Economic data management optimization system and method based on multi-source data fusion technology

Abstract

The invention discloses an economic data management optimization system and method based on a multi-source data fusion technology, and belongs to the technical field of economic data management optimization, the economic data management optimization system comprises a data center and a processing center, the data center comprises a data source identification module, an acquisition module, a data preprocessing module, a data fusion module and a database, the processing center comprises an analysis module, an interface design module and a feedback module, the data integration time can be shortened by 30%, it is ensured that the data accuracy rate reaches 95% or above, a decision maker can obtain needed information within 5 minutes, the labor cost related to data processing is reduced by 20%, data collected through multiple channels can be integrated, and the data processing efficiency is improved. According to the method, the data retrieval efficiency is high, multiple pieces of data with repeated data or the same meaning are avoided, the data management efficiency is improved, and meanwhile, sufficient and effective basis can be provided for data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of economic data management optimization, and in particular to an economic data management optimization system and method based on multi-source data fusion technology. Background Art

[0002] With the development of computer technology, economic data management has gone through three stages of development: manual management, file systems and database systems. The data structure established in the database system more fully describes the internal connections between data, facilitates data modification, updating and expansion, and at the same time ensures the independence, reliability, security and integrity of the data, reduces data redundancy, and thus improves data sharing and data management efficiency.

[0003] After searching, Chinese patent number CN115373857A discloses a data optimization system for economic management. Although it improves the data processing efficiency, distributes batches of user financial data from the cloud server to the selected port for simultaneous processing, improves the optimization efficiency of economic data, reduces the computing pressure of the server, and facilitates economic management, it is unable to integrate data collected from multiple channels, resulting in low data retrieval efficiency. At the same time, it is easy to generate duplicate data or multiple data with the same meaning, which reduces data management efficiency. At the same time, it is also unable to provide sufficient and effective basis for data analysis. Moreover, the source of data collection cannot be fully expanded, making the data insufficient and narrow, thereby failing to meet reference requirements and easily causing data analysis errors. Summary of the invention

[0004] The purpose of the present invention is to solve the defects in the prior art and to propose an economic data management optimization system and method based on multi-source data fusion technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An economic data management optimization system based on multi-source data fusion technology includes a data center and a processing center, wherein the data center includes a data source identification module, a collection module, a data preprocessing module, a data fusion module and a database, and the processing center includes an analysis module, an interface design module and a feedback module;

[0007] The data source identification module is used to identify and list all possible data sources; the acquisition module is used to collect raw data from the data source; the data preprocessing module is used to preprocess the raw data to form the data to be fused, and its specific preprocessing methods are: data deduplication, missing value processing, format conversion and data standardization; the data fusion module adopts multi-source data fusion technology to integrate the data to be fused from different sources to form multi-source fused data; the database is used to store multi-source fused data;

[0008] The interface design module is used to design a user-friendly interface and provide data visualization tools; the analysis module applies statistical analysis and machine learning models to conduct in-depth analysis of multi-source fusion data to form trend analysis charts, predictive modeling and risk assessment reports; the feedback module application interface displays trend analysis charts, predictive modeling and risk assessment reports.

[0009] Furthermore, the specific steps of the data fusion module to form multi-source fusion data are:

[0010] a. Key field identification: Identify key fields for matching, such as ID, name, or timestamp. These key fields should have the same meaning in different data sources.

[0011] b. Similarity calculation: Use similarity algorithms, such as Jaccard similarity or cosine similarity, to determine whether records in different data to be fused match;

[0012] c. Data merging: Merge matching records into a new record to retain important information and avoid information loss;

[0013] d. Choose the appropriate data fusion method according to the specific situation, such as: simple fusion, directly merging data from multiple data sources by row or column, suitable for data with similar structure; hierarchical fusion, processing data in layers, first merging low-level data, and then gradually merging upwards, suitable for complex data structures; model-driven fusion, using machine learning or statistical models to predict and infer data to achieve higher-level data fusion;

[0014] e. Data verification: Verify the fused data to ensure its accuracy and consistency and form multi-source fused data.

