Big data-based purchase data analysis method and device, medium and equipment

Abstract

The invention provides a purchase data analysis method and device based on big data, a medium and equipment. The method comprises the following steps: acquiring purchase data of a plurality of users for preset types of commodities from an e-commerce purchase platform through a crawler technology, extracting required field data from the purchase data, and storing the extracted field data in a corresponding data file; the data file is sent to the theme of the middleware, so that the data processing component preprocesses field data in the data file, and the preprocessed data file is stored in a distributed storage system; summarizing and analyzing field data in the data file from multiple dimensions through a big data analysis engine to obtain analysis data tables corresponding to the multiple dimensions, and storing the analysis data tables in a relational database; and performing visualization processing on each analysis data table in the relational database through a visualization tool. According to the invention, the e-commerce platform is helped to make related decisions by using the analysis data.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and in particular to a method, apparatus, medium, and equipment for purchasing data analysis based on big data. Background Technology

[0002] With the advancement of internet technology, consumers have gradually developed the habit of shopping on e-commerce platforms, generating massive amounts of user behavior data. How to effectively analyze and mine this data and leverage its value is a pressing need for e-commerce platforms today. Summary of the Invention

[0003] To address at least one of the above technical problems, embodiments of the present invention provide a method, apparatus, medium, and equipment for purchasing data analysis based on big data.

[0004] According to a first aspect, the big data-based purchase data analysis method provided by embodiments of the present invention includes:

[0005] The web crawler technology is used to obtain purchase data of multiple users for preset types of products from e-commerce platforms, extract the required field data from the purchase data, and store the extracted field data in the corresponding data file.

[0006] The data file is sent to a topic in the middleware so that the data processing component subscribed to the topic can preprocess the field data in the data file and store the preprocessed data file in a distributed storage system.

[0007] The big data analysis engine summarizes and analyzes the field data in the data files of the distributed storage system from multiple dimensions to obtain analysis data tables corresponding to the multiple dimensions, and stores each analysis data table in a relational database.

[0008] The various analytical data tables in the relational database are visualized using visualization tools.

[0009] In one embodiment, the step of extracting the required field data from the purchased data and storing the extracted field data in a corresponding data file includes:

[0010] Extract the first field data related to the purchase product details from the purchase data, and store the first field data in the first file corresponding to the purchase product details;

[0011] Extract a second field from the purchase data that is related to the user's review information of the purchased product, and store the second field data in the second file corresponding to the review information.

[0012] In one embodiment, the preset type of product is a mobile phone; correspondingly, the first field data includes at least one of the following: product brand, product name, product ID, product gross weight, product place of origin, operating system, body thickness, camera features, battery capacity, screen size, body color, RAM, front camera pixels, rear camera pixels, internal memory, and screen configuration data; the second field data includes at least one of the following: product ID, globally unique identifier, comment content, comment time, rating value, user nickname, customer membership level, and the platform used for shopping.

[0013] In one embodiment, the step of summarizing and analyzing the field data in the data file of the distributed storage system from multiple dimensions using a big data analysis engine includes: analyzing the sales volume summary data of different brands of goods based on the first field data in the first file of the distributed storage system using the big data analysis engine, extracting the sales volume summary data of the top ten brands in terms of sales volume, forming a top ten sales ranking table based on the extracted sales volume summary data; and summarizing the attribute information of each brand of goods to obtain an attribute information summary table.

[0014] In one embodiment, the step of summarizing and analyzing the field data in the data file of the distributed storage system from multiple dimensions using a big data analysis engine includes: using the big data analysis engine to analyze the purchase volume summary data of different levels of members, the user proportion data under various ratings, and the summarized keyword frequency based on the second field data in the second file of the distributed storage system, to obtain a purchase volume summary table, an evaluation proportion table formed by the user proportion under various ratings, and a high-frequency word statistics table.

