CN105117388A - Intelligent robot interaction system - Google Patents
Intelligent robot interaction system Download PDFInfo
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- CN105117388A CN105117388A CN201510603519.3A CN201510603519A CN105117388A CN 105117388 A CN105117388 A CN 105117388A CN 201510603519 A CN201510603519 A CN 201510603519A CN 105117388 A CN105117388 A CN 105117388A
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Abstract
The invention relates to an intelligent robot interaction system which comprises an operation system, a knowledge base module, a robot central module, an artificial intelligence engine and a robot interaction module. The knowledge base module constructs a basic module of the interaction system based on the operation system, the robot central module and the artificial intelligence engine intelligently process user input, and the robot interaction module is used for collecting the user input and feeding the result back to a user. The accuracy of instruction recognition of a robot is improved in the interaction process with the robot.
Description
Technical field
The present invention relates to a kind of intelligent interactive system, relate to a kind of intelligent robot interactive system in particular.
Background technology
In traditional intelligent interaction, the general employing template way of intelligent interaction deals with complicated dialogue, and accuracy is lower, or analyzes after carrying out various participle to information, but general word segmentation result kind is many, and accuracy is lower.
Along with the development of computer hardware and the maturation of large data, traditional intelligent interaction field and computational linguistics can use more technology to improve accuracy.
Summary of the invention
The invention discloses a kind of intelligent robot interactive system, comprise operating system, base module, robot maincenter module, artificial intelligence engine, robot interactive module, described base module builds based on operating system, the basic module of described interactive system, described robot maincenter module, artificial intelligence engine carries out Intelligent treatment to user's input, described robot interactive module collection user input by result feedback to client.
Described base module comprises the example of body and body.
Described artificial intelligence engine comprises word-dividing mode, Lexical Analysis Module and syntactic analysis module.
Described robot interactive module is by immediate communication tool and user interactions.
Described robot interactive module is by embedded system and user interactions.
Described system adopts following steps to process user profile:
A, participle is carried out to the information that user sends;
Whether B, word, word and phrase to after participle described in steps A belong to entity identifies;
C, word, word and phrase to after participle described in steps A carry out semantic tagger analysis;
D, word, word and phrase to after participle described in steps A carry out text error correction;
E, syntactic analysis is carried out to the information that user sends;
Word, word and phrase after participle described in F, the information sent user and steps A carry out weight correction;
G, context process is carried out to the information that user sends;
H, result according to described step B-G, carry out Similarity Measure to the information that user sends, obtain threshold value;
I, preset knowledge base according to threshold value result queries, return results to user.
Semantic tagger analysis in described step C comprises field, importance degree, similar word, synonym, cyberspeak.
Text error correction in described step D is included in the service class word in field and phrase carries out phonetic error correction;
Syntactic analysis in described step F adopts rule and mask method.
Accompanying drawing explanation
Fig. 1-system framework figure
The example of Fig. 2-body and instantiation, succession
Fig. 3-part of speech management
Fig. 4-synonym, weight corrects
Embodiment
As Fig. 1, a kind of intelligent robot interactive system, comprise operating system, base module, robot maincenter module, artificial intelligence engine, robot interactive module, described base module based on the basic module of interactive system described in operating system component, described robot maincenter module, artificial intelligence engine carries out Intelligent treatment to user's input, described robot interactive module collection user input by result feedback to client.
Described base module comprises the example of body and body.
Described artificial intelligence engine comprises word-dividing mode, Lexical Analysis Module and syntactic analysis module.
Described robot interactive module is by immediate communication tool and user interactions.
Described robot interactive module is by embedded system and user interactions.
System described in this patent adopts following steps to carry out intelligent interaction:
A, participle is carried out to the information that user sends;
Participle is the common technology means of Computational Linguistics or artificial intelligence field, general employing " maximum coupling divides morphology " or " most probable number method participle ",
Whether B, word, word and phrase to after participle described in steps A belong to entity identifies;
For entity, be the instantiation of body,
So-called body, being clear and definite to the one of concept and detailed description, is a kind of describing method to real world.In other words, in fact body is exactly the Formal Representation to certain cover concept and relation each other thereof among specific area.Generally comprise:
---concrete instances of ontology (object Object)
---the attribute of body
---affiliated Ontological classifications.
After instances of ontology, just can inherit the attribute of body, be semantic tagger ready for analysis thereafter;
Specifically, as accompanying drawing 1, there is a lot of basic business for banking, all basic businesses are exactly a kind of body, for a certain concrete body, such as handle rule, marketing activity is exactly a kind of succession to basic business, and its all attribute just can be inherited.
C, word, word and phrase to after participle described in steps A carry out semantic tagger analysis;
For semantic tagger analysis, comprise part-of-speech tagging and word sense tagging two parts:
For part-of-speech tagging: the general magnetic mask method adopting Hidden Markov Model (HMM) or drive based on the mistake of conversion;
For word sense tagging: generally adopt the word sense disambiguation method based on mutual information or the row's discrimination method based on dictionary;
For this step, when user inputs a problem in robot front end, first this problem can carry out word segmentation processing, then mates according to the result of participle, and therefore the construction of part of speech is good and bad, is closely connected with the degree of intelligence of robot.All can realize in [part of speech management] with amendment the additions and deletions of part of speech.
