CN106649524A - Improved advanced study intelligent response system based on computer cloud data - Google Patents

Improved advanced study intelligent response system based on computer cloud data Download PDF

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CN106649524A
CN106649524A CN201610910988.4A CN201610910988A CN106649524A CN 106649524 A CN106649524 A CN 106649524A CN 201610910988 A CN201610910988 A CN 201610910988A CN 106649524 A CN106649524 A CN 106649524A
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answer
information
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CN106649524B (en
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张兆万
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Tianju Dihe Suzhou Technology Co ltd
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Ningbo Jiangdong Daikin Information Technology Co Ltd
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    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/9032—Query formulation
    • G06F16/90332—Natural language query formulation or dialogue systems
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • 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
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Abstract

The invention provides an improved advanced study intelligent response system based on the computer cloud data. The intelligent response system comprises a positioning unit, which is used to locate a user in real time and to send the real-time location information to a cartographic information delineation unit; and the intelligent response system comprises the cartographic information delineation unit, which is used to demarcate an activity zone of the user based on the user description information of a trajectory segment within a preset time and is also used to demarcate in advance an entire map of a specific region and marks in detail on the map each interesting point in the specific region; the intelligent response system further comprises an information receiving unit which is used to send the processed information to a database unit, and a database unit, which is used to build a knowledge library by collecting and organizing the questions raised by each user and the solutions of the example knowledge materials answered by each user, and to extract the candidate keywords from the example materials, and to manually mark the contents of the keywords, in order to subsequently reference the keywords; the intelligent response system also comprises a social network description unit, which is used to obtain the correlations and the interactions between different users and the different social roles of different role players between the users.

Description

A kind of deep learning intelligent response system of modified based on computer cloud data
Technical field
The present invention relates to intelligent answer robotics, more particularly to a kind of modified is based on computer cloud data Deep learning intelligent response system.
Background technology
Based on the mobile Internet platform of current popular, the research for carrying out intelligent answer robot system is one emerging Research field.The research field being related to includes based on mobile Internet platform, user in campus real time position it is dynamic The Changing Pattern of state variation monitoring, User Activity position and scope, the regular course of User Activity is defined and pushed away with personalized task Recommend, the fusion of the artificial intelligence knowledge base in combination with user context key element and question and answer expert system etc. all with mobile Internet On the related technical field of campus intelligent answer robot.
Not yet there is the campus intelligent answer machine in combination with big data excavation for mobile Internet in prior art The correlation technique of device people's system.
The content of the invention
In view of this, the present invention proposes a kind of deep learning intelligent response system of modified based on computer cloud data.
A kind of deep learning intelligent response system of modified based on computer cloud data, it is included such as lower unit：
Positioning unit, for positioning to user's real time position, is sent to real-time position information cartographic information and describes Unit；
Cartographic information delineation unit, for receiving the real-time position information of positioning unit transmission, and real-time position information shape Into the action trail of user, the action trail of user is segmented, and trajectory segment is described；Based in Preset Time The description information of the trajectory segment of user delimit User Activity region；It is additionally operable to depict the complete map of specific region in advance, And on map detailed each point of interest marked in the specific region, be a certain specific in the real-time position information of user During point of interest, the relevant information of the particular point of interest is pushed；
Information receiving unit, for the text query or speech query information of receiving user's input, is believing for text query During breath, through the text mining and process of Chinese word segmentation, semantic analysis and syntactic analysis, and the information after process is sent to into number According to library unit；For speech query information when, first speech query information is identified as into text query information, and through Chinese point The text mining and process of word, semantic analysis and syntactic analysis, and the information after process is sent to into Database Unit；For text During this Query Information, the information after process is sent to into Emotion identification unit, for speech query information when, while will process after Information and speech query information be sent to Emotion identification unit；
Emotion identification unit, the information for being sent according to information receiving unit judges the emotional state of user, user's Emotional state is divided into general, low, sad, happiness；For it is low or sad when, the relevant information of particular point of interest is pushed strong The instruction that degree is turned down is sent to cartographic information delineation unit；
The Emotion identification unit includes following word Emotion identification subelement and voice mood identification subelement：
Word Emotion identification subelement includes that classification storage represents general, low, sad, glad standard vocabulary；
By classifier functions the vocabulary of test input is trained and is categorized in each classification, and according to classification As a result judgment models are set up；
The information of reception is matched with each single linguistic context of target word in standard vocabulary, semantic contrast is caught Information, and the mood of Word message is judged to acquired semantic comparative information；
Voice mood identification subelement identification subelement is used to recognize the emotional information of voice by speech recognition technology；
Database Unit, proposes that problem and the answer knowledge material of answer set up knowledge for collecting and arranging each user Storehouse, to story extraction candidate keywords, and the content to keyword carries out handmarking, to carry out follow-up keyword rope Draw；It is problem knowledge storehouse and answer knowledge base by Knowledge base partition, is used to deposit each of user's proposition wherein in problem knowledge storehouse The demand of kind and problem, there are problems that and the demand in problem knowledge storehouse and the answer content for matching, answer in answer knowledge base Thinking and answer detailed process；And the association that question and answer matching degree is assessed is set up between problem knowledge storehouse and answer knowledge base, close The matching degree being combined between metric question and answer, and by this process in combination with follow-up user satisfaction is fed back, It is provided commonly for the optimization to knowledge base, and the renewal to related content in problem knowledge storehouse and answer knowledge base and optimization；
Social networks describes unit, for obtaining interrelated between the user in specific region and influencing each other and use The different social role of performer between family, so that it is determined that each user each has different user property and user task, And the interaction between user constitutes respective customer relationship and social circle.
