Efficient distributed multi-source data acquisition method

Abstract

The invention discloses an efficient distributed multi-source data acquisition method, which realizes efficient acquisition of large-scale multi-source data by identifying and classifying multi-source data, reasonably deploying distributed acquisition nodes and formulating an intelligent acquisition strategy. Firstly, data sources are grouped according to characteristics such as types, formats and updating frequencies; then deploying a plurality of acquisition nodes in the network and cooperating through an efficient communication protocol; making an intelligent acquisition strategy according to the characteristics of the data source, for example, performing real-time acquisition on a high-update-frequency data source and the like; the acquisition nodes pre-process and fuse the data and then transmit the data to the central storage system through a safe and efficient transmission channel. Meanwhile, an acquisition strategy is continuously optimized by monitoring and analyzing an acquisition process index. The method improves data acquisition efficiency, reduces resource consumption, enhances reliability, has good expansibility, can be widely applied to various scenes needing multi-source data acquisition, remarkably reduces acquisition cost, and improves data acquisition efficiency and quality.

Description

Technical Field

[0001] The present invention relates to an efficient distributed multi-source data acquisition method. Background Art

[0002] With the rapid development of information technology, multi-source data acquisition has been widely applied in many fields. Traditional data acquisition methods usually face many problems and are difficult to meet the needs of modern data processing. At the same time, although there are already some data acquisition technologies, the existing multi-source data acquisition technologies still have some deficiencies: 1. Low efficiency and resource waste of centralized acquisition In the traditional centralized data acquisition method, a central node is responsible for collecting data from each data source. With the increase in the number of data sources and the expansion of the data scale, this method is prone to problems such as network congestion and high acquisition latency, resulting in resource waste. In large-scale data processing, centralized acquisition is difficult to handle high-concurrency data requests, leading to low overall efficiency.

[0003] 2. Limited adaptability of existing multi-source data acquisition The existing multi-source data acquisition technologies lack sufficient flexibility and adaptability when facing data sources of different types and formats. The characteristics of different data sources vary greatly, including data update frequency, data volume, data transmission protocol, etc. Existing methods are difficult to make effective adjustments and optimizations according to these differences and cannot meet the requirements of complex and changing actual application scenarios.

[0004] 3. Difficulty in guaranteeing data quality and reliability of existing methods In the process of multi-source data acquisition, data quality and reliability are key issues. Existing technologies perform poorly in dealing with problems such as data anomalies, data loss, and data duplication, and it is difficult to ensure that the collected data has high quality and reliability. Especially in fields with high requirements for data accuracy, such as financial data analysis and scientific research, existing methods cannot meet the strict data quality requirements.

[0005] In summary, the existing technologies have significant deficiencies in acquisition efficiency, adaptability, and data quality. There is an urgent need for a more efficient, flexible, and reliable distributed multi-source data acquisition strategy and method to improve work efficiency and data quality in large-scale data processing tasks. Summary of the Invention

[0006] The purpose of the present invention is to provide an efficient distributed multi-source data acquisition method, which is an efficient distributed multi-source data acquisition strategy and method that can efficiently and accurately acquire multi-source data in a complex data environment, adapt to various data acquisition tasks, and is especially suitable for fields that require large-scale multi-source data acquisition, such as enterprise data analysis, Internet of Things applications, financial transaction monitoring, etc.

