CN106354708A - Client interaction information search engine system based on electricity information collection system - Google Patents
Client interaction information search engine system based on electricity information collection system Download PDFInfo
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Abstract
The invention provides a client interaction information search engine system based on an electricity information collection system. The search engine system is constructed based on an open-source search engine solr and comprises an electric power word bank module, a document analysis module, a Chinese word segmentation module, an index library module and a retrieval interface module. The Chinese word segmentation technology and an electric power word bank are combined to establish a standard index; during search, the standard word bank is also used, so that search is accurate, comprehensive and fast.
Description
Technical field
The present invention relates to a kind of method of field of power, in particular to a kind of client based on power information acquisition system is mutual
Dynamic information search engine system.
Background technology
According to Chinese intelligent grid development plan, 2,011 2015 years, the intelligent grid of China entered the all-round construction stage.2015
Year, 41 intelligent grid innovative demonstration engineering construction tasks, power information acquisition system user interaction in intelligent grid will be completed
Function is realized substantially, including information interaction, electric energy and business interaction.Pass through " tou power price ", " rank on the basis of interactive information
The means such as terraced electricity price " and " bi-directional scheduling " " peak load shifting " effect substantially, and achieves negative to user under not power-off condition
Lotus is controlled and then advances ordered electric.Structurized client connection information Store adopts server database memory module,
The development trend in the future of destructuring interactive information is the electric power data center based on hadoop framework.The connecing of general big and medium-sized cities
Enter electric number of users and reach million orders of magnitude, the thing followed is magnanimity interactive information, efficient, accurate and comprehensive lookup information becomes
Improve operating efficiency, make full use of the bottleneck of data resource.
Content of the invention
For overcoming above-mentioned the deficiencies in the prior art, present invention offer is a kind of to be searched based on the client connection information of power information acquisition system
Rope automotive engine system, can achieve the search for being stored in unit or intranet shared file content at this stage, thus realize right
The accurate lookup of customer information.
Realizing the solution that above-mentioned purpose adopted is:
A kind of client connection information search engine system based on power information acquisition system, wherein, described search engine system base
In increasing income, search engine solr builds, including electric power dictionary module, document parsing module, Chinese word segmentation module, index library module
With Retrieval Interface module.
Preferably, two aspect sources are had in described electric power dictionary module, one is with reference to country and power industry standard and state
The standard of the border electric power network technique committee, specialized vocabulary conventional in user interaction information is included into dictionary；It two will be crucial for core publication
Relational language in the summary such as word and field term extraction algorithm extraction " Proceedings of the CSEE " using regular Distribution Entropy
It is included into dictionary.
Preferably, described document parsing module is responsible for resolution file, from unstructured data such as pdf, word, excel and
In the document of the forms such as powerpoint extract description document word, these descriptive information include Document Title, author,
Main contents etc., are carrying out syntactic analysis and Language Processing further i.e. using tf idf (term frequency-inverse
Document frequency) weighting algorithm is estimated to the word in text, and choose weights and be more than the word of threshold value and extract as document
Core vocabulary go forward side by side a step application message gain method (information gain) preferably core vocabulary and then formed comprise content and
The text of core vocabulary.
Complete the document process of different-format using multiple resources in storehouse of increasing income.For example, apache poi program can complete
The function that microsoft office format file is read and write.Its structure includes: hssf provides read-write microsoft excel xls
The function of form archives；Xssf provides the function of read-write microsoft excel ooxml xlsx form archives；Hwpf provides
The function of read-write microsoft word doc form archives；Hslf provides read-write microsoft powerpoint form archives
Function；Hdgf provides function of read-write microsoft visio form archives etc..The establishment of pdfbox offer pdf document,
Process and document content abstraction function.
Preferably, described Chinese word segmentation module is responsible for using Chinese Word Automatic Segmentation, and text content is carried out full text participle, will
Word segmentation result compares with electric power dictionary Plays term one by one, deletes the participle not having in dictionary, using the standard of electric power dictionary
Word, forms index file, and Chinese word segmentation adopts " ik analyzer " kit, participle when setting up index data base and search
When, it is required for comparing with electric power dictionary java standard library, the index data base being built such that is easily by using identical standard dictionary
Search engine searches.
