Statistics-assisted multi-source data integration device

Abstract

The invention discloses a statistics-assisted multi-source data integration device, and relates to the field of data processing. Comprising a data acquisition module, a data preprocessing module, a statistical analysis module, a data integration module and a storage module which are connected through signals; the data acquisition module is used for acquiring data from a plurality of data sources; the data preprocessing module is used for cleaning, converting and standardizing the collected data; and the statistical analysis module is used for performing statistical analysis on the preprocessed data and mining potential relationships and modes among the data. According to the method, the potential relationship among the data can be mined by the analysis module, so that the data integration is more scientific and accurate, the quality of the integrated data is improved, the potential relationship and mode among the data are deeply mined, information which is difficult to perceive by a traditional method can be found, the potential value of the multi-source data is fully played, and more comprehensive and deep support is provided for decision making.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a statistics-assisted multi-source data integration device. Background Art

[0002] In today's information technology environment, as enterprises expand and their operations become more complex, a single database system may not be able to meet all needs. Different business modules or departments may require different database systems to support their specific data structures and query requirements. These databases may be based on different architectures or platforms, resulting in fragmented data and difficult to manage uniformly. While each database can effectively store and manage its own data, cross-database data integration presents numerous challenges.

[0003] Multi-source data refers to a collection of data from multiple different data sources with different data types and structures. Traditional data integration methods often lack effective statistical assistance and find it difficult to deeply explore the potential relationships between data. As a result, the integrated data cannot fully realize its value and cannot provide comprehensive and accurate support for decision-making. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a statistically assisted multi-source data integration device to solve the problem that traditional data integration methods lack effective statistical means to assist, making it difficult to deeply explore the potential relationships between data, resulting in the integrated data being unable to fully realize its value and unable to provide comprehensive and accurate support for decision-making.

[0005] To achieve the above-mentioned object of the invention, the present invention provides a statistically assisted multi-source data integration device comprising a data acquisition module, a data preprocessing module, a statistical analysis module, a data integration module and a storage module, wherein the modules are connected via signals; The data acquisition module is used to collect data from multiple data sources; The data preprocessing module is used to clean, convert and standardize the collected data; The statistical analysis module is used to perform statistical analysis on the preprocessed data and explore the potential relationships and patterns between the data; The data integration module is used to integrate multi-source data based on the results of the statistical analysis module; The storage module is used to store the integrated data.

[0006] Furthermore, the data acquisition module operation specifically includes the following: Data sources include database data, file data, and network interface data; For database data, obtain data from the relational database; For file data, read text files and parse data by line or specific delimiter; For network interface data, obtain data through the Web API and construct requests according to the requirements of the interface documentation.

[0007] Furthermore, the data preprocessing module specifically includes the following: By checking and comparing the unique identification fields of the data and using the deduplication function in the data processing tool, duplicate data can be removed to ensure data accuracy and consistency; Identify and correct erroneous data based on the data's business rules and logic, and mark or delete erroneous data that cannot be corrected. For data with missing values, statistics are selected based on the characteristics of the data to fill in the missing values.

[0008] Furthermore, the data preprocessing module operation specifically includes the following: Determine the goal of data conversion and conduct a comprehensive analysis of the original data, including data type, value range, distribution and relationship between data; Convert data based on conversion goals and data characteristics; Map the data to a specific interval and normalize the data based on its mean and standard deviation.

[0009] Furthermore, the statistical analysis module includes the following specific contents: Calculate mean and median statistics, draw statistical graphs, test data distribution, and grasp the overall characteristics of the data; Select the correlation coefficient, calculate the correlation coefficient matrix, and use the significance test to determine whether the correlation between variables is significant; Determine dependent and independent variables, select regression models, perform model fitting and evaluation, and explore causal relationships between variables; Determine clustering features, perform clustering, and evaluate the results to divide the data into different categories; Perform factor analysis and principal component analysis, calculate the correlation matrix and eigenvalues ​​after data standardization, determine the number of principal components or factors and interpret them, achieve data dimensionality reduction and extract key information; Propose the null hypothesis and alternative hypothesis, select the test statistic and significance level, calculate the test statistic and P value, and make a decision based on the comparison of the P value and significance level.

[0010] Furthermore, the data integration module operation specifically includes the following: Refer to the correlation analysis results to integrate strongly correlated data, group similar data by category based on cluster analysis, and integrate data from different sources for similar products; Merge fields with the same meaning, unify fields with the same meaning in each data source into one, and splice complementary records.