[0007] To achieve the above object, the solution of the present invention is as follows: An efficient distributed multi-source data acquisition method, including multiple data sources in a data acquisition network, the method comprising: Step 1, data source identification and classification: identifying and classifying multiple data sources, grouping them according to the type of data source, data format, and data update frequency, and adopting different acquisition strategies, including: classifying the data sources into real-time data sources, batch data sources, and important data sources, and formulating acquisition schemes for different data sources respectively; Step 2, deployment of distributed acquisition nodes: deploying multiple distributed acquisition nodes in the network, each acquisition node being responsible for acquiring data sources within a specified range, and the acquisition nodes collaborating through a communication protocol to achieve distributed data acquisition, including: determining the number, location, and coverage range of the acquisition nodes to ensure full coverage of all data sources and avoid duplicate acquisition; Step 3, formulation of intelligent acquisition strategies: formulating intelligent acquisition strategies for each acquisition node according to the classification of the data sources, including: adopting a real-time acquisition strategy for data sources with a high data update frequency; adopting a batch acquisition strategy for data sources with a large amount of data; adopting a redundant acquisition strategy for data sources with high importance to improve data reliability; Step 4, data preprocessing and fusion: after the acquisition nodes acquire data, preprocessing the data, including data cleaning and format conversion operations, and then fusing the data from different data sources to form a unified data format and structure for subsequent analysis and processing, including: removing duplicate data and abnormal data, converting data in different formats into a standard format, and performing data merging and integration; Step 5, data transmission and storage: the acquisition nodes transmit the preprocessed and fused data to a central storage system for storage through a transmission channel, and at the same time, adopting data compression and encryption technologies, specifically including: selecting appropriate transmission protocols and storage devices to ensure the secure storage and fast access of data; Step 6, optimization of acquisition strategies: continuously optimizing the acquisition strategies by monitoring and analyzing the data traffic, acquisition time, and data quality indicators during the acquisition process to improve the efficiency and quality of data acquisition, including: adjusting the deployment of the acquisition nodes and the parameters of the acquisition strategies according to the monitoring results.

[0008] The solution is further: in Step 2: the distributed acquisition nodes are optimized by means of dynamic adjustment, specifically including: dynamically increasing or decreasing the number and location of the acquisition nodes according to changes in the data sources and adjustments in acquisition requirements to improve the acquisition efficiency and coverage; during the node deployment process, considering network topology structure and data source distribution factors, optimizing the layout of the nodes to reduce data transmission latency.

[0009] The solution further is: in step 3: the intelligent acquisition strategy further includes an acquisition mechanism based on priority, specifically including: allocating different acquisition priorities to different data sources according to the importance and urgency of the data sources to ensure that important data is acquired first; in the case of limited resources, giving priority to acquiring data sources with high priorities to meet the requirements of critical business.

[0010] The solution further is: the method is applicable to the following data acquisition application scenarios: Data acquisition in enterprise-level data centers to meet the needs of large-scale data storage and analysis; Sensor data acquisition in the Internet of Things environment to achieve real-time monitoring of the physical world; Data acquisition in the financial field to support risk assessment and decision-making analysis; Scientific research data acquisition to provide rich data resources for scientific research.

[0011] The solution further is: the method can be integrated into a cloud platform or a local system, supporting the provision of data acquisition services through API interfaces, which is convenient for application in the actual production environment.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. It realizes the high-efficiency and automation of multi-source data acquisition, reduces resource waste, and improves the acquisition efficiency.

[0013] 2. It provides an intelligent acquisition strategy to adapt to the requirements in different data acquisition tasks.

[0014] 3. Through the data preprocessing and fusion mechanism, it improves the quality and usability of the data.

[0015] 4. By introducing the method for dynamically optimizing the acquisition strategy, the acquisition strategy can be continuously adjusted and optimized in actual applications, enhancing its adaptability.

[0016] 5. The method of the present invention has high scalability and flexibility, and is applicable to large-scale multi-source data acquisition tasks in multiple fields.

[0017] Through the present invention, the efficiency and quality of multi-source data acquisition can be greatly improved, and it is especially suitable for scenarios that require large-scale processing and high-precision acquisition.

[0018] The present invention will be described in detail below in conjunction with the drawings and embodiments. Description of the Drawings

[0019] Figure 1 is the implementation flowchart of the present invention Figure 2 is the detailed flowchart of the present invention. Detailed Implementation Manner

[0020] An efficient distributed multi-source data acquisition method, which is an efficient distributed multi-source data acquisition strategy and method, includes multiple data sources in a data acquisition network, such as Figure 1 and Figure 2 As shown, its process includes: Data source identification: Analyze multiple data sources through an intelligent identification module to determine characteristics such as the type, format, and update frequency of the data sources.