Preferably, described index library module disappears again by interactive information data prediction using Digital Signature Algorithm, empty using phasor
Between model (vsm:vector space model) represent text characteristic information, set up index data base, for user search for provide
Retrieval source；
The index file of described index library module comprises index terms and index list.
It is different from other topic-specified search engines, the feature when forming index database for the system is that index terms is to build based on electric power dictionary
Vertical, thus forming standardized index database.
Preferably, described Retrieval Interface module is the interface that user uses, and accepts inputting and exporting Query Result of user.Retrieval
When will input term participle after formed keyword, segmenter analysis of key word, parsed and with electric power dictionary comparison, shape
Become multiple search words, then index file is scanned for, and result is ranked up with output to user.
It is different from other search engines, the feature in retrieval for the system is that the keyword after participle is compared with electric power dictionary, from
And form standardized term.
Preferably, described retrieval includes: 1) sets up characteristic item: set up characteristic item, document=d (t to the word of document, word, sentence1,
t2,…tk,…tn), it is expressed as a dimension, wherein tkRepresent k-th characteristic item；
2) calculate the weight of characteristic item: in an object to be retrieved, each characteristic item is endowed weight cj, to represent
Significance level in the text for the characteristic item；
3) set up vector space model: after having given up the order information between each characteristic item, text mean that into
Amount, i.e. feature space a point；Text d1Expression: v (d1)=(wi1, wi2... ... wik... wim), wherein, wik=f (tk,
cj) it is weighting function, reflection weight is cjFeature phase tkDetermine document diBelong to the degree of feature set；
4) Similarity Measure: all documents are mapped as the vector space of this document by vector space model, thus by document information
Matching problem is converted into the vector matching problem in vector space, the distance at n-dimensional space midpoint with the cosine angle between vector Lai
Tolerance, that is, illustrate similarity degree between document it is assumed that destination document is u, during lookup and destination document u compare certain not
Know that document is vi, the similarity of angle less expository writing shelves is higher, similar computing formula (1):
Wherein, wikIt is unknown document viIn k-th characteristic item weighting function, wkIt is k-th characteristic item in destination document u
Weighting function, characteristic item has the m i.e. value of k from 1 to m；Calculating weighting function using word frequency is wik=tfk(di)1/2,
It is normalized:tfk(di) represent k-th characteristic item in unknown document viThe frequency of middle appearance,
The value of j travels through all characteristic items from 1 to m；djRepresent jth item document；The computational methods of wk are identical with wik, in mesh
Calculating weighting function using word frequency in mark document u is wk=tfk(d)1/2, and be normalized:
tfkD frequency that k-th characteristic item of () expression occurs in destination document u, the value of j travels through all characteristic items from 1 to m.
When returning user search information, similarity is ranked up from high to low, provides retrieval entry.
Compared with prior art, the method have the advantages that
Chinese words segmentation of the present invention and electric power dictionary combines it is established that the index of standard, the keyword after participle during search with
Electric power dictionary compares, thus forming standardized term, makes search accurately, comprehensively and quickly.
Brief description
Fig. 1 is the search engine framework figure of the present invention；
Fig. 2 is the index frame diagram of the present invention；
Fig. 3 is the retrieval frame diagram of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings the specific embodiment of the present invention is described in further detail.
The present invention is based on search engine solr kit of increasing income, and builds a kind of gopher of search fixed disk file content, including electricity
Power dictionary module, document parsing module, Chinese word segmentation module, index library module and Retrieval Interface module.Electric power dictionary module is built
Specialized dictionary conventional in user interaction information is particularly sorted out by vertical industry standard term.Document parsing module is responsible for resolution file；
Chinese word segmentation module is responsible for using Chinese Word Automatic Segmentation, and file content is carried out full text participle, in conjunction with electric power dictionary, sets up in full
Index.Index database data storage；Retrieval Interface module is the interface that user uses, and accepts inputting and exporting Query Result of user.
System framework such as Fig. 1.
In document analysis module, from the document of the unstructured data such as form such as pdf, word, excel and powerpoint
Extract the word of description document, these descriptive information include Document Title, author, main contents etc., carrying out language further
Method analysis and Language Processing and then formation index.Complete the document process of different-format using multiple resources in storehouse of increasing income.Example
As apache poi program can complete the function that microsoft office format file is read and write.Its structure includes: hssf carries
For reading and writing the function of microsoft excel xls form archives；Xssf provides read-write microsoft excel ooxml xlsx
The function of form archives；Hwpf provides the function of read-write microsoft word doc form archives；Hslf provides read-write
The function of microsoft powerpoint form archives；Hdgf provides function of read-write microsoft visio form archives etc..
