Method and system for building database cluster based on AI consultation

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a database cluster building method and system based on AI consultation, and the method comprises the following steps: S1, data source acquisition; s2, data storage; s3, AI model training; s4, designing a query interface; and S5, a response mechanism. By adopting a distributed database cluster architecture, the system can process a plurality of query requests at the same time, response time is effectively shortened, query experience of a user is remarkably improved, the system is combined with an advanced artificial intelligence technology, more accurate and more personalized consultation services can be provided, diversified requirements of the user are met, decision support capacity is improved, and the system is suitable for popularization and application. The system architecture supports transverse expansion, resource configuration can be flexibly adjusted according to changes of the data size and query requirements, it is ensured that the system still stably operates under the high-load condition, and through integration and processing of multiple data sources, the system improves the integrity and accuracy of data.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for building an AI-based consulting database cluster. Background Art

[0002] With the rapid development of the information age, the generation and accumulation of data has exploded. According to statistics, the amount of data generated globally reaches tens of billions of GB every day. Effectively managing, storing, and analyzing this massive amount of data has become a pressing challenge. In this context, traditional monolithic database solutions face severe performance bottlenecks, especially when faced with high-concurrency access and large-scale data queries. Neither the response speed nor the stability required by users can be met.

[0003] Distributed database technology is currently widely used for data storage and processing, with many enterprises adopting distributed databases such as Apache Hadoop and Apache Cassandra. However, these traditional distributed databases remain underutilized in the field of intelligent consulting services, lacking intelligent analysis and decision support tailored to user needs. While some AI-based systems are capable of data analysis, they often lack integration with underlying data storage systems, impacting overall system performance and user experience.

[0004] Furthermore, with the rapid development of artificial intelligence (AI), technologies such as natural language processing (NLP), machine learning, and big data analysis are maturing. These technologies offer new possibilities for building intelligent consulting systems. However, integrating these advanced AI technologies with efficient database clusters to achieve real-time, efficient intelligent consulting services has become a pressing technical challenge for the industry.

[0005] Therefore, the present invention proposes a method and system for building an AI consulting database cluster, aiming to improve data processing capabilities and the intelligence level of consulting services by integrating distributed database technology and artificial intelligence technology. Summary of the Invention

[0006] The present invention mainly solves the technical problems existing in the above-mentioned prior art and provides a method and system for building an AI consulting database cluster.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for building an AI consulting database cluster, comprising the following steps:

[0008] S1: Data source acquisition, obtaining structured and unstructured data from various data sources and preprocessing them to improve data quality;

[0009] S2: Data storage, which stores the pre-processed data in a distributed database cluster and establishes efficient data indexes to optimize retrieval efficiency;

[0010] S3: AI model training: Based on the acquired data, multiple AI models are trained, including natural language processing (NLP) models and machine learning models, to meet different types of consulting needs.

[0011] S4: Query interface design: Design a query interface based on RESTful API, through which users can send query requests to achieve convenient access;

[0012] S5: Response mechanism: Based on the user's query request, it selects the appropriate AI model for data analysis and returns intelligent consulting results.

[0013] As a further limitation of the above solution, in the data source acquisition step, the data sources include but are not limited to:

[0014] Internet public data, internal corporate databases, and user feedback and behavior data.

[0015] As a further limitation of the above scheme, the pre-processing step includes the following processing:

[0016] Data cleaning: Remove redundant and erroneous data to improve data accuracy.

[0017] Data formatting: Convert data into a unified format for storage and processing, reducing the complexity of subsequent processing.

[0018] Feature extraction: Extract key features from unstructured data to enhance data usability.

[0019] A system for building a cluster of AI consulting databases, including:

[0020] Data acquisition module: used to obtain data from various data sources and perform preliminary processing;

[0021] Data storage module: stores data based on a distributed database architecture and supports efficient data retrieval;

[0022] AI model module: used to store and train artificial intelligence models to achieve intelligent analysis;

[0023] Query processing module: responsible for receiving user query requests, analyzing the requests and returning corresponding results;

[0024] User interaction module: provides user interface and feedback mechanism to enhance user experience.

[0025] As a preferred technical solution of the present invention, the distributed database adopts a master-slave replication architecture to improve data availability and fault tolerance, ensuring that the slave database can take over the service in time when the master database fails.

[0026] As a preferred technical solution of the present invention, the AI ​​model module includes a natural language processing model, a recommendation system model and a prediction model to meet different types of consulting needs and enhance the intelligence level of the system.

[0027] As a preferred technical solution of the present invention, the query interface also supports multiple query methods, including keyword query, voice query and intelligent recommendation query, so as to improve the user's convenience and flexibility.

[0028] As a preferred technical solution of the present invention, the user interaction module has a user feedback function, which can collect user feedback information during the consultation process and optimize the AI ​​model in real time to improve the system's intelligent response capability.

