Amazon merchant data collection method and device, computer equipment and storage medium

Abstract

The invention discloses an Amazon merchant data collection method and device, computer equipment and a storage medium. The method comprises the following steps: after a user logs in an Amazon merchant background, acquiring an account number of a merchant which is selected by the user and needs to collect data and a sales area; automatically collecting related data according to the merchant account and the sales area; analyzing and sorting the related data to obtain a sorting result; generating a financial analysis report according to the arrangement result, and performing risk prediction to obtain a prediction result; and outputting the financial analysis report and the prediction result. By implementing the method provided by the invention, the data collection and analysis efficiency can be improved, meanwhile, the data missing and leakage risks are avoided, the difficulty in multi-dimensional data analysis is solved, and the quality of merchants can be effectively evaluated.

Description

Technical Field

[0001] The present invention relates to a data processing method, and more specifically to a method, apparatus, computer equipment, and storage medium for collecting Amazon merchant data. Background Art

[0002] Amazon merchant data aggregation helps consolidate and analyze key data such as sales and inventory, thereby optimizing operational decisions and improving management efficiency. It also supports financial and inventory management, drives data-driven marketing strategies, and enhances customer service and experience. The Amazon merchant data aggregation process typically requires staff to manually operate the Amazon merchant management system, download relevant reports, and collect the required data, either remotely or by visiting the merchant's location. This process not only relies on coordination between merchants and staff to ensure time coordination, but also requires manual downloading of reports and data collation, resulting in high overall time costs and a cumbersome process. Merchants need to spend a significant amount of time communicating with staff to ensure accurate data download and collation, but this still requires a significant amount of manual intervention and time consumption, leading to low work efficiency.

[0003] Furthermore, the complex nature of data dimensions makes it prone to omissions and statistical errors, further impacting data accuracy and reliability. To ensure smooth data collation and analysis, relevant personnel must be highly familiar with the Amazon merchant platform. Data updates are also frequent and costly, requiring merchants to regularly invest significant manpower and resources. Furthermore, these operations can impact merchant store management, preventing them from efficiently conducting both daily operations and data management, leading to potential business losses and management challenges.

[0004] Therefore, it is necessary to design a new method to improve the efficiency of data collection and analysis, eliminate the risk of data omissions and leakage, solve the difficulties of multi-dimensional data analysis, and facilitate the effective evaluation of merchant quality. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method, device, computer equipment and storage medium for collecting Amazon merchant data.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Amazon merchant data collection method, comprising:

[0007] When the user logs in to the Amazon merchant backend, the merchant account and sales area selected by the user for data collection are obtained;

[0008] Automatically collect relevant data based on the merchant account and the sales area;

[0009] Analyzing and collating the relevant data to obtain a collation result;

[0010] Generate a financial analysis report based on the collated results and perform risk prediction to obtain prediction results;

[0011] Output the financial analysis report and the forecast result.

[0012] A further technical solution is: the automatic collection of relevant data based on the merchant account and the sales area includes:

[0013] The relevant data corresponding to the merchant account and the sales area are automatically downloaded through the browser plug-in.

[0014] Its further technical solution is: the relevant data includes order reports, inventory reports, business reports, delivery reports, and policy compliance indicators.

[0015] A further technical solution is: analyzing and arranging the relevant data to obtain an arrangement result, including:

[0016] The relevant data are classified, cleaned and standardized to obtain the sorting results.

[0017] A further technical solution is: generating a financial analysis report based on the collation results and performing risk prediction to obtain prediction results, including:

[0018] Performing statistical analysis, trend analysis, and indicator comparison on the collated results to obtain analysis results;

[0019] generating a financial analysis report based on the analysis results;

[0020] Potential risk points are identified from the sorted results to obtain prediction results.

[0021] A further technical solution is: generating a financial analysis report based on the analysis results includes:

[0022] A multi-dimensional report template is used to fill in the analysis results and generate a financial analysis report.

[0023] A further technical solution is: after performing statistical analysis, trend analysis and indicator comparison on the collated results to obtain analysis results, the method further includes:

[0024] The analysis results are stored in a database according to different categories, and database indexing technology is used for user retrieval and query.