Pdfbox provides establishment, process and the document content abstraction function of pdf document.
The content Primary Reference country of electric power dictionary and power industry standard, and the standard of international grid technical committee.Due to
Intelligent grid be new things in constantly improve, some of which common words need collection to be individually added into.
Chinese word segmentation adopts " ik analyzer " kit, when setting up index data base and search during participle, is required for and electric power
Dictionary java standard library compares, and the index data base being built such that easily is searched using the search engine of identical standard dictionary.
Index framework such as Fig. 2.In the hard disks such as word, excel, txt, pdf, the different types of file application of storage is corresponding
Kit extraction document content from file forms text and gives segmenter, and segmenter combines power specialty dictionary and sets up index
File, comprise in index file is the keyword that the key message extracting in text simultaneously compares with electric power dictionary and sets up.
It is different from other topic-specified search engines, the feature when forming index database for the system is that index terms is to build based on electric power dictionary
Vertical, thus forming standardized index database.
Retrieval framework such as Fig. 3.After user input keyword, segmenter analysis of key word, parsed and with the comparison of electric power dictionary,
Form multiple search words, then index file is scanned for, and result is ranked up with output to user.
It is different from other search engines, the feature in retrieval for the system is that the keyword after participle is compared with electric power dictionary, from
And form standardized term.
It is implemented as follows:
1) set up characteristic item: the word of document, word, sentence etc. are set up with characteristic item, document=d (t1,t2,…tk,…tn),
Wherein tkRepresent k-th characteristic item, be expressed as a dimension.Specifically, can be by the payment of certain customer electricity payment information
The words such as unit, Payment Amount, Subscriber Number, customer address, project name, electricity charge month, this paid, total RMB
Respectively as d (t1,t2,…tk,…tn) one of characteristic item.
2) calculate the weight of characteristic item: in an object to be retrieved (such as text), each characteristic item is endowed a power
Weight cj, to represent significance level in the text for the characteristic item.Specifically, characteristic item user being concerned about: electricity charge month,
Project name, fee to be charged, account balance etc. give heavier weight, and for other more sparse with this retrieval relation
Characteristic item: customer address, serial number, client etc. give less weight.
3) set up vector space model: after having given up the order information between each characteristic item, text mean that into
Amount, i.e. feature space a point.As text d1Expression: v (d1)=(wi1, wi2... ... wik... wim).Wherein,
wik=f (tk, cj) it is weighting function, reflect feature tkDetermine document diWhether belong to cjImportance.
4) Similarity Measure: all documents are mapped as the vector space of this document by vector space model, thus by document information
Matching problem is converted into the vector matching problem in vector space.The distance at n-dimensional space midpoint with vector between cosine angle Lai
Tolerance, namely illustrate the similarity degree between document.Assume that destination document vector is u, unknown document is vi, angle gets over novel
The similarity of plaintext shelves is higher, similar computing formula (1):
Weight w thereinikIt is the function of characteristic item institute's frequency of occurrences in a document, use tfk(di) represent tkIn document diMiddle appearance
Frequency, using word frequency wik=tfk(di)1/2Calculate weighting function, and be normalized post processing:
When returning user search information, it is ranked up with similarity, provides retrieval entry.
Finally it should be noted that: above example is merely to illustrate technical scheme rather than the restriction to its protection domain of the application,
Although being described in detail to the application with reference to above-described embodiment, those of ordinary skill in the art are it is understood that this area
Technical staff still can carry out a variety of changes, modification or equivalent to the specific embodiment of application after reading the application, but
These changes, modification or equivalent, are all applying within pending claims.
Claims (7)
1. a kind of client connection information search engine system based on power information acquisition system is it is characterised in that described search is drawn
System of holding up is based on the search engine solr that increases income and builds, including electric power dictionary module, document parsing module, Chinese word segmentation module, rope
Draw library module and Retrieval Interface module.
2. search engine system as claimed in claim 1 comes it is characterised in that having two aspects in described electric power dictionary module
Source, one, will be conventional in user interaction information with reference to the standard of country and power industry standard and international grid technical committee
Specialized vocabulary is included into dictionary；It two is extracted by core publication keyword and using the field term extraction algorithm of regular Distribution Entropy
Relational language in summary is included into dictionary.
