Intelligent data access and integration system based on dynamic expansion architecture

Abstract

The invention discloses an intelligent data access and integration system based on a dynamic expansion architecture, and the system comprises a data source automatic recognition module which automatically detects and recognizes a plurality of data sources and the characteristics of the data sources; the data adaptation module carries out adaptation according to the characteristics of the data source; the intelligent data quality management module predicts and automatically corrects a data quality problem based on a self-learning mechanism; the data preprocessing module is used for cleaning and converting original data; the data integration module is used for adjusting a data processing flow according to a real-time load through a dynamic expansion framework and a load balancing mechanism; the data storage module supports multiple database types and realizes separated storage of cold and hot data; the API management module is used for developing and maintaining a data access interface; and the monitoring and optimizing module monitors the system performance and the data flow in real time and guarantees the stable and efficient operation of the system. According to the invention, the data access efficiency and quality and the utilization rate of system resources are improved, and the system stability is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent data access and integration system based on a dynamic expansion architecture. Background Art

[0002] With the rapid development of big data and Internet of Things (IoT) technologies, more and more enterprises and organizations are facing the challenge of how to process and manage massive amounts of information from multiple data sources. These data sources may be structured, semi-structured, or unstructured, covering different formats, protocols, and storage methods. Especially in the fields of financial services, healthcare, smart cities, and industrial IoT, data access, integration, and management have become a key technical issue. How to obtain high-quality, usable data from different data sources and ensure that this data can be processed and analyzed efficiently and securely has become an urgent issue to be solved in modern data-driven systems.

[0003] Traditional data access and integration methods mainly rely on manual or semi-automatic methods to identify and process data sources. These methods often require a large number of predefined rules and manual intervention to adapt to the formats and protocols of different data sources. This approach may be feasible when processing small amounts of data, but its limitations gradually become apparent as the number and scale of data sources increase. Especially when dealing with large-scale and diverse data sources, traditional methods face many problems in efficiency, flexibility, and scalability.

[0004] Common solutions in existing technologies include data warehouses, ETL (extract, transform, and load) tools, and data lakes.

[0005] A data warehouse is a centralized system for storing and managing large amounts of structured data, and is widely used in business intelligence (BI) and analysis scenarios. The design goal of a data warehouse is to provide high-quality data for decision support systems, by integrating and cleaning data from multiple different sources to establish a unified view, thereby supporting data analysis. However, a major limitation of traditional data warehouses is that they can only process structured data, and their support for unstructured or semi-structured data is relatively weak. With the diversification of data types (such as text, images, sensor data, etc.), relying solely on data warehouses can no longer meet the needs of modern data management.

[0006] As a common data processing tool, ETL tools are mainly used to extract data from different data sources, transform it, and then load it into the data warehouse. ETL tools play an important role in the data integration process, especially in enterprise information systems. However, traditional ETL tools have shown some shortcomings when facing large-scale, multi-source heterogeneous data. First, the ETL process usually requires complex manual configuration, and the data processing speed is slow when processing large-scale data. Secondly, ETL tools have poor adaptability to data sources, limited scalability, and cannot effectively process unstructured data. In addition, ETL tools usually rely on hard-coded transformation rules and lack intelligent adaptation capabilities.

[0007] A data lake is a centralized storage platform that can store structured and unstructured data, solving the problem that data warehouses cannot process unstructured data. Data lakes are usually used to store raw data without any preprocessing or conversion of the data, so that they can accommodate data of various types and formats. Although data lakes have advantages in processing large-scale, heterogeneous data, they have obvious deficiencies in data governance, data quality management, and data query efficiency. Since the data in the data lake is not preprocessed, users often need to perform complex preprocessing operations when using the data, and due to the large amount of data, data redundancy and poor data quality are prone to problems. In addition, the architecture of the data lake is relatively complex, and the maintenance and management costs are high.

[0008] As enterprise data demands grow and data sources diversify, traditional data warehouses, ETL tools, and data lakes face many technical limitations when dealing with modern data environments. Specifically, existing technologies have problems in the following aspects: Existing systems usually rely on manual configuration to handle the access of different data sources. This approach is not only time-consuming and labor-intensive, but also difficult to adapt to the rapid changes in multi-source data. Every time a new data source is added, it is often necessary to redesign and configure the corresponding adaptation rules, resulting in inefficient data access. In addition, traditional data systems have limited capabilities and lack flexibility and scalability when dealing with unstructured data (such as text, images, videos, etc.).

[0009] Data quality is a key challenge in large-scale data processing. Most existing systems rely on manual post-data cleaning and are unable to monitor and correct data quality issues in real time. For example, in ETL tools, the identification and correction of data quality issues are usually performed after data loading is completed. This approach not only prolongs data processing time, but may also lead to a decrease in the accuracy of subsequent analysis results. In addition, traditional systems lack intelligent self-learning mechanisms and are unable to predict and optimize based on historical data quality issues.