[0025] The present invention also provides an Amazon merchant data collection device, comprising:

[0026] The acquisition unit is used to obtain the merchant account and sales area selected by the user for data collection after the user logs in to the Amazon merchant backend;

[0027] An automatic collection unit, configured to automatically collect relevant data based on the merchant account and the sales area;

[0028] A collating unit, configured to analyze and collate the relevant data to obtain a collation result;

[0029] An analysis and prediction unit, configured to generate a financial analysis report based on the collation results and perform risk prediction to obtain a prediction result;

[0030] An output unit is used to output the financial analysis report and the forecast result.

[0031] The present invention further provides a computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0032] The present invention also provides a storage medium storing a computer program, wherein the computer program implements the above method when executed by a processor.

[0033] Compared with the existing technology, the beneficial effects of the present invention are as follows: after the user logs in to the Amazon merchant backend, the system automatically obtains the selected merchant account and sales area, and then collects relevant data based on this information, analyzes and organizes it, and generates accurate financial analysis reports and risk forecasts; the entire process does not require human intervention, ensuring efficient and complete data collection, and avoiding the risks of data omissions and leakage; through standardized report output, the system solves the problem of difficult multi-dimensional data analysis, making merchant quality assessment more intuitive and convenient, thereby improving the efficiency and accuracy of data analysis.

[0034] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A schematic diagram of an application scenario of the Amazon merchant data aggregation method provided by an embodiment of the present invention;

[0037] Figure 2A flowchart of an Amazon merchant data aggregation method provided by an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of a sub-process of the Amazon merchant data collection method provided by an embodiment of the present invention;

[0039] Figure 4 A schematic block diagram of an Amazon merchant data collection device provided by an embodiment of the present invention;

[0040] Figure 5 A schematic block diagram of the **** unit of the Amazon merchant data collection device provided by an embodiment of the present invention;

[0041] Figure 6 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe 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 them. 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.

[0043] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0044] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0045] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0046] See also Figure 1 and Figure 2 , Figure 1 Schematic diagram of an application scenario of the Amazon merchant data aggregation method provided by an embodiment of the present invention. Figure 2A schematic flow chart of an Amazon merchant data aggregation method provided in an embodiment of the present invention. This Amazon merchant data aggregation method is applied to a server. The server interacts with terminals to automatically collect and analyze data related to merchant accounts and sales regions, including multi-dimensional reports such as orders, inventory, business, and delivery. The server then uses a browser plug-in to download the data and perform classification, cleaning, and standardization. Through statistical analysis, trend analysis, and indicator comparison, financial analysis reports are generated and potential risks are predicted. This improves data collection and analysis efficiency, avoids data omissions and leakage risks, and addresses the difficulties of multi-dimensional data analysis, facilitating accurate assessment of merchant performance.

[0047] Figure 2 FIG. 1 is a flow chart of the Amazon merchant data collection method provided by an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S150.

[0048] S110. After the user logs in to the Amazon merchant backend, the merchant account and sales area selected by the user for which data needs to be aggregated are obtained.

[0049] In this example, a merchant account is a unique identifier used by merchants to register and manage their stores on the Amazon platform. Each merchant account corresponds to an Amazon merchant dashboard, through which merchants can log in and view and manage their store's sales data, orders, inventory, delivery, customer feedback, and other information. The use of merchant accounts is fundamental to data aggregation and report generation, ensuring that only authorized users can access and process store data.

[0050] A sales region refers to the geographic area or country in which a merchant chooses to sell products on the Amazon platform. Amazon typically divides sales markets by region, such as North America, Europe, and Asia, and further subdivides them into multiple countries or regions (e.g., the United States, Canada, Germany, the United Kingdom, Japan, etc.). Each sales region may have different market characteristics, consumer demands, sales strategies, and logistics models. Therefore, merchants may choose to sell in specific regions in order to develop appropriate marketing strategies for different markets.

[0051] After logging into the Amazon merchant backend, the system prompts merchants to select the merchant account and sales region for which data needs to be aggregated. Based on the user's selection, the system retrieves relevant information about the merchant account and the sales region data associated with that account. This ensures that data aggregation is customized for each merchant account and sales region, providing accurate data support for subsequent data analysis and report generation.