Modified of the present invention based on computer cloud data deep learning intelligent response system in, it include as Lower unit：
Statistical analysis unit, for according to the description information of the trajectory segment of user, each user in Preset Time each Respective customer relationship and social circle's life are constituted with the interaction between different user property and user task, and user Into and preserve the stroke rule information table of user；Include user within each time period in the stroke rule information table of user The interest of zone of action, activity item and user.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data, the map Information portrayal unit is additionally operable to when the real-time position information of positioning unit transmission meets the point of interest in specific region, and this is emerging Interest point is sent to statistical analysis unit；
Statistical analysis unit is judged in the time point in point of interest item whether to be handled；There is backlog When, sent to information receiving unit with the interest point name and backlog；
Information receiving unit through Chinese word segmentation, semantic analysis and syntactic analysis text mining and process, and will process Information afterwards is sent to Database Unit；
The answer content matched with problem, answer thinking and answer detailed process are pushed and shown by Database Unit.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data,
The processing authority of the backlog of user is prestored in social networks describes unit, processing authority is divided into certainly Oneself processes and allows specific user's alternate process in social circle.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data,
Statistical analysis unit there is backlog in certain time period for user and user is in backlog Zone of action outside when, judge that whether the backlog is the pending thing for allowing specific user's alternate process in social circle , if it is the backlog is sent into user social contact circle corresponding other users.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data,
Matching degree between Database Unit vacuum metrics question and answer, and this process is satisfied with follow-up user Degree feedback combines, and is provided commonly for the optimization to knowledge base, and to related content in problem knowledge storehouse and answer knowledge base more Newly include with optimization：
Matching mapping relations will be set up between question and answer after question and answer matching degree evaluation process, and to candidate answers Ranking is carried out with the matching degree of problem, is supplied to user to be selected several candidate answers in the top, while allowing Answer of the user to problem is evaluated, and the result of evaluation is fed back in time into question answering system carries out the amendment of question and answer matching degree Replacement with knowledge in knowledge base and renewal.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data,
Mapping relations matrix M in Database Unit between problem knowledge storehouse and key to the issue wordq,kIt is expressed as follows：
Wherein Rq→kRepresent mapping relations therebetween, Qi(i=1...n) problem of representation and its number, Kj(j= 1...m) problem of representation keyword and its number；One problem can be disassembled and is been described by for multiple keywords, while one Keyword can also apply in multiple problems；Tij(i=1...n, j=1...m) problem of representation i is by key to the issue word j institutes group Into content of text；
Mapping relations matrix M in Database Unit between key to the issue word and answer keywordk,k’It is expressed as follows：
Wherein Rk→k'Represent mapping relations therebetween, Ki(i=1...n) problem of representation keyword and its number, K'j (j=1...m) answer keyword and its number are represented；One key to the issue word can correspond to multiple problem answers keywords, Simultaneously an answer keyword can also apply in multiple key to the issue words；Tij(i=1...n, j=1...m) problem of representation The content of text that keyword i is answered by answer keyword j；
Mapping relations matrix M in Database Unit between answer keyword and answerk’,aIt is expressed as follows：
Wherein Rk'→aRepresent mapping relations therebetween, K'i(i=1...n) answer keyword and its number, A are representedj (j=1...m) answer and its number are represented；One answer keyword can be to describe multiple answers, while an answer Can be described with multiple answer keywords；Tij(i=1...n, j=1...m) represents that answer keyword i enters to answer j The content of text of row description；
Mapping relations matrix M in Database Unit between answer and answer rankinga,rankIt is expressed as follows：
Wherein Ra→rankRepresent mapping relations therebetween, Ai(i=1...n) answer and its number are represented,Represent the ranking of answer i；
Mapping relations matrix M in Database Unit between answer and user satisfactiona,usIt is expressed as follows：
WhereinRepresent mapping relations therebetween, Ai(i=1...n) answer and its number, U are representeds,j(j= 1...m) user satisfaction and its number are represented；One answer can be evaluated by multiple users, can possess multiple users Satisfaction, while a user can also be evaluated multiple answers, provides respectively the customer satisfaction evaluation of multiple answers； Pij(i=1...n, j=1...m) represents the user satisfaction that answer i is evaluated by user j.