[0010] Traditional data processing systems usually suffer from slow processing speeds and long response times when faced with large-scale data. Especially when processing heterogeneous data from multiple sources, existing ETL tools are difficult to maintain high efficiency while taking into account flexibility due to their hard-coded rules and fixed processes. When data traffic fluctuates greatly, the system is prone to overload or reduced processing efficiency. In addition, existing systems lack dynamic expansion capabilities and cannot adjust the allocation of processing resources according to changes in data traffic.

[0011] Traditional data systems are mostly based on fixed architecture design and have poor scalability. As the number of data sources increases and the amount of data grows, the load capacity and processing performance of existing systems often cannot meet the needs. At the same time, traditional systems that lack modular design require a lot of system reconstruction when dealing with the expansion of new functions, which increases the cost of development and maintenance.

[0012] Existing data systems usually only provide limited data access interfaces, which cannot meet the flexible data access requirements in different application scenarios. Especially when it comes to multiple data sources, different user groups and different permission requirements, the API management capabilities of existing systems are relatively single, making it difficult to achieve refined control over data access. At the same time, the security of data access is insufficient and cannot cope with complex permission management and data privacy protection requirements.

[0013] Therefore, developing an intelligent, efficient and automated multi-source data access and integration system has become a key direction to solve existing technical problems. Summary of the invention

[0014] In order to solve the above problems in the prior art, the present invention proposes an intelligent data access and integration system based on a dynamic expansion architecture, comprising: Data source automatic identification module, used to automatically detect and identify multiple data sources and their characteristics; A data adaptation module, connected to the data source automatic identification module, for adapting the data source according to the identified characteristics; Intelligent data quality management module, which is used to predict and automatically correct data quality issues based on a self-learning mechanism; Data preprocessing module, used to clean and transform raw data; The data integration module uses a dynamic expansion architecture to automatically adjust the data processing process according to the real-time data load. It simplifies the data processing steps when the data load is low, starts additional ETL services when the data load is high, and uses a dynamic load balancing mechanism to start or shut down additional processing nodes according to data processing needs; Data storage module, used to store data, supporting multiple types of databases; API management module, used to develop and maintain data access interfaces; Monitoring and optimization module, used to monitor system performance and data processing flow.

[0015] The data source automatic identification module includes: a data collection unit for detecting and identifying multiple data sources and their characteristics, wherein the characteristics include data format, structure and access rights; an automated script or standardized interface for collecting and analyzing metadata information of the data source; a data source directory establishment unit for establishing a data source directory based on the collected metadata information; a data connection verification unit for verifying the accessibility of the data source connection; and a data sampling unit for sampling data from the data source.

[0016] The data adaptation module includes: an interface connected to the data source automatic identification module, used to receive data source characteristic information; a data format conversion unit, used to convert data formats of different data sources; A field mapping unit is used to generate field mapping rules according to data source characteristics to achieve automatic matching of field correspondences; The structural standardization unit is used to standardize the structures of different data sources to ensure the compatibility and accessibility of the data.

[0017] The intelligent data quality management module includes: a self-learning mechanism for optimizing data quality detection rules through historical data; A quality monitoring unit to monitor data integrity, consistency, and anomaly detection; A statistical analysis and machine learning unit for predicting and automatically correcting data quality issues, wherein the machine learning unit includes algorithms for missing value detection, duplicate data detection, and anomaly detection; A collaborative interface with the data preprocessing module to trigger data correction operations when data quality issues are detected.

[0018] The dynamic expansion architecture adopted by the data integration module is to automatically adjust the data processing flow based on the automatic scheduling algorithm of the load threshold; the formula of the automatic scheduling algorithm is as follows:

[0019] in: : CPU load ratio, which indicates the ratio of the current CPU usage to the total capacity; : Memory load ratio, which indicates the ratio of current memory usage to total capacity; : The load ratio of network bandwidth, which indicates the ratio of the current usage of network bandwidth to the total bandwidth; : Disk I / O load ratio, which indicates the ratio of the current usage of disk I / O to the total I / O capacity; is the weight coefficient.

[0020] when When , the system will expand the nodes, and the number of expanded nodes is calculated as follows:

[0021] in: is the number of nodes that need to be expanded; is the current total system load; is the upper load threshold; is the expansion ratio function.

[0022] The specific formula of the expansion ratio function is: in: There are separate upper thresholds for CPU, memory, network bandwidth, and disk I / O.

[0023] The data storage module includes: a storage unit that supports multiple database types, including relational databases, NoSQL databases, and cloud-based storage systems; Dynamic selection unit, used to dynamically select storage solutions based on data access frequency and importance, to achieve separate storage of hot and cold data; The data migration unit is used to automatically migrate data between different storage systems when the data access frequency changes.

[0024] The API management module includes: Supports data access unit with multiple interfaces; Multi-version management unit, used to support simultaneous maintenance of multiple API versions; Authentication and authorization unit, which performs user identity authentication and controls data access permissions; Use monitoring and compliance review units to monitor access frequency, response time, and usage patterns of API requests and generate log reports.

