Industrial and commercial data processing method and system

Abstract

The invention discloses an industrial and commercial data processing method and system, and relates to the technical field of data processing, industrial and commercial data processing is realized based on Apache Spark, and the method comprises the following steps: data acquisition: acquiring industrial and commercial data from different data sources; data preprocessing: preprocessing the collected data, including data cleaning, duplicate removal and format conversion; data storage, wherein the preprocessed data can be stored in various distributed storage systems; data analysis: performing multi-dimensional analysis on the stored data through an algorithm model based on Spark; data visualization, wherein the analysis result is visually displayed; and user feedback and optimization: adjusting a data processing flow and an analysis model according to the user feedback to realize continuous optimization. The industrial and commercial data processing efficiency and accuracy can be improved, and the requirements of enterprises on data analysis and decision making are met.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for processing industrial and commercial data. Background Art

[0002] With the rapid development of the Internet and information technology, the speed and scale of industrial and commercial data are increasing dramatically. Traditional database management systems often face performance bottlenecks when processing and analyzing big data. Especially in terms of data storage, computing and real-time analysis, traditional systems are difficult to meet the needs of enterprises for real-time data processing and decision-making. Summary of the invention

[0003] The technical task of the present invention is to address the above shortcomings and to provide an industrial and commercial data processing method and system, which, through the improvement of the scoring model, can improve the efficiency and accuracy of industrial and commercial data processing and meet the needs of enterprises in data analysis and decision-making.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A method for processing industrial and commercial data based on Apache Spark, comprising:

[0006] Data collection, collecting industrial and commercial data from different data sources;

[0007] Data preprocessing: preprocessing the collected data, including data cleaning, deduplication and format conversion;

[0008] Data storage, the preprocessed data can be stored in a variety of distributed storage systems;

[0009] Data analysis: multi-dimensional analysis of stored data through algorithm models based on Spark;

[0010] Data visualization: Visualize the analysis results through plug-ins or integrated BI tools (such as Tableau and Power BI) to facilitate user decision support;

[0011] User feedback and optimization: adjust data processing procedures and analysis models based on user feedback to achieve continuous optimization and improve the intelligence level of the system.

[0012] Furthermore, the data collection includes crawler technology, API interface, FTP, and message queue; the collected industrial and commercial data includes:

[0013] Structured data, including database table data and Excel table data;

[0014] Semi-structured data, including data in JSON and XML formats;

[0015] Unstructured data, including text, audio, and video data;

[0016] Develop corresponding collection strategies for different data sources to ensure the integrity and accuracy of the data.

[0017] Furthermore, the data preprocessing specifically includes the following steps:

[0018] Data cleaning: remove duplicate data, process missing values, remove outliers, and improve data quality;

[0019] Data standardization: converting data from different sources into a consistent format to facilitate subsequent analysis and processing;

[0020] Data integration: Integrate data from different data sources to form a unified data set;

[0021] Utilize the powerful query capabilities of Spark SQL to achieve efficient data cleaning through data streaming processing.

[0022] Furthermore, the data storage includes:

[0023] Hadoop HDFS: suitable for batch storage of large-scale data;

[0024] Hive: used to build data warehouses, supporting SQL queries and efficient data analysis;

[0025] HBase: Suitable for storing real-time data and data that needs to be accessed quickly;

[0026] Select appropriate data storage methods based on data characteristics and business needs to achieve flexible data access and management.

[0027] Furthermore, the data analysis includes:

[0028] Data mining: user behavior analysis, market trend analysis, etc. based on the data mining algorithms provided by Spark MLlib;

[0029] Machine learning: Build predictive models, including customer churn prediction, sales prediction, etc., and use historical data for model training and verification;

[0030] Real-time analysis: Use Spark Streaming to process real-time data streams and support business applications such as transaction monitoring and anomaly detection.

