CN117540028A - Platform for intelligent interaction of science popularization knowledge - Google Patents
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Abstract
The invention relates to the technical field of knowledge interaction platforms, in particular to a platform for intelligent interaction of science popularization knowledge, which comprises: the user input module is used for receiving inquiry or interaction instructions of a user; knowledge database for storing science popularization knowledge and related information; the content analysis and integration module is used for retrieving related information from the knowledge database according to the user inquiry or interaction instruction and integrating data; the interaction module allows the user to interact with the platform in multiple modes; the content association module analyzes the relation between the integrated data and other related data to form a comprehensive information chain; and the output module is used for displaying the comprehensive information chain formed by integrating the content association modules to the user in a visual mode. The invention improves the information retrieval efficiency of the user and enhances the understanding and absorption of knowledge.
Description
Technical Field
The invention relates to the technical field of knowledge interaction platforms, in particular to a platform for intelligent interaction of science popularization knowledge.
Background
In the information age, the spreading of science popularization knowledge is particularly important, but the existing science popularization knowledge interaction platform has several problems generally. Firstly, the structuring degree of knowledge organization is insufficient, so that a user is difficult to quickly find required accurate information during retrieval; secondly, the relevance display among knowledge points is not visual enough, and a systematic cognitive structure is difficult to form; finally, the user interaction experience is poor, and flexible and various interaction modes cannot be provided to meet the requirements of different users. Therefore, a new intelligent interaction platform is needed to solve the above problems, improve the user experience, and increase the propagation efficiency and effect of popular science knowledge.
The existing popular science knowledge propagation mode is mostly dependent on the traditional linear text or a simple graphical interface, and lacks enough interactivity and depth association presentation of knowledge. Furthermore, users often cannot obtain efficient guidelines to explore the inherent links between knowledge in the face of large amounts of information. While some existing platforms attempt to introduce search engines and recommendation systems to aid in knowledge discovery, these systems often do not have the ability to deeply customize for popular knowledge features, failing to meet the specific needs of users in acquiring, understanding, and applying popular knowledge.
In view of the above, the invention provides an innovative 'platform for intelligent interaction of science popularization knowledge', which aims to establish a structured, highly interactive and highly user-friendly science popularization knowledge interaction environment through advanced information processing technology, user interaction design and visual display technology, and effectively overcomes the defects of the traditional science popularization interaction platform in knowledge structuring, deep association display and user interaction experience, and promotes efficient propagation and popularization of science popularization knowledge.
Disclosure of Invention
Based on the above purpose, the invention provides a platform for intelligent interaction of science popularization knowledge.
A platform for intelligent interaction of science popularization knowledge, the platform comprising:
the user input module is used for receiving inquiry or interaction instructions of a user;
knowledge database for storing science popularization knowledge and related information;
the content analysis and integration module is used for retrieving related information from the knowledge database according to the user inquiry or interaction instruction and integrating data;
the interaction module allows the user to interact with the platform in multiple modes;
the content association module analyzes the relation between the integrated data and other related data to form a comprehensive information chain;
the output module is used for displaying the comprehensive information chain formed by integrating the content association modules to the user in a visual mode,
the user inquiry or interaction instruction received by the user input module is transmitted to the content analysis and integration module; the content analysis and integration module retrieves related information from the knowledge database and transmits the retrieval result to the content association module; after receiving the search result of the content analysis and integration module, the content association module analyzes the relation between the search result and related data in the knowledge database to form an information chain, and transmits the information chain to the output module; and the output module receives the information chain formed by the content association module and displays the information chain to a user in a visual mode.
Further, the user input module is based on a multi-modal recognition process, wherein the multi-modal recognition comprises a voice recognition sub-module and a text recognition sub-module which are respectively used for processing voice information input by a user through a microphone and text information input by a keyboard or a touch screen;
in the voice recognition sub-module, noise filtering and voice signal digitizing are carried out on voice information input by a user, and then the digitized voice signal is converted into text information;
in the text recognition sub-module, carrying out grammar analysis and keyword extraction on text information input by a user so as to recognize query or interaction intention of the user;
and transmitting the processed text information to a content analysis and integration module for subsequent information retrieval and data integration processing.