[0025] The monitoring and optimization module includes: a visual monitoring interface for displaying the system operation status; a key performance indicator monitoring unit for monitoring CPU usage, memory usage, data processing delay, network traffic and database query response time; a system health assessment unit for real-time assessment of system health status through system logs and data traffic analysis; an automatic optimization unit for dynamically adjusting system resource configuration according to real-time monitoring data; and a trend analysis unit for predicting system resource bottlenecks and expanding resources in advance.

[0026] The intelligent data access and integration system of the present invention realizes automatic identification, adaptation and efficient integration of multiple heterogeneous data sources through modular design, and has the following beneficial effects: The data source automatic identification and data adaptation module automatically identifies and adapts to diverse data sources, reduces manual intervention, and improves data access speed. The intelligent data quality management module adopts a self-learning mechanism that can automatically predict and correct data problems to ensure data quality and accuracy. The data integration module adopts a dynamic expansion architecture and load balancing mechanism to adjust processing nodes according to real-time load and optimize resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings: Figure 1 The overall structural block diagram of the intelligent data access and integration system of the present invention is shown; Figure 2 A functional structure diagram of the data integration module is shown. DETAILED DESCRIPTION

[0028] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0029] like Figure 1 As shown, the present application provides an intelligent data access and integration system. The system realizes automatic access, intelligent processing and efficient management of multi-source data through the collaborative work of multiple modules. The main components of the system include: data source automatic identification module 101, data adaptation module 102, intelligent data quality management module 103, data preprocessing module 104, data integration module 105, data storage module 106, API management module 107 and monitoring and optimization module 108.

[0030] The data source automatic identification module 101 is used to automatically detect and identify multiple data sources and their characteristics, including data format, structure, access rights, etc., and can efficiently process heterogeneous data sources from different types, such as relational databases, NoSQL databases, file systems, API interfaces, etc. This module collects and analyzes metadata information of each data source through automated scripts or standardized interfaces, automatically establishes a data source directory, and performs data connection verification and preliminary data sampling.

[0031] The design of this module allows the system to autonomously identify and adapt to the formats and structures of different data sources without pre-defining the data model, significantly reducing the complexity and time cost of manual participation. In large-scale data access scenarios, the data source automatic identification module can greatly improve the efficiency of data access while ensuring the accuracy and consistency of data source access.

[0032] For example, when the system is connected to multiple heterogeneous data sources, the data source automatic identification module can quickly determine the format (such as XML, JSON, CSV, etc.), structure (such as relational table structure, document structure) and access permission settings of each data source, and pass this information to subsequent modules for further processing. This automation feature greatly improves the system's adaptability to diverse data sources, and is particularly suitable for complex scenarios that require processing large amounts of real-time or dynamically updated data, such as IoT device data, financial data streams, cloud service interfaces, etc. This module not only supports common data sources, but can also be expanded according to preset rules to support new types of data sources in the future.

[0033] In addition, the data source automatic identification module can also automatically perform data connection verification during the data source access process to ensure the accessibility of the data source and the integrity of the data. When performing preliminary data sampling, the module can also perform rationality checks on the data source based on the rules set by the system to prevent the access of invalid or erroneous data sources, thereby improving the robustness of the entire system and data processing efficiency.

[0034] The data adaptation module 102 is connected to the data source automatic identification module and is responsible for automatically performing data adaptation and standardization processing according to the identified data source characteristics. This module uses built-in algorithms to unify the formats and structures of different data sources to ensure data compatibility and accessibility. The adaptation operation includes but is not limited to processing steps such as data format conversion, field mapping, and structure standardization to eliminate format and structure differences between various data sources, thereby achieving seamless data integration.

[0035] For example, if the structure of a data source does not match the standard data model of the system, the data adaptation module can automatically generate corresponding mapping rules to convert the incompatible data structure into a unified format that the system can recognize and process. Typical adaptation operations include matching the table structure from the relational database with the standard object model in the system, converting JSON format data into a unified structured data form, or automatically matching the mapping of different field names.

[0036] This flexible adaptation mechanism enables the system to process data from various sources and in different formats, thus significantly enhancing the system's compatibility and adaptability to heterogeneous data. The adaptation module eliminates the need for manual adjustments through automated processes, greatly improving the efficiency of data processing and reducing the error rate caused by format mismatches. The adaptation module can also customize the adaptation logic based on the characteristics of the data source and business requirements to ensure that the system can adapt to the complex data requirements of different application scenarios.

[0037] In addition, the data adaptation module can also seamlessly connect with the subsequent processing module to ensure that the adapted data can smoothly enter the subsequent data processing and storage process. The design of this module is highly flexible and extensible, and can expand new adaptation rules or algorithms as system requirements change, supporting the changing needs of future data processing.

