System for realizing business intelligent data question answering based on semantic recognition

Abstract

The invention provides a construction system for realizing a business intelligent data question-answering system based on semantic recognition, which belongs to the technical field of information and comprises a user interaction layer, a semantic analysis layer, a data access layer, a data analysis layer and a visual presentation layer. A user can input a query request through a natural language, and the system analyzes the intention of the user by using an advanced semantic recognition technology and quickly extracts related information from mass business data. And then, the system visually presents an analysis result to the user in forms of charts, reports and the like through a visual tool, so that a business department is assisted to quickly understand a data trend and make an efficient decision. According to the system, the data query and analysis process is simplified, the dependence on professional data analysts is reduced, the working efficiency is greatly improved through automatic and intelligent means, the operation and maintenance cost is reduced, good expansibility and integration are achieved, and diversified business requirements of business departments are met.

Description

Technical Field

[0001] The present invention relates to the fields of statistics, computer science, information technology, and data science, and in particular to a business intelligence data question-answering system based on semantic recognition. Background Art

[0002] With the rapid development of big data and artificial intelligence technologies, businesses are facing an ever-increasing amount of data in their daily work, increasing its complexity and processing difficulty. Traditional data query and analysis methods often rely on professional data analysts, and the process is cumbersome and time-consuming, making it difficult for businesses to quickly respond to market changes and make efficient decisions. Therefore, it is crucial to develop an intelligent data question-answering system that can automatically understand natural language queries, quickly extract and analyze relevant data, and present the results in a visual format. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a business intelligence data question and answer system based on semantic recognition, which aims to achieve efficient and convenient data query and analysis functions by combining natural language interaction and visualization technology, thereby greatly improving the work efficiency of business departments. The system can automatically understand the query requests entered by users in natural language, use advanced semantic recognition technology to analyze user intentions, and quickly extract relevant information from massive business data. At the same time, the system presents the analysis results to users in an intuitive and easy-to-understand manner through visualization tools, helping users to quickly grasp data trends and provide strong support for business decisions. Through the implementation of the present invention, business departments can get rid of excessive dependence on professional data analysts, lower the threshold for data query and analysis, and improve overall work efficiency and decision-making capabilities.

[0004] The technical solution of the present invention is:

[0005] A business intelligence data question-answering system based on semantic recognition, comprising:

[0006] The user interaction layer is used to receive query requests input by users through natural language;

[0007] The semantic parsing layer is used to parse the query requests entered by the user and identify the user's intention and required information;

[0008] The data access layer is used to connect to the business department's database or data warehouse to extract relevant data based on user intent;

[0009] The data analysis layer is used to analyze and process the extracted data and obtain analysis results;

[0010] The visualization layer is used to present the analysis results to users in the form of charts, reports, etc.

[0011] Further,

[0012] The processing flow of the semantic parsing layer: When the user enters a query request, it is first preprocessed. Then, the deep learning model is used to perform semantic parsing on the preprocessed input to identify the user's intention and required information. During the parsing process, the domain knowledge base is used to assist in improving the accuracy and efficiency of the parsing. Finally, the parsing results are passed to the data access layer for data extraction and analysis.

[0013] The semantic parsing layer uses a deep learning model to perform semantic parsing on the query request input by the user, and the deep learning model includes BERT, GPT and other models.

[0014] Further,

[0015] The processing flow of the data access layer: Based on the user intent conveyed by the semantic parsing layer, the corresponding SQL query statement is constructed or the data API is called to extract relevant data from the database or data warehouse; the extracted data will be passed to the data analysis layer.

[0016] The data analysis layer can automatically select or construct appropriate analysis models according to user intentions, including statistical analysis models, machine learning models, etc.

[0017] Further,

[0018] The workflow of the visualization layer: Generate various types of charts based on user query requests and data analysis results to intuitively display data trends and patterns; users operate charts through interactive controls; at the same time, it also supports exporting charts as images or PDF formats to facilitate user sharing and reporting.

[0019] The visualization presentation layer supports a variety of visualization forms, including bar charts, line charts, pie charts, radar charts, etc., and can be customized according to user preferences.

[0020] Further,

[0021] It also includes a model optimization module for continuously optimizing the deep learning model in the system to improve the accuracy and efficiency of semantic parsing.

[0022] It also includes a user rights management module for managing user query rights to ensure data security and privacy.

[0023] It also includes a system integration interface for integration with other systems to achieve data sharing and exchange.

[0024] The beneficial effects of the present invention are

[0025] Improve work efficiency: Users can obtain the required information through natural language queries without having professional data analysis skills, greatly shortening the time for data query and analysis.

[0026] Enhanced decision-making capabilities: The visual analysis results provided by the system help users quickly understand the trends and patterns behind the data, providing strong support for business decisions.

[0027] Reduced operation and maintenance costs: The system has a high degree of automation, which reduces manual intervention and operation and maintenance costs.