[0052] In this way, the system can more accurately collect data from different regions and different merchant accounts, and classify and process them according to corresponding rules.

[0053] S120: Automatically collect relevant data according to the merchant account and the sales area.

[0054] In this embodiment, the relevant data includes order reports, inventory reports, business reports, delivery reports, and policy compliance indicators.

[0055] The relevant data corresponding to the merchant account and the sales area are automatically downloaded through the browser plug-in.

[0056] Before collecting data, the user's merchant account and the sales region they belong to must be determined. This step is determined by the system based on the user's login information and selected sales region. After the merchant logs in to the Amazon backend, the system will identify the merchant account information and provide the corresponding options on the system interface, allowing the user to select the specific sales region for which data is required. The sales region selected determines the specific scope of the data, as order, inventory, delivery, and other data will vary in different regions.

[0057] After confirming the merchant account and sales area, the browser plug-in will automatically initiate the data crawling task according to the user's instructions. The specific process includes:

[0058] The browser extension automatically initiates requests based on the merchant backend's page structure to retrieve relevant data. The extension identifies different pages in the backend, such as order reports, inventory reports, business reports, and delivery reports, and extracts data from each page.

[0059] The plugin downloads the appropriate report based on the requested data type. For example, if a user requests an order report, the plugin will request the order data in the background and save it in the specified format (e.g., CSV, Excel, etc.). Similarly, reports related to inventory, delivery, and business operations will also be automatically downloaded based on the user's needs.

[0060] Downloaded data contains a variety of information, so the plug-in will perform a preliminary screening of the data after downloading, extracting the information relevant to the user's needs. For example, an order report may include fields such as order number, product information, sales quantity, and customer reviews. The system will filter out valid information based on preset rules and remove irrelevant data.

[0061] After downloading and extracting data, it is categorized and stored according to its type. To facilitate subsequent analysis and querying, the system stores the data in separate database tables or folders. This storage method is customized based on the nature and type of the data, as well as its subsequent use. For example, order report data is stored separately from inventory data, and all data is stored in an efficient database for quick querying and processing.

[0062] During the data capture and download process, the system design takes into account the normal operations of merchants. The browser plug-in runs in the background, which does not interfere with merchants' current operations, ensuring that merchants' use of the platform is not affected by data collection. In addition, automated data collection eliminates the possibility of manual intervention and reduces the risk of data omissions, leaks, or inaccuracies.

[0063] To ensure data security, the browser plug-in uses encryption technology to protect data during transmission. The system also encrypts downloaded data for storage, preventing unauthorized access or tampering. Furthermore, during subsequent report generation and data presentation, the system ensures data confidentiality and integrity, preventing the leakage of any sensitive information.

[0064] In step S120, the system uses browser plug-in technology to efficiently and automatically collect various data related to merchant accounts and sales regions. This process automates data downloading, extraction, and storage, ensuring normal merchant operations while minimizing manual intervention and improving the efficiency and accuracy of data collection.

[0065] S130: Analyze and organize the relevant data to obtain an organization result.

[0066] In this embodiment, the above-mentioned sorting results include the results obtained after sorting the automatically collected relevant data.

[0067] The relevant data are classified, cleaned and standardized to obtain the sorting results.

[0068] The purpose of data classification is to divide the collected data into different categories according to different data types and business needs for subsequent analysis and processing. Data classification can be based on multiple dimensions such as data source, type, and purpose.

[0069] For example, Amazon merchant data often includes categories like order data, inventory data, sales reports, customer feedback, logistics timeliness data, and business opportunity detector data. Each category of data has different analytical value, and categorization ensures that each category receives specialized analysis and reporting.

[0070] Specifically, the system classifies data according to data sources (such as different modules of Amazon backend) and data types (such as sales volume, negative review rate, delivery time, etc.), and stores it in the corresponding data warehouse or database table.

[0071] The purpose of data cleaning is to remove invalid information such as errors, redundancies, duplications, and null values ​​from the data, thereby ensuring the accuracy and reliability of the analysis results. During the automatic collection process of the merchant backend, some useless or erroneous data is often collected and must be cleaned.