[0038] Through the collaborative work of the data source automatic identification module and the data adaptation module, the system can automatically complete the entire process from data source detection and identification to format adaptation and standardized processing, providing an efficient and stable basic guarantee for subsequent data analysis and management.

[0039] The intelligent data quality management module 103 is responsible for comprehensive quality management of the data entering the system, ensuring that the data has been fully quality tested and corrected before entering each link of the system. This module contains a self-learning mechanism that can predict and automatically correct data quality problems through statistical analysis and machine learning algorithms, thereby improving the overall data quality of the system.

[0040] The data quality dimensions that the intelligent data quality management module mainly monitors include data integrity, consistency, and anomaly detection. Among them, integrity detection is used to ensure that there are no missing values ​​or null values ​​in the data set, consistency detection ensures that there are no conflicts or duplications between data, and anomaly detection is used to identify abnormal data points outside the reasonable range.

[0041] To achieve these detection functions, the module uses a variety of statistical analysis and machine learning techniques. Statistical analysis methods include descriptive statistics (such as mean, variance, standard deviation, etc.), while machine learning algorithms include supervised learning and unsupervised learning algorithms. Supervised learning algorithms are trained based on existing labeled data to identify potential quality issues. Unsupervised learning can be used to detect new data anomaly patterns, such as using the K-means clustering algorithm to detect possible abnormal groups in a data set.

[0042] The data quality management module uses these methods to continuously update its understanding of different data sets and generate corresponding quality detection rules. When new data comes in, the module can quickly verify the data according to the existing rules and predict the quality problems that may exist in the data. The system will also continuously improve and update the detection algorithm through common quality problems in historical data, and gradually improve the ability to predict data quality problems.

[0043] The intelligent data quality management module has an embedded self-learning mechanism that enhances the accuracy of quality management by continuously learning from historical data. Specifically, the system regularly collects processed data sets, analyzes common quality issues (such as missing values, format errors, numerical anomalies, etc.), and continuously optimizes its detection and correction strategies through these analyses. For example, using decision trees or random forest algorithms can identify the most common features that cause data problems and readjust the parameters of the detection model based on the feature weights.

[0044] This self-learning mechanism can identify new types of problems based on the historical behavior patterns of the data. For example, when a specific data source is found to have long-term anomalies in a specific field, the system can dynamically adjust the detection rules to detect similar errors earlier. In addition, the system can also automatically generate remediation plans. For example, when missing values ​​often appear in certain fields, the module can automatically recommend the most suitable filling values ​​(such as mean filling, nearest neighbor interpolation, etc.) based on historical data, thereby reducing manual intervention.

[0045] When the intelligent data quality management module detects data quality problems, the system will work with the data preprocessing module 104 to automatically trigger the corresponding processing operation. For example, when the module detects that a data set has missing values, the system will call the corresponding operation in the data preprocessing module to fill the missing values. The system supports a variety of filling strategies, including common mean filling, mode filling, interpolation methods (such as linear interpolation or polynomial interpolation), etc. The specific strategy is automatically selected based on the data type and context.

[0046] In addition, for duplicate data detection and deletion, the intelligent data quality management module identifies data redundancy by comparing different records in the same data set. For example, by comparing string similarity algorithms, the module can effectively identify potential duplicate records and automatically delete redundant records through collaboration with the data preprocessing module.

[0047] The processing of abnormal data is automatically achieved through anomaly detection algorithms. The intelligent data quality management module uses anomaly detection based on support vector machines (SVM) or isolation forest algorithms to identify abnormal points in the data, and marks these data points as potential problems, which are then corrected by the data preprocessing module. Anomaly correction can include simple outlier discarding operations, or outlier replacement or interpolation processing based on business needs.

[0048] The intelligent data quality management module can not only detect and correct problems in existing data, but also predict potential problems that may occur in the future through a self-learning mechanism. For example, by analyzing common error types and patterns in historical data, the system can generate a data quality warning model to issue quality warnings for data that is about to enter the system. For example, if a certain type of data source has frequently had null value problems in the past, the system will automatically increase the focus of monitoring on this data source and even correct possible data problems in advance.

[0049] This prediction function effectively avoids the accumulation of erroneous data, prevents data quality issues from spreading in the system, and ensures long-term stable and efficient operation of the system.

[0050] The intelligent data quality management module also has a report generation function that can generate detailed data quality analysis reports. The report includes detailed statistical information on data integrity, consistency and anomaly detection, and can display historical trends and processing records of various data problems. This report function not only provides system managers with a comprehensive data quality monitoring tool, but also provides a reliable basis for data analysis and business decision-making.

[0051] Through the self-learning mechanism and real-time data correction function of the intelligent data quality management module, the system can continuously optimize the data quality management process, achieve efficient quality detection, prediction and processing, and ensure the accuracy and consistency of all data in the system.

