Local database docking method based on artificial intelligence
By automating the database docking process through an AI-based method, the problems of low efficiency, many errors and high maintenance costs in existing technologies are solved, and efficient and accurate data docking and flexible system maintenance are achieved.
Patent Information
- Application Number
- CN202510689283.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-27
- Publication Date
- 2025-09-09
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing database docking methods rely on manual operations, which are inefficient, prone to errors, have high maintenance costs, poor flexibility, and cannot adapt to changes in database structure or updated business needs.
Using artificial intelligence-based methods, we can automate the database docking process through demand analysis and planning, building AI models, data preprocessing, establishing data mapping relationships, building intelligent docking programs, testing and optimization, deployment and monitoring.
It improves the efficiency of database docking, reduces the probability of human error, ensures data accuracy and integrity, reduces maintenance costs, improves system flexibility and scalability, and enables real-time monitoring and problem solving.
Smart Images
Figure CN120610983A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of database docking, and in particular to a local database docking method based on artificial intelligence. Background Art
[0002] In the information age, enterprises and organizations often have multiple local databases within them, storing data of different types and purposes. For example, the sales department's database stores customer orders and sales records, while the finance department's database records income and expenditure details and financial statement data. As business grows and data application needs increase, it is often necessary to connect these dispersed local databases to achieve functions such as data sharing and comprehensive analysis.
[0003] Traditional database docking methods rely primarily on manually writing complex codes and scripts to manually define data mapping relationships and conversion rules. This approach presents numerous problems: First, manual operations are inefficient, especially when processing large-scale, complex data structures, which is time-consuming and labor-intensive. Second, they are prone to human errors, such as inaccurate data mapping and omitted conversion rules, which can lead to data loss or errors. Third, when the database structure changes or business requirements are updated, the code and rules need to be manually modified, resulting in high maintenance costs and poor flexibility. Therefore, we propose an artificial intelligence-based local database docking method to address these issues. Summary of the Invention
[0004] The problem to be solved by the present invention is that the existing database is prone to human errors during the docking process, such as inaccurate data mapping, omission of conversion rules, etc., which leads to data loss or errors; furthermore, when the database structure changes or business needs are updated, the code and rules need to be manually modified, which has high maintenance costs and poor flexibility.
[0005] In order to solve the above technical problems, the present invention provides a local database docking method based on artificial intelligence, which specifically includes:
[0006] S1: Demand analysis and planning, clarifying the goals of data docking and determining the data output format and requirements after docking;
[0007] S2: Build an AI model and select or train an appropriate AI model based on your needs;
[0008] S3: Data preprocessing, which implements data cleaning, standardization, and conversion functions;
[0009] S4: Establish data mapping relationships, complete automatic mapping and manual verification and adjustment operations;
[0010] S5: Build an intelligent docking program, write code to implement docking logic and integrate AI models;
[0011] S6: Testing and optimization, performing functional testing, performance testing, and exception handling testing;
[0012] S7: Deployment and monitoring, responsible for program deployment and real-time monitoring;
[0013] Preferably, the demand analysis and planning specifically include:
[0014] S101: Clarify business requirements: Use natural language processing technology to perform semantic analysis on business requirement documents and conversations entered by users, extract key information, clarify the goals of data integration, and determine the data output format and requirements after integration;
[0015] S102: Sort out data resources: Automatically obtain the table structure, field meaning, data type, data volume, and data update frequency in the local database through database metadata acquisition technology. Based on knowledge graph technology, draw a data relationship diagram to clearly present the relationship between data, providing a basis for subsequent docking.
[0016] Preferably, the building of the AI model specifically includes:
[0017] S201: Natural Language Processing: If the connection requirements include data queries or instructions described in natural language, select an appropriate NLP pre-trained model and fine-tune it to enable it to accurately interpret user intent and convert natural language into query statements that the database can recognize;
[0018] S202: Machine Learning Algorithms: Targeting data cleaning and automatic learning of data transformation rules, selecting clustering algorithms to identify abnormal data, and using linear regression algorithms to predict missing values. Machine learning libraries such as Scikit-learn and TensorFlow are also used to implement algorithmic functions.
[0019] S203: Combining database middleware with AI framework: With the help of database middleware such as MyBatis-Plus and Hibernate, combined with the AI framework, efficient reading, writing and processing of data can be achieved. At the same time, ETL tools are used to complete the data extraction, conversion and loading process with the assistance of AI.