[0072] First, check whether there are duplicate records in the dataset, such as the same order information or the same data entered multiple times, to avoid repeated analysis or statistics. If certain data items are missing, the system may need to fill in, infer, or delete them based on business needs. For example, if the logistics timeliness data for an order is missing, it can be supplemented based on historical data or marked as "missing." For example, if the return rate of some merchants' orders is abnormally high, this may be due to data entry errors, system failures, or other abnormalities. This data needs to be marked and corrected.

[0073] Specifically, automated scripts or algorithms can check and clean up duplicates, null values, and unreasonable anomalies in the data. During the cleaning process, the system may also use some external data sources for comparison to ensure data accuracy.

[0074] Standardization ensures that data from different sources is formatted consistently and adheres to pre-defined standards, facilitating subsequent analysis and report generation. Data standardization ensures uniformity across all types of data in the system, enabling interoperability and interoperability between data from different sources.

[0075] For example, timestamps in orders should be standardized to the "year-month-day hour:minute:second" format. Price data should be expressed in currencies such as "USD" or "RMB," ensuring consistency in the number of decimal places. For example, the "Delivery Time" and "Logistics Time" fields in the order table can be standardized to "DeliveryTime," and the "Inventory Quantity" field in the inventory table can be standardized to "StockQuantity."

[0076] If data from different sources uses different units of measurement, the system can automatically convert the units. For example, if order data involves sales amounts from different countries (primarily in different currencies), it can be converted to a unified currency unit for easier analysis.

[0077] During this process, the system will unify data in different formats into a format that meets business requirements based on preset standardization rules, and perform unified unit conversion, field name replacement and other operations on all data items.

[0078] The sorted data will form a "sorted result", providing a clear, standardized and unified data basis for subsequent report generation and risk assessment.

[0079] The classified, cleaned, and standardized data is organized into structured tables or databases for easy query and analysis. The system can generate an intermediate report that lists changes made during the data cleaning and standardization process, as well as the results of abnormal data processing, to ensure data transparency and traceability.

[0080] The sorted data will be stored in the database. The system may attach labels to each type of data, mark its processing status, and generate standardized report templates based on different business needs for subsequent analysis.

[0081] In summary, the data classification, cleaning, and standardization steps in step S130 are designed to improve data quality, ensuring its accuracy and consistency to support subsequent report generation, risk analysis, and operational forecasting. Through this series of processes, the system can extract valuable information from large amounts of raw data, providing strong support for merchant decision-making.

[0082] S140: Generate a financial analysis report based on the sorting results, and perform risk prediction to obtain prediction results.

[0083] In this embodiment, the financial analysis report refers to a detailed report generated based on data analysis to assess the financial health, risks, and future trends of merchants or markets.

[0084] Forecast results refer to the predictions of future risks, trends or performances derived through data analysis and model calculations.

[0085] In one embodiment, see Figure 3 , the above-mentioned step S140 may include steps S141 to S143.

[0086] S141. Perform statistical analysis, trend analysis, and indicator comparison on the sorting results to obtain analysis results.

[0087] In this embodiment, the analysis result refers to a comprehensive assessment of the current situation obtained through methods such as statistics, trends, and indicator comparison.

[0088] Summarize and classify different types of data, and use statistical methods (such as mean, standard deviation, maximum, minimum, etc.) to conduct statistical analysis on order sales, user reviews, inventory status and other data to obtain a preliminary understanding of the current merchant operations.

[0089] By analyzing historical data and existing data, the system can identify changing trends within a certain period of time, such as an increase or decrease in sales, an increase in negative review rates, an increase in logistics timeliness issues, etc., thereby reflecting the dynamic changes in the merchant's operating status.

[0090] Compare this data to industry standards, competitors, or historical data to assess a merchant's performance in different areas. For example, the system can compare a merchant's negative review rate to the industry average, or its inventory turnover rate to that of other merchants in the same industry, to identify potential issues.

[0091] Through these analyses, the system can obtain a detailed analysis result, revealing the strengths and weaknesses of the merchant's current operations.

[0092] S142. Generate a financial analysis report based on the analysis results.

[0093] In this embodiment, a multi-dimensional report template is used to fill in the analysis results to generate a financial analysis report.