[0052] The data preprocessing module 104 is closely integrated with the intelligent data quality management module to clean and convert the original data. Data preprocessing includes missing value processing, error correction, format standardization and data fusion. The system performs preprocessing operations according to different rule sets (such as data cleaning rules, format conversion rules) to ensure that the data can meet the requirements of subsequent storage and analysis.

[0053] For example, when processing data from multiple data sources, the data preprocessing module can automatically perform encoding conversion operations based on different data characteristics to ensure format compatibility between all data sources. In this way, the system can effectively remove redundant and abnormal information in the data, laying the foundation for subsequent data analysis.

[0054] The data integration module 105 is based on a containerized microservice architecture and uses a dynamic expansion architecture and a dynamic load balancing mechanism (such as Figure 2 This module uses ETL tools to extract and transform pre-processed data from the source system and load it into the target data storage to achieve unified data management and analysis.

[0055] Automatically adjust the data processing flow according to the real-time data load of the system. When the data load is low, simplify the processing steps to save resources; when the data load is high, the system automatically starts additional ETL services to ensure data processing efficiency.

[0056] Dynamically start or shut down processing nodes according to system load to ensure on-demand expansion of data processing capabilities. When dealing with large-scale data access and high-concurrency access, the load balancing mechanism can effectively allocate system resources to ensure smooth and efficient data processing.

[0057] The data storage module 106 is used to efficiently and securely store the integrated and processed data, and supports various types of database systems, including relational databases, NoSQL databases, and storage solutions based on cloud architecture. The core of this module is that it can dynamically select the optimal storage solution based on the access frequency and importance of the data, thereby realizing the separate storage of hot and cold data, which not only meets the needs of efficient query, but also minimizes storage costs.

[0058] For "hot data" with high access frequency, such as data that needs to be processed in real time or frequently queried, the system will give priority to storage methods with higher performance. Common storage options include relational databases (such as MySQL and PostgreSQL), which have powerful transaction processing capabilities and efficient query performance and can quickly respond to query requests. In addition, in scenarios that require high concurrent access, the data storage module can also use distributed databases (such as Cassandra and CockroachDB) to ensure high data availability and high throughput.

[0059] For "cold data" with low access frequency, such as historical data or data that is not updated frequently, the system will use low-cost storage methods, such as NoSQL databases (such as MongoDB, Couchbase) or cloud storage services (such as Amazon S3, Azure Blob Storage). These storage methods not only have low operating costs, but also provide large-scale data storage capabilities through distributed storage architecture, which is suitable for storing large data sets that do not require frequent access. In addition, cloud storage solutions also provide flexible expansion capabilities and can dynamically adjust capacity according to data storage needs.

[0060] The data storage module ensures the efficiency and economy of the system when processing large amounts of data by classifying and storing data in a hierarchical manner. By combining the access pattern, update frequency and importance of the data, the module can realize the automatic migration of data between different storage systems to ensure the balance between the timeliness of data access and storage costs. For example, some data is frequently accessed over a period of time and is marked as "hot data" by the system and stored in a high-performance relational database. When the access frequency decreases, the system will automatically migrate it to a lower-cost NoSQL database or cloud storage to reduce unnecessary high-performance storage usage.

[0061] The API management module 107 is used to develop and maintain the data access interface of the system, and supports modern data interface standards such as RESTful API and GraphQL interface. Through this module, the system can provide flexible and unified data access methods for different types of users, applications or third-party services, ensuring the accessibility, scalability and security of data.

[0062] The API management module has a powerful multi-version management function, which enables developers to maintain multiple versions of APIs at the same time to meet the needs of different applications and user groups. Through version management, the system can push new functions or interface updates without affecting existing services, ensuring that users can choose the appropriate API version according to their needs, thereby achieving a smooth transition and avoiding business interruptions.

[0063] The API management module also has a complete identity authentication and authorization mechanism to ensure the security of data access. For example, the API management module supports standard authentication methods such as OAuth 2.0 and JWT (JSON Web Token), allowing the system to verify the identity of users and control their access to data based on the permissions of different users. Fine-grained permission control ensures that different users can only access data that matches their permissions, preventing the leakage of sensitive data.

[0064] In addition, the API management module has real-time usage monitoring and compliance review functions. The system can monitor the access frequency, response time and usage pattern of each API request, and generate detailed log reports based on user behavior. This information not only helps administrators identify potential abuse, but can also be used for system optimization, adjusting data access policies, and improving API response efficiency. By monitoring API usage, the system can also dynamically adjust access rights or restrictions based on the behavior of different users to ensure the security and compliance of data access.

[0065] The monitoring and optimization module 108 is responsible for real-time monitoring of the overall operation of the system and displays the system's operating status by providing a visual monitoring interface. This module can monitor key performance indicators (KPIs), such as CPU and memory usage, data processing latency, network traffic, database query response time, etc., and evaluate the health of the system in real time through system logs and data traffic analysis.