[0015] In one embodiment, the process by which the data processing component preprocesses the field data in the data file includes: data cleaning, data deduplication, filtering out data that does not conform to preset standards, and data format conversion.

[0016] In one embodiment, the method further includes:

[0017] Acquire shopping behavior data of multiple users on the e-commerce platform; wherein, the shopping behavior data includes at least one of the following: number of times a user browses products, purchase records, shopping cart records, favorite records, and timestamps of purchases; and analyze the purchase preference data of the multiple users based on the shopping behavior data.

[0018] According to a second aspect, the big data-based purchase data analysis device provided in the embodiments of the present invention includes:

[0019] The first acquisition module is used to acquire purchase data of multiple users for preset types of goods from an e-commerce platform through web crawling technology, extract the required field data from the purchase data, and store the extracted field data in the corresponding data file.

[0020] The data processing module is used to send the data file to the topic of the middleware, so that the data processing component subscribed to the topic can preprocess the field data in the data file and store the preprocessed data file in the distributed storage system;

[0021] The data analysis module is used to summarize and analyze the field data in the data file of the distributed storage system from multiple dimensions through a big data analysis engine, obtain the analysis data tables corresponding to the multiple dimensions respectively, and store each analysis data table in a relational database.

[0022] The visualization module is used to visualize the various analytical data tables in the relational database using visualization tools.

[0023] In one embodiment, the first acquisition module is specifically used to: extract a first field data related to the details of the purchased product from the purchase data, and store the first field data in a first file corresponding to the details of the purchased product; extract a second field data related to the user's review information of the purchased product from the purchase data, and store the second field data in a second file corresponding to the review information.

[0024] In one embodiment, the preset type of product is a mobile phone; correspondingly, the first field data includes at least one of the following: product brand, product name, product ID, product gross weight, product place of origin, operating system, body thickness, camera features, battery capacity, screen size, body color, RAM, front camera pixels, rear camera pixels, internal memory, and screen configuration data; the second field data includes at least one of the following: product ID, globally unique identifier, comment content, comment time, rating value, user nickname, customer membership level, and the platform used for shopping.

[0025] In one embodiment, the data analysis module is specifically used to: analyze the sales volume summary data of different brands of goods based on the first field data in the first file of the distributed storage system through a big data analysis engine, and extract the sales volume summary data of the top ten brands in terms of sales volume, and form a top ten sales ranking table based on the extracted sales volume summary data; and summarize the attribute information of each brand of goods to obtain an attribute information summary table.

[0026] In one embodiment, the data analysis module is specifically used to: analyze the purchase volume summary data of different levels of members, the user proportion data under various ratings, and the summary keyword frequency based on the second field data in the second file of the distributed storage system through the big data analysis engine, and obtain the purchase volume summary table, the evaluation proportion table formed by the user proportion under various ratings, and the high-frequency word statistics table.

[0027] In one embodiment, the process by which the data processing component preprocesses the field data in the data file includes: data cleaning, data deduplication, filtering out data that does not conform to preset standards, and data format conversion.

[0028] In one embodiment, the apparatus further includes:

[0029] The preference analysis module is used to acquire shopping behavior data of multiple users on the e-commerce platform; wherein, the shopping behavior data includes at least one of the following: the number of times a user browses products, purchase records, shopping cart records, favorite records, and timestamps of purchase actions; and the module analyzes the purchase preference data of the multiple users based on the shopping behavior data.

[0030] According to a third aspect, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method provided in the first aspect.

[0031] According to a fourth aspect, the computing device provided in the embodiments of the present invention includes a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method provided in the first aspect.

