Efficient data acquisition system and method for data report behaviors

Abstract

The invention discloses an efficient data acquisition system for data report behaviors, which comprises a data acquisition module, a data processing module, a data storage module and a visual analysis module, and is characterized in that the data acquisition module is responsible for acquiring data report behavior data from various data sources in real time; the data acquisition module supports various database types (such as MySQL, Oracle, SQL Server and the like) and file formats (such as CSV, Excel, JSON and the like), and in the acquisition process, the data acquisition module firstly receives acquisition tasks configured by a user, including data source information, acquisition frequency, acquisition fields and the like. And then, generating a corresponding data query statement according to the acquisition task, and positioning the data query statement to a corresponding database or file to be acquired. The system has the advantages of efficient collection, intelligent processing, flexible storage, richness, visualization, safety and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data report forms, and in particular to an efficient data collection system and method for data report forms. Background Art

[0002] In the current information age, data has become an important basis for business operations and decision-making. Data reports, as an important form of data display, are widely used in all walks of life. However, the traditional data report data collection method has many shortcomings, such as low collection efficiency, difficult to ensure data quality, and susceptibility to external interference. In addition, the processing of multi-source heterogeneous data also faces huge challenges, resulting in data analysis results that are often not accurate and timely, and there are problems. Summary of the invention

[0003] In view of the defects in the prior art, the present invention provides an efficient data collection system and method for data report behavior to solve the existing problems.

[0004] The present invention is realized by the following technical scheme: an efficient data collection system for data report behavior, including a data collection module, a data processing module, a data storage module and a visual analysis module, characterized in that: the data collection module is responsible for real-time collection of data report behavior data from various data sources, and the data collection module supports multiple database types (such as MySQL, Oracle, SQL Server, etc.) and file formats (such as CSV, Excel, JSON, etc.). During the collection process, the data collection module first receives the collection task configured by the user, including data source information, collection frequency, collection field, etc. Then, the corresponding data query statement is generated according to the collection task, and the corresponding database or file to be collected is located.

[0005] Preferably, the data processing module is responsible for cleaning, converting and aggregating the collected data. The data processing module first performs cleaning operations such as deduplication, missing value filling, and outlier processing on the data to generate a data set that meets the analysis requirements. A distributed processing architecture is used to distribute data processing tasks to multiple computing nodes for parallel execution.

[0006] Preferably, the data storage module supports multiple database types, including relational databases and non-relational databases, and adopts distributed storage technology to store data in a dispersed manner on multiple storage nodes.

[0007] Preferably, the visualization analysis module is responsible for presenting the processed data to the user in a visual form. The visualization analysis module provides chart types (such as line charts, bar charts, pie charts, scatter plots, etc.) and custom options. Users can choose appropriate chart types and styles for display according to their needs.

[0008] An efficient data collection method for data report behavior includes the following steps: S1. Collection task configuration: users configure collection tasks through the system interface, including data source information (such as database type, address, port, user name, password, etc.), collection frequency (such as once an hour), and collection fields (such as report name, generation time, data items, etc.). The system generates corresponding data query statements and collection plans based on user configuration; S2, data collection, automatically executes data query statements according to the collection plan, collects data report behavior data from the specified data source, and dynamically adjusts the collection strategy through intelligent scheduling algorithms.

[0009] S3, data processing, first remove duplicate data and fill missing values ​​to ensure the accuracy and completeness of the data, perform necessary format conversion or unit conversion on the data according to business needs, and integrate the processed data into a data set that meets the analysis needs through aggregation algorithms; S4, data storage, the processed data is stored in a pre-configured database; S5. Visual analysis: users can choose the appropriate chart type according to their analysis needs. Users can interact with the chart by clicking, dragging, and other operations.

[0010] The beneficial effects of the present invention are as follows: 1. Efficient collection, using multi-threading and asynchronous processing technology, supporting real-time collection of multi-source heterogeneous data, and significantly improving data collection efficiency.

[0011] 2. Intelligent processing: Built-in intelligent scheduling algorithm and efficient data processing algorithm automatically optimize the collection and processing strategies to ensure the accuracy and efficiency of data processing.

[0012] 3. Flexible storage: Supports multiple database types and distributed storage technologies, and provides flexible data storage solutions to meet different data storage needs.

[0013] 4. Rich visualization: Provides a variety of chart types and customization options, supports dynamic interaction and advanced analysis functions, and helps users quickly understand the patterns and trends behind the data.

[0014] 5. Safe and reliable: Built-in data backup and recovery functions ensure data security and reliability; at the same time, strict permission management and data encryption technology are used to protect user privacy and data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0016] Figure 1 This is the architecture diagram of the data report behavior data collection system of the present invention; Figure 2 This is a diagram showing the effects of the data visualization analysis module of the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element at the same time.

