E-commerce platform-based network shop operation evaluation method and device, and medium

Abstract

The invention discloses a network shop operation evaluation method and device based on an e-commerce platform and a medium, and the method comprises the steps: receiving an encrypted mobile phone number uploaded by an applet, and decrypting the encrypted mobile phone number to obtain a user mobile phone number; querying a list of registered stores associated with the mobile phone number of the user in a database; when the registered shop list is a non-empty list, returning the shop list to the applet; when a target shop query request uploaded by the applet is received, searching operation data meeting the query request; the query request comprises a target store ID, query time and a front-end return format; and returning the operation data to the applet according to the front-end return format. Based on the constructed applet, the applet and the e-commerce platform are subjected to front-end and back-end design, so that diversified requirements of users can be met, and shop data statistics and transverse and longitudinal comparison analysis are performed from multiple dimensions.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, and medium for evaluating the operation of an online store based on an e-commerce platform. Background Art

[0002] With the rapid development of the modern internet and e-commerce, more and more people are starting to run online stores. However, faced with a wide variety of product categories and a large number of competing merchants, how individual operators can dynamically optimize their business direction and better achieve cost reduction and efficiency improvement often becomes the key to the survival of a store.

[0003] At present, operation evaluation is usually carried out based on e-commerce platforms. Although e-commerce platforms provide basic data analysis, most of them only provide data statistics of a single dimension (such as sales volume and sales revenue), and the statistical data are usually displayed in a fixed default format. Operators lack vertical analysis and prediction and horizontal comparative insights, resulting in simple and inflexible dimensions of online store operation evaluation, which cannot meet diverse evaluation needs. Summary of the Invention

[0004] The embodiments of the present application provide a method, device, and medium for evaluating online store operations based on an e-commerce platform, which are used to solve the problem that online store operations evaluation dimensions are simple and inflexible and cannot meet diverse evaluation needs.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] On the one hand, an embodiment of the present application provides an online store operation evaluation method based on an e-commerce platform, the method comprising: receiving an encrypted mobile phone number uploaded by a mini program, decrypting the encrypted mobile phone number to obtain a user mobile phone number; querying a database for a list of registered stores associated with the user mobile phone number; when the list of registered stores is a non-empty list, returning the list of stores to the mini program; upon receiving a target store query request uploaded by the mini program, searching for business data that meets the query request; the query request includes a target store ID, a query time, and a front-end return format; and returning the business data to the mini program in accordance with the front-end return format.

[0007] In one example, after returning the business data to the mini program in the front-end return format, the method further includes: receiving a time range update request uploaded by the mini program when a user clicks on the time filter of the mini program; searching for updated business data that meets the time range update request, and returning the updated business data to the mini program; displaying indicator data in a dashboard in the mini program, displaying trend changes of indicators in a chart, and displaying detailed data of the data point in a table when a user clicks on a data point in a chart.

[0008] In one example, the method further includes: receiving a market situation analysis request uploaded by the mini program;

[0009] According to the market situation analysis request, query the market public data of the preset top-ranked stores; the market situation analysis request includes analysis indicators, the category to which the store belongs, and the target store ID; according to the target store ID, query the target market data of the target store under the analysis indicators; return the store market public data and the target market data to the mini program.

[0010] In one example, after returning the store market public data and the target market data to the mini program, the method also includes: receiving an indicator update request uploaded by the mini program when the user clicks the indicator filter of the mini program; searching for updated store market public data that meets the indicator update request, and querying the updated target market data of the target store under the indicator update request; returning the updated store market public data and the updated target market data to the mini program.

[0011] In one example, the method also includes: receiving new store binding information uploaded by the mini program, and after the new store binding information is verified, binding the new store to the user's mobile phone number; adding the new store to the registered store list; and / or, receiving store unbinding information uploaded by the mini program, unbinding the unbound store from the user's mobile phone number; deleting the unbound store from the registered store list.

[0012] In one example, the method further includes: scoring the competitiveness index data of each store in the registered store list; generating a competitiveness analysis radar chart for each store based on the competitiveness index data of each store; and sending the competitiveness analysis radar chart for each store to the mini program.

[0013] In one example, the method also includes: receiving a store subscription report opening notification uploaded by the mini program; generating an operation report for the target store based on a scheduled task, and returning the operation report to the mini program; calling a social application notification API to send an operation report notification information; the operation report notification information includes a report title and a mini program path.