[0021] Classification and grouping: Group the data sources according to the identified characteristics, providing a basis for subsequent distributed acquisition node deployment and intelligent acquisition strategy formulation.

[0022] Distributed node deployment: Reasonably deploy multiple distributed acquisition nodes in the network to ensure that each node can efficiently acquire data sources within a specific range.

[0023] Intelligent acquisition strategy formulation: According to the classification results of the data sources, formulate corresponding intelligent acquisition strategies for each acquisition node to improve the efficiency and accuracy of data acquisition.

[0024] Data preprocessing and fusion: The acquisition nodes perform preprocessing operations on the acquired data, including data cleaning, format conversion, etc., and then fuse the data from different data sources to form a unified data format and structure.

[0025] Acquisition strategy optimization: Monitor and analyze indicators such as data traffic, acquisition time, and data quality during the acquisition process, and dynamically optimize the acquisition strategy to ensure continuous improvement of acquisition performance The specific implementation method steps include: Step 1: Data source identification and classification: Identify and classify multiple data sources, group them according to the type of data source, data format, and data update frequency, and adopt different acquisition strategies, including: classifying the data sources into real-time data sources, batch data sources, and important data sources, and formulating acquisition schemes for different data sources respectively; First, use the data source identification module to scan and analyze multiple potential data sources. This module can determine characteristics such as the type, format, and update frequency of the data sources by detecting information such as the storage location, access method, and data structure of the data sources. According to these characteristics, the data sources are divided into different categories, such as text data source category, image data source category, high-update-frequency data source category, low-data-volume data source category, etc. The following formula is often used for feature calculation in data classification: C = FW * T + FW * F + UFW * UF, where C is the classification result, FW, FW, and UFW are the weight coefficients of the corresponding features, T is the data source type, F is the data format, and UF is the data update frequency; Step 2, Deployment of Distributed Acquisition Nodes: Deploy multiple distributed acquisition nodes in the network. Each acquisition node is responsible for acquiring data sources within a specified range. The acquisition nodes cooperate through communication protocols to achieve distributed data acquisition, including: determining the number, location, and coverage range of the acquisition nodes to ensure full coverage of all data sources and avoid duplicate acquisition; After determining the classification of the data sources, deploy multiple distributed acquisition nodes reasonably according to the network topology structure and the distribution of the data sources. Each acquisition node is responsible for acquiring data sources within a specific range to ensure full coverage of all data sources and avoid duplicate acquisition. Assign a unique identifier and IP address to each acquisition node, and establish a communication protocol between the nodes to ensure efficient data exchange and cooperation. The deployment of distributed nodes can be optimized through the following formula: NP = NT * SD + CCW * CC, where NP is the location of the acquisition node, NT is the network topology structure, SD is the distribution of the data sources, CCW is the weight coefficient of the communication cost, and CC is the communication cost between the nodes; Step 3, Formulation of Intelligent Acquisition Strategies: According to the classification results of the data sources, formulate intelligent acquisition strategies for each acquisition node, including: for data sources with a high data update frequency, adopt a real-time acquisition strategy; regularly check whether there is new data update for the data sources and acquire it in a timely manner. For data sources with a large amount of data, adopt a batch acquisition strategy to divide the data into several batches for acquisition to reduce the acquisition pressure. For data sources with a large amount of data, adopt a batch acquisition strategy; for data sources with a high importance level, adopt a redundant acquisition strategy to improve the reliability of the data; The intelligent acquisition strategy can be selected through the following formula: S = ST * UF + DVW * DV + IW * I, where S is the acquisition strategy, ST is the data source type, UF is the data update frequency, DVW is the weight coefficient of the data volume, DV is the data volume size, IW is the weight coefficient of the importance level, and I is the importance degree of the data source; Step 4, Data Preprocessing and Fusion: After the acquisition nodes acquire the data, preprocess the data, including using data cleaning algorithms to remove abnormal data, noise data, and duplicate data, and using format conversion operation tools to convert the data formats of different data sources into a unified data format. Then, fuse the data from different data sources to form a unified data format and structure for subsequent analysis and processing, including: removing duplicate data and abnormal data, converting data in different formats into a standard format, and performing data merging and integration; Data preprocessing and fusion can be described by the following formula: PD = DC * OD + FC * CD + DF * FD, where PD is the preprocessed data, DC is the data cleaning operation, OD is the original data, FC is the format conversion operation, CD is the converted data, DF is the data fusion operation, and FD is the fused data; where: Data cleaning: Use data cleaning algorithms to remove outliers, noise data, and duplicate data from the collected data. Statistical methods, machine learning algorithms, etc. can be used for data cleaning.

