CN109446344B - Intelligent analysis report automatic generation system based on big data - Google Patents
Intelligent analysis report automatic generation system based on big data Download PDFInfo
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Abstract
The invention discloses an intelligent analysis report automatic generation system based on big data, which comprises a big data resource pool, a knowledge graph system, a model engine system and an XML expert viewpoint base automatic pushing system; the big data resource pool is used for gathering data resources and knowledge resources of different data sources and intelligently pushing associated data and associated knowledge according to topics; the knowledge graph system is constructed based on knowledge logic and attribute relation, and automatically associates knowledge related to the subject words according to the subject words to realize intelligent retrieval and question answering; the model engine system is used for associating analysis models related to application fields corresponding to the subject terms to realize in-depth analysis of the index data; the automatic pushing system of the XML expert viewpoint base is used for automatically associating the knowledge points corresponding to the subject terms, automatically pushing the problem solving knowledge points and automatically generating an intelligent report. Compared with the traditional intelligent report, the intelligent analysis report automatically generated based on big data is objective and authoritative, and the working efficiency of researchers can be greatly improved.
Description
Technical Field
The invention belongs to the technical field of intelligent analysis and automatic generation systems of reports developed based on various intelligent information technical means, and particularly relates to an intelligent analysis report automatic generation system of a big data mining analysis technology, a data exchange and sharing technology, a fragment indexing and indexing technology and a knowledge graph technology.
Background
Traditional intelligent data analysis mines the most common and fundamental data processing behaviors in BI projects. During the construction of the data warehouse, data of various service systems can enter the data warehouse only through a strict ETL process, and further support is provided for subsequent data presentation and analysis. Generally, because the data apertures of the business systems of the enterprises are not consistent, the BI project must implement ETL work, otherwise, it is useless and meaningless to conduct various data behaviors on ambiguous and inaccurate data.
In the multi-media and cross-media digital age, the traditional data media cannot meet the requirements of report writers on remote collaborative writing, personalized customization of requirements, intelligent identification, automation of editing and the like in the process of content organization and service. Therefore, breaking the constraint of the traditional flow and concept, establishing a content object-based, collaborative, "one-time production, multi-element publishing" dynamic report generation mechanism becomes a key technology.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent analysis report automatic generation system based on big data.
The purpose of the invention is realized by the following technical scheme:
an intelligent big data-based analysis report automatic generation system, comprising: the system comprises a big data resource pool, a knowledge graph system, a model engine system and an automatic XML expert viewpoint library pushing system; the above-mentioned
The big data resource pool is used for gathering data resources and knowledge resources of different data sources and intelligently pushing associated data and associated knowledge according to topics;
the knowledge graph system is constructed based on knowledge logic and attribute relation, and automatically associates knowledge related to the subject words according to the subject words to realize intelligent retrieval and question answering;
the model engine system is used for associating analysis models related to application fields corresponding to the subject terms to realize in-depth analysis of the index data;
the automatic pushing system of the XML expert viewpoint base is used for automatically associating the knowledge points corresponding to the subject terms, automatically pushing the problem solving knowledge points and automatically generating an intelligent report.
One or more embodiments of the present invention may have the following advantages over the prior art:
the report generated based on the big data is guest-looking and scientific in content, data and knowledge labeling sources can be traced in terms of sources, and the report is automatically generated in terms of generation speed and is high in efficiency.
Drawings
FIG. 1 is a block diagram of a big data based intelligent analytics report automatic generation system.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following embodiments and accompanying drawings.
As shown in fig. 1, the system architecture for automatically generating intelligent analysis report based on big data includes: the system comprises a big data resource pool, a knowledge graph system, a model engine system and an automatic XML expert viewpoint library pushing system; the above-mentioned
The big data resource pool is used for gathering data resources and knowledge resources of different data sources and intelligently pushing associated data and associated knowledge according to topics;
the knowledge graph system is constructed based on knowledge logic and attribute relation, and automatically associates knowledge related to the subject words according to the subject words to realize intelligent retrieval and question answering;
the model engine system is used for associating analysis models related to application fields corresponding to the subject terms to realize in-depth analysis of the index data;
the automatic pushing system of the XML expert viewpoint base is used for automatically associating the knowledge points corresponding to the subject terms, automatically pushing the problem solving knowledge points and automatically generating an intelligent report.