[0011] Furthermore, the operation of the data integration module specifically includes the following: In the case of data conflicts, the correct value is determined based on the reliability of the data source and the update time rules, and duplicate and redundant data is identified and removed. In cases where full-field exact matching is not possible but some key fields are unique, partial field matching is used to identify duplicate order records. Check data accuracy, completeness, and consistency to ensure integration is correct. If data performance is poor, optimize the storage structure or create indexes.

[0012] Furthermore, the operation of the storage module specifically includes the following: Design a reasonable database table structure based on the logical relationship of the data and business needs, and divide the data into different areas according to time partitions; Regularly perform full and incremental backups of data and establish a data recovery mechanism to ensure rapid recovery in the event of data problems; Set different user roles and permissions to limit access to data, and use encryption algorithms to encrypt and store sensitive data; Determine the data life cycle based on the importance and frequency of use of the data, clean up and delete expired or useless data to free up storage space.

[0013] Compared with the prior art, the present invention has the following technical effects and advantages: 1. The data preprocessing module cleans, converts, and standardizes multi-source data, while the statistical analysis module mines the potential relationships between data, making data integration more scientific and accurate and improving the quality of the integrated data. 2. Deeply explore the potential relationships and patterns between data, discover information that is difficult to detect with traditional methods, give full play to the potential value of multi-source data, provide more comprehensive and in-depth support for decision-making, meet the needs of large-scale data storage, and have data backup and recovery functions to ensure the security and integrity of data, adapting to the requirements of data processing and storage in the big data era. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0015] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0016] The present invention provides a statistically assisted multi-source data integration device, which mainly includes a data acquisition module, a data preprocessing module, a statistical analysis module, a data integration module and a storage module, wherein the modules are connected via signals; The data acquisition module is used to collect data from multiple data sources; The data preprocessing module is used to clean, convert and standardize the collected data; The statistical analysis module is used to perform statistical analysis on the preprocessed data and explore the potential relationships and patterns between the data; The data integration module is used to integrate multi-source data based on the results of the statistical analysis module; The storage module is used to store the integrated data.

[0017] The operation of the data acquisition module specifically includes the following: Data sources include database data, file data, and network interface data; For database data, obtain data from the relational database; For file data, read text files and parse data by line or specific delimiter; For network interface data, obtain data through the Web API and construct requests according to the requirements of the interface documentation.

[0018] It's important to note that data sources encompass database data, file data, and network interface data, enabling data acquisition from multiple perspectives. For example, business data in relational databases can provide structured transaction information, log data in text files can record system operations, and network interface data can access real-time data from external platforms, such as weather data and stock quotes. This makes the data more comprehensive and provides a richer basis for subsequent analysis and decision-making.

[0019] Retrieving data from relational databases allows you to leverage their powerful query and indexing capabilities to quickly and accurately extract the required data. Relational databases offer mature technologies and comprehensive mechanisms for storing and managing large-scale structured data, ensuring data consistency and integrity, and improving the efficiency and quality of data collection. Reading text files and parsing data by line or by specific delimiters allows the data collection module to adapt to text data in various formats. Whether it's simple log files and configuration files, or complex data files like CSV and TSV, customized parsing methods can extract valuable data, offering high flexibility and adaptability. Retrieving data through Web APIs facilitates data interaction with external systems. Simply constructing requests according to the interface documentation allows you to retrieve the latest data provided by external platforms, without having to understand the internal architecture or data storage methods of the other system. This enables enterprises to obtain timely external data such as industry trends and market information, supporting strategic decision-making.

[0020] By collecting different types of data through various methods, it's possible to integrate and analyze these data in relation to each other, uncovering more valuable information. Data from different sources may have potential relationships, and comprehensive analysis can uncover these relationships, providing businesses with deeper insights. This multi-source data collection architecture makes the system highly scalable. When business expansion requires the addition of new data sources, businesses simply add the appropriate collection and processing logic to the data collection module based on the type and characteristics of the new data source, eliminating the need for a large-scale overhaul of the entire system.

[0021] The data preprocessing module operation specifically includes the following: By checking and comparing the unique identification fields of the data and using the deduplication function in the data processing tool, duplicate data can be removed to ensure data accuracy and consistency; Identify and correct erroneous data based on the data's business rules and logic, and mark or delete erroneous data that cannot be corrected. For data with missing values, statistics are selected based on the characteristics of the data to fill in the missing values.