[0026] Format conversion: Convert the data formats of different data sources into a unified data format for subsequent analysis and processing. Data conversion tools, etc. can be used.

[0027] Data fusion: Integrate data from different data sources to form a unified data structure and format. Data fusion algorithms such as weighted average method, principal component analysis method, etc. can be adopted.

[0028] Data verification: Verify the fused data to ensure the accuracy and integrity of the data. Data verification algorithms, data comparison, etc. can be used for data verification; Step 5, Data transmission and storage: The acquisition node transmits the preprocessed and fused data to the central storage system through the transmission channel for storage. At the same time, data compression and encryption technologies are adopted, specifically including: selecting appropriate transmission protocols and storage devices to ensure the secure storage and fast access of data; Step 6, Acquisition strategy optimization: Continuously monitor various indicators during the acquisition process, such as monitoring and analyzing the data flow, acquisition time, and data quality indicators during the acquisition process, and continuously optimize the acquisition strategy to improve the efficiency and quality of data acquisition, including: adjusting the deployment of acquisition nodes and the parameters of the acquisition strategy according to the monitoring results; Analyze these indicators through data analysis tools to determine the effectiveness and performance bottlenecks of the acquisition strategy. According to the analysis results, dynamically adjust the acquisition strategy, optimize the deployment of acquisition nodes and the selection of intelligent acquisition strategies. Acquisition strategy optimization can be calculated by the following formula: OS = PM * CS + AF * AR, where OS is the optimized acquisition strategy, PM is the performance monitoring operation, CS is the current acquisition strategy, AF is the adjustment coefficient, and AR is the analysis result; where: Performance monitoring: Use performance monitoring tools to monitor various indicators during the acquisition process in real time, such as data flow, acquisition time, data quality, etc.

[0029] Analysis and evaluation: Analyze and evaluate the monitored metrics to determine the effectiveness of the acquisition strategy and performance bottlenecks. Data analysis techniques, machine learning algorithms, etc. can be used for analysis and evaluation.

[0030] Strategy adjustment: Dynamically adjust the acquisition strategy according to the analysis and evaluation results, and optimize the deployment of acquisition nodes and the selection of intelligent acquisition strategies. Parameters such as acquisition frequency, batch size, redundancy, etc. can be adjusted to improve acquisition efficiency and data quality.

[0031] Continuous optimization: Continuously monitor and optimize the acquisition strategy to continuously improve acquisition performance and data quality. Performance evaluation and strategy adjustment can be carried out regularly to adapt to changing data sources and acquisition requirements.

[0032] In step 2: The distributed acquisition nodes are optimized by means of dynamic adjustment, specifically including: dynamically increasing or decreasing the number and location of acquisition nodes according to changes in data sources and adjustments in acquisition requirements, so as to improve acquisition efficiency and coverage; during the node deployment process, considering network topology structure and data source distribution factors, optimize the layout of nodes to reduce data transmission latency.