[0066] The data processing latency analysis provided by the monitoring and optimization module can help system administrators identify performance bottlenecks in the system. For example, in the data processing process, if the latency of a processing node continues to increase, the system can automatically allocate more resources through the feedback of this module, or start new processing nodes under the dynamic expansion architecture, thereby ensuring that the overall processing capacity of the system will not be affected by local bottlenecks.

[0067] The monitoring and optimization module also has the function of automatic optimization, which can dynamically adjust the system resource configuration according to the real-time monitoring data to ensure the optimal use of resources. In the case of a surge in data traffic, the monitoring module can sense it in time and automatically start the expansion mechanism to add additional processing nodes to avoid system overload. When the traffic returns to normal, the system will automatically release excess resources to reduce unnecessary resource consumption.

[0068] In addition, the monitoring and optimization module can predict possible system resource bottlenecks in advance through trend analysis. For example, if the data flow or processing request volume shows an increasing trend in a certain period of time, the system can expand resources in advance to avoid performance degradation or response delays during peak periods. This predictive capability improves the elasticity of the system and ensures that the system can still maintain efficient and stable operation when facing high concurrency and large-scale data processing.

[0069] The specific implementation of the data integration module is as follows: like Figure 2As shown, the data integration module 105 is based on a containerized microservice architecture, efficiently processes pre-processed data from multiple data sources, and integrates it into the target data storage system through extraction, transformation, and loading (ETL) operations to achieve unified management and analysis of data. The module achieves high efficiency and flexibility in data integration through a dynamic expansion architecture and a dynamic load balancing mechanism, and is particularly suitable for integrated processing of large-scale, multi-source heterogeneous data.

[0070] The data integration module adopts a containerized microservice architecture, so that each ETL task or data processing service can be encapsulated as an independent microservice unit. Through containerization, the system can realize independent deployment, operation and expansion of modules, ensuring high isolation and flexibility of services. Each ETL task runs as an independent microservice in its own container environment, ensuring that different data processing tasks do not interfere with each other, and the number of ETL services can be dynamically expanded or reduced through container orchestration tools (such as Kubernetes, DockerSwarm) to adapt to different data processing needs. Containerized services can be quickly started and shut down, which is suitable for data processing scenarios that require fast response.

[0071] The dynamic expansion architecture of the data integration module can automatically adjust the data processing flow according to the current data load of the system to ensure data processing efficiency and resource utilization. The specific implementation plan is as follows: Data load monitoring: The system monitors the input flow and system load of the data source in real time. Through data flow statistical analysis and historical data pattern recognition, the system can predict future data load trends and plan resource scheduling in advance.

[0072] Low-load scenarios: When the system detects that the data load is low, the dynamic expansion architecture can automatically simplify the data processing process. For example, the ETL service will automatically adjust the frequency of extraction and conversion, postpone some processing tasks until the load is higher to save resources, and may reduce some unnecessary data conversion operations, retaining only the core data integration process to ensure efficient use of resources.

[0073] High-load scenarios: When the system load increases (such as in the case of large-scale data access or high concurrent access), the dynamic expansion architecture automatically starts additional ETL services to ensure that the system processing capacity matches the data traffic. The system automatically increases the number of ETL container instances based on the current load to ensure that data processing tasks are carried out smoothly during peak periods.

[0074] The key technology of this dynamic expansion architecture is the automatic scheduling algorithm based on load thresholds. This algorithm dynamically expands or contracts system resources by setting upper and lower thresholds of system load (such as CPU usage, memory occupancy, data traffic size, etc.).

[0075] The specific formula of the automatic scheduling algorithm based on multi-dimensional load is as follows:

[0076] in: The current total system load is an overall load index calculated by the load ratio of each resource in the system, which is used to indicate the comprehensive utilization of the current system resources.

[0077] Under reasonable conditions, Usually in within the scope of the system, making it easier to compare and monitor.

[0078] Load ratios: The formula includes four load ratios, which represent the utilization of CPU, memory, network bandwidth, and disk I / O: : CPU load ratio, which indicates the ratio of the current CPU usage to the total capacity; : Memory load ratio, which indicates the ratio of current memory usage to total capacity; : The load ratio of network bandwidth, which indicates the ratio of the current usage of network bandwidth to the total bandwidth; : Disk I / O load ratio, which indicates the ratio of the current disk I / O usage to the total I / O capacity.

[0079] These ratios range from 0 to 1 and quantify how heavily each resource is being utilized. The closer the ratio is to 1, the more heavily loaded the resource is.

[0080] Weight coefficient ,The weight coefficient is used to reflect the relative importance of each resource in the overall load.,They flexibly adjust the system scheduling strategy by assigning different priorities to different,resources.

[0081] For example, for memory-intensive tasks, increasing The value can increase the weight of the memory load ratio in the overall load calculation; for I / O intensive tasks, increase To emphasize the impact of disk I / O load in system scheduling. The weight can be adjusted dynamically according to different task types.