As Fig. 2, having " public part of speech ", " proprietary part of speech " under [part of speech management] label, is wherein the part of speech that body generic attribute is corresponding under " public part of speech ", is the self-defining peculiar part of speech of project under " proprietary part of speech ".
D, word, word and phrase to after participle described in steps A carry out text error correction;
E, syntactic analysis is carried out to the information that user sends;
Word, word and phrase after participle described in F, the information sent user and steps A carry out weight correction;
As Fig. 3, select the classification right click needing to be linked into, select [newly-built subclassification] in a menu, in pop-up box, insert typonym preserved.
In native system, " * " " # " of item name side mark is used for distinguishing the importance degree of part of speech and similarity respectively, and " * " represents important, and weight is higher; " # " represents dissmilarity, and similarity is very low; " " word represented under this classification has phonetic error correction.Subclassification inherits " * " " # " " " setting of parent classification automatically.
Native system also can adjust weight according to user data daily record.Such as: " no " word Corpus--based Method is inessential, but through statistical study, " no " word occur and sentence tail ratio higher, its implication is completely different, so when " no " word appears at tail, such as " I can open CRBT not " adjustment " no " word weight.
G, context process is carried out to the information that user sends;
Native system can realize context and process together, and such as: after user asks " how open-minded CRBT is ", user is only with asking " micro-letter ", and system can taken in context user wish to understand " how open-minded micro-letter is " automatically
H, result according to described step B-G, carry out Similarity Measure to the information that user sends, obtain threshold value;
In addition, native system can also realize " hybrid operation of semantic formula and common question sentence ".
Such as: standard ask for: " cosmetics mark exaggerate effect, falsely represent how to investigate and prosecute? "
To should the standard semantic formula of asking can be analyzed to: [cosmetics | cosmetic brand] [falseness] [mark] [punishment] [method? ]
To should the standard a certain expansion of asking ask as " what method is the information that cosmetics mark mark is false take punish for this behavior industrial and commercial bureau "
Suppose to comprise above-mentioned knowledge in knowledge base, the information that system of users provides can carry out hybrid processing.Namely judge that the problem of user is asked can directly answer as being close to standard; As being decomposed into semantic formula, then answer according to semantic formula; Be close to expansion as can not semantic formula be resolved into ask, then ask answer according to expansion; And non-individual adopts above-mentioned any one party formula, to obtain max-thresholds.The i.e. answer of the most identical user's request.
I, preset knowledge base according to threshold value result queries, return results to user.
Semantic tagger analysis in described step C comprises field, importance degree, similar word, synonym, cyberspeak.
Specifically, after carrying out semantic tagger analysis according to above-mentioned aspect, the semanteme of the word divided is accurate, and ambiguity is eliminated substantially.
Text error correction in described step D is included in the service class word in field and phrase carries out phonetic error correction;
Syntactic analysis in described step e adopts rule and mask method.
Claims (10)
1. an intelligent robot interactive system, comprise operating system, base module, robot maincenter module, artificial intelligence engine, robot interactive module, described base module builds based on operating system, the basic module of described interactive system, described robot maincenter module, artificial intelligence engine carries out Intelligent treatment to user's input, described robot interactive module collection user input by result feedback to client.
2. an intelligent robot interactive system according to claim 1, is characterized in that: described base module comprises the example of body and body.
3. an intelligent robot interactive system according to claim 1, is characterized in that: described artificial intelligence engine comprises word-dividing mode, Lexical Analysis Module and syntactic analysis module.
4. an intelligent robot interactive system according to claim 1, is characterized in that: described robot interactive module is by immediate communication tool and user interactions.
5. an intelligent robot interactive system according to claim 1, is characterized in that: described robot interactive module is by embedded system and user interactions.
6. an intelligent robot interactive system according to claim 1, is characterized in that: described system adopts following steps to process user profile:
A, participle is carried out to the information that user sends;
Whether B, word, word and phrase to after participle described in steps A belong to entity identifies;
C, word, word and phrase to after participle described in steps A carry out semantic tagger analysis;
D, word, word and phrase to after participle described in steps A carry out text error correction;
E, syntactic analysis is carried out to the information that user sends;
Word, word and phrase after participle described in F, the information sent user and steps A carry out weight correction;
G, context process is carried out to the information that user sends;
H, result according to described step B-G, carry out Similarity Measure to the information that user sends, obtain threshold value;
I, preset knowledge base according to threshold value result queries, return results to user.
7. a kind of intelligent robot interactive system according to claim 6, is characterized in that:
Semantic tagger analysis in described step C comprises field, importance degree, similar word, synonym, cyberspeak.