Deep learning intelligent response system and existing skill of the modified of present invention offer based on computer cloud data is provided Art is compared and had the advantages that：For aiding in specific region, the new user for adding (such as teaches (such as in campus) Teacher and student etc.) faster be familiar with and adapt to environment (University Environments and cultural atmosphere of such as user place universities and colleges), preferably with Friend relation is set up between other users, and the artificial intelligence knowledge base set up by situation key element is movable crowd in specific region Daily life preferably service.So that user faster, is more preferably familiar with specific region internal and external environment, hardware/software infrastructure, area Domain culture etc., and systematicness is carried out with question and answer mode represent with a series of.Can be so that according to the mood of user, it is strong that adjustment is pushed Degree, hommization degree is higher.
Description of the drawings
Fig. 1 is the deep learning intelligent response system architecture frame of the modified based on computer cloud data of the embodiment of the present invention Figure.
Specific embodiment
As shown in figure 1, for the defect of prior art, the present invention proposes a kind of modified based on computer cloud data Deep learning intelligent response system, it is included such as lower unit：
Positioning unit, for positioning to user's real time position, is sent to real-time position information cartographic information and describes Unit.Alternatively, positioning unit can pass through the function that the app such as mobile phone realize positioning.
Cartographic information delineation unit, for receiving the real-time position information of positioning unit transmission, and real-time position information shape Into the action trail of user, the action trail of user is segmented, and trajectory segment is described；Based in Preset Time The description information of the trajectory segment of user delimit User Activity region；It is additionally operable to depict the complete map of specific region in advance, And on map detailed each point of interest marked in the specific region, be a certain specific in the real-time position information of user During point of interest, the relevant information of the particular point of interest is pushed.It is alternatively possible to the algorithm for being based on user collaborative filtration pushes the spy Determine the relevant information of point of interest.Such as through some point of interest, when being coffee, then coffee phase in annex preset range is pushed The information of pass is to user.
The action trail of user is segmented, and trajectory segment is described, for example, user is if a university It is raw, the trajectory segment from dormitory to teaching building is described as " turning out for work ".These are only schematically.
Information receiving unit, for the text query or speech query information of receiving user's input, is believing for text query During breath, through the text mining and process of Chinese word segmentation, semantic analysis and syntactic analysis, and the information after process is sent to into number According to library unit；For speech query information when, first speech query information is identified as into text query information, and through Chinese point The text mining and process of word, semantic analysis and syntactic analysis, and the information after process is sent to into Database Unit；For text During this Query Information, the information after process is sent to into Emotion identification unit, for speech query information when, while will process after Information and speech query information be sent to Emotion identification unit；
Emotion identification unit, the information for being sent according to information receiving unit judges the emotional state of user, user's Emotional state is divided into general, low, sad, happiness；For it is low or sad when, the relevant information of particular point of interest is pushed strong The instruction that degree is turned down is sent to cartographic information delineation unit；
The Emotion identification unit includes following word Emotion identification subelement and voice mood identification subelement：
Word Emotion identification subelement includes that classification storage represents general, low, sad, glad standard vocabulary；
By classifier functions the vocabulary of test input is trained and is categorized in each classification, and according to classification As a result judgment models are set up；
The information of reception is matched with each single linguistic context of target word in standard vocabulary, semantic contrast is caught Information, and the mood of Word message is judged to acquired semantic comparative information；
Voice mood identification subelement identification subelement is used to recognize the emotional information of voice by speech recognition technology.
Alternatively, the formula of judgment models is as follows：
Wherein, w is target classification word in context window, and c is the input vocabulary in situational meaning；K is existing for negative word remittance abroad Probability；Sim represents similarity cosine value, and σ is Dynamic gene.