[0020] Preferably, the data preprocessing specifically includes:
[0021] S301: Data Cleaning: Training a data cleaning machine learning model. This model is trained on a large number of labeled data sets containing missing values, duplicate values, and outliers. The model automatically detects and processes missing values, duplicate values, and outliers by analyzing the distribution characteristics of the data and the relationship between fields.
[0022] S302: Data Standardization: Build a data standardization rule model, use natural language processing technology to analyze the semantic information of fields in each database, and combine machine learning algorithms to automatically learn the conversion rules of data formats and encoding methods;
[0023] S303: For unstructured data, use computer vision technology and natural language processing technology to extract key information and convert it into structured data.
[0024] Preferably, the establishing of the data mapping relationship specifically includes:
[0025] S401: Automatic Mapping: Based on a deep learning pattern matching algorithm, a pattern matching neural network model is constructed. The model inputs the table structure and field feature information of the source and target databases, and automatically generates a data mapping relationship by calculating the similarity of field names, data types, and semantics.
[0026] S402: Manual verification and adjustment: The automatically generated data mapping relationship is displayed to the user, who can perform manual verification and adjustment. The system provides a visual mapping relationship editing interface, where users can manually modify the mapping rules. For complex business logic relationships, users can easily configure them. At the same time, the user's adjustment operations are recorded for further optimization of the automatic mapping model.
[0027] Preferably, the building of the intelligent docking program specifically includes:
[0028] S501: Write code to implement the docking logic: Use a programming language, combined with the selected AI framework and database driver, to write programs to read, process, and write data. Utilize code generation technology to automatically generate part of the docking code framework based on the established data mapping relationships and data processing rules. Developers only need to add a small amount of business logic code.
[0029] S502: Integrate AI models: Embed trained AI models such as data cleaning models, data standardization models, and data conversion models into the docking program to achieve automated data processing. Before writing data into the target database, use AI models to perform data quality detection and content review.
[0030] Preferably, the testing and optimization specifically include:
[0031] S601: Functional testing: Build a test case generation model that automatically generates functional test cases based on demand analysis and data mapping relationships;
[0032] S602: Performance Testing: Use performance testing tools to simulate large-scale data transmission and processing scenarios, test the performance indicators of the docking program, and use machine learning performance analysis models to analyze the test results and identify performance bottlenecks.
[0033] S603: Exception handling test: artificially create various abnormal situations to test the exception handling capabilities of the docking program, build an exception handling evaluation model, and evaluate the rationality and effectiveness of exception handling based on the performance of the program under abnormal situations.
[0034] Preferably, the deployment and monitoring specifically include:
[0035] S701: Deployment and launch: Deploy the tested and optimized docking program to the production environment to ensure stable connection and data interaction with the local database;
[0036] S702: Real-time monitoring: Establish a monitoring indicator system, including data transmission volume, processing time, and error rate, and use a deep learning anomaly detection model to monitor the data docking process in real time. The model learns the distribution characteristics of monitoring indicator data under normal circumstances. When the indicators fluctuate abnormally, it automatically issues an alert and quickly locates the cause of the problem by analyzing logs and operating data.
[0037] The technical effects and advantages of the present invention are as follows:
[0038] This invention uses artificial intelligence technology to automatically complete tasks such as data combing, mapping relationship establishment, and code generation, significantly reducing manual operations, improving the efficiency of local database docking, and shortening project cycles; using AI algorithms to clean, standardize, and convert data, as well as automatically generate data mapping relationships, reduces the probability of human error and ensures accurate and complete data docking; when the database structure or business needs change, the AI model can quickly learn and adapt to the new situation, automatically adjust data processing and docking rules, reduce maintenance costs, and improve the flexibility and scalability of the system; through real-time monitoring functions combined with AI anomaly detection models, problems in the data docking process can be discovered in a timely manner, and solution suggestions can be provided to ensure the stable operation of data docking. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION
[0040] The present invention provides a local database docking method based on artificial intelligence, such as Figure 1 Shown, including:
[0041] S1: Demand analysis and planning, clarifying the goals of data docking and determining the data output format and requirements after docking;
[0042] Furthermore, demand analysis and planning specifically include:
[0043] S101: Clarify Business Requirements: Use natural language processing technology to perform semantic analysis on user-entered business requirement documents and conversations, extract key information, and clarify the goals of data integration, such as data analysis, business process automation, and data sharing. The system also determines the output format and requirements for the data after integration. For example, if a user enters "I need to integrate the sales database and inventory database to analyze the correlation between product sales and inventory," the system can automatically identify the integration goal as data analysis and the two databases involved.