[0094] After completing the statistical analysis, trend analysis, and indicator comparison of the data, the system will use these analysis results to generate a financial analysis report. The generation of this report usually includes the following aspects:

[0095] Multi-dimensional report template filling: The system uses pre-set multi-dimensional report templates to embed analysis results into corresponding report formats. Different data dimensions (such as order status, logistics performance, user reviews, etc.) are displayed in the report with clear charts and data items.

[0096] Report Content: The report content usually includes:

[0097] Operational status: such as sales trends, impact analysis of negative review rates, effectiveness of customer feedback, etc.

[0098] Financial health: Calculate the merchant's profitability, cash flow and other indicators through sales data.

[0099] Risk analysis: Assess potential financial risks based on information such as order anomalies, negative reviews, and logistics delays.

[0100] Compliance Report: Based on policy compliance indicators, check whether merchants meet the relevant requirements of the Amazon platform.

[0101] Market forecast: Through data analysis, the system can provide future business forecasts and give growth potential or decline risks.

[0102] Visual charts: To better understand the data, the system embeds visual charts in the report, such as trend charts, pie charts, bar charts, etc., to help users intuitively understand the report content.

[0103] The generated financial analysis reports can provide a comprehensive view of merchants' operations and help merchants and financial personnel make more informed decisions.

[0104] S143: Identify potential risk points based on the sorting results to obtain prediction results.

[0105] In this embodiment, the core of this step is to use the collected and analyzed data information to identify potential risks in the merchant's operations and make predictions based on these risks. The specific process includes:

[0106] By analyzing the results, the system identifies potential risk points based on different risk dimensions (such as product negative review rate, order return rate, logistics timeliness issues, etc.). For example, if the negative review rate of a product surges within a short period of time, the system will mark this risk point and alert the merchant.

[0107] Based on historical data and trend analysis, the system can use machine learning algorithms or big data analytics to predict future risks. For example, by analyzing data from the past few months, it can predict the risks that may affect a product's sales in the coming quarter, or the potential customer complaints caused by delivery issues for a certain product type.

[0108] Through charts, reports or early warning systems, the system will present the forecast results to merchants or financial analysts in an easy-to-understand form, ensuring that they can understand potential risks in a timely manner and take appropriate countermeasures.

[0109] Additionally, it includes:

[0110] The analysis results are stored in a database according to different categories, and database indexing technology is used for user retrieval and query.

[0111] During the report generation and risk prediction process, the system stores all analysis results in the database according to different categories and uses database indexing technology for efficient storage and retrieval. This function is achieved by:

[0112] The data will be stored according to different types such as order information, user feedback, inventory data, etc. to ensure that the data is clearly organized and convenient for subsequent query and analysis.

[0113] By indexing the stored data, the system can achieve efficient retrieval, and users can quickly filter out the required data, thereby speeding up the analysis and decision-making process.

[0114] The database has backup and recovery functions to ensure the data security and integrity of merchants and financial personnel, and avoid data loss or tampering.

[0115] In summary, the goal of step S140 is to generate a high-quality financial analysis report by comprehensively analyzing the collected data, and to use advanced technology to predict potential risks, thereby providing strong decision-making support for merchants and financial-related fields.

[0116] S150: Output the financial analysis report and the forecast result.

[0117] Output financial analysis reports and forecast results to the terminal for display.

[0118] The method of this embodiment uses browser plug-in related technology to automatically collect basic information, order reports, inventory reports, business reports, delivery reports, policy compliance indicators, inventory indicators, emails, business opportunity detector indicators and market segmentation information in the Amazon merchant management background. After receiving user instructions, the module will automatically collect relevant data in the platform according to the user's current account, filter out valid information, and complete the data collection work without interfering with the merchant's operations. Based on the collected data, according to different data types and time periods, through the designed preset templates, it automatically organizes and generates reports. The report content will highlight the risk points and high-quality items of the store, ensure the standardization and professionalism of the data, and meet the needs of the financial field. According to different risk levels, corresponding preset reports are generated to help merchants and financial personnel assess relevant risks.