8. a kind of intelligent robot interactive system according to claim 6, is characterized in that: the text error correction in described step D is included in the service class word in field and phrase carries out phonetic error correction.
9. a kind of intelligent robot interactive system according to claim 6, is characterized in that: the syntactic analysis in described step e adopts rule and mask method.
10. a kind of intelligent robot interactive system according to claim 6, is characterized in that: the Similarity Measure in described step H takes the hybrid operation of semantic formula and common question sentence.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106154876A (en) * | 2016-07-15 | 2016-11-23 | 北京光年无限科技有限公司 | A kind of intelligent robot and robot operating system |
| CN106541412A (en) * | 2016-10-19 | 2017-03-29 | 北京光年无限科技有限公司 | The changing method of intelligent robot status mechanism, intelligent robot and device |
| CN106599163A (en) * | 2016-12-08 | 2017-04-26 | 上海云信留客信息科技有限公司 | Data mining method and device for big data |
| CN107135247A (en) * | 2017-02-16 | 2017-09-05 | 江苏南大电子信息技术股份有限公司 | A kind of service system and method for the intelligent coordinated work of person to person's work |
| CN110175230A (en) * | 2019-05-29 | 2019-08-27 | 广州伟宏智能科技有限公司 | Intelligent robot interactive system |
| CN110858096A (en) * | 2018-08-23 | 2020-03-03 | 中瑞福宁机器人（沈阳）有限公司 | Robot-human-computer interaction method based on semantic recognition and assisted by other modes |
| CN107818781B (en) * | 2017-09-11 | 2021-08-10 | 远光软件股份有限公司 | Intelligent interaction method, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101286161A (en) * | 2008-05-28 | 2008-10-15 | 华中科技大学 | Intelligent Chinese request-answering system based on concept |
| US20090162824A1 (en) * | 2007-12-21 | 2009-06-25 | Heck Larry P | Automated learning from a question and answering network of humans |
| CN101510221A (en) * | 2009-02-17 | 2009-08-19 | 北京大学 | Enquiry statement analytical method and system for information retrieval |
| CN103400054A (en) * | 2013-08-27 | 2013-11-20 | 哈尔滨工业大学 | Computer-assisted psychological consulting automatic question-answering robot system |
| CN103902652A (en) * | 2014-02-27 | 2014-07-02 | 深圳市智搜信息技术有限公司 | Automatic question-answering system |
-
2015
- 2015-09-21 CN CN201510603519.3A patent/CN105117388B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090162824A1 (en) * | 2007-12-21 | 2009-06-25 | Heck Larry P | Automated learning from a question and answering network of humans |
| CN101286161A (en) * | 2008-05-28 | 2008-10-15 | 华中科技大学 | Intelligent Chinese request-answering system based on concept |
| CN101510221A (en) * | 2009-02-17 | 2009-08-19 | 北京大学 | Enquiry statement analytical method and system for information retrieval |
| CN103400054A (en) * | 2013-08-27 | 2013-11-20 | 哈尔滨工业大学 | Computer-assisted psychological consulting automatic question-answering robot system |
| CN103902652A (en) * | 2014-02-27 | 2014-07-02 | 深圳市智搜信息技术有限公司 | Automatic question-answering system |
Non-Patent Citations (1)
| Title |
|---|
| 朱旺南: "基于本体的自动问答客服系统研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106154876A (en) * | 2016-07-15 | 2016-11-23 | 北京光年无限科技有限公司 | A kind of intelligent robot and robot operating system |
| CN106541412A (en) * | 2016-10-19 | 2017-03-29 | 北京光年无限科技有限公司 | The changing method of intelligent robot status mechanism, intelligent robot and device |
| CN106541412B (en) * | 2016-10-19 | 2019-04-26 | 北京光年无限科技有限公司 | Switching method, intelligent robot and the device of intelligent robot status mechanism |
| CN106599163A (en) * | 2016-12-08 | 2017-04-26 | 上海云信留客信息科技有限公司 | Data mining method and device for big data |
| CN106599163B (en) * | 2016-12-08 | 2019-11-22 | 上海云信留客信息科技有限公司 | A kind of data digging method and device for big data |
| CN107135247A (en) * | 2017-02-16 | 2017-09-05 | 江苏南大电子信息技术股份有限公司 | A kind of service system and method for the intelligent coordinated work of person to person's work |
| CN107135247B (en) * | 2017-02-16 | 2019-11-29 | 江苏南大电子信息技术股份有限公司 | A kind of service system and method for the intelligent coordinated work of person to person's work |
| CN107818781B (en) * | 2017-09-11 | 2021-08-10 | 远光软件股份有限公司 | Intelligent interaction method, equipment and storage medium |
| CN110858096A (en) * | 2018-08-23 | 2020-03-03 | 中瑞福宁机器人（沈阳）有限公司 | Robot-human-computer interaction method based on semantic recognition and assisted by other modes |
| CN110175230A (en) * | 2019-05-29 | 2019-08-27 | 广州伟宏智能科技有限公司 | Intelligent robot interactive system |
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