[0044] S102: Sort out data resources: Automatically obtain information such as table structure, field meaning, data type, data volume, and data update frequency in the local database through database metadata acquisition technology. Draw a data relationship diagram based on knowledge graph technology to clearly present the relationship between data and provide a basis for subsequent docking. For example, construct a knowledge graph with database tables as nodes and field relationships as edges to intuitively display relationships such as foreign key associations between tables.
[0045] S2: Build an AI model and select or train an appropriate AI model based on your needs;
[0046] Furthermore, building an AI model specifically includes:
[0047] S201: Natural Language Processing: If the connection requirements include data queries or instructions described in natural language, select an appropriate NLP pre-trained model, such as BERT or GPT, and fine-tune it to accurately interpret user intent and convert natural language into query statements that the database can recognize. For example, if a user inputs "Query products with sales greater than 100 last month," the NLP model will process it and convert it into the SQL query statement "SELECT * FROM sales_table WHERE sales > 100 AND sale_date > = DATE_SUB(CURDATE(), INTERVAL 1MONTH)";
[0048] S202: Machine Learning Algorithms: For data cleaning and automatic learning of data transformation rules, select clustering algorithms (such as the K-Means algorithm) to identify abnormal data, and use regression algorithms (such as linear regression) to predict missing values. At the same time, use machine learning tool libraries such as Scikit-learn and TensorFlow to implement algorithm functions. For example, use the K-Means algorithm to cluster and identify abnormal order data in sales data, and then use the linear regression algorithm to predict missing sales based on historical sales data.
[0049] S203: Combining database middleware with AI framework: With the help of database middleware such as MyBatis-Plus and Hibernate, combined with AI framework, efficient data reading, writing and processing can be achieved. At the same time, ETL tools such as Apache NiFi and Kettle can be used to complete the data extraction, conversion and loading process with the assistance of AI. For example, MyBatis-Plus can be used to simplify database operations, combined with TensorFlow models to process the extracted data in real time, and then Apache NiFi can be used to realize automatic data loading.
[0050] S3: Data preprocessing, which implements data cleaning, standardization, and conversion functions;
[0051] Furthermore, data preprocessing specifically includes:
[0052] S301: Data Cleaning: Training a data cleaning machine learning model. This model is trained on a large number of labeled data sets containing missing values, duplicate values, and outliers. The model automatically detects and processes missing values, duplicate values, and outliers by analyzing the distribution characteristics of the data and the relationships between fields. For example, if a missing customer age field is correlated with fields such as the customer's spending amount and purchase frequency, the model can predict the missing age value through regression analysis. For duplicate order records, the model identifies and deletes duplicate data based on fields such as the order number and product information.
[0053] S302: Data Standardization: Build a data standardization rule model, use natural language processing technology to analyze the semantic information of fields in each database, and combine it with machine learning algorithms to automatically learn the conversion rules of data formats and encoding methods. For example, for date formats, if one database uses the "YYYY-MM-DD" format and another uses the "DD / MM / YYYY" format, the model can automatically identify and unify them into the specified format. For string case, all product names can be automatically converted to uppercase or lowercase according to business rules.
[0054] S303: For unstructured data, such as text descriptions in product images and customer review texts, use computer vision technology and natural language processing technology to extract key information and convert it into structured data. For example, use optical character recognition technology to extract text information from product images, and then use NLP technology to perform semantic analysis on the extracted text and convert it into structured fields that can be stored in the database, such as product model, specifications, etc.
[0055] S4: Establish data mapping relationships, complete automatic mapping and manual verification and adjustment operations;
[0056] Furthermore, establishing a data mapping relationship specifically includes:
[0057] S401: Automatic Mapping: Based on a deep learning pattern matching algorithm, a pattern matching neural network model is constructed. The model inputs the table structure and field feature information of the source and target databases. By calculating field name similarity (such as using the edit distance algorithm to calculate string similarity), data type similarity, and semantic similarity (using a word vector model such as Word2Vec to calculate the vector distance of field semantics), it automatically generates a data mapping relationship. For example, if there is a "product_id" field in the source database and a "goods_id" field in the target database, the model analyzes and finds that the two have similar semantics and the same data type, and thus recommends establishing a mapping relationship.
[0058] S402: Manual verification and adjustment: The automatically generated data mapping relationship is displayed to the user, who can perform manual verification and adjustment. The system provides a visual mapping relationship editing interface, where users can manually modify the mapping rules. For complex business logic relationships, such as those involving the combined mapping of multiple fields, users can easily configure them. At the same time, the system records the user's adjustment operations for further optimization of the automatic mapping model.