[0029] Data source acquisition and processing: Acquire massive amounts of structured and unstructured data through multiple data sources and pre-process the data to ensure its accuracy and completeness.

[0030] Distributed database architecture: Adopts a distributed database cluster architecture to achieve high availability, scalability, and fault tolerance to meet the storage and query needs of large-scale data.

[0031] Application of intelligent AI models: Utilize natural language processing and machine learning technologies to build intelligent AI models, analyze and process user query requests, and provide intelligent consulting services.

[0032] User-friendly query interface: Design a RESTful API interface to enable users to easily make query requests and improve user experience.

[0033] Feedback and optimization mechanism: Establish a user feedback mechanism to improve the accuracy of AI models and the quality of consulting services through continuous learning and optimization.

[0034] The present invention provides a method and system for building an AI-based consulting database cluster. It has the following beneficial effects:

[0035] 1. Improve query efficiency: By adopting a distributed database cluster architecture, the system can process multiple query requests simultaneously, effectively reducing response time and significantly improving the user's query experience.

[0036] 2. Intelligent analysis and decision support: The system combines advanced artificial intelligence technology to provide more accurate and personalized consulting services, meet the diverse needs of users, and enhance decision support capabilities.

[0037] 3. Flexible expansion and high availability: The system architecture supports horizontal expansion and can flexibly adjust resource allocation according to changes in data volume and query requirements, ensuring that the system continues to operate stably under high load conditions.

[0038] 4. Data integration capability and accuracy: By integrating and processing multiple data sources, the system improves data integrity and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The structures, proportions, sizes, etc. depicted in this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance.

[0040] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0041] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely illustrative, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0042] The structures, proportions, sizes, etc. illustrated in this specification are intended only to complement the contents disclosed herein and to facilitate understanding and reading by persons familiar with the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall still fall within the scope of the technical contents disclosed herein.

[0043] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not require further definition or explanation in subsequent drawings.

[0044] In the description of the embodiments of the present invention, it should be noted that the terms "center," "upper," "lower," "inner," "outer," and "side" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or the orientations or positional relationships in which the inventive product is typically placed when in use. These terms are intended solely to facilitate the description of the present invention and to simplify the description, and are not intended to indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and the like are used solely to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0045] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention according to specific circumstances.

[0046] Example: A method and system for building a cluster of AI-based consulting databases, such as Figure 1 As shown, the following steps are included:

[0047] S1: Data source acquisition, obtaining structured and unstructured data from various data sources and preprocessing them to improve data quality;

[0048] S2: Data storage, which stores the pre-processed data in a distributed database cluster and establishes efficient data indexes to optimize retrieval efficiency;

[0049] S3: AI model training: Based on the acquired data, multiple AI models are trained, including natural language processing (NLP) models and machine learning models, to meet different types of consulting needs.

[0050] S4: Query interface design: Design a query interface based on RESTful API, through which users can send query requests to achieve convenient access;

[0051] S5: Response mechanism: Based on the user's query request, it selects the appropriate AI model for data analysis and returns intelligent consulting results.

[0052] In the data source acquisition step, data sources include but are not limited to:

[0053] Internet public data, internal corporate databases, and user feedback and behavior data.

[0054] The preprocessing step includes the following processing:

[0055] Data cleaning: Remove redundant and erroneous data to improve data accuracy.

[0056] Data formatting: Convert data into a unified format for storage and processing, reducing the complexity of subsequent processing.

[0057] Feature extraction: Extract key features from unstructured data to enhance data usability.

[0058] A system for building a cluster of AI consulting databases, including:

[0059] Data acquisition module: used to obtain data from various data sources and perform preliminary processing;

[0060] Data storage module: stores data based on a distributed database architecture and supports efficient data retrieval;

[0061] AI model module: used to store and train artificial intelligence models to achieve intelligent analysis;

[0062] Query processing module: responsible for receiving user query requests, analyzing the requests and returning corresponding results;

[0063] User interaction module: provides user interface and feedback mechanism to enhance user experience.

[0064] The distributed database adopts a master-slave replication architecture to improve data availability and fault tolerance, ensuring that the slave database can take over the service in time when the master database fails.

[0065] The AI ​​model module includes natural language processing models, recommendation system models and prediction models to meet different types of consulting needs and enhance the intelligence level of the system.

[0066] The query interface also supports multiple query methods, including keyword query, voice query and intelligent recommendation query, to improve user convenience and flexibility.

[0067] The user interaction module has a user feedback function, which can collect user feedback information during the consultation process and optimize the AI ​​model in real time to improve the system's intelligent response capabilities.

[0068] Data source acquisition and processing: Acquire massive amounts of structured and unstructured data through multiple data sources and pre-process the data to ensure its accuracy and completeness.