[0019] For ease of description, spatial relative terms such as "upper," "lower," "left," and "right" may be used herein to describe the relationship of one element or feature shown in a figure relative to another element or feature. It should be understood that the spatial terms are intended to include different orientations of the device in use or operation in addition to the orientation shown in the figures. For example, if the device in the figure is inverted, elements described as being "below" other elements or features will be positioned "above" the other elements or features. Thus, the exemplary term "below" may include both upper and lower orientations.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation of the present invention is described in detail below in conjunction with specific embodiments: Figure 1-Figure 2The present invention is implemented by the following technical scheme: an efficient data collection system for data report behavior, including a data collection module, a data processing module, a data storage module and a visual analysis module, characterized in that: the data collection module is responsible for real-time collection of data report behavior data from various data sources, and the data collection module supports multiple database types (such as MySQL, Oracle, SQL Server, etc.) and file formats (such as CSV, Excel, JSON, etc.). During the collection process, the data collection module first receives the collection task configured by the user, including data source information, collection frequency, collection field, etc. Then, the corresponding data query statement is generated according to the collection task, and the corresponding database or file to be collected is located.

[0022] The data processing module is responsible for cleaning, converting and aggregating the collected data. The data processing module first removes duplicates, fills missing values, processes outliers, etc., to generate a data set that meets the analysis requirements. It uses a distributed processing architecture to distribute data processing tasks to multiple computing nodes for parallel execution.

[0023] The data storage module supports multiple database types, including relational databases and non-relational databases, and uses distributed storage technology to store data in multiple storage nodes.

[0024] In addition, the data storage module also provides data backup and recovery functions to ensure data security and reliability.

[0025] The visualization analysis module is responsible for presenting the processed data to the user in a visual form. The visualization analysis module provides chart types (such as line charts, bar charts, pie charts, scatter charts, etc.) and custom options. Users can choose the appropriate chart type and style for display according to their needs.

[0026] Automatically recommend appropriate chart types and styles based on the user's historical analysis behavior and current data characteristics. At the same time, it also provides a drill-down analysis function, where users can click on a data point or area in the chart to further view the detailed information of the data point or area.

[0027] An efficient data collection method for data report behavior includes the following steps: S1. Collection task configuration: users configure collection tasks through the system interface, including data source information (such as database type, address, port, user name, password, etc.), collection frequency (such as once an hour), and collection fields (such as report name, generation time, data items, etc.). The system generates corresponding data query statements and collection plans based on user configuration; S2, data collection, automatically executes data query statements according to the collection plan, collects data report behavior data from the specified data source, and dynamically adjusts the collection strategy through intelligent scheduling algorithms.

[0028] S3, data processing, first remove duplicate data and fill missing values ​​to ensure the accuracy and completeness of the data, perform necessary format conversion or unit conversion on the data according to business needs, and integrate the processed data into a data set that meets the analysis needs through aggregation algorithms; S4, data storage, the processed data is stored in a pre-configured database; S5. Visual analysis: users can choose the appropriate chart type according to their analysis needs. Users can interact with the chart by clicking, dragging, and other operations.

[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An efficient data collection system for data report behavior, comprising a data collection module, a data processing module, a data storage module and a visual analysis module, characterized in that: The data collection module is responsible for collecting data report behavior data from various data sources in real time. The data collection module supports multiple database types (such as MySQL, Oracle, SQL Server, etc.) and file formats (such as CSV, Excel, JSON, etc.). During the collection process, the data collection module first receives the collection task configured by the user, including data source information, collection frequency, collection fields, etc., and then generates corresponding data query statements according to the collection task, and locates the corresponding database or file to be collected.

2. The efficient data collection system for data report behavior according to claim 1, characterized in that: The data processing module is responsible for cleaning, converting and aggregating the collected data. The data processing module first removes duplicates, fills missing values, processes outliers, etc., generates a data set that meets the analysis requirements, and adopts a distributed processing architecture to distribute data processing tasks to multiple computing nodes for parallel execution.

3. The efficient data collection system for data report behavior according to claim 1, characterized in that: The data storage module supports multiple database types, including relational databases and non-relational databases, and adopts distributed storage technology to store data in multiple storage nodes in a dispersed manner.

4. The efficient data collection system for data report behavior according to claim 1, characterized in that: The visualization analysis module is responsible for presenting the processed data to the user in a visual form. The visualization analysis module provides chart types (such as line charts, bar charts, pie charts, scatter plots, etc.) and custom options. Users can choose the appropriate chart type and style for display according to their needs.

5. An efficient data collection method for data reporting behavior, comprising the following steps: S1. Collection task configuration. Users configure collection tasks through the system interface, including data source information (such as database type, address, port, user name, password, etc.), collection frequency (such as once an hour), and collection fields (such as report name, generation time, data items, etc.).

6. The system generates corresponding data query statements and collection plans based on user configuration; S2, data collection, automatically executes data query statements according to the collection plan, collects data report behavior data from the specified data source, and dynamically adjusts the collection strategy through intelligent scheduling algorithms. 7.S3, data processing, first remove duplicate data and fill missing values ​​to ensure the accuracy and completeness of the data, perform necessary format conversion or unit conversion on the data according to business needs, and integrate the processed data into a data set that meets the analysis requirements through aggregation algorithms; S4, data storage, the processed data is stored in a pre-configured database; S5. Visual analysis: users can choose the appropriate chart type according to their analysis needs. Users can interact with the chart by clicking, dragging, and other operations.

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