.If the length of W forwards step e to greater than 1, otherwise forward step I to.
E) get the initial character string L of W, according to alphabetical L[0] (being first letter among the character string L) search all initials combinations of this letter in the initial management data block.Check the initial combination, see whether therein character string L.If forward step F therein to, otherwise forward step H to.
F) in initial index data piece, obtain the index information of character string L, in the keyword data piece, obtain its corresponding keyword according to index information, and save as the participle dictionary.
G) check the participle dictionary, see that W is whether in dictionary.If do not forward step H therein to, otherwise forward step I to.
H) word of W rightmost is removed, judge whether W is individual character.If individual character forwards step I to, otherwise forward step G to.
I) W is saved among the word segmentation result VetStr, and from S1, removes candidate character strings W and save as new S1 then, forward step C at last to.
J) return word segmentation result VetStr.
Such as user's input " People's Republic of China (PRC) ", the input characters number will be carried out word segmentation processing greater than 4.At first take out leftmost 4 words " the Chinese people " and corresponding initial " zhrm ".In the initial management data block, search all initial combinations of letter " z " then, comprise character strings such as " zc ", " zhd ", " zhrm ", " zjt ".In these initial combinations, search character string " zhrm ",, in initial index data piece, take out the index information of this character string because " zhrm " existence wherein.Obtain all keywords of this string matching more then according to index information, comprise keywords such as " the Chinese people ", and save as the participle dictionary.Last " the Chinese people " that in the participle dictionary, search user's input whether therein, so, and be saved in the word segmentation result because this speech exists that it is cut apart.According to identical processing mode, the character string " republic " of user's input also has been saved in the word segmentation result.
S304) keyword index coupling: according to the character string S in the word segmentation result
1, S
2S
n, search corresponding index information.Wherein search and mate S fully
1, S
2S
N-1Community string index community information, save as set A
1, A
2A
N-1Search all part coupling S
nIndex information, save as m set A
N1, A
N2... A
Nm(L is S to L character if keyword is from the 1st character
nLength) successively with S
nIn character equate, be called the part coupling.If keyword and S
nEqual fully, be called coupling fully.N and m are natural number.
S305) the key word association result shows: keyword and these keywords that the energy association of institute of explicit user input goes out can inquire POI quantity as a result.The user imports through after the word segmentation processing, has generated S
1, S
2S
nCharacter string, S
1, S
2S
N-1Be complete phrase, and S
nThen be word or the speech that does not possess complete meaning, so the association of user's input, actual is exactly S
nThe result of association.With S
nThe keyword of part coupling is exactly S
nThe result of association, the POI results set R of its coupling
i=A
1∩ A
2∩ ... ∩ A
Ni-1∩ A
Ni(ni≤m), the POI that the result of association can inquire quantity as a result gathers R exactly
iNumber.When user's input error, the POI search engine can be searched the corresponding result of association according to the partials of input of character string.When the user repeatedly imports, the POI search engine can seek common ground to the result of association of every single input, obtains the result of association of query composition.
S306) Query Result shows: the result of association who selects according to the user, the POI results set R that can mate by the result of this association
i, in POI solid data piece, obtain corresponding POI object information.
In the above-mentioned steps, POI search engine information index provides initial management data block, initial index data piece and keyword data piece for S303, for S304 provides matching result index data piece, for S306 provides the solid data piece.
Claims (9)
1. based on the vehicle navigation POI (Point of Interest) search engine of internetwork word segmentation idea, it is characterized in that may further comprise the steps:
1) the POI name in the former data of POI is called word segmentation processing;
2) generate POI search engine information index according to word segmentation result;
3) carry out the POI name query according to POI search engine information index.
2. method according to claim 1 is characterized in that step 1) specifically comprises:
S101) go out the title of POI from the former extracting data of POI;
S102) the POI title that extracts is saved as text;
S103) the POI name is called word segmentation processing: the Chinese of POI title in the text is divided into Chinese keyword smaller or equal to maximum participle length according to algorithm; Numeral and English word are then cut apart as a whole separately, generate digital keyword and English keyword respectively; And the symbol in the removal POI title;
S104) the Chinese keyword smaller or equal to maximum participle length that generates after the POI title word segmentation processing, digital keyword and English keyword are saved as word segmentation result.