Database Unit, proposes that problem and the answer knowledge material of answer set up knowledge for collecting and arranging each user Storehouse, to story extraction candidate keywords, and the content to keyword carries out handmarking, to carry out follow-up keyword rope Draw；It is problem knowledge storehouse and answer knowledge base by Knowledge base partition, is used to deposit each of user's proposition wherein in problem knowledge storehouse The demand of kind and problem, there are problems that and the demand in problem knowledge storehouse and the answer content for matching, answer in answer knowledge base Thinking and answer detailed process；And the association that question and answer matching degree is assessed is set up between problem knowledge storehouse and answer knowledge base, close The matching degree being combined between metric question and answer, and by this process in combination with follow-up user satisfaction is fed back, It is provided commonly for the optimization to knowledge base, and the renewal to related content in problem knowledge storehouse and answer knowledge base and optimization.
Social networks describes unit, for obtaining interrelated between the user in specific region and influencing each other and use The different social role of performer between family, so that it is determined that each user each has different user property and user task, And the interaction between user constitutes respective customer relationship and social circle.Alternatively, customer relationship and social circle can draw Graduation, the user in different grades of social circle has different information browse authorities.
Modified of the present invention based on computer cloud data deep learning intelligent response system in, it include as Lower unit：
Statistical analysis unit, for according to the description information of the trajectory segment of user, each user in Preset Time each Respective customer relationship and social circle's life are constituted with the interaction between different user property and user task, and user Into and preserve the stroke rule information table of user；Include user within each time period in the stroke rule information table of user The interest of zone of action, activity item and user.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data, the map Information portrayal unit is additionally operable to when the real-time position information of positioning unit transmission meets the point of interest in specific region, and this is emerging Interest point is sent to statistical analysis unit.
Statistical analysis unit is judged in the time point in point of interest item whether to be handled；There is backlog When, sent to information receiving unit with the interest point name and backlog.
Information receiving unit through Chinese word segmentation, semantic analysis and syntactic analysis text mining and process, and will process Information afterwards is sent to Database Unit.
The answer content matched with problem, answer thinking and answer detailed process are pushed and shown by Database Unit.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data,
The processing authority of the backlog of user is prestored in social networks describes unit, processing authority is divided into certainly Oneself processes and allows specific user's alternate process in social circle.Implement the present embodiment, the use of user can be greatly improved just Profit.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data,
Statistical analysis unit there is backlog in certain time period for user and user is in backlog Zone of action outside when, judge that whether the backlog is the pending thing for allowing specific user's alternate process in social circle , if it is the backlog is sent into user social contact circle corresponding other users.Embodiment the present embodiment, is carrying While high convenience for users, security is also improved.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data,
Matching degree between Database Unit vacuum metrics question and answer, and this process is satisfied with follow-up user Degree feedback combines, and is provided commonly for the optimization to knowledge base, and to related content in problem knowledge storehouse and answer knowledge base more Newly include with optimization：
Matching mapping relations will be set up between question and answer after question and answer matching degree evaluation process, and to candidate answers Ranking is carried out with the matching degree of problem, is supplied to user to be selected several candidate answers in the top, while allowing Answer of the user to problem is evaluated, and the result of evaluation is fed back in time into question answering system carries out the amendment of question and answer matching degree Replacement with knowledge in knowledge base and renewal.
In deep learning intelligent response system of the modified of the present invention based on computer cloud data,
Mapping relations matrix M in Database Unit between problem knowledge storehouse and key to the issue wordq,kIt is expressed as follows：
Wherein Rq→kRepresent mapping relations therebetween, Qi(i=1...n) problem of representation and its number, Kj(j= 1...m) problem of representation keyword and its number；One problem can be disassembled and is been described by for multiple keywords, while one Keyword can also apply in multiple problems；Tij(i=1...n, j=1...m) problem of representation i is by key to the issue word j institutes group Into content of text；
Mapping relations matrix M in Database Unit between key to the issue word and answer keywordk,k’It is expressed as follows：
Wherein Rk→k'Represent mapping relations therebetween, Ki(i=1...n) problem of representation keyword and its number, K'j (j=1...m) answer keyword and its number are represented；One key to the issue word can correspond to multiple problem answers keywords, Simultaneously an answer keyword can also apply in multiple key to the issue words；Tij(i=1...n, j=1...m) problem of representation The content of text that keyword i is answered by answer keyword j；
Mapping relations matrix M in Database Unit between answer keyword and answerk’,aIt is expressed as follows：
Wherein Rk'→aRepresent mapping relations therebetween, K'i(i=1...n) answer keyword and its number, A are representedj (j=1...m) answer and its number are represented；One answer keyword can be to describe multiple answers, while an answer Can be described with multiple answer keywords；Tij(i=1...n, j=1...m) represents that answer keyword i enters to answer j The content of text of row description；
Mapping relations matrix M in Database Unit between answer and answer rankinga,rankIt is expressed as follows：
Wherein Ra→rankRepresent mapping relations therebetween, Ai(i=1...n) answer and its number are represented,Represent the ranking of answer i；
Mapping relations matrix M in Database Unit between answer and user satisfactiona,usIt is expressed as follows：
WhereinRepresent mapping relations therebetween, Ai(i=1...n) answer and its number, U are representeds,j(j= 1...m) user satisfaction and its number are represented；One answer can be evaluated by multiple users, can possess multiple users Satisfaction, while a user can also be evaluated multiple answers, provides respectively the customer satisfaction evaluation of multiple answers； Pij(i=1...n, j=1...m) represents the user satisfaction that answer i is evaluated by user j.