[0032] The present invention provides a big data-based purchase data analysis method, apparatus, medium, and device. First, it uses web crawling technology to obtain purchase data from multiple users for preset types of goods from an e-commerce platform. Then, it extracts the required field data from the purchase data and stores the extracted field data in corresponding data files. Next, it sends the data files to a middleware topic. A data processing component subscribed to the topic preprocesses the field data in the data files and stores the preprocessed data files in a distributed storage system. Then, a big data analysis engine summarizes and analyzes the field data in the data files in the distributed storage system from multiple dimensions, obtaining analysis data tables corresponding to each dimension, and storing each analysis data table in a relational database. Finally, it uses a visualization tool to visualize each analysis data table in the relational database. Therefore, by analyzing and summarizing user purchase data from multiple dimensions, multi-dimensional analysis data is obtained, which helps e-commerce platforms make relevant decisions and realize the value of purchase data. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating a big data-based purchase data analysis method according to an embodiment of the present invention.

[0034] Figure 2 This is an overall flowchart of a big data-based purchase data analysis method in one embodiment of the present invention;

[0035] Figure 3 This is a sales chart of the top ten mobile phone brands in one embodiment of the present invention.

[0036] Figure 4 This is a bar chart showing the percentage of users under various ratings in one embodiment of the present invention;

[0037] Figure 5 This is a structural block diagram of a big data-based purchase data analysis device according to an embodiment of the present invention. Detailed Implementation

[0038] Firstly, embodiments of the present invention provide a purchase data analysis method based on big data, see [link to relevant documentation]. Figure 1 and Figure 2 The method includes the following steps S110 to S140:

[0039] S110. Obtain purchase data of multiple users for preset types of goods from e-commerce platforms through web crawling technology, extract the required field data from the purchase data, and store the extracted field data in the corresponding data file;

[0040] This includes using web crawling technology to obtain purchase data from e-commerce platforms, for example, using a Python web crawler to retrieve purchase data from e-commerce platforms. A web crawler is a program that automatically retrieves information from the internet according to specific rules.

[0041] For example, using a Python web crawler to scrape mobile phone purchase data from the JD.com platform can generally include the following steps:

[0042] Step 1: Open the JD.com website, enter "mobile phone" in the search box to search, access the mobile phone list page, and get the mobile phone list information;

[0043] Step 2: Obtain the webpage source code and use XPath to locate the `li` tags for each product in the webpage source code. The `li` tag is an HTML tag. In HTML, tags can be used to define lists; lists defined using tags can be unordered or ordered. XPath, or XML Path Language, is a language used to determine the location of a part of an XML document.

[0044] Step 3: Extract purchase data based on li tags using XPath, including product name, product ID, price, reviews, store name, details link, and whether it is self-operated, etc.

[0045] Step 4: Repeat the above steps to obtain various data from the phone's details page and the comments page, and extract the corresponding field data, storing them in the corresponding data files. The data files can be CSV files, which store the purchase data in plain text format.

[0046] In one embodiment, the step of extracting the required field data from the purchase data and storing the extracted field data in a corresponding data file in S110 may include:

[0047] S111. Extract the first field data related to the purchase product details from the purchase data, and store the first field data in the first file corresponding to the purchase product details;

[0048] S112. Extract the second field data related to the user's review information of the purchased product from the purchase data, and store the second field data in the second file corresponding to the review information.

[0049] For example, the preset type of product is a mobile phone; the first field data may include product brand, product name, product ID, product gross weight, product place of origin, operating system, body thickness, camera features, battery capacity, screen size, body color, RAM, front camera pixels, rear camera pixels, internal memory, and screen configuration data; the second field data may include product ID, globally unique identifier, review content, review time, rating value, user nickname, customer membership level, and the platform used for shopping.

[0050] S120. The data file is sent to a topic in the middleware so that the data processing component subscribed to the topic preprocesses the field data in the data file and stores the preprocessed data file in a distributed storage system.

[0051] Understandably, data preprocessing is a crucial step in data analysis and visualization, laying the foundation for various subsequent data analyses.

[0052] The middleware used can be Kafak. Kafak is an event stream processing platform that acts as a message middleware, importing and exporting data as event streams to achieve integration with existing systems.