[0015] Furthermore, the format conversion is: converting data from one format to another format according to needs, such as converting text data into numerical data; the data standardization is: unifying the data format, such as date format or currency unit, so that the original data from different data sources can be compared with each other.

[0016] The economic data management optimization method based on multi-source data fusion technology, the specific operation steps of the economic data management optimization method based on multi-source data fusion technology are:

[0017] S1. Determine system goals: clarify the main functions and goals of the system, such as: data integration and management; real-time data analysis and visualization; predictive analysis and decision support; improve data quality and consistency;

[0018] S2. Data source identification: Identify and list all possible data sources, including internal data, external data, structured data and unstructured data;

[0019] S3. Data collection: Design a data collection module to ensure that raw data can be obtained from different data sources. You can use API, crawler technology or direct database connection. For data that cannot be obtained automatically, you can consider manual input or import;

[0020] S4, data preprocessing: cleaning and preprocessing the collected raw data;

[0021] S5 Data Fusion: Use multi-source data fusion technology to integrate raw data from different sources. The following methods can be considered: Data matching: associate related data from different data sources through key fields; Data synthesis: synthesize information from multiple data sources to form a new data set based on business needs; Use machine learning algorithms: use clustering, classification and other algorithms to identify and fuse similar data to form multi-source fused data;

[0022] S6. Data storage: Choose a suitable database system to store the fused multi-source data. Consider using relational databases such as MySQL and PostgreSQL or non-relational databases such as MongoDB and Cassandra, and choose according to data characteristics and access requirements;

[0023] S7. Data analysis and modeling: Build data analysis modules, apply statistical analysis, machine learning models and other technologies, conduct in-depth analysis of multi-source fusion data, and achieve trend analysis, predictive modeling and risk assessment;

[0024] S8. Visualization and reporting: Design a user-friendly interface and provide data visualization tools to help users intuitively understand multi-source fusion data. At the same time, generate regular reports to support the decision-making process.

[0025] Furthermore, the specific operations for determining the system objectives described in step S1 are: collecting user needs: interviews and questionnaires, interviewing or distributing questionnaires to potential users such as financial personnel, market analysts and decision makers to understand their needs and expectations for data management, and determining the core functions of the system based on the collected needs.

[0026] Furthermore, the internal data in step S2 is: financial statements, sales records or inventory data; the external data is: market trends, economic indicators, competitor information or social media data; the structured data and unstructured data are: databases, documents or images.

[0027] Furthermore, the data visualization tool in step S8 is Tableau or PowerBI.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention reduces data integration time by 30% and improves data quality through automated data collection and fusion: data cleaning and standardization are achieved to ensure that data accuracy reaches more than 95%, and decision support capabilities are enhanced: through real-time analysis and visualization tools, decision makers can obtain the required information within 5 minutes, and reduce operating costs: by optimizing data management processes, the labor costs related to data processing are reduced by 20%.

[0030] 2. The present invention can integrate data collected from multiple channels through multi-source data fusion technology, so as to improve data retrieval efficiency, avoid the generation of duplicate data or multiple data with the same meaning, improve the efficiency of data management, and also provide sufficient and effective basis for data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0032] Figure 1 This is a schematic diagram of the operation modules of the economic data management optimization system based on multi-source data fusion technology proposed in the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0034] In the description of the present invention, it is necessary to understand that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0035] Embodiment 1:

[0036] See also Figure 1 , the present invention provides a technical solution: an economic data management optimization system based on multi-source data fusion technology, including a data center and a processing center, the data center includes a data source identification module, a collection module, a data preprocessing module, a data fusion module and a database, and the processing center includes an analysis module, an interface design module and a feedback module;

[0037] The data source identification module is used to identify and list all possible data sources; the acquisition module is used to collect the original data of the data source; the data preprocessing module is used to preprocess the original data to form the data to be fused, and its specific preprocessing methods are: data deduplication, missing value processing, format conversion and data standardization; the data fusion module adopts multi-source data fusion technology to integrate the data to be fused from different sources to form multi-source fused data; the database is used to store multi-source fused data;