Deep learning intelligent response system and existing skill of the modified of present invention offer based on computer cloud data is provided Art is compared and had the advantages that：For aiding in specific region, the new user for adding (such as teaches (such as in campus) Teacher and student etc.) faster be familiar with and adapt to environment (University Environments and cultural atmosphere of such as user place universities and colleges), preferably with Friend relation is set up between other users, and the artificial intelligence knowledge base set up by situation key element is movable crowd in specific region Daily life preferably service.So that user faster, is more preferably familiar with specific region internal and external environment, hardware/software infrastructure, area Domain culture etc., and systematicness is carried out with question and answer mode represent with a series of.
It is understood that for the person of ordinary skill of the art, can be done with technology according to the present invention design Go out other various corresponding changes and deformation, and all these changes and deformation should all belong to the protection model of the claims in the present invention Enclose.

Claims (6)

1. a kind of modified is based on the deep learning intelligent response system of computer cloud data, it is characterised in that it includes as follows Unit：
Positioning unit, for positioning to user's real time position, by real-time position information cartographic information delineation unit is sent to；
Cartographic information delineation unit, for receiving the real-time position information of positioning unit transmission, and real-time position information is formed and used The action trail at family, the action trail of user is segmented, and trajectory segment is described；Based on user in Preset Time The description information of trajectory segment delimit User Activity region；It is additionally operable to depict the complete map of specific region in advance, and Detailed each point of interest marked in the specific region on map, is a certain special interests in the real-time position information of user During point, the relevant information of the particular point of interest is pushed；
Information receiving unit, for the text query or speech query information of receiving user's input, for text query information when, Through the text mining and process of Chinese word segmentation, semantic analysis and syntactic analysis, and the information after process is sent to into database Unit；For speech query information when, first speech query information is identified as into text query information, and through Chinese word segmentation, language Justice analysis and the text mining and process of syntactic analysis, and the information after process is sent to into Database Unit；Looking into for text During inquiry information, the information after process is sent to into Emotion identification unit, for speech query information when, while by the letter after process Breath and speech query information are sent to Emotion identification unit；
Emotion identification unit, the information for being sent according to information receiving unit judges the emotional state of user, the mood of user State is divided into general, low, sad, happiness；For it is low or sad when, the relevant information of particular point of interest is pushed into intensity and is adjusted Low instruction is sent to cartographic information delineation unit；
The Emotion identification unit includes following word Emotion identification subelement and voice mood identification subelement：
Word Emotion identification subelement includes that classification storage represents general, low, sad, glad standard vocabulary；
By classifier functions the vocabulary of test input is trained and is categorized in each classification, and according to the result of classification Set up judgment models；
The information of reception is matched with each single linguistic context of target word in standard vocabulary, semantic contrast letter is caught Breath, and the mood of Word message is judged to acquired semantic comparative information；
Voice mood identification subelement identification subelement is used to recognize the emotional information of voice by speech recognition technology；
Database Unit, proposes that problem and the answer knowledge material of answer set up knowledge base for collecting and arranging each user, To story extraction candidate keywords, and the content to keyword carries out handmarking, to carry out follow-up keyword index；Will Knowledge base partition is problem knowledge storehouse and answer knowledge base, is used to deposit the various demands of user's proposition wherein in problem knowledge storehouse And problem, there are problems that in answer knowledge base with problem knowledge storehouse in demand and match answer content, answer thinking and Answer detailed process；And the association that question and answer matching degree is assessed is set up between problem knowledge storehouse and answer knowledge base, associating is used for Matching degree between metric question and answer, and by this process in combination with follow-up user satisfaction is fed back, it is common to use In the optimization to knowledge base, and the renewal to related content in problem knowledge storehouse and answer knowledge base and optimization；
Social networks describes unit, for obtain between the user in specific region it is interrelated and influence each other and user it Between the different social role of performer, so that it is determined that each user each has different user property and user task, and Interaction between user constitutes respective customer relationship and social circle；
Statistical analysis unit, for each being had according to the description information of the trajectory segment of user, each user in Preset Time Different user properties and the interaction between user task, and user constitutes respective customer relationship and social circle generates simultaneously Preserve the stroke rule information table of user；Include activity of the user within each time period in the stroke rule information table of user The interest in region, activity item and user.