Further, the knowledge database adopts a hierarchical data structure, and each layer corresponds to a specific classification of science popularization knowledge and is subdivided into more specific sub-classifications in each classification;
each science popularization knowledge point exists in a database in the form of data records, and each data record comprises a description of the knowledge point, a related image, a video, a data table, a reference source, a cross-referenced related knowledge point and a position of the cross-referenced related knowledge point in a knowledge graph;
the knowledge point data record comprises metadata fields for recording the update time, access frequency and user evaluation information of the knowledge point so as to support the efficient operation of the content analysis and integration module and the content association module;
the knowledge database also comprises an index system for positioning knowledge points related to the user query instruction, and the index system supports fuzzy query, and the knowledge database adopts a redundant storage mechanism, so that the safety and high availability of data are ensured, and uninterrupted service can be provided when any part of data is damaged.
Further, the content parsing and integrating module specifically includes:
after receiving the query or interactive text instruction processed and converted by the user input module, starting a natural language processing engine, wherein the engine is pre-provided with a set of semantic analysis rules, core keywords in the user instruction and corresponding operation intentions thereof can be extracted, the keywords and the user intentions extracted by the natural language processing engine are converted into database query commands, and the commands correspond to knowledge points stored in a database by utilizing an index mechanism built in a knowledge database so as to retrieve related data records;
in the data retrieval stage, a content analysis and integration module invokes a data association engine which operates in a back-end service and is dedicated to analyzing the correlation between metadata and knowledge points contained in a data record, identifying and extracting additional knowledge points associated with a core knowledge point, wherein the data association engine carries out personalized recommendation of the knowledge points by combining historical query data of a user based on a reference source and cross-reference information of the data record, and the recommendation comprises user query habits, preference setting and historical interaction records;
the content parsing and integration module aggregates the retrieved data records and their associated knowledge point data and constructs a multi-dimensional information model detailing the relationships and hierarchy among the knowledge points and their locations in the overall knowledge system.
Further, the multi-dimensional information model is constructed based on a hierarchical data processing flow, firstly, a unified knowledge representation protocol is defined, and the knowledge point data structure is defined by the protocol, and comprises identifiers, names, definitions, associated knowledge point lists and attribute fields;
the method comprises the steps that a graph database is adopted to store and manage knowledge point data, each knowledge point is used as a node, the relationship of the knowledge point is used as an edge, the edge and the node can be attached with attributes, the attributes comprise weight, category and state information, a knowledge point relationship analysis engine is preset, the engine automatically recognizes and infers the hidden relationship among the knowledge points according to a predefined knowledge point relationship template, the hierarchy and the position of the knowledge points are determined through a dynamic calculation module, and the module scores the importance of the knowledge points in real time according to the interaction history of users, the interaction data statistical result of all users, the update frequency and the reference frequency dynamic index of the knowledge points;
the multidimensional information model is expressed as a visual knowledge graph which is dynamically generated by a front-end rendering engine and displays a complex knowledge structure in a 3D or 2D form.
Further, the interaction module specifically includes:
the interactive module is provided with a plurality of interactive interfaces, including text input, voice command, touch operation and gesture recognition, so as to meet the operation habits and interactive preferences of different users, and when the users provide inquiry or interactive instructions through the text input interfaces, the interactive module directly transmits text contents to the user input module so as to carry out subsequent analysis and processing;
if the user adopts a voice command to interact with the platform, the interaction module activates a built-in voice recognition engine, and the engine can convert voice signals into text data and process spoken language inquiry of the user so as to perform a natural language interaction mode;
under the condition of touch operation or gesture recognition, capturing a touch path and gesture actions of a user by an interaction module through a touch screen or a camera, recognizing instruction intention of the user through a preset touch or gesture analysis algorithm, and converting an analysis result into a corresponding instruction format;
the interaction module also provides visual interaction elements, and a user sends out a query request or performs setting adjustment by clicking or sliding the elements so as to interact in a non-text mode.