[0038] The interface design module is used to design a user-friendly interface and provide data visualization tools; the analysis module uses statistical analysis and machine learning models to conduct in-depth analysis of multi-source fusion data to form trend analysis charts, predictive modeling and risk assessment reports; the feedback module uses the interface to display trend analysis charts, predictive modeling and risk assessment reports;

[0039] The specific steps of the data fusion module to form multi-source fusion data are as follows:

[0040] a. Key field identification: Identify key fields for matching, such as ID, name, or timestamp. These key fields should have the same meaning in different data sources.

[0041] b. Similarity calculation: Use similarity algorithms, such as Jaccard similarity or cosine similarity, to determine whether records in different data to be fused match;

[0042] c. Data merging: Merge matching records into a new record to retain important information and avoid information loss;

[0043] d. Choose the appropriate data fusion method according to the specific situation, such as: simple fusion, directly merging data from multiple data sources by row or column, suitable for data with similar structure; hierarchical fusion, processing data in layers, first merging low-level data, and then gradually merging upwards, suitable for complex data structures; model-driven fusion, using machine learning or statistical models to predict and infer data to achieve higher-level data fusion;

[0044] e. Data verification: Verify the fused data to ensure its accuracy and consistency and form multi-source fused data;

[0045] Format conversion: converting data from one format to another according to needs, such as converting text data to numerical data; data standardization: unifying data formats, such as date formats or currency units, so that raw data from different data sources can be compared with each other.

[0046] Specifically, the collected raw data is cleaned and preprocessed, and then multi-source data fusion technology is used to integrate the raw data from different sources. The following methods can be considered: data matching: related data in different data sources are associated through key fields; data synthesis: information from multiple data sources is synthesized according to business needs to form a new data set; use machine learning algorithms: use clustering, classification and other algorithms to identify and fuse similar data to form multi-source fusion data, select a suitable database system to store the fused multi-source fusion data, consider using relational databases such as MySQL and PostgreSQL or non-relational databases such as MongoDB and Cassandra, select according to data characteristics and access requirements, and then build a data analysis module. Apply statistical analysis, machine learning models and other technologies to conduct in-depth analysis of multi-source fusion data to achieve trend analysis, predictive modeling and risk assessment, and then design a user-friendly interface and provide data visualization tools to help users intuitively understand multi-source fusion data. At the same time, generate regular reports to support the decision-making process, integrate data collected from multiple channels, make data retrieval efficient, avoid data duplication or multiple data with the same meaning, improve the efficiency of data management, and provide sufficient and effective basis for data analysis.

[0047] Embodiment 2:

[0048] See also Figure 1 The present invention provides a technical solution: an economic data management optimization method based on multi-source data fusion technology. The specific operation steps of the economic data management optimization method based on multi-source data fusion technology are:

[0049] S1. Determine system goals: clarify the main functions and goals of the system, such as: data integration and management; real-time data analysis and visualization; predictive analysis and decision support; improve data quality and consistency;

[0050] S2. Data source identification: Identify and list all possible data sources, including internal data, external data, structured data and unstructured data;

[0051] S3. Data collection: Design a data collection module to ensure that raw data can be obtained from different data sources. You can use API, crawler technology or direct database connection. For data that cannot be obtained automatically, you can consider manual input or import;

[0052] S4, data preprocessing: cleaning and preprocessing the collected raw data;

[0053] S5 Data Fusion: Use multi-source data fusion technology to integrate raw data from different sources. The following methods can be considered: Data matching: associate related data from different data sources through key fields; Data synthesis: synthesize information from multiple data sources to form a new data set based on business needs; Use machine learning algorithms: use clustering, classification and other algorithms to identify and fuse similar data to form multi-source fused data;

[0054] S6. Data storage: Choose a suitable database system to store the fused multi-source data. Consider using relational databases such as MySQL and PostgreSQL or non-relational databases such as MongoDB and Cassandra, and choose according to data characteristics and access requirements;