2. modified as claimed in claim 1 is based on the deep learning intelligent response system of computer cloud data, and its feature exists In the cartographic information delineation unit is additionally operable to the interest that real-time position information meets in specific region sent in positioning unit During point, the point of interest is sent to statistical analysis unit；
Statistical analysis unit is judged in the time point in point of interest item whether to be handled；When there is backlog, Sent to information receiving unit with the interest point name and backlog；
Information receiving unit through Chinese word segmentation, semantic analysis and syntactic analysis text mining and process, and by after process Information is sent to Database Unit；
The answer content matched with problem, answer thinking and answer detailed process are pushed and shown by Database Unit.
3. modified as claimed in claim 2 is based on the deep learning intelligent response system of computer cloud data, and its feature exists In,
The processing authority of the backlog of user is prestored in social networks describes unit, processing authority is divided at oneself Specific user's alternate process in reason and permission social circle.
4. modified as claimed in claim 3 is based on the deep learning intelligent response system of computer cloud data, and its feature exists In,
Statistical analysis unit is used for user and there is backlog in certain time period and work of the user in backlog When outside dynamic region, judge whether the backlog is the backlog for allowing specific user's alternate process in social circle, If it is the backlog is sent into user social contact circle corresponding other users.
5. modified as claimed in claim 1 is based on the deep learning intelligent response system of computer cloud data, and its feature exists In,
Matching degree between Database Unit vacuum metrics question and answer, and this process is anti-with follow-up user satisfaction Feedback combines, and is provided commonly for the optimization to knowledge base, and the renewal to related content in problem knowledge storehouse and answer knowledge base with Optimization includes：
After question and answer matching degree evaluation process will between question and answer set up matching mapping relations, and to candidate answers with ask The matching degree of topic carries out ranking, is supplied to user to be selected several candidate answers in the top, while allowing user Answer to problem is evaluated, and the result of evaluation is fed back in time into question answering system is carried out the amendment of question and answer matching degree and known Know the replacement of knowledge and renewal in storehouse.
6. modified as claimed in claim 1 is based on the deep learning intelligent response system of computer cloud data, and its feature exists In,
Mapping relations matrix M in Database Unit between problem knowledge storehouse and key to the issue wordq,kIt is expressed as follows：
M q , k = K m T 1 m T 2 m T 3 m ... T n m ... ... ... ... ... ... K 3 T 13 T 23 T 33 ... T n 3 K 2 T 12 T 22 T 32 ... T n 2 K 1 T 11 T 21 T 31 ... T n 1 R q → k Q 1 Q 2 Q 3 ... Q n
Wherein Rq→kRepresent mapping relations therebetween, Qi(i=1...n) problem of representation and its number, Kj(j=1...m) table Show key to the issue word and its number；One problem can be disassembled and is been described by for multiple keywords, while a keyword During multiple problems can be applied to；TijThe text that (i=1...n, j=1...m) problem of representation i is made up of key to the issue word j Content；
Mapping relations matrix M in Database Unit between key to the issue word and answer keywordk,k’It is expressed as follows：
M k , k ′ = K ′ m T 1 m T 2 m T 3 m ... T n m ... ... ... ... ... ... K ′ 3 T 13 T 23 T 33 ... T n 3 K ′ 2 T 12 T 22 T 32 ... T n 2 K ′ 1 T 11 T 21 T 31 ... T n 1 R q → k ′ K 1 K 2 K 3 ... K n
Wherein Rk→k'Represent mapping relations therebetween, Ki(i=1...n) problem of representation keyword and its number, K'j(j= 1...m) answer keyword and its number are represented；One key to the issue word can correspond to multiple problem answers keywords, while One answer keyword can also apply in multiple key to the issue words；Tij(i=1...n, j=1...m) problem of representation is crucial The content of text that word i is answered by answer keyword j；