Further, the content association module comprises a relationship calibration unit, wherein the relationship calibration unit is provided with a preset data relationship dictionary, a series of data relationship types and corresponding characteristic parameters thereof are defined in the dictionary, the relationship calibration unit performs characteristic mapping operation on the integrated data, namely, the corresponding elements in the data content are matched with the characteristic parameters in the data relationship dictionary through a set of pre-coded relationship characteristic recognition algorithm so as to calibrate the accurate relationship types among the data, the relationship calibration unit also converts the calibration result into a relationship vector through vectorization processing, and the relationship vector contains the determined relationship types, relationship strength and directionality so as to provide basic data for the follow-up construction of an accurate information chain;
the content association module also comprises a relation braiding unit which is responsible for constructing an information chain among the data nodes according to the relation vector, wherein the information chain connects different data nodes according to relation strength and directivity through a path and sequence determined by an algorithm to form an ordered knowledge structure, the relation braiding unit dynamically adjusts a link structure in the construction process of the information chain through a decision logic engine, and the engine optimizes the correlation and personalized expression of the information chain according to user interaction feedback and user portrait data;
the content association module stores the constructed information chain in a structured data storage unit, the structured data storage unit optimizes a data access path for efficient retrieval, ensures that the ordered and highly relevant information chain can be quickly retrieved when a user inquires, and when the user inquires, the content association module calls a link display unit, extracts the corresponding information chain from the structured data storage unit according to the inquiry requirement and preset display logic of the user and displays the information chain through a user interface.
Further, the relation braiding unit adopts a graph-based ordering algorithm, and for each node v in the graph, a calculation formula is defined based on the PageRank algorithm to calculate the weight PR (v) of the node, wherein the weight is used for representing the importance of the node, and the calculation formula is as follows:
wherein: PR (v) is the PageRank value of node v, d is the damping factor, set to 0.85, representing the probability of a transition from one node to another, N is the total number of nodes in the graph, B v Is the set of all nodes pointing to node v, L (u) is the number of links pointed to by node u;
in order to adapt to the construction of an information chain, the PageRank formula is subjected to fine tuning modification to contain the relation strength and the directionality, the modified algorithm is called a knowledge chain PageRank, and the formula is as follows:
wherein: KPR (v) is the knowledge chain PageRank value of the knowledge point v, alpha is a constant term used for ensuring that at least part of ranking is not referenced by other pages, the ranking is set to be a value equivalent to (1-d)/N in the PageRank algorithm, w (u, v) is the relation strength from the node u to the node v, a numerical value determined by a relation calibration unit represents the relation strength between two knowledge points, and C (u) is the sum of the outgoing chain relation strength of the node u and is used for normalizing the contribution degree of different nodes.
Further, the output module displays the comprehensive information chain formed by integrating the content association modules in a visual mode, and specifically comprises a data visual processing unit which is responsible for converting the information chain data in the structured data storage unit into visual elements on a graphical user interface;
the data visualization processing unit utilizes a pre-designed knowledge graph visualization template which defines the visual representation of the data nodes and the visual representation of the data relationships;
the visualization processing unit applies a weight value obtained by a knowledge chain PageRank algorithm to determine the position priority and the display size of corresponding knowledge points in a visualization map, and the higher the weight, the larger the size of the nodes in the map, the more central the position, so as to highlight important knowledge points;
the relation strength between the data nodes is represented by the thickness and the color depth of the connecting lines, and the thicker the line and the darker the color between the two nodes with stronger relation, the more visual the association strength between the knowledge points.
The invention has the beneficial effects that:
the method has remarkable innovation and advantages in the aspects of information retrieval and content display, firstly, the platform can finely and comprehensively classify popular science knowledge by constructing a multidimensional information model, the structuring degree of knowledge is remarkably improved, meanwhile, the platform can accurately analyze and calculate the association strength and the hierarchical relationship between knowledge points by utilizing a content association module and a knowledge chain PageRank algorithm, so that a highly relevant and logically clear knowledge information chain is provided for a user, the information retrieval efficiency of the user is improved, and the understanding and absorption of knowledge are enhanced.