[0014] In one example, the method also includes: receiving a request for opening intelligent assistance for a store uploaded by the mini program; obtaining auxiliary structured data of the target store within a preset period; the auxiliary structured data includes store data, market public data of the category to which the store belongs, and industry public opinion data; inputting the auxiliary structured data and preset prompt words into a large language model to obtain an auxiliary report; the auxiliary report includes market trend forecasts, store status analysis, and business suggestions.

[0015] On the other hand, an embodiment of the present application provides an online store operation evaluation device based on an e-commerce platform, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the above-mentioned online store operation evaluation methods based on the e-commerce platform.

[0016] On the other hand, an embodiment of the present application provides a non-volatile computer storage medium for evaluating online store operations based on an e-commerce platform, which stores computer-executable instructions, and the computer-executable instructions can execute any of the above-mentioned methods for evaluating online store operations based on an e-commerce platform.

[0017] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0018] By building an online store operation evaluation mini program and integrating the mini program with the e-commerce platform for front-end and back-end design, a smooth, secure, and scalable data query foundation is provided for e-commerce operators, which can meet the diverse needs of users and conduct multi-dimensional statistics of store data and horizontal and vertical comparative analysis.

[0019] Based on this, the encrypted mobile phone numbers uploaded through the mini program can prevent the mobile phone numbers from being intercepted during transmission, comply with privacy regulations such as GDPR, and reduce the risk of data leakage.

[0020] Then, the user's mobile phone number is bound to the registered store to implement operations based on the mini program, and the store list is automatically loaded dynamically on the background server to solve the needs of one operator to manage multiple stores without the need to repeatedly log in and switch accounts.

[0021] Under the architecture of mini-programs and back-end servers, online store operators can view the operating conditions of their stores from multiple dimensions on the client anytime and anywhere, and can display statistical data in a diversified horizontal and vertical manner in the required form. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the present application, some embodiments of the present application will be described in detail below with reference to the accompanying drawings, in which:

[0023] Figure 1 A flowchart of a method for evaluating online store operations based on an e-commerce platform provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of an online store operation evaluation system based on an e-commerce platform provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of the structure of an online store operation evaluation device based on an e-commerce platform provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] Some embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0028] Figure 1 This is a flow chart of a method for evaluating online store operations on an e-commerce platform, provided in an embodiment of this application. This method can be applied to various business areas, such as internet finance, e-commerce, instant messaging, gaming, and government affairs. Certain input parameters or intermediate results in this process can be manually adjusted to improve accuracy.

[0029] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For ease of understanding and description, the following embodiments are described in detail using a server as an example.

[0030] It should be noted that the server can be a single device or a system composed of multiple devices, that is, a distributed server, and this application does not make any specific restrictions on this.

[0031] Figure 1 The process in includes the following steps:

[0032] S101: Receive the encrypted mobile phone number uploaded by the mini program, decrypt the encrypted mobile phone number, and obtain the user's mobile phone number.

[0033] In some embodiments of the present application, the mini program obtains the current user's mobile phone number by calling the open API of the social application, and obtains the registered store information from the backend server through the mobile phone number.

[0034] S102: Query the database for a list of registered stores associated with the user's mobile phone number.

[0035] It should be noted that if no registered store is found under the user's mobile phone number, the mini program will guide the user to the store management module, where the user can choose to manually add store information. Otherwise, the system will provide a drop-down menu for the user to select the store they want to view (taking into account the situation that a user may have multiple stores).

[0036] S103: When the list of registered stores is not empty, return the list of stores to the mini program.

[0037] S104: When receiving a target store query request uploaded by the mini program, search for business data that meets the query request; the query request includes the target store ID, query time, and front-end return format.

[0038] For example, if user Zhang San (mobile phone number 13800138000) logs into the mini program, the backend server will find that he is associated with Zhang San Clothing Store and Zhang San Home Furnishing Store. The mini program will display a drop-down menu with a list of stores, and Zhang San will select Zhang San Clothing Store as the current store, making it the target store.

[0039] It should be noted that the front-end return format can include dashboard data, chart data, table data, etc.

[0040] For example, query the sales data of store A in the current month and return it in the form of chart data.