The big data resource pool comprises: data in distributed and heterogeneous data sources, such as relationship data, plane data files and the like, are extracted to a temporary middle layer by using an ETL tool through a natural language processing technology, artificial intelligence, data mining, data processing and knowledge fragmentation technology, then are cleaned, converted and integrated, and finally are loaded to a data warehouse or a data mart to become the basis of online analysis processing and data mining. Data is stored into relational databases, NOSQL, SQL, etc. by classification, estimation, prediction, relevance grouping or association rules, clustering, description and visualization, complex data type (Text, Web, graphic image, video, audio, etc.) mining.
Knowledge graph system: and calling a knowledge graph and index ontology system, constructing the knowledge graph by using a knowledge network system established on the basis of natural language, and displaying the association relation between data related to the knowledge elements and revealing the multi-dimensional semantic relation. The data distributed scattered form a knowledge grid, resources such as production data, scientific research data and market data are fused, and the integrity and relevance of the data are deeply explored. The knowledge map comprises a basic resource layer, a knowledge unit layer, a knowledge organization layer and a knowledge expression layer. Knowledge extraction is carried out on data from different sources to form knowledge unit entities, then the extracted entities are subjected to knowledge fusion, the incidence relation among the entities is discovered, the organization of the knowledge can be realized from the semantic level, the implicit relation among the knowledge is discovered, and a knowledge network is formed.
The knowledge graph system starts from structured, semi-structured and unstructured data, adopts an automatic or semi-automatic technology to extract knowledge facts from an original database and a third-party database, and stores the extracted knowledge facts into a data layer and a mode layer of a knowledge base.
The process of storing the extracted knowledge facts into the knowledge base data layer and the pattern layer comprises the following steps: the method comprises four processes of knowledge extraction, knowledge representation, knowledge fusion and knowledge reasoning, and each updating iteration comprises four stages.
A model engine system: calling a model engine, and constructing a practical model by using a statistical analysis method, such as hypothesis test, significance test, difference analysis, correlation analysis, T test, variance analysis, chi-square analysis, partial correlation analysis, distance analysis, regression analysis, simple regression analysis, multiple regression analysis, stepwise regression, regression prediction and residual analysis, logistic regression analysis, curve estimation, factor analysis, cluster analysis, principal component analysis, factor analysis, fast clustering method and clustering method, discriminant analysis, correspondence analysis, multiple correspondence analysis (optimal scale analysis), bootstrap technology and the like.
The model engine system is based on the authoritative thesis resources in the CNKI agricultural field, and various analysis models are constructed by utilizing the artificial intelligence technology according to different research purposes, contents and data types to form a model engine.
Automatic XML expert opinion library pushing system: in addition to metadata tagging of the whole or entire content, the fragment indexing and indexing technology is utilized to separately index and index the knowledge of each section of the digital publishing resource in more detail. The fragment knowledge after indexing and indexing is easier to acquire and utilize by readers, and the life cycle of the fragment knowledge is longer and more effective than that of the whole book. The main flow of digital content fragmentation organization comprises the following steps: maintaining traditional publishing contents, storing author manuscripts, final review manuscripts and final files, and converting the final files to organize according to the modes of species, volume, piece, chapter and section; classifying the formed contents of the chapter, chapter and section according to the disciplines, the Chinese picture classification, the topics and other modes, and constructing a knowledge system according to a certain discipline, a certain direction and a certain industry by the formed classification; splitting a knowledge system into knowledge units in different directions, splitting the knowledge units into knowledge points, and finally splitting the knowledge points into subject words and keywords; dynamically associating the knowledge points through semantic relations among the keywords to form a mesh interconnection relation; and recombining the content as required, and realizing dynamic publishing by adopting a polymorphic synchronous generation technology.
