Data management system based on big data

Abstract

The invention discloses a data management system based on big data, which belongs to the field of big data processing, and comprises a data acquisition module which is integrated with a multi-source data acquisition interface and supports acquisition of original data from various data sources such as a relational database, an unstructured file and Internet of Things equipment. An intelligent data preprocessing algorithm is built in, data formats are automatically identified, the data are subjected to cleaning, duplicate removal, normalization and other operations, original data are converted into standard formats suitable for storage and management, and the data quality is ensured; according to the method, an intelligent scheduling algorithm model based on reinforcement learning is constructed, the algorithm analyzes multi-dimensional features such as data types, access frequency and storage requirements in real time, and data storage and access strategies are dynamically adjusted; and through a self-adaptive data compression framework, based on a data type and an access mode, an optimal compression algorithm is automatically selected. For structured data, an efficient lossless compression algorithm is adopted to ensure data integrity.

Description

Technical Field

[0001] The present invention relates to the technical field of big data processing, and in particular to a data management system based on big data. Background Art

[0002] With the exponential development of information technology, the era of big data has fully arrived. In today's society, various types of data, including business operations data from enterprises, experimental data from scientific research institutions, and user behavior data from internet platforms, are growing at an unprecedented rate. According to statistics, the amount of data generated globally is increasing by over 50% annually. Efficiently managing this massive amount of data and unlocking its value have become key factors driving the development of various industries.

[0003] Although a variety of big data-based data management systems are currently available on the market, they have exposed numerous problems in practical applications. Data processing efficiency is low when faced with complex data structures. In the financial sector, multi-dimensional data such as customer transaction records and credit ratings are intertwined, making it difficult for traditional systems to quickly integrate and analyze them. In scientific research, the heterogeneous data generated by gene sequencing cannot be efficiently processed. Furthermore, there are bottlenecks in data scalability. When the amount of data increases dramatically, storage and access performance decreases significantly. Furthermore, insufficient system performance optimization cannot meet the real-time response requirements under high-concurrency data requests.

[0004] Therefore, a data management system based on big data with complex data processing capabilities, high scalability and excellent performance is needed. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a big data-based data management system that effectively overcomes existing shortcomings in data processing, storage optimization, and system performance. By introducing innovative technologies, it meets the management needs of multi-dimensional, heterogeneous data, significantly improves data access efficiency, reduces long-term system operation and maintenance costs, and provides users with an efficient and stable data management solution.

[0006] Technical solution: To solve the above technical problems, according to one aspect of the present invention, more specifically, a data management system based on big data, comprising:

[0007] Data Collection Module: This module integrates multi-source data collection interfaces and supports the collection of raw data from various data sources, including relational databases, unstructured files, and IoT devices. Built-in intelligent data preprocessing algorithms automatically identify data formats and perform data cleaning, deduplication, and normalization operations, converting raw data into a standard format suitable for storage and management to ensure data quality.

[0008] Data Storage Module: Utilizing a distributed storage architecture and integrating intelligent data scheduling algorithms, the module dynamically adjusts the storage layout of data across distributed nodes based on factors such as data popularity and importance. When access frequency for a particular type of data increases, it automatically migrates it to a higher-performing storage node, optimizing storage efficiency and access speed. Furthermore, the module supports horizontal scalability by adding storage nodes, easily addressing large-scale data storage and high-concurrency data access needs.

[0009] Data Processing Module: Utilizes adaptive data compression technology to compress collected data in real time. Simultaneously, it constructs a multi-dimensional index structure, creating indexes based on data dimensions such as time, space, and attributes. The module also includes data cleaning and formatting submodules, which perform in-depth processing of raw data, remove noise, and standardize data formats. This lays the foundation for subsequent data processing and analysis, accelerating data processing.

[0010] Data Access Module: Designs high-performance data query and access interfaces, employing a multi-level caching mechanism, including browser caching, server local caching, and distributed caching. Based on the frequency and importance of data access, cache resources are rationally allocated to reduce disk I / O operations, optimize data access performance, and reduce response time. Combined with a multi-level data indexing mechanism, it supports complex, multi-conditional data queries, ensuring efficient system operation in large-scale data environments.

[0011] System monitoring and optimization module: collects system operation data in real time, including but not limited to CPU usage, memory occupancy, data access latency, data storage capacity, etc., analyzes this data through intelligent algorithms, and automatically adjusts storage and computing resource allocation. When the system load increases, it dynamically increases computing resources to ensure data processing speed. When storage resources approach the threshold, it automatically triggers data migration strategies and expands storage capacity. At the same time, based on system feedback after resource allocation adjustment, it continuously optimizes intelligent algorithm parameters to form a closed-loop mechanism of adaptive and dynamic optimization to ensure that the system is always in the best operating state.

[0012] The beneficial effects of the data management system based on big data of the present invention are:

[0013] (1) The present invention constructs an intelligent scheduling algorithm model based on reinforcement learning. The algorithm analyzes the multi-dimensional characteristics of data in real time, such as type, access frequency, storage requirements, etc., and dynamically adjusts data storage and access strategies. For example, for frequently accessed hot data, it is automatically migrated to high-speed storage media and the access path is optimized; for cold data, it is migrated to low-cost storage devices, thereby reducing storage costs while ensuring data access efficiency.