[0059] S5: Build an intelligent docking program, write code to implement docking logic and integrate AI models;
[0060] Furthermore, building an intelligent docking program specifically includes:
[0061] S501: Write code to implement the docking logic: Use a programming language (such as Python or Java) in conjunction with the selected AI framework and database driver to write programs to read, process, and write data. Utilize code generation technology to automatically generate part of the docking code framework based on the established data mapping relationships and data processing rules. Developers only need to add a small amount of business logic code. For example, use Python's Pandas library for data processing and connect to the database through SQLAlchemy. The system automatically generates the basic code structure for data reading and writing. Developers only need to add code related to data conversion and business rules.
[0062] S502: Integrate AI models: Embed trained AI models such as data cleaning models, data standardization models, and data conversion models into the docking program to achieve automated data processing. Before writing data into the target database, use AI models to perform data quality detection and content review. For example, use a trained anomaly detection model to perform real-time detection on the data to be written. If abnormal data is found, an alarm will be issued and the data writing will be blocked. At the same time, the abnormal data will be recorded for subsequent analysis.
[0063] S6: Testing and optimization, performing functional testing, performance testing, and exception handling testing;
[0064] Furthermore, testing and optimization specifically include:
[0065] S601: Functional Testing: Build a test case generation model that automatically generates functional test cases based on requirements analysis and data mapping relationships. By simulating different data inputs and business scenarios, conduct comprehensive functional testing on the developed docking program to verify that data can be accurately and completely transferred from the source database to the target database, and check whether the data mapping relationship and processing logic are correct. For example, based on the data mapping relationship, generate test data containing various field value combinations to check whether the values of the corresponding fields in the target database are correctly mapped and converted.
[0066] S602: Performance Testing: Use performance testing tools to simulate scenarios involving the transmission and processing of large amounts of data, testing the performance indicators of the docking program, such as data transmission speed and resource utilization. Leveraging machine-learning performance analysis models, analyze the test results to identify performance bottlenecks. For example, if analysis reveals that the data conversion process is time-consuming, the model can recommend optimizing the data conversion algorithm or increasing computing resources. Developers can then implement optimizations based on these recommendations, such as using parallel computing technology to accelerate data conversion.
[0067] S603: Exception Handling Test: Various exceptions are artificially created, such as network outages, database connection failures, and data format errors, to test the exception handling capabilities of the docking program. An exception handling evaluation model is then constructed to assess the rationality and effectiveness of the exception handling based on the program's performance under these conditions. For example, when a network outage occurs, the program should automatically retry the connection and record the time of the outage and the number of retries. The exception handling evaluation model uses these records to determine whether the exception handling mechanism meets the requirements and, if not, provides improvement suggestions.
[0068] S7: Deployment and monitoring, responsible for program deployment and real-time monitoring;
[0069] Furthermore, deployment and monitoring specifically include:
[0070] S701: Deployment and rollout: Deploy the tested and optimized integration program to the production environment, ensuring stable connection and data interaction with the local database. Use containerization technology (such as Docker) to package the integration program and its dependent environments into containers, facilitating rapid deployment and migration across different server environments.
[0071] S702: Real-time monitoring: Establish a monitoring indicator system, including indicators such as data transmission volume, processing time, and error rate, and use a deep learning anomaly detection model to monitor the data docking process in real time. The model learns the distribution characteristics of monitoring indicator data under normal circumstances. When the indicators fluctuate abnormally, it automatically issues an alert and quickly locates the cause of the problem by analyzing logs and operating data. For example, when the data transmission volume suddenly drops sharply, the model analyzes the logs and finds that the database connection pool is full, thus providing the operation and maintenance personnel with a solution suggestion of "increasing the size of the database connection pool."
[0072] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are intended to be protected by the present invention.
Claims
1. A local database docking method based on artificial intelligence, characterized by: The local database docking method specifically includes: S1: Demand analysis and planning, clarifying the goals of data docking and determining the data output format and requirements after docking; S2: Build an AI model and select or train an appropriate AI model based on your needs; S3: Data preprocessing, which implements data cleaning, standardization, and conversion functions; S4: Establish data mapping relationships, complete automatic mapping and manual verification and adjustment operations; S5: Build an intelligent docking program, write code to implement docking logic and integrate AI models; S6: Testing and optimization, performing functional testing, performance testing, and exception handling testing; S7: Deployment and monitoring, responsible for program deployment and real-time monitoring.