[0031] The present invention also claims protection for a business data processing system, comprising:

[0032] Data collection module, used to collect industrial and commercial data from different data sources;

[0033] Data preprocessing module, used to preprocess the collected data, including data cleaning, deduplication and format conversion;

[0034] A data storage module, used to store the pre-processed data in a variety of distributed storage systems;

[0035] The data analysis module performs multi-dimensional analysis on stored data through algorithm models based on Spark;

[0036] Data visualization module, which is used to visualize the analysis results through plug-ins or integrated BI tools (such as Tableau and Power BI) to facilitate user decision support;

[0037] User feedback and optimization module, which is used to adjust the data processing flow and analysis model according to user feedback, achieve continuous optimization, and improve the intelligence level of the system;

[0038] The system realizes industrial and commercial data processing through the above-mentioned industrial and commercial data processing method.

[0039] Furthermore, the system is an online retail data processing system, and the system implements data processing in the following manner:

[0040] 1) Data collection:

[0041] Collect sales data, user reviews, inventory information, etc. from multiple online retail platforms through regular crawlers, and obtain user click data in real time through Kafka;

[0042] 2) Data preprocessing:

[0043] Use Spark to clean the collected data, remove invalid comments, fill in missing user information, and format it into a unified structure;

[0044] 3) Data storage:

[0045] Store the processed data in HDFS and use Hive to create data tables to facilitate subsequent query and analysis;

[0046] 4) Data analysis:

[0047] Use Spark MLlib to analyze user behavior, generate user portraits, and train product recommendation models based on historical sales information;

[0048] 5) Data Visualization:

[0049] Use Tableau to create visual reports based on the analysis results for management’s decision-making reference;

[0050] 6) Optimization and feedback:

[0051] Adjust the product recommendation algorithm based on user feedback, continuously optimize the model, and improve the accuracy of recommendations.

[0052] Furthermore, the system is a financial risk monitoring system, and the system implements data processing in the following manner:

[0053] 1) Data collection:

[0054] Connect with the data system of financial institutions through API interface to obtain financial data, including transaction records, credit applications, user asset allocation and other data;

[0055] 2) Data preprocessing:

[0056] Use Spark SQL to clean the data and screen out potential risk indicators, including debt ratio, credit score, etc.

[0057] 3) Data storage:

[0058] Store the processed data in HBase for fast access and calculation;

[0059] 4) Data analysis:

[0060] Implement machine learning algorithms to detect abnormal trading behaviors and generate risk assessment reports to support real-time alerts;

[0061] 5) Data Visualization:

[0062] Build a real-time visual dashboard to provide key indicators for risk monitoring and help decision makers respond quickly;

[0063] 6) Optimization and feedback:

[0064] Regularly adjust the risk assessment model based on monitoring results and improve data processing strategies.

[0065] The present invention also claims protection for an industrial and commercial data processing device, comprising: at least one memory and at least one processor;

[0066] The at least one memory is used to store a machine-readable program;

[0067] The at least one processor is used to call the machine-readable program to implement the above method.

[0068] The present invention also claims protection for a computer-readable medium having computer instructions stored thereon, which implement the above method when executed by a processor.

[0069] Compared with the prior art, the industrial and commercial data processing method and system of the present invention have the following beneficial effects:

[0070] 1. Integration: The present invention integrates data collection, preprocessing, storage, analysis, visualization and other modules into one system to form a complete industrial and commercial data processing chain.

[0071] 2. Efficiency: Utilizing the memory computing features of Apache Spark, the processing speed can be significantly improved when processing large-scale data compared to traditional data processing technologies.

[0072] 3. Scalability: The system adopts a distributed architecture, which can be flexibly expanded as the amount of data grows to meet the company's growing data processing needs.

[0073] 4. Intelligence: Introduce machine learning algorithms into the data analysis module to realize intelligent data analysis and help enterprises deeply explore the value of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a diagram showing a data processing flow of an online retail data processing system provided by an embodiment of the present invention;

[0075] Figure 2 It is a diagram of the data processing flow of the financial risk monitoring system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0077] The embodiment of the present invention provides a method for processing industrial and commercial data, which implements industrial and commercial data processing based on Apache Spark. The implementation of the method includes:

[0078] 1. Data collection,

[0079] Use crawler technology, API interface, FTP, message queue and other methods to collect industrial and commercial data from different data sources, including:

[0080] Structured data, such as database table data, Excel table data, etc.;

[0081] Semi-structured data, such as data in JSON and XML formats;

[0082] Unstructured data, such as text, audio, video, etc.