[0082] Among them, when When , the system will expand the node, and the expansion number is calculated as follows:

[0083] in: Number of nodes to be expanded: Indicates the number of nodes that need to be expanded. This number is calculated by the ratio of the system load exceeding the upper threshold and is calculated by the function Precise adjustments ensure that the system can quickly replenish resources and relieve pressure when overloaded.

[0084] Since the number of expanded nodes should be an integer, the ceiling function is used To ensure that the calculation results are consistent with the actual number of scalable nodes.

[0085] (Overload ratio): Indicates the relative ratio of the current load exceeding the threshold. This item quantifies the deviation of the current system load from the acceptable upper limit, reflecting the current degree of system resource insufficiency.

[0086] The calculation method is: use the current system total load Subtract the upper load threshold , and then divided by , thus obtaining the overload ratio.

[0087] (Expanded ratio function): Function Used to adjust the expansion ratio so that the expansion process can allocate resources more accurately and efficiently.

[0088] It is a weight coefficient for different resources (such as CPU, memory, network bandwidth, disk I / O). Each coefficient has different priorities for different types of tasks. For example, it can be increased for I / O intensive tasks. , thereby giving priority to expanding I / O-related nodes.

[0089] Generates a scaling factor based on the load imbalance of the system. Pay attention to whether the load of each resource exceeds a separate upper limit, so that you can more specifically scale the resources that need it most.

[0090] The specific formula for the expansion adjustment coefficient is:

[0091] in: They are separate upper thresholds for CPU, memory, network bandwidth, and disk I / O, and their value ranges are 0-1. They are set according to the performance requirements and load capacity of the system.

[0092] The max function selects the maximum value between 0 and the difference; when the ratio of each resource exceeds the corresponding threshold, the expansion ratio function increases the contribution of the weight, otherwise it will be regarded as zero.

[0093] function The expansion ratio is calculated based on resource usage patterns, making expansion more accurate and effective.

[0094] The dynamic load balancing mechanism of the data integration module ensures that data processing capabilities can be expanded and allocated on demand according to load changes. The system continuously monitors the load of each ETL node. When the load of a node exceeds the threshold, the task will be redistributed to the node with lower load to ensure load balance. When the load increases significantly, the system will trigger the automatic expansion mechanism, start new ETL nodes and assign new tasks. When the load decreases, redundant nodes are automatically shut down to reduce resource waste. The ETL tool is highly adaptable in the data integration module, including adaptive data extraction, incremental data processing, and transformation load optimization.

[0095] Adjust the extraction strategy based on the real-time status of the data source and the output of the preprocessing module. Frequently updated data sources will speed up the extraction frequency; low-update data sources will reduce the extraction frequency through batch processing.

[0096] ETL tools only extract and convert new or updated data, avoiding duplicate processing and significantly improving processing efficiency.

[0097] During the data loading phase, the system optimizes the import speed through batch loading technology.

[0098] In order to further improve system efficiency, this application conducts a load forecasting model based on time series analysis. The specific forecasting model formula is:

[0099] in: For predicted future loads; is the average load, and are the autoregressive coefficient and the moving average coefficient, respectively.

[0100] (Forecasted load at future time): This is the predicted load value at future time t, which is used to estimate future system resource requirements. If the predicted load exceeds a certain set threshold, the system can expand resources in advance; conversely, if the load decreases, some resources will be released to optimize system costs.

[0101] (Load Average): is a constant term that represents the average level of historical load data. This constant ensures that the forecast value is based on the overall average load of the system in the past, reducing errors caused by occasional fluctuations.

[0102] The specific value of can be obtained by calculating the mean of historical data or by fitting the model.

[0103] (Autoregressive coefficients): It is the coefficient of the autoregressive term, which indicates the weight of the impact of historical load values ​​on the current forecast and reflects the continuity of past load data in time.

[0104] The parameter p is the order of the autoregressive part, that is, how many load values ​​in the past are included in the prediction calculation. By optimally selecting p, the model can better balance prediction accuracy and computational complexity.

[0105] (Moving average coefficient): It is the coefficient of the moving average (MA) term, which represents the amount of correction made by the previous error term to the forecast value. It is usually used to smooth short-term fluctuations caused by accidental events.

[0106] The parameter q is the order of the moving average part, which indicates how many past error terms are used for the current prediction calculation. By properly selecting the q value, the cumulative deviation of the error term can be effectively reduced.

[0107] (Error term): Represents the prediction error at the past i moment, which is a white noise sequence, that is, a random variable with zero mean. The introduction of the error term is to correct the random disturbance and make the model more robust. Through this model, the system can predict the load peak in advance, expand or release resources in advance, and optimize processing capacity; Represents the actual load value at the past i-th moment. It is the historical data used by the autoregressive part, that is, when predicting the load at the future moment t, the model will refer to the actual load data of the past i time steps ; These historical load values ​​are used to capture the trends and patterns of system load changes over time, allowing the model to predict future load conditions by analyzing past load behavior.