The design of the platform effectively solves the limitation of the prior art in knowledge transmission and intelligent interaction, ensures the real-time updating and dynamic display of knowledge content through the application of the real-time data rendering engine and the dynamic knowledge graph, provides continuously updated knowledge information and good interactive experience for users, promotes the wide transmission of science popularization knowledge, increases the attraction and the intelligibility of knowledge information, and is particularly helpful for users to quickly obtain deep understanding for complex or emerging science popularization knowledge subjects.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram illustrating the operation of a platform function module according to an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1, a platform for intelligent interaction of science popularization knowledge, the platform comprising:
the user input module is used for receiving inquiry or interaction instructions of a user;
knowledge database for storing science popularization knowledge and related information;
the content analysis and integration module is used for retrieving related information from the knowledge database according to the user inquiry or interaction instruction and integrating data;
the interaction module allows the user to interact with the platform in multiple modes;
the content association module analyzes the relation between the integrated data and other related data to form a comprehensive information chain;
the output module is used for displaying the comprehensive information chain formed by integrating the content association modules to the user in a visual mode,
the user inquiry or interaction instruction received by the user input module is transmitted to the content analysis and integration module; the content analysis and integration module retrieves related information from the knowledge database and transmits the retrieval result to the content association module; after receiving the search result of the content analysis and integration module, the content association module analyzes the relation between the search result and the related data in the knowledge database to form an information chain, and transmits the information chain to the output module; and the output module receives the information chain formed by the content association module and displays the information chain to a user in a visual mode.
The user input module is based on a multi-modal recognition process, and the multi-modal recognition comprises a voice recognition sub-module and a text recognition sub-module which are respectively used for processing voice information input by a user through a microphone and text information input by a keyboard or a touch screen;
in the voice recognition sub-module, noise filtering and voice signal digitizing are carried out on voice information input by a user, and then the digitized voice signal is converted into text information;
in the text recognition sub-module, carrying out grammar analysis and keyword extraction on text information input by a user so as to recognize query or interaction intention of the user;
and transmitting the processed text information to a content analysis and integration module for subsequent information retrieval and data integration processing.
Through the steps, the user input module not only can receive and accurately understand query or interaction instructions which are proposed by a user in various modes, but also can ensure that the instructions can be accurately understood and effectively processed by other modules in the platform, thereby providing more accurate and rich interaction experience.
The knowledge database adopts a layered data structure, and each layer corresponds to a specific classification of science popularization knowledge, such as natural science, engineering technology, medical health and the like, and is subdivided into more specific sub-classifications in each classification;
each science popularization knowledge point exists in a database in the form of data records, and each data record comprises a description of the knowledge point, a related image, a video, a data table, a reference source, a cross-referenced related knowledge point and a position of the cross-referenced related knowledge point in a knowledge graph;
the knowledge point data record comprises metadata fields for recording the update time, access frequency and user evaluation information of the knowledge point so as to support the efficient operation of the content analysis and integration module and the content association module;
the knowledge database also comprises an index system for positioning knowledge points related to the user query instruction, and the index system supports fuzzy query, and adopts a redundant storage mechanism to ensure the safety and high availability of data, so that uninterrupted service can be provided when any part of data is damaged;
through the database structure and the management mode, the knowledge database can effectively organize, store and rapidly search science popularization knowledge and related information, and the user can obtain rapid, accurate and content-rich feedback during query.
The content analysis and integration module specifically comprises:
after receiving the query or interactive text instruction processed and converted by the user input module, starting a natural language processing engine, wherein the engine is pre-provided with a set of semantic analysis rules, core keywords in the user instruction and corresponding operation intentions thereof can be extracted, the keywords and the user intentions extracted by the natural language processing engine are converted into database query commands, and the commands correspond to knowledge points stored in a database by utilizing an index mechanism built in a knowledge database so as to retrieve related data records;
in the data retrieval stage, a content analysis and integration module invokes a data association engine which operates in a back-end service and is dedicated to analyzing the correlation between metadata and knowledge points contained in a data record, identifying and extracting additional knowledge points associated with a core knowledge point, wherein the data association engine carries out personalized recommendation of the knowledge points by combining historical query data of a user based on a reference source and cross-reference information of the data record, and the recommendation comprises user query habits, preference setting and historical interaction records;
the content analysis and integration module aggregates the retrieved data records and the knowledge point data associated with the retrieved data records, and constructs a multi-dimensional information model which details the relationship and the hierarchy among knowledge points and the positions of the knowledge points in the overall knowledge system;
the content analysis and integration module converts the information model into a data format which can be read and displayed by a front-end interface, such as JSON or XML, so that the front-end interface can intuitively display the information in the form of a chart, a map or an interactive list.