[0083] Develop corresponding collection strategies for different data sources to ensure the integrity and accuracy of the data.

[0084] 2. Data preprocessing,

[0085] The collected data is preprocessed, mainly including data cleaning, deduplication and format conversion to ensure data quality. The preprocessing steps include:

[0086] Data cleaning: remove duplicate data, process missing values, remove outliers, and improve data quality;

[0087] Data standardization: converting data from different sources into a consistent format to facilitate subsequent analysis and processing;

[0088] Data integration: Integrate data from different data sources to form a unified data set;

[0089] During the preprocessing process, the powerful query capabilities of Spark SQL can be used to achieve efficient data cleaning through data streaming processing.

[0090] 3. Data storage,

[0091] The preprocessed data can be stored in a variety of distributed storage systems to ensure data persistence and reliability. Storage solutions include:

[0092] Hadoop HDFS: suitable for batch storage of large-scale data;

[0093] Hive: used to build data warehouses, supporting SQL queries and efficient data analysis;

[0094] HBase: Suitable for storing real-time data and data that needs to be accessed quickly;

[0095] Select appropriate data storage methods based on data characteristics and business needs to achieve flexible data access and management.

[0096] 4. Data analysis,

[0097] Based on Spark, the algorithm model is used to analyze the stored data in multiple dimensions to help enterprises explore business value. Including:

[0098] Data mining: user behavior analysis, market trend analysis, etc. based on the data mining algorithms provided by Spark MLlib;

[0099] Machine learning: Build predictive models, such as customer churn prediction, sales prediction, etc., and use historical data for model training and verification;

[0100] Real-time analysis: Use Spark Streaming to process real-time data streams and support business applications such as transaction monitoring and anomaly detection.

[0101] 5. Data visualization,

[0102] Visualize the analysis results through plug-ins or integrated BI tools (such as Tableau and Power BI) to facilitate user decision support;

[0103] 6. User feedback and optimization,

[0104] Adjust data processing procedures and analysis models based on user feedback to achieve continuous optimization and improve the intelligence level of the system.

[0105] Apache Spark is an open source distributed computing framework designed for big data processing and capable of efficiently processing large-scale data sets. With its powerful in-memory computing capabilities and rich ecosystem, Spark has become one of the most popular big data processing frameworks. However, the application of Spark technology to industrial and commercial data processing still needs further optimization and integration based on industry characteristics. This industrial and commercial data processing method based on Apache Spark uses modern big data technology to form an integrated solution in data collection, processing, storage and analysis. Enterprises can efficiently process and analyze massive industrial and commercial data, thereby improving the accuracy and real-time nature of decision-making and enhancing market competitiveness. It has broad application prospects and economic value and is suitable for the industrial and commercial data processing needs of various types of enterprises.

[0106] The embodiment of the present invention further provides an industrial and commercial data processing system, which implements industrial and commercial data processing through the above industrial and commercial data processing method.

[0107] The system includes:

[0108] 1. Data collection module, which is used to collect industrial and commercial data from different data sources using crawler technology, API interface, FTP, message queue, etc., including:

[0109] Structured data, such as database table data, Excel table data, etc.;

[0110] Semi-structured data, such as data in JSON and XML formats;

[0111] Unstructured data, such as text, audio, video, etc.

[0112] Develop corresponding collection strategies for different data sources to ensure the integrity and accuracy of the data.

[0113] 2. Data preprocessing module is used to preprocess the collected data, mainly including data cleaning, deduplication and format conversion to ensure data quality. The preprocessing steps include:

[0114] Data cleaning: remove duplicate data, process missing values, remove outliers, and improve data quality;

[0115] Data standardization: converting data from different sources into a consistent format to facilitate subsequent analysis and processing;

[0116] Data integration: Integrate data from different data sources to form a unified data set;

[0117] During the preprocessing process, the powerful query capabilities of Spark SQL can be used to achieve efficient data cleaning through data streaming processing.