[0108] The data integration module uses containerized architecture, dynamic expansion and load balancing mechanisms, combined with adaptive ETL tools and predictive models, to ensure efficient data integration and unified management under different loads.

[0109] The above description is only a preferred embodiment of the present invention, so all equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. An intelligent data access and integration system, characterized by: The intelligent data access and integration system comprises: Data source automatic identification module, used to automatically detect and identify multiple data sources and their characteristics; A data adaptation module, connected to the data source automatic identification module, for adapting the data source according to the identified characteristics; Intelligent data quality management module, which is used to predict and automatically correct data quality issues based on a self-learning mechanism; Data preprocessing module, used to clean and transform raw data; The data integration module uses a dynamic expansion architecture to automatically adjust the data processing process according to the real-time data load. It simplifies the data processing steps when the data load is low, starts additional ETL services when the data load is high, and uses a dynamic load balancing mechanism to start or shut down additional processing nodes according to data processing needs; Data storage module, used to store data, supporting multiple types of databases; API management module, used to develop and maintain data access interfaces; Monitoring and optimization module, used to monitor system performance and data processing flow.

2. An intelligent data access and integration system as claimed in claim 1, characterized in that: The data source automatic identification module includes: a data collection unit for detecting and identifying multiple data sources and their characteristics, wherein the characteristics include data format, structure and access rights; an automated script or standardized interface for collecting and analyzing metadata information of the data source; a data source directory establishment unit for establishing a data source directory based on the collected metadata information; a data connection verification unit for verifying the accessibility of the data source connection; and a data sampling unit for sampling data from the data source.

3. An intelligent data access and integration system as claimed in claim 1, characterized in that: The data adaptation module includes: an interface connected to the data source automatic identification module, used to receive data source characteristic information; a data format conversion unit, used to convert data formats of different data sources; A field mapping unit is used to generate field mapping rules according to data source characteristics to achieve automatic matching of field correspondences; The structural standardization unit is used to standardize the structures of different data sources to ensure the compatibility and accessibility of the data.

4. The intelligent data access and integration system according to claim 1, characterized in that: The intelligent data quality management module includes: a self-learning mechanism for optimizing data quality detection rules through historical data; A quality monitoring unit to monitor data integrity, consistency, and anomaly detection; A statistical analysis and machine learning unit for predicting and automatically correcting data quality issues, wherein the machine learning unit includes algorithms for missing value detection, duplicate data detection, and anomaly detection; A collaborative interface with the data preprocessing module to trigger data correction operations when data quality issues are detected.

5. The intelligent data access and integration system according to claim 1, characterized in that: The dynamic expansion architecture adopted by the data integration module is to automatically adjust the data processing flow based on the automatic scheduling algorithm of the load threshold; the formula of the automatic scheduling algorithm is as follows: in: is the current total system load; : CPU load ratio, indicating the current CPU usage With total capacity The ratio of : The memory load ratio, indicating the current memory usage With total capacity The ratio of : The load ratio of the network bandwidth, indicating the current usage of the network bandwidth Total bandwidth The ratio of : Disk I / O load ratio, indicating the current usage of disk I / O Total I / O Capacity The ratio of is the weight coefficient.

6. An intelligent data access and integration system as claimed in claim 5, characterized in that: when When , the system will expand the nodes, and the number of expanded nodes is calculated as follows: in: is the number of nodes that need to be expanded; is the current total system load; is the upper load threshold; is the expansion ratio function.

7. An intelligent data access and integration system as claimed in claim 6, characterized in that: The specific formula of the expansion ratio function is: in: There are separate upper thresholds for CPU, memory, network bandwidth, and disk I / O.

8. An intelligent data access and integration system as claimed in claim 1, characterized in that: The data storage module includes: a storage unit that supports multiple database types, including relational databases, NoSQL databases, and cloud-based storage systems; Dynamic selection unit, used to dynamically select storage solutions based on data access frequency and importance, to achieve separate storage of hot and cold data; The data migration unit is used to automatically migrate data between different storage systems when the data access frequency changes.

9. The intelligent data access and integration system according to claim 1, characterized in that: The API management module includes: Supports data access unit with multiple interfaces; Multi-version management unit, used to support simultaneous maintenance of multiple API versions; Authentication and authorization unit, which performs user identity authentication and controls data access permissions; Use monitoring and compliance review units to monitor access frequency, response time, and usage patterns of API requests and generate log reports.

10. An intelligent data access and integration system as claimed in claim 1, characterized in that: The monitoring and optimization module includes: a visual monitoring interface for displaying the system operation status; a key performance indicator monitoring unit for monitoring CPU usage, memory usage, data processing delay, network traffic and database query response time; a system health assessment unit for real-time assessment of system health status through system logs and data traffic analysis; an automatic optimization unit for dynamically adjusting system resource configuration according to real-time monitoring data; and a trend analysis unit for predicting system resource bottlenecks and expanding resources in advance.

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