[0053] Among these, the distributed storage system can use HDFS. HDFS is a distributed storage component in the Hadoop ecosystem. It uses a master-slave structure to store data in blocks on cluster nodes, achieving fault tolerance and disaster recovery.

[0054] The data processing component can be Storm. Storm is an open-source distributed real-time computing system with features such as low latency, high availability, distributed architecture, and scalability, while ensuring that data is not lost.

[0055] For example, the Storm component reads topic data from Kafka in real time, preprocesses the read data, and stores the processed data in HDFS.

[0056] The process by which the data processing component preprocesses the field data in the data file may include: data cleaning, data deduplication, filtering out data that does not meet preset standards, and data format conversion.

[0057] S130. The field data in the data file of the distributed storage system is summarized and analyzed from multiple dimensions by a big data analysis engine to obtain analysis data tables corresponding to the multiple dimensions, and each analysis data table is stored in a relational database.

[0058] Among these, the big data analytics engine can use Spark. Spark is a memory-based big data computing framework that obtains data sources from HDFS and uses various components to process the data.

[0059] Among them, relational databases can use MySQL.

[0060] In one embodiment, the step S130, which involves summarizing and analyzing the field data in the data file of the distributed storage system from multiple dimensions using a big data analytics engine, may include:

[0061] The big data analysis engine analyzes the sales volume summary data of different brands of goods based on the first field data of the first file in the distributed storage system, and extracts the sales volume summary data of the top ten brands in terms of sales volume. A top ten sales ranking table is formed based on the extracted sales volume summary data. The attribute information of each brand of goods is summarized to obtain an attribute information summary table.

[0062] For example, Figure 3 The chart shows the sales figures for the top ten mobile phone brands.

[0063] In one embodiment, the step S130, which involves summarizing and analyzing the field data in the data file of the distributed storage system from multiple dimensions using a big data analytics engine, may include:

[0064] The big data analysis engine analyzes the purchase volume summary data of different membership levels, the user proportion data under various ratings, and the summary keyword frequency based on the second field data in the second file of the distributed storage system, and obtains the purchase volume summary table, the evaluation proportion table formed by the user proportion under various ratings, and the high-frequency word statistics table.

[0065] The keywords are extracted by segmenting the evaluation information using word segmentation. The frequency of each keyword is then calculated as the corresponding word frequency. Keywords with a word frequency higher than a certain value are sorted according to their word frequency to obtain a high-frequency vocabulary statistics table.

[0066] For example, see Figure 4 The chart shows the percentage of users across the five rating categories. The horizontal axis represents buyers' ratings of the phone, and the vertical axis represents the number of users who rated it (in thousands).

[0067] S140. Visualize the various analytical data tables in the relational database using visualization tools.

[0068] For visualization, Echarts can be used. Echarts is an open-source JavaScript library focused on creating advanced data charts, offering various chart types including line charts, bar charts, scatter plots, and pie charts.

[0069] As can be seen, Echarts can be used to visualize various summary data. Through charts, users can more easily and intuitively understand relevant information about user behavior data, providing better decision support for e-commerce platforms.

[0070] In one embodiment, the method may further include:

[0071] A1. Obtain shopping behavior data of multiple users on the e-commerce platform; wherein, the shopping behavior data includes at least one of the following: number of times a user browses products, purchase records, shopping cart records, favorite records, and timestamps of purchase events;

[0072] A2. Analyze the purchase preference data of the multiple users based on the shopping behavior data.

[0073] Understandably, analyzing and mining massive amounts of user behavior data is crucial for optimizing merchants' marketing strategies and improving customer service. Dynamically analyzing user shopping behavior data and combining it with product feature data can reveal user shopping preferences, behavioral patterns, and help accurately identify high-quality and highly engaged users.

[0074] Understandably, e-commerce platform user behavior data has the following characteristics: large scale, diverse data source structures, and high timeliness. This embodiment of the invention uses Spark as the computing engine, Hadoop Distributed File System (HDFS) as storage, and leverages technologies such as Python web crawlers, Kafka, and Storm for data collection and preprocessing, finally using Echarts for visualization.