[0022] It should be noted that deduplicating data can prevent duplicate records from biasing data analysis results. For example, in market research data, if there are a large number of duplicate questionnaire records, certain data features will be over-amplified, leading to incorrect judgments about market trends. After deduplicating data, market analysis conclusions based on accurate data can truly reflect market conditions and provide a reliable basis for corporate decision-making. Ensuring data accuracy and consistency by checking and comparing unique identification fields helps maintain the logical relationships of the data. In a customer relationship management system, duplicate or inconsistent customer unique identifiers such as ID numbers or customer numbers can lead to confusion in customer information and make it impossible to accurately track customer transaction records and service history.

[0023] Marking or deleting uncorrectable erroneous data can reduce noise and outliers in the data, improving overall data quality. Filling missing values ​​can make the data complete, providing a complete data sample for data analysis and modeling. In credit rating models, if certain customer financial indicators contain missing values, the model may not be able to accurately assess the customer's credit risk. By properly filling missing values, the model input data can be complete, improving the model's stability and accuracy, and more reliably predicting the customer's credit status.

[0024] The data preprocessing module operation specifically includes the following: Determine the goal of data conversion and conduct a comprehensive analysis of the original data, including data type, value range, distribution and relationship between data; Convert data based on conversion goals and data characteristics; Map the data to a specific interval and normalize the data based on its mean and standard deviation.

[0025] It's important to note that a comprehensive analysis of raw data—understanding the data type, value range, distribution, and relationships between data—helps data analysts grasp the overall characteristics and inherent patterns of the data. For example, analysis of e-commerce sales data reveals that sales of certain products exhibit seasonal distributions and correlations with factors such as user age and region. This provides a crucial basis for subsequent data conversion and model building, helping analysts select more appropriate processing methods and model algorithms.

[0026] Different data analysis models have different requirements for data formats and features. For example, when using a support vector machine (SVM) model for classification, data often needs to be converted into a specific vector space representation so that the model can better learn and identify data features. By performing data conversion based on model requirements, model fit and prediction accuracy can be improved. Appropriate data conversion can make the data easier to understand and interpret. For example, converting complex text data into word vectors or topic vectors can present text information in a more intuitive manner, making it easier for analysts to understand the semantic information contained in the text data and discover underlying patterns and regularities within the data. Data conversion can highlight important features in the data and suppress noise and irrelevant information. In image recognition, grayscale conversion and normalization of raw image data can highlight key features such as edges and texture, reduce interference from factors such as lighting and color, and improve the accuracy of image recognition algorithms.

[0027] Mapping data to a specific interval and normalizing it can eliminate differences in the dimensions and value ranges of different variables. For example, when analyzing sales data for different products, the dimensions and value ranges of indicators such as price and sales volume can vary significantly. Normalization can unify these indicators onto the same scale, giving them equal weight and influence in data analysis and avoiding bias in analytical results caused by dimensionality issues. In machine learning and deep learning model training, standardized data can accelerate model convergence. For example, in the gradient descent algorithm, standardized data can make gradient updates more stable and efficient, avoiding problems such as vanishing or exploding gradients caused by data with excessively large or small value ranges. This shortens model training time and improves training efficiency. Standardized data can also improve model generalization across different datasets. Because the data has been uniformly normalized, the model can better learn the inherent characteristics and patterns of the data during training and testing, without being affected by the data's value range and distribution. This improves the model's prediction accuracy and stability on unknown data.

[0028] The statistical analysis module operation includes the following specific contents: Calculate mean and median statistics, draw statistical graphs, test data distribution, and grasp the overall characteristics of the data; Select the correlation coefficient, calculate the correlation coefficient matrix, and use the significance test to determine whether the correlation between variables is significant; Determine dependent and independent variables, select regression models, perform model fitting and evaluation, and explore causal relationships between variables; Determine clustering features, perform clustering, and evaluate the results to divide the data into different categories; Perform factor analysis and principal component analysis, calculate the correlation matrix and eigenvalues ​​after data standardization, determine the number of principal components or factors and interpret them, achieve data dimensionality reduction and extract key information; Propose the null hypothesis and alternative hypothesis, select the test statistic and significance level, calculate the test statistic and P value, and make a decision based on the comparison of the P value and significance level.

[0029] It is important to note that testing the data distribution can determine whether the data conforms to a specific distribution type, such as normal distribution or Poisson distribution. This provides an important basis for selecting appropriate statistical methods and models. If the data conforms to a normal distribution, parametric statistical methods based on the normal distribution assumption can be selected; if not, nonparametric statistical methods may be necessary.