[0055] S7. Data analysis and modeling: Build data analysis modules, apply statistical analysis, machine learning models and other technologies, conduct in-depth analysis of multi-source fusion data, and achieve trend analysis, predictive modeling and risk assessment;

[0056] S8. Visualization and reporting: Design a user-friendly interface and provide data visualization tools to help users intuitively understand multi-source fusion data. At the same time, generate regular reports to support the decision-making process;

[0057] The specific operations for determining the system objectives in step S1 are: Collecting user needs: Interviews and questionnaires, interviewing or distributing questionnaires to potential users such as financial personnel, market analysts, and decision makers to understand their needs and expectations for data management, and determining the core functions of the system based on the collected needs;

[0058] In step S2, internal data is: financial statements, sales records or inventory data; external data is: market trends, economic indicators, competitor information or social media data; structured data and unstructured data are: databases, documents or images;

[0059] In step S8, the data visualization tool is Tableau or PowerBI.

[0060] Specifically, clarify the main functions and goals of the system, such as: data integration and management; real-time data analysis and visualization; predictive analysis and decision support; improve data quality and consistency, and then identify and list all possible data sources based on the main functions and goals of the system, including: internal data, external data, structured data and unstructured data. After the data source is determined, design the data collection module to ensure that the original data can be obtained from different data sources. You can use API, crawler technology or direct database connection. For data that cannot be obtained automatically, consider manual input or importing, so that the source of data collection can be fully expanded, so that the data is sufficient and extensive, which can meet reference needs and avoid data analysis errors.

[0061] The working principle and use process of the present invention are as follows: first, the main functions and goals of the system are clarified, such as: data integration and management; real-time data analysis and visualization; predictive analysis and decision support; improving data quality and consistency, and then identifying and listing all possible data sources according to the main functions and goals of the system, including: internal data, external data, structured data and unstructured data. After the data source is determined, the data acquisition module is designed to ensure that the original data can be obtained from different data sources. API, crawler technology or direct database connection can be used. For data that cannot be automatically obtained, manual input or import can be considered, so that the source of data collection can be fully expanded, so that the data is sufficient and extensive, so as to meet the reference requirements and avoid errors in data analysis;

[0062] Clean and preprocess the collected raw data, and then use multi-source data fusion technology to integrate the raw data from different sources. The following methods can be considered: Data matching: associate related data in different data sources through key fields; Data synthesis: synthesize information from multiple data sources to form a new data set according to business needs; Use machine learning algorithms: Use clustering, classification and other algorithms to identify and fuse similar data to form multi-source fusion data, select a suitable database system to store the fused multi-source fusion data, consider using relational databases such as MySQL and PostgreSQL or non-relational databases such as MongoDB and Cassandra, select according to data characteristics and access requirements, and then build a data analysis module. Apply statistical analysis, machine learning models and other technologies to conduct in-depth analysis of multi-source fusion data to achieve trend analysis, predictive modeling and risk assessment, and then design a user-friendly interface and provide data visualization tools to help users intuitively understand multi-source fusion data. At the same time, generate regular reports to support the decision-making process, integrate data collected from multiple channels, make data retrieval efficient, avoid data duplication or multiple data with the same meaning, improve the efficiency of data management, and provide sufficient and effective basis for data analysis to complete the operation.

[0063] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. Economic data management optimization system based on multi-source data fusion technology, characterized by: It includes a data center and a processing center, wherein the data center includes a data source identification module, a collection module, a data preprocessing module, a data fusion module and a database, and the processing center includes an analysis module, an interface design module and a feedback module; The data source identification module is used to identify and list all possible data sources; the acquisition module is used to collect raw data from the data source; the data preprocessing module is used to preprocess the raw data to form the data to be fused, and its specific preprocessing methods are: data deduplication, missing value processing, format conversion and data standardization; the data fusion module adopts multi-source data fusion technology to integrate the data to be fused from different sources to form multi-source fused data; The database is used to store multi-source fusion data; The interface design module is used to design a user-friendly interface and provide data visualization tools; the analysis module applies statistical analysis and machine learning models to conduct in-depth analysis of multi-source fusion data to form trend analysis charts, predictive modeling and risk assessment reports; the feedback module application interface displays trend analysis charts, predictive modeling and risk assessment reports.