[0075] Secondly, embodiments of the present invention provide a purchase data analysis device based on big data, see [link to relevant documentation]. Figure 5 The device 100 includes:

[0076] The first acquisition module 110 is used to acquire purchase data of multiple users for preset types of goods from an e-commerce platform through web crawling technology, extract the required field data from the purchase data, and store the extracted field data in the corresponding data file.

[0077] Data processing module 120 is used to send the data file to a topic in the middleware, so that the data processing component subscribed to the topic can preprocess the field data in the data file and store the preprocessed data file in a distributed storage system;

[0078] Data analysis module 130 is used to summarize and analyze the field data in the data file in the distributed storage system from multiple dimensions through a big data analysis engine, obtain analysis data tables corresponding to the multiple dimensions respectively, and store each analysis data table in a relational database.

[0079] The visualization module 140 is used to visualize the various analytical data tables in the relational database using visualization tools.

[0080] In one embodiment, the first acquisition module is specifically used to: extract a first field data related to the details of the purchased product from the purchase data, and store the first field data in a first file corresponding to the details of the purchased product; extract a second field data related to the user's review information of the purchased product from the purchase data, and store the second field data in a second file corresponding to the review information.

[0081] In one embodiment, the preset type of product is a mobile phone; correspondingly, the first field data includes at least one of the following: product brand, product name, product ID, product gross weight, product place of origin, operating system, body thickness, camera features, battery capacity, screen size, body color, RAM, front camera pixels, rear camera pixels, internal memory, and screen configuration data; the second field data includes at least one of the following: product ID, globally unique identifier, comment content, comment time, rating value, user nickname, customer membership level, and the platform used for shopping.

[0082] In one embodiment, the data analysis module is specifically used to: analyze the sales volume summary data of different brands of goods based on the first field data in the first file of the distributed storage system through a big data analysis engine, and extract the sales volume summary data of the top ten brands in terms of sales volume, and form a top ten sales ranking table based on the extracted sales volume summary data; and summarize the attribute information of each brand of goods to obtain an attribute information summary table.

[0083] In one embodiment, the data analysis module is specifically used to: analyze the purchase volume summary data of different levels of members, the user proportion data under various ratings, and the summary keyword frequency based on the second field data in the second file of the distributed storage system through the big data analysis engine, and obtain the purchase volume summary table, the evaluation proportion table formed by the user proportion under various ratings, and the high-frequency word statistics table.

[0084] In one embodiment, the process by which the data processing component preprocesses the field data in the data file includes: data cleaning, data deduplication, filtering out data that does not conform to preset standards, and data format conversion.

[0085] In one embodiment, the apparatus further includes:

[0086] The preference analysis module is used to acquire shopping behavior data of multiple users on the e-commerce platform; wherein, the shopping behavior data includes at least one of the following: the number of times a user browses products, purchase records, shopping cart records, favorite records, and timestamps of purchase actions; and the module analyzes the purchase preference data of the multiple users based on the shopping behavior data.

[0087] It is understood that explanations, specific implementation methods, beneficial effects, examples, etc. of the contents of the device provided in the embodiments of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0088] Thirdly, embodiments of the present invention provide a computer-readable medium storing computer instructions, which, when executed by a processor, cause the processor to perform the method provided in the first aspect.

[0089] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0090] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0091] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0092] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0093] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0094] It is understood that explanations, specific implementation methods, beneficial effects, examples, etc. of the contents in the computer-readable medium provided in the embodiments of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0095] Fourthly, one embodiment of this specification provides a computing device including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method of any embodiment of the specification.

[0096] It is understood that explanations, specific implementation methods, beneficial effects, examples, etc. of the computing device provided in the embodiments of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0097] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0098] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using hardware, software, widgets, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium.