[0030] Determining whether the correlation between variables is significant through significance tests helps to screen out variables that have an important impact on the research problem from among many variables, and exclude those variables that are too highly correlated with other variables or have no significant relationship with the dependent variable, thereby simplifying the data analysis model and improving the accuracy and interpretability of the model.

[0031] Regression models not only explain the relationships between variables but can also be used for forecasting. Regression models built based on historical data can predict the future value of the dependent variable given the value of the independent variable, providing strong support for a company's production planning, market forecasting, and other decision-making.

[0032] After dividing the data into different categories, each category can be analyzed and processed separately, which simplifies the data processing process and improves the efficiency of data analysis. At the same time, the clustering results can also be used as input for other analysis methods, providing a basis for further data analysis.

[0033] When processing high-dimensional data, factor analysis and principal component analysis can reduce data dimensionality by calculating correlation matrices and eigenvalues, determining the number of principal components or factors, and transforming multiple original variables into a small number of uncorrelated composite variables. In customer satisfaction surveys, there may be numerous survey indicators. Principal component analysis can be used to combine these indicators into a few key factors, such as product quality factors and service attitude factors. This reduces the data dimension while retaining most of the original data information.

[0034] In hypothesis testing, setting a significance level can control the probability of making a Type I error (rejecting a correct null hypothesis), ensuring the reliability and scientific nature of the analysis results. Furthermore, making decisions based on the comparison of the P value and the significance level can, to a certain extent, avoid erroneous conclusions due to chance factors and improve the accuracy and credibility of data analysis.

[0035] The operation of the data integration module specifically includes the following: Refer to the correlation analysis results to integrate strongly correlated data, group similar data by category based on cluster analysis, and integrate data from different sources for similar products; Merge fields with the same meaning, unify fields with the same meaning in each data source into one, and splice complementary records.

[0036] It's important to note that this integration allows for faster location and acquisition of required data during data analysis, eliminating the tedious process of navigating through large amounts of disorganized data. This significantly improves the efficiency and accuracy of data analysis. Analysts can focus on in-depth analysis of valuable data rather than spending time on data organization.

[0037] Unified fields and complete data records provide a higher-quality data foundation for data mining and modeling. When implementing data mining algorithms and building data analysis models, neatly formatted data facilitates algorithm convergence, resulting in higher accuracy and stability of the models, thereby improving the reliability and effectiveness of data-driven decision-making.

[0038] The operation of the data integration module specifically includes the following: In the case of data conflicts, the correct value is determined based on the reliability of the data source and the update time rules, and duplicate and redundant data is identified and removed. In cases where full-field exact matching is not possible but some key fields are unique, partial field matching is used to identify duplicate order records. Check data accuracy, completeness, and consistency to ensure integration is correct. If data performance is poor, optimize the storage structure or create indexes.

[0039] It's important to note that determining the correct value based on data source reliability and update time rules can effectively resolve data conflicts and ensure data accuracy and authenticity. For example, when multiple systems record the same customer information, the more reliable data source or the most recently updated data will prevail, preventing misleading business decisions from erroneous information.

[0040] Checking data for accuracy, completeness, and consistency ensures that the consolidated data meets business requirements and logic in all respects. This helps avoid errors caused by incomplete or inconsistent data during data use. For example, in financial data consolidation, ensuring the integrity and consistency of revenue and expenditure data is crucial for the accuracy of financial statements and the reliability of financial analysis.

[0041] The operation of the storage module specifically includes the following: Design a reasonable database table structure based on the logical relationship of the data and business needs, and divide the data into different areas according to time partitions; Regularly perform full and incremental backups of data and establish a data recovery mechanism to ensure rapid recovery in the event of data problems; Set different user roles and permissions to limit access to data, and use encryption algorithms to encrypt and store sensitive data; Determine the data life cycle based on the importance and frequency of use of the data, clean up and delete expired or useless data to free up storage space.

[0042] It should be noted that a reasonable database table structure design can make data storage more orderly and facilitate data management and maintenance. Designing based on the logical relationships of the data and business needs can ensure that different types of data are stored in the appropriate locations, improving the efficiency of data operations.

[0043] Establishing a data recovery mechanism allows for rapid recovery when data issues arise, minimizing business interruptions. Rapid data recovery is crucial for critical business systems, minimizing the impact of data loss or system failures on business operations, ensuring business continuity, and avoiding significant financial losses and reputational damage caused by data issues.