2. The economic data management optimization system based on multi-source data fusion technology according to claim 1 is characterized in that: The specific steps of the data fusion module to form multi-source fusion data are as follows: a. Key field identification: Identify key fields for matching, such as ID, name, or timestamp. These key fields should have the same meaning in different data sources. b. Similarity calculation: Use similarity algorithms, such as Jaccard similarity or cosine similarity, to determine whether records in different data to be fused match; c. Data merging: Merge matching records into a new record to retain important information and avoid information loss; d. Choose the appropriate data fusion method according to the specific situation, such as: simple merging, directly merging data from multiple data sources by row or column, suitable for data with similar structures; hierarchical fusion, processing data in layers, first merging low-level data, and then gradually merging upwards, suitable for complex data structures; Model-driven fusion uses machine learning or statistical models to predict and infer data to achieve a higher level of data fusion; e. Data verification: Verify the fused data to ensure its accuracy and consistency and form multi-source fused data.

3. The economic data management optimization system based on multi-source data fusion technology according to claim 1 is characterized in that: The format conversion is: converting data from one format to another format according to needs, such as converting text data into numerical data; the data standardization is: unifying the data format, such as date format or currency unit, so that the original data from different data sources can be compared with each other.

4. The economic data management optimization method based on multi-source data fusion technology is characterized by: The specific operation steps of the economic data management optimization method based on multi-source data fusion technology are as follows: S1. Determine system goals: clarify the main functions and goals of the system, such as: data integration and management; real-time data analysis and visualization; predictive analysis and decision support; Improve data quality and consistency; S2. Data source identification: Identify and list all possible data sources, including internal data, external data, structured data and unstructured data; S3. Data collection: Design a data collection module to ensure that raw data can be obtained from different data sources. You can use API, crawler technology or direct database connection. For data that cannot be obtained automatically, you can consider manual input or import; S4, data preprocessing: cleaning and preprocessing the collected raw data; S5 Data Fusion: Use multi-source data fusion technology to integrate raw data from different sources. The following methods can be considered: Data matching: associate related data from different data sources through key fields; Data synthesis: synthesize information from multiple data sources to form a new data set based on business needs; Use machine learning algorithms: use clustering, classification and other algorithms to identify and fuse similar data to form multi-source fused data; S6. Data storage: Choose a suitable database system to store the fused multi-source data. Consider using relational databases such as MySQL and PostgreSQL or non-relational databases such as MongoDB and Cassandra, and choose according to data characteristics and access requirements; S7. Data analysis and modeling: Build data analysis modules, apply statistical analysis, machine learning models and other technologies, conduct in-depth analysis of multi-source fusion data, and achieve trend analysis, predictive modeling and risk assessment; S8. Visualization and reporting: Design a user-friendly interface and provide data visualization tools to help users intuitively understand multi-source fusion data. At the same time, generate regular reports to support the decision-making process.

5. The economic data management optimization method based on multi-source data fusion technology according to claim 4 is characterized in that: The specific operations for determining the system objectives described in step S1 are: Collecting user needs: Interviews and questionnaires, interviewing or distributing questionnaires to potential users such as financial personnel, market analysts, and decision makers to understand their needs and expectations for data management, and determining the core functions of the system based on the collected needs.

6. The economic data management optimization method based on multi-source data fusion technology according to claim 4 is characterized in that: The internal data in step S2 is: financial statements, sales records or inventory data; the external data is: market trends, economic indicators, competitor information or social media data; the structured data and unstructured data are: databases, documents or images.

7. The economic data management optimization method based on multi-source data fusion technology according to claim 4 is characterized in that: The data visualization tool in step S8 is Tableau or PowerBI.

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