[0033] In step 3: The intelligent acquisition strategy also includes a priority-based acquisition mechanism, specifically including: assigning different acquisition priorities to different data sources according to the importance and urgency of the data sources to ensure that important data is acquired first; in the case of limited resources, give priority to acquiring data sources with high priorities to meet key business needs.

[0034] Among them: The method is applicable to the following data acquisition application scenarios: Data acquisition in enterprise-level data centers to meet the needs of large-scale data storage and analysis; Sensor data acquisition in the Internet of Things environment to achieve real-time monitoring of the physical world; Data acquisition in the financial field to support risk assessment and decision-making analysis; Scientific research data acquisition to provide rich data resources for scientific research.

[0035] The method can be integrated into a cloud platform or a local system, and support the provision of data acquisition services through API interfaces, which is convenient for application in the actual production environment.

Claims

1. An efficient distributed multi-source data acquisition method, comprising multiple data sources in a data acquisition network, characterized in that: The method comprises: Step 1: Data source identification and classification: Identify and classify multiple data sources, group them according to the type, data format, and data update frequency of the data source, and adopt different collection strategies, including: classifying data sources into real-time data sources, batch data sources, and important data sources, and formulating collection plans for different data sources; Step 2: Deploy distributed collection nodes: deploy multiple distributed collection nodes in the network. Each collection node is responsible for collecting data sources within a specified range. The collection nodes collaborate through communication protocols to achieve distributed data collection, including: determining the number, location, and coverage of collection nodes to ensure full coverage of all data sources and avoid duplicate collection; Step 3: Formulate intelligent collection strategies: According to the classification of data sources, formulate intelligent collection strategies for each collection node, including: adopting real-time collection strategies for data sources with high data update frequency; adopting batch collection strategies for data sources with large data volumes; and adopting redundant collection strategies for data sources with high importance to improve data reliability. Step 4: Data preprocessing and fusion: After collecting data, the collection node preprocesses the data, including data cleaning and format conversion operations. Then, the data from different data sources are merged to form a unified data format and structure to facilitate subsequent analysis and processing, including: removing duplicate data and abnormal data, converting data in different formats into a standard format, and merging and integrating data; Step 5, data transmission and storage: The collection node transmits the pre-processed and fused data to the central storage system through the transmission channel for storage. At the same time, data compression and encryption technology are used, including: selecting appropriate transmission protocols and storage devices to ensure secure storage and fast access to data; Step 6: Optimize the collection strategy: By monitoring and analyzing the data flow, collection time, and data quality indicators during the collection process, continuously optimize the collection strategy to improve the efficiency and quality of data collection, including: adjusting the deployment of collection nodes and the parameters of the collection strategy according to the monitoring results.

2. The efficient distributed multi-source data acquisition method according to claim 1, characterized in that: In step 2: the distributed collection nodes are optimized by dynamic adjustment, specifically including: dynamically increasing or decreasing the number and location of collection nodes according to changes in data sources and adjustments to collection requirements to improve collection efficiency and coverage; during node deployment, network topology and data source distribution factors are considered to optimize node layout and reduce data transmission delays.

3. The efficient distributed multi-source data acquisition method according to claim 1, characterized in that: In step 3: the intelligent collection strategy also includes a priority-based collection mechanism, specifically including: assigning different collection priorities to different data sources according to the importance and urgency of the data source to ensure that important data is collected first; in the case of limited resources, high-priority data sources are collected first to meet key business needs.

4. The efficient distributed multi-source data acquisition method according to claim 1, characterized in that: The method is applicable to the following data collection application scenarios: Data collection in enterprise-level data centers to meet the needs of large-scale data storage and analysis; Sensor data collection in IoT environments enables real-time monitoring of the physical world; Data collection in the financial field to support risk assessment and decision analysis; Scientific research data collection provides rich data resources for scientific research.

5. The efficient distributed multi-source data acquisition method according to claim 1, characterized in that: The method can be integrated into a cloud platform or a local system, and supports providing data collection services through an API interface, which is convenient for application in actual production environments.

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