[0118] 3. Data storage module, which is used to store pre-processed data in a variety of distributed storage systems to ensure data persistence and reliability. Storage solutions include:

[0119] Hadoop HDFS: suitable for batch storage of large-scale data;

[0120] Hive: used to build data warehouses, supporting SQL queries and efficient data analysis;

[0121] HBase: Suitable for storing real-time data and data that needs to be accessed quickly;

[0122] Select appropriate data storage methods based on data characteristics and business needs to achieve flexible data access and management.

[0123] 4. Data analysis module: Based on Spark, the algorithm model is used to perform multi-dimensional analysis on the stored data to help enterprises explore commercial value. Including:

[0124] Data mining: user behavior analysis, market trend analysis, etc. based on the data mining algorithms provided by Spark MLlib;

[0125] Machine learning: Build predictive models, such as customer churn prediction, sales prediction, etc., and use historical data for model training and verification;

[0126] Real-time analysis: Use Spark Streaming to process real-time data streams and support business applications such as transaction monitoring and anomaly detection.

[0127] 5. Data visualization module, which is used to visualize the analysis results through plug-ins or integrated BI tools (such as Tableau and Power BI) to facilitate user decision support.

[0128] 6. User feedback and optimization module, which is used to adjust the data processing flow and analysis model according to user feedback, achieve continuous optimization, and improve the intelligence level of the system.

[0129] When the system is an online retail data processing system, the system implements data processing in the following manner:

[0130] (1) Data collection:

[0131] Collect sales data, user reviews, inventory information, etc. from multiple online retail platforms through regular crawlers, and obtain user click data in real time through Kafka;

[0132] (2) Data preprocessing:

[0133] Use Spark to clean the collected data, remove invalid comments, fill in missing user information, and format it into a unified structure;

[0134] (3) Data storage:

[0135] Store the processed data in HDFS and use Hive to create data tables to facilitate subsequent query and analysis;

[0136] (4) Data analysis:

[0137] Use Spark MLlib to analyze user behavior, generate user portraits, and train product recommendation models based on historical sales information;

[0138] (5) Data visualization:

[0139] Use Tableau to create visual reports based on the analysis results for management’s decision-making reference;

[0140] (6) Optimization and feedback:

[0141] Adjust the product recommendation algorithm based on user feedback, continuously optimize the model, and improve the accuracy of recommendations.

[0142] When the system is a financial risk monitoring system, the system implements data processing in the following manner:

[0143] (1) Data collection:

[0144] Connect with the data system of financial institutions through API interface to obtain financial data, including transaction records, credit applications, user asset allocation and other data;

[0145] (2) Data preprocessing:

[0146] Use Spark SQL to clean the data and screen out potential risk indicators, including debt ratio, credit score, etc.

[0147] (3) Data storage:

[0148] Store the processed data in HBase for fast access and calculation;

[0149] (4) Data analysis:

[0150] Implement machine learning algorithms to detect abnormal trading behaviors and generate risk assessment reports to support real-time alerts;

[0151] (5) Data visualization:

[0152] Build a real-time visual dashboard to provide key indicators for risk monitoring and help decision makers respond quickly;

[0153] (6) Optimization and feedback:

[0154] Regularly adjust the risk assessment model based on monitoring results and improve data processing strategies.

[0155] An embodiment of the present invention further provides an industrial and commercial data processing device, comprising: at least one memory and at least one processor;

[0156] The at least one memory is used to store a machine-readable program;

[0157] The at least one processor is used to call the machine-readable program to implement the industrial and commercial data processing method described in the above embodiment.

[0158] The embodiment of the present invention further provides a computer-readable medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the processor executes the industrial and commercial data processing method described in the above embodiment. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code that implements the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0159] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0160] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0161] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0162] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0163] The present invention is shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the protection scope of the present invention.

Claims

1. A method for processing industrial and commercial data, characterized in that: Industrial and commercial data processing based on Apache Spark, including: Data collection, collecting industrial and commercial data from different data sources; Data preprocessing: preprocessing the collected data, including data cleaning, deduplication and format conversion; Data storage, the preprocessed data can be stored in a variety of distributed storage systems; Data analysis: multi-dimensional analysis of stored data through algorithm models based on Spark; Data visualization, visual display of analysis results; User feedback and optimization: adjust data processing procedures and analysis models based on user feedback to achieve continuous optimization.