[0119] Collected data is categorized and stored according to its type, using high-quality data storage units and efficient database indexing and retrieval technology to ensure that financial personnel can easily screen, query, compile statistics, and analyze data. The database also features backup and recovery capabilities to ensure data security and integrity.

[0120] Specifically, the system determines whether the user has logged into the Amazon merchant backend and asks the user to select the corresponding account and the sales area to be obtained. The system then submits the information and begins data collection.

[0121] The system automatically collects relevant data, downloads reports and analyzes the data, organizes the data according to different categories, and automatically saves it to the database.

[0122] Based on the collected data, the system generates corresponding financial analysis reports by analyzing dimensions such as order sales, negative user review rates, and logistics timeliness. Through intelligent predictions using large-scale models, it assesses risks and provides visual displays such as risk analysis and operational forecasts for each data category, helping merchants make accurate decisions.

[0123] Compared to traditional data collection methods, which typically take one to two days, this solution reduces this time to approximately ten minutes. This solution intelligently collects the required information based on different scenarios, flexibly applying different solutions to different business types. Furthermore, the system addresses issues that can impact normal merchant operations. Through efficient, automated backend data collection, it avoids the risk of data leakage and loss.

[0124] The shift from traditional manual data analysis to automated report generation in this solution significantly reduces labor costs and improves data analysis efficiency and accuracy. Visual charts allow users to easily identify upward and downward trends and cyclical changes in data, making complex data intuitive and reducing the time and effort required for data analysis. Furthermore, labeling each data type facilitates the identification of abnormal data, improving the convenience and accuracy of data processing.

[0125] The system automatically collects and archives relevant data about target merchants, categorizing it and generating visual reports. The entire process requires no human intervention; simply installing the plug-in and following the on-screen instructions to complete a simple authorization process ensures data integrity and effectively mitigates the risk of data leakage.

[0126] This solution greatly improves the speed of data collection and the efficiency of analysis, allowing businesses to quickly obtain key data and make efficient business decisions.

[0127] By automatically generating standardized reports, the system solves the problems of numerous data dimensions, difficult analysis, and difficulty in evaluating the quality of merchants. It provides clear and intuitive results to help users judge the business status of merchants more effectively.

[0128] The above-mentioned Amazon merchant data collection method is that after the user logs in to the Amazon merchant backend, the system automatically obtains the selected merchant account and sales area, and then collects relevant data based on this information, analyzes and organizes it, and generates accurate financial analysis reports and risk forecasts; the entire process does not require human intervention, ensuring efficient and complete data collection, and avoiding the risk of data omissions and leakage; through standardized report output, the system solves the problem of difficult multi-dimensional data analysis, making merchant quality assessment more intuitive and convenient, thereby improving the efficiency and accuracy of data analysis.

[0129] Figure 4 FIG is a schematic block diagram of an Amazon merchant data collection device 300 provided by an embodiment of the present invention. Figure 4 As shown, corresponding to the above Amazon merchant data collection method, the present invention also provides an Amazon merchant data collection device 300. The Amazon merchant data collection device 300 includes a unit for executing the above Amazon merchant data collection method, and the device can be configured in a server. Figure 4 The Amazon merchant data collection device 300 includes an acquisition unit 301, an automatic collection unit 302, a sorting unit 303, an analysis and prediction unit 304 and an output unit 305.

[0130] The acquisition unit 301 is used to obtain the merchant account and sales area selected by the user for data collection after the user logs in to the Amazon merchant backend; the automatic collection unit 302 is used to automatically collect relevant data based on the merchant account and the sales area; the sorting unit 303 is used to analyze and sort the relevant data to obtain sorting results; the analysis and prediction unit 304 is used to generate a financial analysis report based on the sorting results and perform risk prediction to obtain prediction results; the output unit 305 is used to output the financial analysis report and the prediction results.

[0131] In one embodiment, the automatic collection unit 302 is configured to automatically download the merchant account and related data corresponding to the sales area through a browser plug-in.

[0132] In one embodiment, the arranging unit 303 is used to classify, clean and standardize the relevant data to obtain an arranging result.

[0133] In one embodiment, if Figure 5 As shown, the analysis and prediction unit 304 includes an analysis subunit 3041 , a report generation subunit 3042 and a risk point identification subunit 3043 .