[0099] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A purchase data analysis method based on big data, characterized in that: include: Acquire purchase data of multiple users for preset types of goods from an e-commerce purchase platform through crawler technology, extract required field data from the purchase data, and store the extracted field data in a corresponding data file; Sending the data file to the topic of the middleware, so that the data processing component subscribing to the topic pre-processes the field data in the data file, and stores the pre-processed data file in the distributed storage system; Summarizing and analyzing the field data in the data file in the distributed storage system from multiple dimensions through a big data analysis engine to obtain analysis data tables corresponding to the multiple dimensions, and storing each analysis data table in a relational database; Each analysis data table in the relational database is visualized by using a visualization tool.

2. The method according to claim 1, characterized in that The step of extracting required field data from the purchase data and storing the extracted field data in a corresponding data file includes: Extracting first field data related to the purchased commodity details from the purchase data, and storing the first field data in a first file corresponding to the purchased commodity details; Second field data related to the user's comment information on the purchased product is extracted from the purchase data, and the second field data is stored in a second file corresponding to the comment information.

3. The method according to claim 2, characterized in that The preset type of product is a mobile phone; correspondingly, the first field data includes at least one of the product brand, product name, product ID, product gross weight, product origin, operating system, body thickness, camera features, battery capacity, screen size, body color, running memory, front camera pixels, rear camera pixels, body memory and screen configuration data; the second field data includes at least one of the product ID, globally unique identifier, comment content, comment time, rating value, user nickname, customer membership level and shopping platform.

4. The method according to claim 2, characterized in that: The field data in the data file in the distributed storage system is summarized and analyzed from multiple dimensions by the big data analysis engine, including: analyzing the sales volume summary data of different brands of goods according to the first field data in the first file in the distributed storage system by the big data analysis engine, and extracting the sales volume summary data of the top ten brands in sales volume, and forming a top ten sales ranking table based on the extracted sales volume summary data; summarizing the attribute information of each brand of goods to obtain an attribute information summary table.

5. The method according to claim 2, characterized in that: The method uses a big data analysis engine to summarize and analyze the field data in the data file in the distributed storage system from multiple dimensions, including: using the big data analysis engine to analyze the purchase volume summary data of members of different levels, user proportion data under various ratings and summarized keyword frequency according to the second field data in the second file in the distributed storage system, to obtain a purchase volume summary table, an evaluation proportion table formed by user proportions under various ratings, and a high-frequency vocabulary statistics table.

6. The method according to claim 1, characterized in that The process of preprocessing the field data in the data file by the data processing component includes: data cleaning, data deduplication, filtering out data that does not meet the preset standards, and format conversion of the data.

7. The method according to claim 1, characterized in that Also includes: Acquire shopping behavior data of multiple users on the e-commerce purchase platform; wherein the shopping behavior data includes at least one of the number of times a user browses a product, purchase records, shopping cart records, favorite records, and a timestamp of a purchase behavior; The purchasing preference data of the multiple users are analyzed based on the shopping behavior data.

8. A purchase data analysis device based on big data, characterized in that: include: A first acquisition module is used to acquire purchase data of multiple users for preset types of commodities from an e-commerce purchase platform by using crawler technology, extract required field data from the purchase data, and store the extracted field data in a corresponding data file; A data processing module, used for sending the data file to the subject of the middleware, so that the data processing component subscribed to the subject pre-processes the field data in the data file, and stores the pre-processed data file in the distributed storage system; A data analysis module, configured to summarize and analyze the field data in the data file in the distributed storage system from multiple dimensions through a big data analysis engine, obtain analysis data tables corresponding to the multiple dimensions, and store each analysis data table in a relational database; The visualization module is used to visualize each analysis data table in the relational database through a visualization tool.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

10. A computing device, characterized in that The method comprises a memory and a processor, wherein the memory stores executable codes, and when the processor executes the executable codes, the method according to any one of claims 1 to 7 is implemented.

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