The construction of the multidimensional information model is based on a hierarchical data processing flow, firstly, a unified knowledge representation protocol is defined, and the knowledge point data structure is defined by the protocol and comprises identifiers, names, definitions, associated knowledge point lists and attribute fields;
the method comprises the steps of adopting a graph database to store and manage knowledge point data, wherein the graph database is naturally suitable for representing various relations among knowledge points, such as causal relations, hierarchical relations and incidence relations, each knowledge point is used as a node, the relation is used as an edge, the edge and the node can be attached with attributes including weight, category and state information, a knowledge point relation analysis engine is preset, the engine automatically recognizes and infers the hidden relations among the knowledge points according to a predefined knowledge point relation template, retrieval and recommendation precision of the associated knowledge points is enhanced, for example, through information such as common citation documents, similar attribute values or user interaction histories, the hierarchy and the position of the knowledge points are determined through a dynamic calculation module, and the module scores the importance of the knowledge points in real time according to user interaction histories, interaction data statistics results of all users, update frequency and citation frequency dynamic indexes of the knowledge points;
the multidimensional information model is expressed as a visual knowledge graph, and the graph is dynamically generated by a front-end rendering engine and displays a complex knowledge structure in a 3D or 2D form;
therefore, the construction and specific expression form of the multidimensional information model are established through the technical means, so that the knowledge representation of the intelligent interaction platform is more visual and easy to understand and operate, and an effective knowledge learning and exploring tool is provided.
The interaction module specifically comprises:
the interactive module is provided with a plurality of interactive interfaces, including text input, voice command, touch operation and gesture recognition, so as to meet the operation habits and interactive preferences of different users, and when the users provide inquiry or interactive instructions through the text input interfaces, the interactive module directly transmits text contents to the user input module so as to carry out subsequent analysis and processing;
if the user adopts a voice command to interact with the platform, the interaction module activates a built-in voice recognition engine, and the engine can convert voice signals into text data and process spoken language inquiry of the user so as to perform a natural language interaction mode;
under the condition of touch operation or gesture recognition, capturing a touch path and gesture actions of a user by an interaction module through a touch screen or a camera, recognizing instruction intention of the user through a preset touch or gesture analysis algorithm, and converting an analysis result into a corresponding instruction format;
the interaction module also provides visual interaction elements, a user sends out a query request or performs setting adjustment by clicking or sliding the elements so as to interact in a non-text mode, and for complex interaction requirements, the interaction module combines all interaction modes to allow the user to initiate a query or execute an instruction through combined operation, such as using a voice command to accurately query data in combination with touch operation;
through the steps, the interaction module provides a flexible and visual interaction environment for the user, enhances the user experience, and ensures that the user can effectively communicate and exchange with the science popularization knowledge intelligent interaction platform in various modes.
The content association module comprises a relation calibration unit, a relation vector and a relation vector, wherein the relation calibration unit is provided with a preset data relation dictionary, a series of data relation types and corresponding characteristic parameters are defined in the dictionary, the relation calibration unit performs characteristic mapping operation on integrated data, namely, matches corresponding elements in data content with the characteristic parameters in the data relation dictionary through a set of pre-coded relation characteristic recognition algorithm so as to calibrate the accurate relation types among the data, and converts a calibration result into a relation vector through vectorization processing, and the relation vector contains the determined relation types, relation strength and directivity so as to provide basic data for the follow-up construction of an accurate information chain;
the content association module also comprises a relation braiding unit which is responsible for constructing an information chain among the data nodes according to the relation vector, wherein the information chain connects different data nodes according to relation strength and directivity through a path and sequence determined by an algorithm to form an ordered knowledge structure, the relation braiding unit dynamically adjusts a link structure in the construction process of the information chain through a decision logic engine, and the engine optimizes the correlation and personalized expression of the information chain according to user interaction feedback and user portrait data;
the content association module stores the constructed information chain in a structured data storage unit, the structured data storage unit optimizes a data access path for efficient retrieval, ensures that the ordered and highly relevant information chain can be quickly retrieved when a user inquires, and when the user inquires, the content association module calls a link display unit, extracts the corresponding information chain from the structured data storage unit according to the inquiry requirement and preset display logic of the user and displays the information chain through a user interface.