Mapping relations matrix M in Database Unit between answer keyword and answerk’,aIt is expressed as follows：
M k ′ a = A m T 1 m T 2 m T 3 m ... T n m ... ... ... ... ... ... A 3 T 13 T 23 T 33 ... T n 3 A 2 T 12 T 22 T 32 ... T n 2 A 1 T 11 T 21 T 31 ... T n 1 R k ′ → a K ′ 1 K ′ 2 K ′ 3 ... K ′ n
Wherein Rk'→aRepresent mapping relations therebetween, K'i(i=1...n) answer keyword and its number, A are representedj(j= 1...m) answer and its number are represented；One answer keyword can be to describe multiple answers, while an answer can also It is described with multiple answer keywords；Tij(i=1...n, j=1...m) represents that answer keyword i is retouched to answer j The content of text stated；
Mapping relations matrix M in Database Unit between answer and answer rankinga,rankIt is expressed as follows：
M a , r a n k = R a n k R A 1 R A 2 R A 3 ... R A n R a → r a n k A 1 A 2 A 3 ... A n
Wherein Ra→rankRepresent mapping relations therebetween, Ai(i=1...n) answer and its number are represented, Represent the ranking of answer i；
Mapping relations matrix M in Database Unit between answer and user satisfactiona,usIt is expressed as follows：
M a , u s = U s , m P 1 m P 2 m P 3 m ... P n m ... ... ... ... ... ... U s , 3 P 13 P 23 P 33 ... P n 3 U s , 2 P 12 P 22 P 32 ... P n 2 U s , 1 P 11 P 21 P 31 ... P n 1 R a → u s A 1 A 2 A 3 ... A n
WhereinRepresent mapping relations therebetween, Ai(i=1...n) answer and its number, U are representeds,j(j=1...m) Represent user satisfaction and its number；One answer can be evaluated by multiple users, can possess multiple user satisfaction, Simultaneously a user can also be evaluated multiple answers, and the customer satisfaction evaluation of multiple answers is given respectively；Pij(i= 1...n, j=1...m) represent the user satisfaction that answer i is evaluated by user j.
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Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107301168A (en) * 2017-06-01 2017-10-27 深圳市朗空亿科科技有限公司 Intelligent robot and its mood exchange method, system
CN107590274A (en) * 2017-09-27 2018-01-16 合肥博力生产力促进中心有限公司 A kind of intelligent Answer System for patent service
CN108038230A (en) * 2017-12-26 2018-05-15 北京百度网讯科技有限公司 Information generating method and device based on artificial intelligence
CN108717413A (en) * 2018-03-26 2018-10-30 浙江大学 It is a kind of based on the assumption that property semi-supervised learning Opening field answering method
CN108958247A (en) * 2018-07-02 2018-12-07 深圳市益鑫智能科技有限公司 A kind of guided robot
CN109145092A (en) * 2017-06-15 2019-01-04 阿里巴巴集团控股有限公司 A kind of database update, intelligent answer management method, device and its equipment
CN109145084A (en) * 2018-07-10 2019-01-04 阿里巴巴集团控股有限公司 Data processing method, data processing equipment and server
CN109299240A (en) * 2018-09-30 2019-02-01 北京小谛机器人科技有限公司 Chat robots information interacting method and device
CN109598658A (en) * 2018-11-30 2019-04-09 暨南大学 A kind of newborn service system and method for entering a school
CN109815322A (en) * 2018-12-27 2019-05-28 东软集团股份有限公司 Method, apparatus, storage medium and the electronic equipment of response
CN109997127A (en) * 2017-06-05 2019-07-09 谷歌有限责任公司 Logic section data processing system
CN110046903A (en) * 2019-01-15 2019-07-23 阿里巴巴集团控股有限公司 Data processing method, system, server and readable storage medium storing program for executing
CN110727793A (en) * 2018-06-28 2020-01-24 百度在线网络技术（北京）有限公司 Method, device, terminal and computer readable storage medium for area identification
WO2021056127A1 (en) * 2019-09-23 2021-04-01 Beijing Didi Infinity Technology And Development Co., Ltd. Systems and methods for analyzing sentiment
CN114706795A (en) * 2022-06-07 2022-07-05 湖南智擎科技有限公司 Turing test method, device and system for SaaS artificial intelligence application

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103823844A (en) * 2014-01-26 2014-05-28 北京邮电大学 Question forwarding system and question forwarding method on the basis of subjective and objective context and in community question-and-answer service