2. The method for connecting to a local database based on artificial intelligence according to claim 1, characterized in that: The demand analysis and planning specifically include: S101: Clarify business requirements: Use natural language processing technology to perform semantic analysis on business requirement documents and conversations entered by users, extract key information, clarify the goals of data integration, and determine the data output format and requirements after integration; S102: Sort out data resources: Automatically obtain the table structure, field meaning, data type, data volume, and data update frequency in the local database through database metadata acquisition technology. Based on knowledge graph technology, draw a data relationship diagram to clearly present the relationship between data, providing a basis for subsequent docking.
3. The method for connecting to a local database based on artificial intelligence according to claim 1, characterized in that: The construction of the AI model specifically includes: S201: Natural Language Processing: If the connection requirements include data queries or instructions described in natural language, select an appropriate NLP pre-trained model and fine-tune it to enable it to accurately interpret user intent and convert natural language into query statements that the database can recognize; S202: Machine Learning Algorithms: Targeting data cleaning and automatic learning of data transformation rules, selecting clustering algorithms to identify abnormal data, and using linear regression algorithms to predict missing values. Machine learning libraries such as Scikit-learn and TensorFlow are also used to implement algorithmic functions. S203: Combining database middleware with AI framework: With the help of database middleware such as MyBatis-Plus and Hibernate, combined with the AI framework, efficient reading, writing and processing of data can be achieved. At the same time, ETL tools are used to complete the data extraction, conversion and loading process with the assistance of AI.
4. The method for connecting to a local database based on artificial intelligence according to claim 1, characterized in that: The data preprocessing specifically includes: S301: Data Cleaning: Training a data cleaning machine learning model. This model is trained on a large number of labeled data sets containing missing values, duplicate values, and outliers. The model automatically detects and processes missing values, duplicate values, and outliers by analyzing the distribution characteristics of the data and the relationship between fields. S302: Data Standardization: Build a data standardization rule model, use natural language processing technology to analyze the semantic information of fields in each database, and combine machine learning algorithms to automatically learn the conversion rules of data formats and encoding methods; S303: For unstructured data, use computer vision technology and natural language processing technology to extract key information and convert it into structured data.
5. The local database docking method based on artificial intelligence according to claim 1, characterized in that: The establishment of the data mapping relationship specifically includes: S401: Automatic Mapping: Based on a deep learning pattern matching algorithm, a pattern matching neural network model is constructed. The model inputs the table structure and field feature information of the source and target databases, and automatically generates a data mapping relationship by calculating the similarity of field names, data types, and semantics. S402: Manual verification and adjustment: The automatically generated data mapping relationship is displayed to the user, who can perform manual verification and adjustment. The system provides a visual mapping relationship editing interface, where users can manually modify the mapping rules. For complex business logic relationships, users can easily configure them. At the same time, the user's adjustment operations are recorded for further optimization of the automatic mapping model.
6. The local database docking method based on artificial intelligence according to claim 1, characterized in that: The smart docking program specifically includes: S501: Write code to implement the docking logic: Use a programming language, combined with the selected AI framework and database driver, to write programs to read, process, and write data. Utilize code generation technology to automatically generate part of the docking code framework based on the established data mapping relationships and data processing rules. Developers only need to add a small amount of business logic code. S502: Integrate AI models: Embed trained AI models such as data cleaning models, data standardization models, and data conversion models into the docking program to achieve automated data processing. Before writing data into the target database, use AI models to perform data quality detection and content review.
7. The local database docking method based on artificial intelligence according to claim 1, characterized in that: The testing and optimization specifically include: S601: Functional testing: Build a test case generation model that automatically generates functional test cases based on demand analysis and data mapping relationships; S602: Performance Testing: Use performance testing tools to simulate large-scale data transmission and processing scenarios, test the performance indicators of the docking program, and use machine learning performance analysis models to analyze the test results and identify performance bottlenecks. S603: Exception handling test: artificially create various abnormal situations to test the exception handling capabilities of the docking program, build an exception handling evaluation model, and evaluate the rationality and effectiveness of exception handling based on the performance of the program under abnormal situations.
8. The local database docking method based on artificial intelligence according to claim 1, characterized in that: The deployment and monitoring specifically include: S701: Deployment and launch: Deploy the tested and optimized docking program to the production environment to ensure stable connection and data interaction with the local database; S702: Real-time monitoring: Establish a monitoring indicator system, including data transmission volume, processing time, and error rate, and use a deep learning anomaly detection model to monitor the data docking process in real time. The model learns the distribution characteristics of monitoring indicator data under normal circumstances. When the indicators fluctuate abnormally, it automatically issues an alert and quickly locates the cause of the problem by analyzing logs and operating data.