The relation braiding unit adopts a graph-based ordering algorithm, and for each node v in the graph, a calculation formula is defined based on the PageRank algorithm to calculate the weight PR (v) of the node, wherein the weight is used for representing the importance of the node, and the calculation formula is as follows:
wherein: PR (v) is the PageRank value of node v, d is the damping factor, set to 0.85, representing the probability of a transition from one node to another, N is the total number of nodes in the graph, B v Is the set of all nodes pointing to node v, L (u) is the number of links pointed to by node u;
in order to adapt to the construction of an information chain, the PageRank formula is subjected to fine tuning modification to contain the relation strength and the directionality, the modified algorithm is called a knowledge chain PageRank, and the formula is as follows:
wherein: KPR (v) is the knowledge chain PageRank value of the knowledge point v, alpha is a constant term used for ensuring that at least part of ranking is not referenced by other pages, the ranking is set to be a value equivalent to (1-d)/N in the PageRank algorithm, w (u, v) is the relation strength from the node u to the node v, a numerical value determined by a relation calibration unit represents the relation strength between two knowledge points, and C (u) is the sum of the outgoing chain relation strength of the node u and is used for normalizing the contribution degree of different nodes.
The output module displays the comprehensive information chain formed by integrating the content association modules in a visual mode, and specifically comprises a data visual processing unit which is responsible for converting the information chain data in the structured data storage unit into visual elements on a graphical user interface;
the data visualization processing unit utilizes a pre-designed knowledge graph visualization template which defines the visual representation (such as color, size, shape) of the data nodes and the visual representation (such as line thickness, color, arrow) of the data relationships;
the visualization processing unit applies a weight value obtained by a knowledge chain PageRank algorithm to determine the position priority and the display size of corresponding knowledge points in a visualization map, and the higher the weight, the larger the size of the nodes in the map, the more central the position, so as to highlight important knowledge points;
the relation strength between the data nodes is represented by the thickness and the color depth of a connecting line, and the thicker the line and the darker the color between two nodes with stronger relation are, the more the association strength between knowledge points is intuitively displayed;
the output module further comprises an interaction logic control unit, and the unit dynamically adjusts the display of the visual map according to the interaction operation (such as clicking, zooming, dragging and the like) of the user, for example, clicking a certain knowledge point can expand the related sub knowledge chain, or adjust the view focus to a specific knowledge region; the visual display of the output module is not limited to static knowledge patterns, but also comprises dynamic effects such as progressive loading and relation dynamic highlighting so as to improve the interactive experience and knowledge absorption efficiency of users; when the user performs query operation, the output module invokes a real-time data rendering engine, and the engine updates the knowledge graph in real time according to the query requirement and the interaction behavior of the user, so that the user can acquire the latest information chain visual display.
Through the technical scheme, the output module can not only present complex information chain data to a user in an intuitive and dynamic mode, but also provide rich user interaction experience through the interaction logic control unit, and greatly enhance the availability of a platform and the effectiveness of knowledge propagation.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (9)
1. A platform for intelligent interaction of science popularization knowledge, the platform comprising:
the user input module is used for receiving inquiry or interaction instructions of a user;
knowledge database for storing science popularization knowledge and related information;
the content analysis and integration module is used for retrieving related information from the knowledge database according to the user inquiry or interaction instruction and integrating data;
the interaction module allows the user to interact with the platform in multiple modes;
the content association module analyzes the relation between the integrated data and other related data to form a comprehensive information chain;
the output module is used for displaying the comprehensive information chain formed by integrating the content association modules to the user in a visual mode,
the user inquiry or interaction instruction received by the user input module is transmitted to the content analysis and integration module; the content analysis and integration module retrieves related information from the knowledge database and transmits the retrieval result to the content association module; after receiving the search result of the content analysis and integration module, the content association module analyzes the relation between the search result and related data in the knowledge database to form an information chain, and transmits the information chain to the output module; and the output module receives the information chain formed by the content association module and displays the information chain to a user in a visual mode.