[0028] Easy to expand and integrate: The system provides an open API interface to facilitate integration and expansion with other systems to meet diverse business needs. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] The present invention provides a business intelligence data question-and-answer system based on semantic recognition. The system is designed specifically for business departments and aims to improve the work efficiency of business departments by combining natural language interaction and visualization technology. The system mainly includes a user interaction layer, a semantic parsing layer, a data access layer, a data analysis layer, and a visualization presentation layer. Users can input query requests in natural language, and the system uses advanced semantic recognition technology to parse user intentions and quickly extract relevant information from massive business data. Subsequently, the system uses visualization tools to intuitively present the analysis results to users in the form of charts, reports, etc., helping business departments to quickly understand data trends and make efficient decisions. The system not only simplifies the data query and analysis process and reduces dependence on professional data analysts, but also greatly improves work efficiency and reduces operation and maintenance costs through automation and intelligent means. It has good scalability and integration to meet the diverse business needs of business departments.

[0031] The present invention consists of five main components: a user interaction layer, a semantic parsing layer, a data access layer, a data analysis layer, and a visualization layer. The user interaction layer, located at the top, provides an interactive interface between the user and the system, allowing users to enter queries via natural language. The semantic parsing layer receives user input and uses a deep learning model to perform semantic analysis and identify user intent. The data access layer connects to the business department's database or data warehouse and extracts relevant data based on user intent. The data analysis layer analyzes and processes the extracted data to produce analysis results. Finally, the visualization layer presents the analysis results to the user in the form of charts, reports, and other intuitive forms.

[0032] The system user interface of this invention features a concise and clear design, providing an input box for users to enter natural language queries. Below the input box, the system displays query suggestions automatically generated based on the user input, improving query efficiency and accuracy. Query results are displayed visually at the bottom of the interface, allowing users to view different types of charts or reports as needed, and further manipulate and analyze the results through interactive controls.

[0033] The semantic parsing layer's processing flow specifically includes the following: When a user enters a query, the system first performs preprocessing, including removing irrelevant information and performing word segmentation. A deep learning model then performs semantic parsing on this preprocessed input, identifying the user's intent and the required information. During the parsing process, the system also utilizes a domain knowledge base to assist in improving parsing accuracy and efficiency. Finally, the system passes the parsing results to the data access layer for data extraction and analysis.

[0034] The processing flow of the data access and analysis layers includes the following: At the data access layer, the system constructs SQL queries or calls data APIs based on the user intent conveyed by the semantic analysis layer to extract relevant data from the business department's database or data warehouse. The extracted data is then passed to the data analysis layer, which selects or constructs an appropriate analysis model based on the user intent, processes and analyzes the data, and generates analytical results.

[0035] Regarding example charts in the system's visualization layer, the system generates various chart types, such as bar charts, line charts, and pie charts, based on user query requests and data analysis results, to intuitively display data trends and patterns. Users can use interactive controls to zoom, rotate, and perform other operations on the charts, providing a deeper understanding of the data. The system also supports exporting charts as images or PDFs for convenient sharing and reporting.

[0036] The above description is only a preferred embodiment of the present invention and is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A business intelligence data question-answering system based on semantic recognition, characterized in that: include: The user interaction layer is used to receive query requests input by users through natural language; The semantic parsing layer is used to parse the query requests entered by the user and identify the user's intention and required information; The data access layer is used to connect to the business department's database or data warehouse to extract relevant data based on user intent; The data analysis layer is used to analyze and process the extracted data and obtain analysis results; The visualization layer is used to present the analysis results to users in the form of charts, reports, etc.

2. The system according to claim 1, wherein: The processing flow of the semantic parsing layer: When a user enters a query request, it is first preprocessed. Then, a deep learning model is used to perform semantic parsing on the preprocessed input to identify the user's intent and the required information. During the parsing process, the domain knowledge base is used to assist in improving the accuracy and efficiency of the parsing; finally, the parsing results are passed to the data access layer for data extraction and analysis.

3. The system according to claim 1 or 2, characterized in that The semantic parsing layer uses a deep learning model to perform semantic parsing on the query request input by the user, and the deep learning model includes BERT and GPT models.

4. The system according to claim 1, wherein: The processing flow of the data access layer is as follows: based on the user intent transmitted by the semantic parsing layer, the corresponding SQL query statement is constructed or the data API is called to extract relevant data from the database or data warehouse; the extracted data will be passed to the data analysis layer.

5. The system according to claim 4, characterized in that The data analysis layer automatically selects or constructs an appropriate analysis model based on user intent to process and analyze the data, including statistical analysis models and machine learning models.

6. The system according to claim 1, wherein: The workflow of the visualization layer is as follows: various types of charts are generated based on user query requests and data analysis results to intuitively display data trends and patterns; users operate the charts through interactive controls; at the same time, it also supports exporting charts as images or PDF formats to facilitate user sharing and reporting.

7. The system according to claim 6, characterized in that The visualization presentation layer supports multiple visualization forms, including bar charts, line charts, pie charts, and radar charts, and can be customized according to user preferences.

8. The system according to claim 1, wherein: It also includes a model optimization module for continuously optimizing the deep learning model in the system to improve the accuracy and efficiency of semantic parsing.

9. The system according to claim 1, wherein: It also includes a user rights management module for managing user query rights to ensure data security and privacy.

10. The system according to claim 1, wherein: It also includes a system integration interface for integration with other systems to achieve data sharing and exchange.