[0134] The analysis subunit 3041 is used to perform statistical analysis, trend analysis and indicator comparison on the sorting results to obtain analysis results; the report generation subunit 3042 is used to generate a financial analysis report based on the analysis results; the risk point identification subunit 3043 is used to identify potential risk points in the sorting results to obtain prediction results.

[0135] In one embodiment, the report generation subunit 3042 is configured to fill in the analysis results using a multi-dimensional report template to generate a financial analysis report.

[0136] In one embodiment, the analysis and prediction unit 304 further includes:

[0137] The storage subunit is used to store the analysis results into a database according to different categories and use database indexing technology to provide users with retrieval and query.

[0138] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned Amazon merchant data collection device 300 and each unit can refer to the corresponding description in the aforementioned method embodiment. For the convenience and brevity of the description, it will not be repeated here.

[0139] The above-mentioned Amazon merchant data collection device 300 can be implemented in the form of a computer program. The computer program can be used in Figure 6 Runs on the computer equipment shown.

[0140] See also Figure 6 , Figure 6 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0141] See Figure 6 The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0142] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, which, when executed, can cause the processor 502 to execute an Amazon merchant data aggregation method.

[0143] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500.

[0144] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an Amazon merchant data collection method.

[0145] The network interface 505 is used to communicate with other devices through the network. Figure 6 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0146] The processor 502 is configured to execute a computer program 5032 stored in the memory to implement the following steps:

[0147] After a user logs in to the Amazon merchant backend, the merchant account and sales area selected by the user for data collection are obtained; relevant data is automatically collected based on the merchant account and the sales area; the relevant data is analyzed and sorted to obtain sorting results; a financial analysis report is generated based on the sorting results, and risk prediction is performed to obtain prediction results; and the financial analysis report and the prediction results are output.

[0148] The relevant data include order reports, inventory reports, business reports, delivery reports, and policy compliance indicators.

[0149] In one embodiment, when the processor 502 implements the step of automatically collecting relevant data according to the merchant account and the sales area, it specifically implements the following steps:

[0150] The relevant data corresponding to the merchant account and the sales area are automatically downloaded through the browser plug-in.

[0151] In one embodiment, when the processor 502 implements the step of analyzing and arranging the relevant data to obtain an arrangement result, the processor 502 specifically implements the following steps:

[0152] The relevant data are classified, cleaned and standardized to obtain the sorting results.

[0153] In one embodiment, when the processor 502 generates a financial analysis report based on the sorting results and performs risk prediction to obtain a prediction result, the processor 502 specifically implements the following steps:

[0154] Perform statistical analysis, trend analysis, and indicator comparison on the collated results to obtain analysis results; generate a financial analysis report based on the analysis results; and identify potential risk points on the collated results to obtain prediction results.

[0155] In one embodiment, when the processor 502 implements the step of generating a financial analysis report based on the analysis results, the processor 502 specifically implements the following steps:

[0156] A multi-dimensional report template is used to fill in the analysis results and generate a financial analysis report.

[0157] In one embodiment, after performing the step of performing statistical analysis, trend analysis, and indicator comparison on the sorting results to obtain analysis results, the processor 502 further performs the following steps:

[0158] The analysis results are stored in a database according to different categories, and database indexing technology is used for user retrieval and query.

[0159] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0160] Those skilled in the art will appreciate that all or part of the steps in the method of the above-described embodiment can be implemented by instructing the relevant hardware through a computer program. The computer program includes program instructions, which can be stored in a storage medium that is computer-readable. The program instructions are executed by at least one processor in the computer system to implement the steps in the method of the above-described embodiment.

[0161] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor performs the following steps:

[0162] After a user logs in to the Amazon merchant backend, the merchant account and sales area selected by the user for data collection are obtained; relevant data is automatically collected based on the merchant account and the sales area; the relevant data is analyzed and sorted to obtain sorting results; a financial analysis report is generated based on the sorting results, and risk prediction is performed to obtain prediction results; and the financial analysis report and the prediction results are output.

[0163] The relevant data include order reports, inventory reports, business reports, delivery reports, and policy compliance indicators.