[0014] (2) This invention uses an adaptive data compression framework to automatically select the optimal compression algorithm based on data type and access mode. For structured data, an efficient lossless compression algorithm is used to ensure data integrity. For unstructured data such as images and videos, deep learning image recognition and video encoding technology are combined to achieve a balance between high compression ratio and high quality.

[0015] (3) The present invention creates a multi-level index system consisting of a global index, a local index, and a real-time index. The global index is used to quickly locate the area where the data is located, the local index further refines the data search scope, and the real-time index targets dynamically updated data to ensure the real-time and accuracy of the index. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0017] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0019] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Reference Figure 1 , a data management system based on big data, comprising:

[0021] Data Collection Module: Capable of collecting heterogeneous data from multiple sources, it can collect raw data in real time from relational and non-relational databases, IoT devices, cloud storage, and other data sources. A built-in intelligent preprocessing engine processes raw data into a standardized format through data cleaning, format conversion, and data validation, laying the foundation for subsequent storage and processing.

[0022] Data Storage Module: Utilizes a distributed storage architecture, combined with intelligent data scheduling algorithms. Dynamically adjusts the storage layout of data across distributed nodes based on factors such as data popularity and timeliness. Supporting horizontal expansion, by adding storage nodes, it easily accommodates petabyte-level or even zettabyte-level data storage needs, ensuring system storage stability and scalability in large-scale data environments.

[0023] The data processing module compresses data using adaptive data compression technology and constructs a multi-dimensional index structure. The data cleaning and formatting submodule performs in-depth processing on raw data, removing noise and unifying the data format. The collaborative work of these modules significantly improves data processing efficiency, meeting the performance requirements of complex data analysis.

[0024] Data Access Module: Designs a high-performance data query and access interface, employing a multi-level caching mechanism, including browser caching, server local caching, and distributed caching. Combined with a multi-level data indexing mechanism, it supports complex, multi-conditional data queries, ensuring efficient system operation in highly concurrent data access scenarios and reducing data access response times to milliseconds.

[0025] System monitoring and optimization module: collects system operation data in real time, including but not limited to CPU usage, memory occupancy, data access latency, data storage capacity, etc., analyzes this data through intelligent algorithms, and automatically adjusts storage and computing resource allocation. When the system load increases, it dynamically increases computing resources to ensure data processing speed. When storage resources approach the threshold, it automatically triggers data migration strategies and expands storage capacity. At the same time, based on system feedback after resource allocation adjustment, it continuously optimizes intelligent algorithm parameters to form a closed-loop mechanism of adaptive and dynamic optimization to ensure that the system is always in the best operating state.

[0026] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A data management system based on big data, comprising a data acquisition module, a data storage module, a data processing module, a data access module, and a system monitoring and optimization module, characterized in that: The data acquisition module is used to collect raw data from various data sources and perform preprocessing; The data storage module adopts a distributed storage architecture and combines intelligent data scheduling algorithms to optimize data storage strategies; The data processing module compresses the data using adaptive data compression technology and constructs a multi-dimensional index structure; The data access module supports efficient data query and access through a multi-level cache mechanism and a multi-level data index structure; The system monitoring and optimization module is used to monitor and optimize system configuration in real time.

2. A data management system based on big data according to claim 1, characterized in that: The intelligent data scheduling algorithm is based on deep learning technology and automatically adjusts data storage and access strategies according to data type, access frequency and storage requirements.

3. A data management system based on big data according to claim 1, characterized in that: Data compression technology automatically selects different compression algorithms by establishing a data type-access pattern analysis model, and dynamically adjusts based on the data type and access pattern.

4. A data management system based on big data according to claim 1, characterized in that: The data processing module further includes a data cleaning and formatting submodule for converting raw data into a standardized format for storage and processing.

5. The data management system based on big data according to claim 1, characterized in that: The data access module adopts a multi-level caching mechanism to optimize data access performance and reduce response time. The caching mechanism includes browser cache, server local cache and distributed cache.

6. A data management system based on big data according to claim 1, characterized in that: The system monitoring and optimization module includes the function of automatically adjusting the allocation of storage and computing resources to adapt to changes in system load.

7. The data management system based on big data according to claim 1, characterized in that: The data storage module supports horizontal expansion and can adapt to large-scale data storage and high-concurrency data access requirements.

8. The data management system based on big data according to claim 1, characterized in that: The system monitoring and optimization module collects system operation data in real time, including but not limited to CPU usage, memory occupancy, data access latency, data storage capacity, etc., analyzes this data through intelligent algorithms, and automatically adjusts storage and computing resource allocation. When the system load increases, computing resources are dynamically increased to ensure data processing speed. When storage resources approach the threshold, data migration strategies are automatically triggered and storage capacity is expanded. At the same time, based on system feedback after resource allocation adjustment, intelligent algorithm parameters are continuously optimized to form a closed-loop mechanism of adaptive and dynamic optimization to ensure that the system is always in the best operating state.

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