[0069] Distributed database architecture: Adopts a distributed database cluster architecture to achieve high availability, scalability, and fault tolerance to meet the storage and query needs of large-scale data.

[0070] Application of intelligent AI models: Utilize natural language processing and machine learning technologies to build intelligent AI models, analyze and process user query requests, and provide intelligent consulting services.

[0071] User-friendly query interface: Design a RESTful API interface to enable users to easily make query requests and improve user experience.

[0072] Working principle of the present invention:

[0073] The method and system for building an AI-based consulting database cluster described in the present invention achieve the goal of intelligent consulting services mainly through the following steps:

[0074] 1. Data source acquisition and processing:

[0075] The system first collects data from multiple data sources (such as the Internet, internal enterprise databases, user behavior data, etc.) through the data acquisition module. This data may include various forms such as text, images, and audio.

[0076] During the data preprocessing phase, the system cleans, formats, and extracts features from the data to ensure data quality and usability. The processed data is then stored in a distributed database, where efficient data indexes are established for fast retrieval.

[0077] 2. Training and application of intelligent AI models:

[0078] The system uses preprocessed data to train multiple AI models. Natural language processing models are used to understand the user's query intent, recommendation system models are used to provide personalized suggestions based on the user's historical behavior, and predictive models are used to analyze data trends and make future predictions.

[0079] The trained AI model will be stored in the system's AI model module for subsequent query processing module calls.

[0080] 3. User query and response mechanism:

[0081] Users submit query requests through the RESTful API interface. After receiving the request, the query processing module will analyze the user's intention and select the appropriate AI model for data analysis based on the type of request.

[0082] After the analysis is completed, the system will return the intelligent consulting results to the user to achieve efficient service.

[0083] 4. Feedback and Optimization:

[0084] The user interaction module provides a feedback channel, where users can submit their evaluation of the query results during use. This feedback information will be recorded by the system and used to optimize the AI ​​model.

[0085] Through continuous feedback and learning, the system can continuously improve its intelligence level and adapt to changing user needs.

[0086] The basic principles, main features and advantages of the present invention are shown and described above. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for building a database cluster based on AI consulting, characterized in that: The following steps are involved: S1: Data source acquisition, obtaining structured and unstructured data from various data sources and preprocessing them to improve data quality; S2: Data storage, which stores the pre-processed data in a distributed database cluster and establishes efficient data indexes to optimize retrieval efficiency; S3: AI model training: Based on the acquired data, multiple AI models are trained, including natural language processing (NLP) models and machine learning models, to meet different types of consulting needs. S4: Query interface design: Design a query interface based on RESTful API, through which users can send query requests to achieve convenient access; S5: Response mechanism: Based on the user's query request, it selects the appropriate AI model for data analysis and returns intelligent consulting results.

2. The method for building an AI-based consulting database cluster according to claim 1, characterized in that: In the data source acquisition step, data sources include but are not limited to: Internet public data, internal corporate databases, and user feedback and behavior data.

3. The method for building a database cluster based on AI query according to any one of claims 1 or 2, characterized in that: The pre-processing step includes the following processing: Data cleaning: Remove redundant and erroneous data to improve data accuracy. Data formatting: Convert data into a unified format for storage and processing, reducing the complexity of subsequent processing. Feature extraction: Extract key features from unstructured data to enhance data usability.

4. A system for building a cluster of AI consulting databases, characterized in that: include: Data acquisition module: used to obtain data from various data sources and perform preliminary processing; Data storage module: stores data based on a distributed database architecture and supports efficient data retrieval; AI model module: used to store and train artificial intelligence models to achieve intelligent analysis; Query processing module: responsible for receiving user query requests, analyzing the requests and returning corresponding results; User interaction module: provides user interface and feedback mechanism to enhance user experience.

5. The AI-based consulting database cluster building system according to claim 4 is characterized in that: The distributed database adopts a master-slave replication architecture to improve data availability and fault tolerance, ensuring that the slave database can take over the service in a timely manner when the master database fails.

6. The AI-based consulting database cluster building system according to any one of claims 4 or 5, characterized in that: The AI ​​model module includes a natural language processing model, a recommendation system model and a prediction model to meet different types of consulting needs and enhance the intelligence level of the system.

7. The method or system for building an AI-based consulting database cluster according to any one of claims 1 to 6, characterized in that: The query interface also supports multiple query methods, including keyword query, voice query and intelligent recommendation query, to improve user convenience and flexibility.

8. The method or system for building an AI-based consulting database cluster according to any one of claims 1 to 7, characterized in that: The user interaction module has a user feedback function, which can collect user feedback information during the consultation process and optimize the AI ​​model in real time to improve the system's intelligent response capabilities.

Images