[0164] In one embodiment, when the processor executes the computer program to implement the step of automatically collecting relevant data based on the merchant account and the sales area, the processor specifically implements the following steps:

[0165] The relevant data corresponding to the merchant account and the sales area are automatically downloaded through the browser plug-in.

[0166] In one embodiment, when the processor executes the computer program to implement the step of analyzing and collating the relevant data to obtain a collation result, the processor specifically implements the following steps:

[0167] The relevant data are classified, cleaned and standardized to obtain the sorting results.

[0168] In one embodiment, when the processor executes the computer program to implement the step of generating a financial analysis report based on the sorting results and performing risk prediction to obtain a prediction result, the processor specifically implements the following steps:

[0169] Perform statistical analysis, trend analysis, and indicator comparison on the collated results to obtain analysis results; generate a financial analysis report based on the analysis results; and identify potential risk points on the collated results to obtain prediction results.

[0170] In one embodiment, when the processor executes the computer program to implement the step of generating a financial analysis report based on the analysis results, the processor specifically implements the following steps:

[0171] A multi-dimensional report template is used to fill in the analysis results and generate a financial analysis report.

[0172] In one embodiment, after the processor executes the computer program to implement the step of performing statistical analysis, trend analysis, and indicator comparison on the sorting results to obtain analysis results, the processor further implements the following steps:

[0173] The analysis results are stored in a database according to different categories, and database indexing technology is used for user retrieval and query.

[0174] The storage medium may be any computer-readable storage medium that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0175] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0176] In the several embodiments provided herein, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the various units is merely a logical functional division, and actual implementation may employ other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0177] The steps in the methods of the embodiments of the present invention may be adjusted in order, combined, or deleted as needed. The units in the devices of the embodiments of the present invention may be combined, divided, or deleted as needed. Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0178] If this integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, terminal, or network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present invention.

[0179] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. Amazon merchant data collection method, characterized by: include: When the user logs in to the Amazon merchant backend, the merchant account and sales area selected by the user for data collection are obtained; Automatically collect relevant data based on the merchant account and the sales area; Analyzing and collating the relevant data to obtain a collation result; Generate a financial analysis report based on the collated results and perform risk prediction to obtain prediction results; Output the financial analysis report and the forecast result.

2. The Amazon merchant data collection method according to claim 1, characterized in that: The automatically collecting relevant data according to the merchant account and the sales area includes: The relevant data corresponding to the merchant account and the sales area are automatically downloaded through the browser plug-in.

3. The Amazon merchant data collection method according to claim 1, characterized in that: The relevant data includes order reports, inventory reports, business reports, delivery reports, and policy compliance indicators.

4. The Amazon merchant data collection method according to claim 1, characterized in that: The analyzing and arranging the relevant data to obtain an arrangement result includes: The relevant data are classified, cleaned and standardized to obtain the sorting results.

5. The Amazon merchant data collection method according to claim 1, characterized in that: Generating a financial analysis report based on the collation results and performing risk prediction to obtain prediction results includes: Performing statistical analysis, trend analysis, and indicator comparison on the collated results to obtain analysis results; generating a financial analysis report based on the analysis results; Potential risk points are identified from the sorted results to obtain prediction results.

6. The Amazon merchant data collection method according to claim 5, characterized in that: Generating a financial analysis report according to the analysis results includes: A multi-dimensional report template is used to fill in the analysis results and generate a financial analysis report.

7. The Amazon merchant data collection method according to claim 5, characterized in that: After performing statistical analysis, trend analysis and indicator comparison on the collated results to obtain analysis results, the method further includes: The analysis results are stored in a database according to different categories, and database indexing technology is used for user retrieval and query.

8. Amazon merchant data collection device, characterized by: include: The acquisition unit is used to obtain the merchant account and sales area selected by the user for data collection after the user logs in to the Amazon merchant backend; An automatic collection unit, configured to automatically collect relevant data based on the merchant account and the sales area; A collating unit, configured to analyze and collate the relevant data to obtain a collation result; An analysis and prediction unit, configured to generate a financial analysis report based on the collation results and perform risk prediction to obtain a prediction result; An output unit is used to output the financial analysis report and the forecast result.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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