2. The platform for intelligent interaction of science popularization knowledge according to claim 1, wherein the user input module is based on a multi-modal recognition process, the multi-modal recognition comprises a voice recognition sub-module and a text recognition sub-module, which are respectively used for processing voice information input by a user through a microphone and text information input by a keyboard or a touch screen;
in the voice recognition sub-module, noise filtering and voice signal digitizing are carried out on voice information input by a user, and then the digitized voice signal is converted into text information;
in the text recognition sub-module, carrying out grammar analysis and keyword extraction on text information input by a user so as to recognize query or interaction intention of the user;
and transmitting the processed text information to a content analysis and integration module for subsequent information retrieval and data integration processing.
3. The platform for intelligent interaction of science popularization knowledge according to claim 2, wherein the knowledge database adopts a hierarchical data structure, each layer corresponds to a specific class of science popularization knowledge and is subdivided into more specific sub-classes in each class;
each science popularization knowledge point exists in a database in the form of data records, and each data record comprises a description of the knowledge point, a related image, a video, a data table, a reference source, a cross-referenced related knowledge point and a position of the cross-referenced related knowledge point in a knowledge graph;
the knowledge point data record comprises metadata fields for recording the update time, access frequency and user evaluation information of the knowledge point so as to support the efficient operation of the content analysis and integration module and the content association module;
the knowledge database also comprises an index system for positioning knowledge points related to the user query instruction, and the index system supports fuzzy query, and the knowledge database adopts a redundant storage mechanism, so that the safety and high availability of data are ensured, and uninterrupted service can be provided when any part of data is damaged.
4. The platform for intelligent interaction of science popularization knowledge according to claim 3, wherein the content parsing and integrating module specifically comprises:
after receiving the query or interactive text instruction processed and converted by the user input module, starting a natural language processing engine, wherein the engine is pre-provided with a set of semantic analysis rules, core keywords in the user instruction and corresponding operation intentions thereof can be extracted, the keywords and the user intentions extracted by the natural language processing engine are converted into database query commands, and the commands correspond to knowledge points stored in a database by utilizing an index mechanism built in a knowledge database so as to retrieve related data records;
in the data retrieval stage, a content analysis and integration module invokes a data association engine which operates in a back-end service and is dedicated to analyzing the correlation between metadata and knowledge points contained in a data record, identifying and extracting additional knowledge points associated with a core knowledge point, wherein the data association engine carries out personalized recommendation of the knowledge points by combining historical query data of a user based on a reference source and cross-reference information of the data record, and the recommendation comprises user query habits, preference setting and historical interaction records;
the content parsing and integration module aggregates the retrieved data records and their associated knowledge point data and constructs a multi-dimensional information model detailing the relationships and hierarchy among the knowledge points and their locations in the overall knowledge system.
5. The platform for intelligent interaction of science popularization knowledge according to claim 4, wherein the multi-dimensional information model is constructed based on hierarchical data processing flow, firstly, a unified knowledge representation protocol is defined, and the knowledge point data structure is defined by the protocol, and comprises identifiers, names, definitions, associated knowledge point lists and attribute fields;
the method comprises the steps that a graph database is adopted to store and manage knowledge point data, each knowledge point is used as a node, the relationship of the knowledge point is used as an edge, the edge and the node can be attached with attributes, the attributes comprise weight, category and state information, a knowledge point relationship analysis engine is preset, the engine automatically recognizes and infers the hidden relationship among the knowledge points according to a predefined knowledge point relationship template, the hierarchy and the position of the knowledge points are determined through a dynamic calculation module, and the module scores the importance of the knowledge points in real time according to the interaction history of users, the interaction data statistical result of all users, the update frequency and the reference frequency dynamic index of the knowledge points;
the multidimensional information model is expressed as a visual knowledge graph which is dynamically generated by a front-end rendering engine and displays a complex knowledge structure in a 3D or 2D form.
6. The platform for intelligent interaction of science popularization knowledge according to claim 5, wherein the interaction module specifically comprises:
the interactive module is provided with a plurality of interactive interfaces, including text input, voice command, touch operation and gesture recognition, so as to meet the operation habits and interactive preferences of different users, and when the users provide inquiry or interactive instructions through the text input interfaces, the interactive module directly transmits text contents to the user input module so as to carry out subsequent analysis and processing;
if the user adopts a voice command to interact with the platform, the interaction module activates a built-in voice recognition engine, and the engine can convert voice signals into text data and process spoken language inquiry of the user so as to perform a natural language interaction mode;
under the condition of touch operation or gesture recognition, capturing a touch path and gesture actions of a user by an interaction module through a touch screen or a camera, recognizing instruction intention of the user through a preset touch or gesture analysis algorithm, and converting an analysis result into a corresponding instruction format;
the interaction module also provides visual interaction elements, and a user sends out a query request or performs setting adjustment by clicking or sliding the elements so as to interact in a non-text mode.