2. A method for processing industrial and commercial data according to claim 1, characterized in that: The data collection method includes crawler technology, API interface, FTP, and message queue; The collected industrial and commercial data include: Structured data, including database table data and Excel table data; Semi-structured data, including data in JSON and XML formats; Unstructured data, including text, audio, and video data; Develop corresponding collection strategies for different data sources.

3. A method for processing industrial and commercial data according to claim 1, characterized in that: The data preprocessing specifically includes the following steps: Data cleaning: remove duplicate data, process missing values, and remove outliers; Data standardization: converting data from different sources into a consistent format; Data integration: Integrate data from different data sources to form a unified data set; Utilize the query capability of Spark SQL to achieve data cleaning through data streaming processing.

4. A method for processing industrial and commercial data according to claim 1, characterized in that: The data storage and storage method include: Hadoop HDFS: suitable for batch storage of large-scale data; Hive: used to build data warehouses, supporting SQL queries and efficient data analysis; HBase: Suitable for storing real-time data and data that needs to be accessed quickly; Select appropriate data storage methods based on data characteristics and business needs to achieve flexible data access and management.

5. A method for processing industrial and commercial data according to claim 1, characterized in that: The data analysis includes: Data mining: user behavior analysis and market trend analysis based on the data mining algorithm provided by Spark MLlib; Machine learning: Build predictive models, including customer churn prediction and sales prediction, and use historical data for model training and verification; Real-time analysis: Use Spark Streaming to process real-time data streams and support transaction monitoring and anomaly detection business applications.

6. An industrial and commercial data processing system, characterized in that: include: Data collection module, used to collect industrial and commercial data from different data sources; Data preprocessing module, used to preprocess the collected data, including data cleaning, deduplication and format conversion; A data storage module, used to store the pre-processed data in a variety of distributed storage systems; The data analysis module performs multi-dimensional analysis on stored data through algorithm models based on Spark; Data visualization module, used to visualize the analysis results; User feedback and optimization module, which is used to adjust data processing flow and analysis model according to user feedback to achieve continuous optimization; The system realizes industrial and commercial data processing through the industrial and commercial data processing method described in any one of claims 1 to 5.

7. The industrial and commercial data processing system according to claim 6, characterized in that: The system is an online retail data processing system, and the system implements data processing in the following way: 1) Data collection: Collect sales data, user reviews, and inventory information from multiple online retail platforms through regular crawlers, and obtain user click data in real time through Kafka; 2) Data preprocessing: Use Spark to clean the collected data, remove invalid comments, fill in missing user information, and format it into a unified structure; 3) Data storage: Store the processed data in HDFS and use Hive to create data tables to facilitate subsequent query and analysis; 4) Data analysis: Use Spark MLlib to analyze user behavior, generate user portraits, and train product recommendation models based on historical sales information; 5) Data Visualization: Use Tableau to create visual reports based on the analysis results for management’s decision-making reference; 6) Optimization and feedback: Adjust the product recommendation algorithm based on user feedback, continuously optimize the model, and improve the accuracy of recommendations.

8. The industrial and commercial data processing system according to claim 6, characterized in that: The system is a financial risk monitoring system, and the system implements data processing in the following way: 1) Data collection: Connect with the data systems of financial institutions through API interfaces to obtain financial data, including transaction records, credit applications, and user asset allocation data; 2) Data preprocessing: Use Spark SQL to clean the data and screen out potential risk indicators, including debt ratio and credit score; 3) Data storage: Store the processed data in HBase; 4) Data analysis: Implement machine learning algorithms to detect abnormal trading behaviors and generate risk assessment reports to support real-time alerts; 5) Data Visualization: Build a real-time visual dashboard to provide key indicators for risk monitoring; 6) Optimization and feedback: Regularly adjust the risk assessment model based on monitoring results and improve data processing strategies.

9. An industrial and commercial data processing device, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is used to call the machine-readable program to implement the method described in any one of claims 1 to 5.

10. A computer-readable medium, characterized in that The computer readable medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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