3. method according to claim 1 and 2 is characterized in that step 2) specifically comprise:
S201) keyword in the word segmentation result is generated initial, wherein the initial of Chinese keyword is a first letter of pinyin; The initial of numeral keyword is itself; The initial of English keyword is its lowercase;
S202) set up the initial inverted index of character 0～9 and a～z for word segmentation result;
S203) in the POI title, search the POI record that contains keyword according to the keyword of word segmentation result;
S204) address and the record quantity that the POI of word segmentation result keyword lookup is recorded in the data is saved in the inverted index of this keyword, generates POI search engine information index at last.
4. method according to claim 3 is characterized in that: POI search engine information index comprises management department's data block, initial management data block, initial index data piece, keyword data piece, matching result index data piece and solid data piece.
5. method according to claim 4 is characterized in that each data block concrete structure of POI search engine information index is:
1) management department's block data structure sees Table 1:
Table 1 management department block data structure
2) structure of initial management data block sees Table 2:
Table 2 initial management data block structure
3) structure of initial index data piece sees Table 3:
Table 3 initial index data block structure
4) structure of keyword data piece sees Table 4:
Table 4 keyword data block structure
5) structure of POI matching result index data piece sees Table 5:
Table 5POI matching result index data block structure
6) structure of POI solid data piece sees Table 6:
Table 6POI solid data block structure
6. method according to claim 4 is characterized in that: step 3) comprises user inquiring request input, keyword conversion process, keyword word segmentation processing, keyword index coupling successively, the key word association result shows and the step of Query Result demonstration.
7. method according to claim 6 is characterized in that step 3) specifically may further comprise the steps:
S301) user inquiring request input: phrase or literal that keyboard button that the user provides by navigational system or handwriting functions input will be inquired about, allow the character types of input that Chinese character, letter and number are arranged, the letter case insensitive, after the user imports or remove last time input first again under the situation of input, the user is input as a literal;
S302) keyword conversion process: the keyword of user's input is converted to corresponding initial, when keyword is Chinese, converts first letter of pinyin to; When keyword is English, convert the lowercase combination to; When keyword is numeral, be itself;
S303) keyword word segmentation processing: when the number of user's input characters surpasses maximum participle length, word segmentation processing being carried out in user's input, is character string S with its participle
1, S
2S
nOtherwise user's input only comprises a character string S
1
S304) keyword index coupling: according to the character string S in the word segmentation result
1, S
2S
n, search corresponding index information by initial; Wherein search and mate S fully
1, S
2S
N-1Community string index community information, save as set A respectively
1, A
2A
N-1Search all part coupling S
nIndex information, save as set A
N1, A
N2... A
Nm
S305) the key word association result shows: keyword and these keywords that the energy association of institute of explicit user input goes out can inquire POI quantity as a result; The association of user's input is S
nThe result of association, the POI results set R of its coupling
i=A
1∩ A
2∩ ... ∩ A
Ni-1∩ A
Ni, and ni≤m, the POI that the result of association can inquire quantity as a result gathers R exactly
iNumber; When user's input error, the POI search engine is searched the corresponding result of association according to the partials of input of character string; When the user repeatedly imports, the POI search engine seeks common ground to the result of association of every single input, obtains the result of association of query composition;
S306) Query Result shows: the POI results set R that the result of association who selects according to the user matches
i, in POI solid data piece, obtain corresponding POI object information.
8. method according to claim 4 is characterized in that step S103) specifically comprise:
A) the user inputs character string is saved as target string S1, the character string sequence container Vector＜string of word segmentation result is preserved in initialization〉VetStr, and maximum participle length M axLen is set;
B) if the length of S1 surpasses MaxLen, forward step C to; Otherwise S1 is saved in word segmentation result VetStr, forwards step J then to;
C) if S1 is empty, forwards step J to, otherwise forward step D to;
D) from the S1 left side, take out candidate character strings W, the length of W is not more than MaxLen; If the length of W forwards step e to greater than 1, otherwise forward step I to;
E) get the initial character string L of W, according to alphabetical L[0] search all initials combinations of this letter in the initial management data block; Check the initial combination, whether therein to see character string L,, otherwise forward step H to if forward step F therein to;
F) in initial index data piece, obtain the index information of character string L, in the keyword data piece, obtain its corresponding keyword according to index information, and save as the participle dictionary;
G) check the participle dictionary, check that W is whether in dictionary; If do not forward step H therein to, then forward step I therein to;
H) word of W rightmost is removed, judge whether W is individual character; If individual character forwards step I to, otherwise forward step G to;
I) W is saved among the word segmentation result VetStr, saves as new S1 after from S1, removing candidate character strings W, forward step C at last to;
J) return word segmentation result VetStr.
9. method according to claim 2 is characterized in that: maximum participle length is 4 words.
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