3. search engine system as claimed in claim 1 is it is characterised in that described document parsing module is responsible for resolution file, from
The word extracting description document in unstructured document carries out syntactic analysis and Language Processing further i.e. using tf idf weighting calculation
Method is estimated to the word in text, chooses weights and is more than the core vocabulary that extracts as document of word of threshold value and goes forward side by side a step application message
Gain method preferred core vocabulary so formed comprise content and the text of core vocabulary.
4. search engine system as claimed in claim 1 is it is characterised in that described Chinese word segmentation module is responsible for using Chinese word segmentation
Algorithm, text content is carried out full text participle, word segmentation result is compared with electric power dictionary Plays term one by one, deletes
The participle not having in dictionary, using the standard word of electric power dictionary, forms index file.
5. search engine system as claimed in claim 1 is it is characterised in that described index library module is pre- by interactive information data
Process and disappeared again using Digital Signature Algorithm, represent the characteristic information of text using vector space model, set up index data base, be
User's search provides retrieval source；
The index file of described index library module comprises index terms and index list.
6. search engine system as claimed in claim 1 it is characterised in that described Retrieval Interface module be user use interface,
Accept inputting and exporting Query Result of user；
Forming keyword by after the term participle of input during retrieval, comparing with electric power dictionary, thus forming standardized term.
7. search engine system as claimed in claim 6 is it is characterised in that described retrieval includes:
1) set up characteristic item: characteristic item, document=d (t are set up to the word of document, word, sentence1,t2,…tk,…tn),
It is expressed as a dimension, wherein tkRepresent k-th characteristic item；
2) calculate the weight of characteristic item: in an object to be retrieved, each characteristic item is endowed weight cj, to represent
Significance level in the text for the characteristic item；
3) set up vector space model: after having given up the order information between each characteristic item, text mean that into
Amount, i.e. feature space a point；Text d1Expression: v (d1)=(wi1, wi2... ... wik... wim), wherein, wik=f (tk,
cj) it is weighting function, reflection weight is cjFeature phase tkDetermine document diBelong to the degree of feature set；
4) Similarity Measure: all documents are mapped as the vector space of this document by vector space model, thus by document information
Matching problem is converted into the vector matching problem in vector space, the distance at n-dimensional space midpoint with the cosine angle between vector Lai
Tolerance, that is, illustrate similarity degree between document it is assumed that destination document is u, during lookup and destination document u compare certain not
Know that document is vi, the similarity of angle less expository writing shelves is higher, similar computing formula (1):
Wherein, wikIt is unknown document viIn k-th characteristic item weighting function, wkIt is k-th characteristic item in destination document u
Weighting function, characteristic item has the m i.e. value of k from 1 to m；Calculating weighting function using word frequency is wik=tfk(di)1/2,
It is normalized:tfk(di) represent k-th characteristic item in unknown document viThe frequency of middle appearance,
The value of j travels through all characteristic items from 1 to m；djRepresent jth item document；
W can be obtained in the same mannerk:
When returning user search information, similarity is ranked up from high to low, provides retrieval entry.