7. The platform for intelligent interaction of science popularization knowledge according to claim 6, wherein the content association module comprises a relationship calibration unit, a relationship vector and a relationship vector, wherein the relationship calibration unit is provided with a preset data relationship dictionary, a series of data relationship types and corresponding characteristic parameters are defined in the dictionary, the relationship calibration unit performs characteristic mapping operation on the integrated data, namely, matches corresponding elements in the data content with the characteristic parameters in the data relationship dictionary through a set of pre-coded relationship characteristic recognition algorithm to calibrate the accurate relationship types among the data, and the relationship calibration unit further converts the calibration result into the relationship vector through vectorization processing, wherein the relationship vector comprises the determined relationship types, relationship strength and directionality, and provides basic data for the follow-up accurate information chain construction;
the content association module also comprises a relation braiding unit which is responsible for constructing an information chain among the data nodes according to the relation vector, wherein the information chain connects different data nodes according to relation strength and directivity through a path and sequence determined by an algorithm to form an ordered knowledge structure, the relation braiding unit dynamically adjusts a link structure in the construction process of the information chain through a decision logic engine, and the engine optimizes the correlation and personalized expression of the information chain according to user interaction feedback and user portrait data;
the content association module stores the constructed information chain in a structured data storage unit, the structured data storage unit optimizes a data access path for efficient retrieval, ensures that the ordered and highly relevant information chain can be quickly retrieved when a user inquires, and when the user inquires, the content association module calls a link display unit, extracts the corresponding information chain from the structured data storage unit according to the inquiry requirement and preset display logic of the user and displays the information chain through a user interface.
8. The platform for intelligent interaction of science popularization knowledge according to claim 7, wherein the relation braiding unit adopts a graph-based ordering algorithm, and for each node v in the graph, a calculation formula is defined based on a PageRank algorithm to calculate the weight PR (v) of the node, and the weight is used for representing the importance of the node, and the calculation formula is as follows:
wherein: PR (v) is the PageRank value of node v, d is the damping factor, set to 0.85, representing the probability of a transition from one node to another, N is the total number of nodes in the graph, B v Is directed to all of node vA set of nodes, L (u) being the number of links indicated by node u;
in order to adapt to the construction of an information chain, the PageRank formula is subjected to fine tuning modification to contain the relation strength and the directionality, the modified algorithm is called a knowledge chain PageRank, and the formula is as follows:
wherein: KPR (v) is the knowledge chain PageRank value of the knowledge point v, alpha is a constant term used for ensuring that at least part of ranking is not referenced by other pages, the ranking is set to be a value equivalent to (1-d)/N in the PageRank algorithm, w (u, v) is the relation strength from the node u to the node v, a numerical value determined by a relation calibration unit represents the relation strength between two knowledge points, and C (u) is the sum of the outgoing chain relation strength of the node u and is used for normalizing the contribution degree of different nodes.
9. The platform for intelligent interaction of science popularization knowledge according to claim 8, wherein the output module visually displays the comprehensive information chain formed by integrating the content association modules, and specifically comprises a data visual processing unit which is responsible for converting the information chain data in the structured data storage unit into visual elements on a graphical user interface;
the data visualization processing unit utilizes a pre-designed knowledge graph visualization template which defines the visual representation of the data nodes and the visual representation of the data relationships;
the visualization processing unit applies a weight value obtained by a knowledge chain PageRank algorithm to determine the position priority and the display size of corresponding knowledge points in a visualization map, and the higher the weight, the larger the size of the nodes in the map, the more central the position, so as to highlight important knowledge points;
the relation strength between the data nodes is represented by the thickness and the color depth of the connecting lines, and the thicker the line and the darker the color between the two nodes with stronger relation, the more visual the association strength between the knowledge points.
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