[0044] Setting different user roles and permissions to restrict data access can effectively prevent unauthorized users from accessing and manipulating data, reducing the risk of data leakage and tampering. For example, within an enterprise, only designated management personnel can access and modify sensitive financial data, while ordinary employees can only view business data within their scope of authority, thus ensuring data security and confidentiality.

[0045] By determining the data lifecycle based on its importance and frequency of use, and cleaning and deleting expired or useless data, storage space can be freed up in a timely manner to avoid wasted storage space. As enterprise data volumes continue to grow, proper space management can reduce storage costs, improve storage device utilization, and enable enterprises to utilize resources more efficiently.

[0046] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A statistically assisted multi-source data integration device, characterized by: It includes data acquisition module, data preprocessing module, statistical analysis module, data integration module and storage module, and each module is connected by signals; The data acquisition module is used to collect data from multiple data sources; The data preprocessing module is used to clean, convert and standardize the collected data; The statistical analysis module is used to perform statistical analysis on the preprocessed data and explore the potential relationships and patterns between the data; The data integration module is used to integrate multi-source data based on the results of the statistical analysis module; The storage module is used to store the integrated data.

2. The statistically assisted multi-source data integration device according to claim 1, characterized in that: The operation of the data acquisition module specifically includes the following: Data sources include database data, file data, and network interface data; For database data, obtain data from the relational database; For file data, read text files and parse data by line or specific delimiter; For network interface data, obtain data through the Web API and construct requests according to the requirements of the interface documentation.

3. The statistically assisted multi-source data integration device according to claim 1, characterized in that: The data preprocessing module operation specifically includes the following: By checking and comparing the unique identification fields of the data and using the deduplication function in the data processing tool, duplicate data can be removed to ensure data accuracy and consistency; Identify and correct erroneous data based on the data's business rules and logic, and mark or delete erroneous data that cannot be corrected. For data with missing values, statistics are selected based on the characteristics of the data to fill in the missing values.

4. The statistically assisted multi-source data integration device according to claim 3, characterized in that: The data preprocessing module operation specifically includes the following: Determine the goal of data conversion and conduct a comprehensive analysis of the original data, including data type, value range, distribution and relationship between data; Convert data based on conversion goals and data characteristics; Map the data to a specific interval and normalize the data based on its mean and standard deviation.

5. The statistically assisted multi-source data integration device according to claim 1, characterized in that: The statistical analysis module operation includes the following specific contents: Calculate mean and median statistics, draw statistical graphs, test data distribution, and grasp the overall characteristics of the data; Select the correlation coefficient, calculate the correlation coefficient matrix, and use the significance test to determine whether the correlation between variables is significant; Determine dependent and independent variables, select regression models, perform model fitting and evaluation, and explore causal relationships between variables; Determine clustering features, perform clustering, and evaluate the results to divide the data into different categories; Perform factor analysis and principal component analysis, calculate the correlation matrix and eigenvalues ​​after data standardization, determine the number of principal components or factors and interpret them, achieve data dimensionality reduction and extract key information; Propose the null hypothesis and alternative hypothesis, select the test statistic and significance level, calculate the test statistic and P value, and make a decision based on the comparison of the P value and significance level.

6. The statistically assisted multi-source data integration device according to claim 1, characterized in that: The operation of the data integration module specifically includes the following: Refer to the correlation analysis results to integrate strongly correlated data, group similar data by category based on cluster analysis, and integrate data from different sources for similar products; Merge fields with the same meaning, unify fields with the same meaning in each data source into one, and splice complementary records.

7. The statistically assisted multi-source data integration device according to claim 6, characterized in that: The operation of the data integration module specifically includes the following: In the case of data conflicts, the correct value is determined based on the reliability of the data source and the update time rules, and duplicate and redundant data is identified and removed. In cases where full-field exact matching is not possible but some key fields are unique, partial field matching is used to identify duplicate order records. Check data accuracy, completeness, and consistency to ensure integration is correct. If data performance is poor, optimize the storage structure or create indexes.

8. The statistically assisted multi-source data integration device according to claim 1, characterized in that: The operation of the storage module specifically includes the following: Design a reasonable database table structure based on the logical relationship of the data and business needs, and divide the data into different areas according to time partitions; Regularly perform full and incremental backups of data and establish a data recovery mechanism to ensure rapid recovery in the event of data problems; Set different user roles and permissions to limit access to data, and use encryption algorithms to encrypt and store sensitive data; Determine the data life cycle based on the importance and frequency of use of the data, clean up and delete expired or useless data to free up storage space.

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