The automatic XML expert viewpoint base pushing system consists of a knowledge base management system, an independent knowledge base, a database, an inference machine, an interpreter, a knowledge acquisition module and a user interface; the above-mentioned
The knowledge base management system is used for checking and retrieving knowledge in a knowledge base;
the database is used for storing the original data and the intermediate information obtained by the inference engine in inference;
an interpreter for explaining a solving process and providing a countermeasure for solving the problem;
the knowledge acquisition module is used for transferring the acquired related knowledge into a knowledge base;
and the user interface is responsible for receiving the information input by the user, converting the information into a system internal representation form, submitting the system internal representation form to a corresponding module for processing, converting the internal information output by the system into a representation form which can be accepted by the user, and returning the representation form to the user.
In the automatic pushing system of the XML expert opinion base, the knowledge points of the problem to be solved include current situation analysis, reason exploration, strategy suggestion, expecting and predicting knowledge resources, and the like.
After fragmentation is solved, multiplexing and reorganization are one of the key technologies for dynamic digital publishing. The traditional content management can manage fragmented content, but cannot manage multiplexing and recombination rules of the fragmented content, particularly dynamic recombination, and needs to realize a series of standardized dynamic reconstruction of application request, combination, output and the like.
For fragmented content and integrated and formatted files, a method for checking whether mass files are damaged or not after storage is necessary, backup and repair are performed on damaged parts, and mass file feature management is established to facilitate checking, management and repair, which is the key in the existing digital content checking and multiplexing technology.
Based on the research base of the collection and fragmentation and recombination of the content, a content dynamic recombination and on-demand publishing platform is designed. The platform overall technical framework route is divided into three relatively independent layers according to the service flow, functions and characteristics: the system comprises a data service layer, a data management layer and a data acquisition layer.
The data service layer mainly comprises a multi-channel digital publishing service system and a mobile reading system. The data management layer mainly comprises a digital resource management system, a data verification management module, a mass data characteristic processing module and the like. The data acquisition layer mainly comprises an online publishing and compiling system, an author, an editor, an expert indexing tool, an Internet-based scientific and technical symbol, a graphic complex editing tool and the like.
The data management layer mainly realizes centralized processing, resource management and digital content output services of digital content resources of publishing houses, including multimedia resources such as books, newspapers, chapters, knowledge points, audios, videos, animations and pictures, and has the main functions of content storage management, general component management, content arrangement management, logic content library management, content display management and the like.
Storing and managing contents: various digital contents are stored in a unified content management platform, then the contents are segmented according to chapters, pictures and the like through content fragmenting processing, semantic labeling is carried out after segmentation, and processed results are stored in a fragment content storage platform. Managing the general components: the system manages the description information (attribute label) of the content uniformly, manages the related information among various contents, and provides a full-text search engine for the managed content to search all the contents uniformly. And managing the content. A semantic engine is provided to help processing personnel label the digital content; meanwhile, a content marking tool is provided, the tool helps to divide the PDF document into chapters and pictures, and semantic tags are added to the fragmented content after cutting; the content retrieval system provides the capability of retrieving contents of different levels and sorts the retrieved contents by weight. Editing the personal space provides a tool for editing and author to accumulate and manage personal content. And fourthly, content display management. After the digital content is sorted, various logic content libraries can be formed, such as an original material library, a picture library, a file library, an audio and video library and the like.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. An intelligent big data-based analysis report automatic generation system, characterized in that the system comprises: the system comprises a big data resource pool, a knowledge graph system, a model engine system and an automatic XML expert viewpoint library pushing system; the above-mentioned
The big data resource pool is used for gathering data resources and knowledge resources of different data sources and intelligently pushing associated data and associated knowledge according to topics;
the knowledge graph system is constructed based on knowledge logic and attribute relation, and automatically associates knowledge related to the subject words according to the subject words to realize intelligent retrieval and question answering;
the model engine system is used for associating analysis models related to application fields corresponding to the subject terms to realize in-depth analysis of the index data;
the automatic pushing system of the XML expert viewpoint base is used for automatically associating the knowledge points corresponding to the subject terms, automatically pushing the problem-solving knowledge points and automatically generating an intelligent report;