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106777405A (en) * | 2017-04-05 | 2017-05-31 | 安徽机器猫电子商务股份有限公司 | Promote the e-business method of low frequency class transaction based on SaaS services |
| CN106844700A (en) * | 2017-02-03 | 2017-06-13 | 山东浪潮商用系统有限公司 | It is a kind of to ask tax system based on Sorl |
| CN106997390A (en) * | 2017-04-05 | 2017-08-01 | 安徽机器猫电子商务股份有限公司 | A kind of equipment part or parts commodity transaction information search method |
| CN107609006A (en) * | 2017-07-24 | 2018-01-19 | 华中师范大学 | A kind of chess game optimization method based on local chronicle research |
| CN108415900A (en) * | 2018-02-05 | 2018-08-17 | 中国科学院信息工程研究所 | A kind of visualText INFORMATION DISCOVERY method and system based on multistage cooccurrence relation word figure |
| CN108984647A (en) * | 2018-06-26 | 2018-12-11 | 北京工业大学 | A kind of water utilities domain knowledge map construction method based on Chinese text |
| CN109388649A (en) * | 2018-09-28 | 2019-02-26 | 湖南土流信息有限公司 | Intelligent land recommendation method and system |
| CN109543182A (en) * | 2018-11-15 | 2019-03-29 | 广东电网有限责任公司信息中心 | A kind of electric power enterprise based on solr engine takes turns interactive semantic analysis method more |
| CN110245822A (en) * | 2019-04-22 | 2019-09-17 | 贵州电网有限责任公司 | A kind of scheduling operation task sequence generation method based on comprehensive power failure |
| CN110674285A (en) * | 2019-09-18 | 2020-01-10 | 国网安徽省电力有限公司芜湖供电公司 | Intelligent retrieval system and method for power dispatching machine accounts |
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| CN112182148A (en) * | 2020-09-14 | 2021-01-05 | 山西和耀科技有限公司 | Standard auxiliary compiling method based on full-text retrieval |
| CN112988953A (en) * | 2021-04-26 | 2021-06-18 | 成都索贝数码科技股份有限公司 | Adaptive broadcast television news keyword standardization method |
| CN113326350A (en) * | 2021-05-31 | 2021-08-31 | 江汉大学 | Keyword extraction method, system, device and storage medium based on remote learning |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106844700A (en) * | 2017-02-03 | 2017-06-13 | 山东浪潮商用系统有限公司 | It is a kind of to ask tax system based on Sorl |
| CN106997390A (en) * | 2017-04-05 | 2017-08-01 | 安徽机器猫电子商务股份有限公司 | A kind of equipment part or parts commodity transaction information search method |
| CN106777405A (en) * | 2017-04-05 | 2017-05-31 | 安徽机器猫电子商务股份有限公司 | Promote the e-business method of low frequency class transaction based on SaaS services |
| CN106777405B (en) * | 2017-04-05 | 2020-04-28 | 安徽机器猫电子商务股份有限公司 | Method for promoting low-frequency commodity transaction based on SaaS service |
| CN107609006A (en) * | 2017-07-24 | 2018-01-19 | 华中师范大学 | A kind of chess game optimization method based on local chronicle research |
| CN107609006B (en) * | 2017-07-24 | 2021-01-29 | 华中师范大学 | Search optimization method based on local log research |
| CN108415900A (en) * | 2018-02-05 | 2018-08-17 | 中国科学院信息工程研究所 | A kind of visualText INFORMATION DISCOVERY method and system based on multistage cooccurrence relation word figure |
| CN108984647A (en) * | 2018-06-26 | 2018-12-11 | 北京工业大学 | A kind of water utilities domain knowledge map construction method based on Chinese text |
| CN109388649A (en) * | 2018-09-28 | 2019-02-26 | 湖南土流信息有限公司 | Intelligent land recommendation method and system |
| CN109543182A (en) * | 2018-11-15 | 2019-03-29 | 广东电网有限责任公司信息中心 | A kind of electric power enterprise based on solr engine takes turns interactive semantic analysis method more |
| CN111767308A (en) * | 2019-04-01 | 2020-10-13 | 广州精选速购网络科技有限公司 | Method, system, electronic device and medium for real-time screening and sorting of commodities |
| CN110245822A (en) * | 2019-04-22 | 2019-09-17 | 贵州电网有限责任公司 | A kind of scheduling operation task sequence generation method based on comprehensive power failure |
| CN110674285A (en) * | 2019-09-18 | 2020-01-10 | 国网安徽省电力有限公司芜湖供电公司 | Intelligent retrieval system and method for power dispatching machine accounts |
| CN112182148A (en) * | 2020-09-14 | 2021-01-05 | 山西和耀科技有限公司 | Standard auxiliary compiling method based on full-text retrieval |
| CN112182148B (en) * | 2020-09-14 | 2023-08-25 | 山西和耀科技有限公司 | Standard aided writing method based on full text retrieval |
| CN112988953A (en) * | 2021-04-26 | 2021-06-18 | 成都索贝数码科技股份有限公司 | Adaptive broadcast television news keyword standardization method |
| CN113326350A (en) * | 2021-05-31 | 2021-08-31 | 江汉大学 | Keyword extraction method, system, device and storage medium based on remote learning |
| CN113326350B (en) * | 2021-05-31 | 2023-05-26 | 江汉大学 | Keyword extraction method, system, equipment and storage medium based on remote learning |
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Application publication date: 20170125 |