CN104699708A (en) * 2013-12-09 2015-06-10 中国移动通信集团北京有限公司 Self-learning method and device for customer service robot
CN104836720A (en) * 2014-02-12 2015-08-12 北京三星通信技术研究有限公司 Method for performing information recommendation in interactive communication, and device
CN104834679A (en) * 2015-04-14 2015-08-12 苏州大学 Representation and inquiry method of behavior track and device therefor
CN104933157A (en) * 2015-06-26 2015-09-23 百度在线网络技术（北京）有限公司 Method and device used for obtaining user attribute information, and server
CN105023047A (en) * 2014-04-15 2015-11-04 上海莫言信息科技有限公司 Tourism service individuation online ordering realization method based on travel itinerary

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104699708A (en) * 2013-12-09 2015-06-10 中国移动通信集团北京有限公司 Self-learning method and device for customer service robot
CN103823844A (en) * 2014-01-26 2014-05-28 北京邮电大学 Question forwarding system and question forwarding method on the basis of subjective and objective context and in community question-and-answer service
CN104836720A (en) * 2014-02-12 2015-08-12 北京三星通信技术研究有限公司 Method for performing information recommendation in interactive communication, and device
CN105023047A (en) * 2014-04-15 2015-11-04 上海莫言信息科技有限公司 Tourism service individuation online ordering realization method based on travel itinerary
CN104834679A (en) * 2015-04-14 2015-08-12 苏州大学 Representation and inquiry method of behavior track and device therefor
CN104933157A (en) * 2015-06-26 2015-09-23 百度在线网络技术（北京）有限公司 Method and device used for obtaining user attribute information, and server

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107301168A (en) * 2017-06-01 2017-10-27 深圳市朗空亿科科技有限公司 Intelligent robot and its mood exchange method, system
CN109997127B (en) * 2017-06-05 2023-07-18 谷歌有限责任公司 Logical segment data processing system
CN109997127A (en) * 2017-06-05 2019-07-09 谷歌有限责任公司 Logic section data processing system
CN109145092A (en) * 2017-06-15 2019-01-04 阿里巴巴集团控股有限公司 A kind of database update, intelligent answer management method, device and its equipment
CN109145092B (en) * 2017-06-15 2022-05-17 阿里巴巴集团控股有限公司 Database updating and intelligent question and answer management method, device and equipment
CN107590274A (en) * 2017-09-27 2018-01-16 合肥博力生产力促进中心有限公司 A kind of intelligent Answer System for patent service
CN108038230A (en) * 2017-12-26 2018-05-15 北京百度网讯科技有限公司 Information generating method and device based on artificial intelligence
CN108717413A (en) * 2018-03-26 2018-10-30 浙江大学 It is a kind of based on the assumption that property semi-supervised learning Opening field answering method
CN108717413B (en) * 2018-03-26 2021-10-08 浙江大学 Open field question-answering method based on hypothetical semi-supervised learning
CN110727793A (en) * 2018-06-28 2020-01-24 百度在线网络技术（北京）有限公司 Method, device, terminal and computer readable storage medium for area identification
CN108958247A (en) * 2018-07-02 2018-12-07 深圳市益鑫智能科技有限公司 A kind of guided robot
CN109145084B (en) * 2018-07-10 2022-07-01 创新先进技术有限公司 Data processing method, data processing device and server
CN109145084A (en) * 2018-07-10 2019-01-04 阿里巴巴集团控股有限公司 Data processing method, data processing equipment and server
CN109299240A (en) * 2018-09-30 2019-02-01 北京小谛机器人科技有限公司 Chat robots information interacting method and device
CN109598658A (en) * 2018-11-30 2019-04-09 暨南大学 A kind of newborn service system and method for entering a school
CN109815322A (en) * 2018-12-27 2019-05-28 东软集团股份有限公司 Method, apparatus, storage medium and the electronic equipment of response
CN109815322B (en) * 2018-12-27 2021-03-12 东软集团股份有限公司 Response method and device, storage medium and electronic equipment
CN110046903A (en) * 2019-01-15 2019-07-23 阿里巴巴集团控股有限公司 Data processing method, system, server and readable storage medium storing program for executing
WO2021056127A1 (en) * 2019-09-23 2021-04-01 Beijing Didi Infinity Technology And Development Co., Ltd. Systems and methods for analyzing sentiment
CN114706795A (en) * 2022-06-07 2022-07-05 湖南智擎科技有限公司 Turing test method, device and system for SaaS artificial intelligence application

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