the automatic XML expert viewpoint base pushing system consists of a knowledge base management system, an independent knowledge base, a database, an inference machine, an interpreter, a knowledge acquisition module and a user interface; the above-mentioned
The knowledge base management system is used for checking and retrieving knowledge in a knowledge base;
the database is used for storing the original data and the intermediate information obtained by the inference engine in inference;
an interpreter for explaining a solving process and providing a countermeasure for solving the problem;
the knowledge acquisition module is used for transferring the acquired related knowledge into a knowledge base;
the user interface is responsible for receiving information input by a user, converting the information into a system internal representation form, submitting the system internal representation form to a corresponding module for processing, and converting the internal information output by the system into a representation form received by the user and returning the representation form to the user;
in the automatic XML expert opinion base pushing system, knowledge points of the solved problems comprise current situation analysis, reason exploration, strategy suggestion and prospect prediction knowledge resources;
the automatic XML expert point of view library pushing system comprises: the fragment indexing and indexing technology is used for marking metadata of the whole book or the whole content, the knowledge of each section of the digital publishing resource needs to be individually indexed and indexed in detail, the indexed and indexed fragment knowledge is easier to be acquired and utilized by readers, and the life cycle of the fragment indexing and indexing technology is longer and more effective than that of the whole book; the digital content fragmentation organization flow comprises the following steps: maintaining traditional publishing contents, storing author manuscripts, final review manuscripts and final files, and converting the final files to organize according to the modes of species, volume, piece, chapter and section; classifying the formed contents of the chapter, chapter and section according to a subject, a middle figure classification and a theme mode, and constructing a knowledge system according to a certain subject, a certain direction and a certain industry by the formed classification; splitting a knowledge system into knowledge units in different directions, splitting the knowledge units into knowledge points, and finally splitting the knowledge points into subject words and keywords; dynamically associating the knowledge points through semantic relations among the keywords to form a mesh interconnection relation; recombining the content as required, and adopting a polymorphic synchronous generation technology to realize dynamic publishing;
after fragmentation is solved, multiplexing and recombining dynamic digital publishing rules of fragmented contents are managed, wherein dynamic recombination realizes a series of standardized dynamic reconstruction of application request, combination and output; based on the research foundation of content collection, fragmentation and recombination, the system comprises a content dynamic recombination and on-demand publishing platform, and the overall technical framework route of the platform is divided into three independent layers according to the service flow, the function and the characteristics: the system comprises a data service layer, a data management layer and a data acquisition layer; the data service layer comprises a multi-channel digital publishing service system and a mobile reading system; the data management layer comprises a digital resource management system, a data verification management module and a mass data characteristic processing module and is used for realizing digital content resources of a publishing company; the data acquisition layer comprises an online publishing and compiling system, an author, an editor, an expert indexing tool, an internet-based scientific symbol and a graphic complex editing tool.
2. The big-data based intelligent analysis report automatic generation system of claim 1, wherein the big data resource pool implements cleaning, transformation and integration of data through natural language processing technology, artificial intelligence, data mining, data processing, knowledge fragmentation technology.
3. The big-data based intelligent analysis report automatic generation system as claimed in claim 1, wherein the knowledge graph system uses automatic or semi-automatic technology to extract knowledge facts from the original database and the third party database starting from structured, semi-structured and unstructured data, and stores the extracted knowledge facts into the data layer and the schema layer of the knowledge base.
4. The big-data-based automatic intelligent analysis report generation system as claimed in claim 1, wherein the model engine system is based on the authoritative paper resources in the agricultural field of CNKI, and utilizes artificial intelligence technology to construct various analysis models to form the model engine according to different research purposes, contents and data types.
5. The intelligent big-data-based analysis report automatic generation system of claim 3, wherein the process of storing the extracted knowledge facts in the knowledge base data layer and the schema layer comprises: the method comprises four processes of knowledge extraction, knowledge representation, knowledge fusion and knowledge reasoning.
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| CN111368515B (en) * | 2020-03-02 | 2021-01-26 | 中国农业科学院农业信息研究所 | Industry dynamic interactive report generation method and system based on PDF document fragmentation |
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| CN111859969B (en) * | 2020-07-20 | 2024-05-03 | 航天科工智慧产业发展有限公司 | Data analysis method and device, electronic equipment and storage medium |
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