[0041] S105: Return the business data to the mini program according to the front-end return format.

[0042] It should be noted that the business situation analysis module supports users to view the current store's business and sales data from multiple dimensions.

[0043] Among them, the front-end display methods can be diverse. The key indicators within the time range can be clearly displayed through the indicator dashboard (such as cumulative sales: ￥500,000, monthly sales: ￥80,000, sales volume year-on-year: +15%), the sales trend within the time range can be displayed through visual charts (line chart: sales / sales volume; bar chart: year-on-year change), and detailed data can be provided through data tables.

[0044] For example, the store's cumulative sales volume, monthly sales volume, cumulative sales, monthly sales, and year-on-year trends can be displayed in the form of a dashboard; at the same time, the store's sales volume, sales volume, and year-on-year trends can be displayed in the form of echart line bar charts and tables.

[0045] It should be noted that it supports switching viewing indicators and time ranges by clicking on the indicator dashboard.

[0046] The user interaction process is as follows:

[0047] Time Switching: The user clicks the time filter and selects the last three months, the last six months, the last year, or a custom time period. The mini program then requests data for the corresponding time range from the backend server and refreshes the page.

[0048] Indicator type switching: When a user clicks the cumulative sales label on the dashboard, the mini program switches the dashboard data and may link a chart to display the long-term trend of the indicator (for example, when clicking cumulative sales, the chart shows the cumulative sales trend for the past year).

[0049] Chart linkage: Clicking a data point on the chart may display detailed data for that point in time in a table.

[0050] Based on this, after returning the business data to the mini program in the front-end return format, the process also includes the following:

[0051] When the user clicks the time filter of the mini program, the mini program receives the time range update request uploaded by the mini program.

[0052] Find the updated business data that meets the time range update request and return the updated business data to the mini program.

[0053] In this way, the indicator data is displayed in the dashboard of the mini program, the trend changes of the indicator are displayed in the chart, and when the user clicks on the data point in the chart, the detailed data of the data point is displayed in the table.

[0054] In some embodiments of the present application, the market situation analysis module displays a list of stores and product catalogs that are ranked high in current market sales (for example, TOP20), and displays the corresponding data of the current user's store, so that the operator can more intuitively compare the sales of the current user's store with those of the top stores in the market.

[0055] It should be noted that the market situation analysis module mainly displays the current market sales rankings of the top stores and store item rankings in the form of echart horizontal bar charts. Indicator name and indicator type filters are set above the chart. Users can filter the cumulative or monthly sales, sales volume, year-on-year sales, and year-on-year sales volume to understand the current market sales structure or trends from different dimensions. At the same time, a table is attached below, as well as the sales data of the corresponding indicators of the current store, so that users can more intuitively compare the sales of the current store with the leading stores in the market.

[0056] For example, a horizontal bar chart could visually display the rankings and values ​​of the top 20 stores / products in a selected indicator (such as cumulative sales). Below the chart or in a prominent position next to the list, it would display: "Your store: ranked 35th, cumulative sales: ￥500,000."

[0057] Based on this, the market analysis request uploaded by the mini program is received. According to the market analysis request, the market public data of the preset top-ranked stores is queried.

[0058] It should be noted that the market situation analysis request includes analysis indicators, store category, and target store ID.

[0059] Based on the target store ID, query the target market data of the target store under the analysis indicators.

[0060] Return the store market public data and target market data to the mini program.

[0061] Furthermore, when the user clicks on the indicator filter of the mini program, an indicator update request uploaded by the mini program is received.

[0062] Find the updated store market public data that meets the indicator update request, and query the updated target market data of the target store under the indicator update request.

[0063] The updated store market public data and updated target market data will be returned to the mini program.

[0064] For example, Zhang San switches the indicator to the year-on-year sales volume for the current month. The chart refreshes to the top 20 stores with the fastest sales growth that month. He sees that the top store saw a 120% increase, while his own store showed a 15% increase, ranking 50th. This makes users realize that there is room for improvement in promotion efficiency.

[0065] In some embodiments of the present application, the store management module is used by users to view / maintain basic store information and view the current store competitiveness analysis.

[0066] Users can bind / unbind personal stores in this module and view / maintain basic store information; at the same time, in the store details, the system will generate a six-dimensional analysis chart to evaluate the store's competitiveness based on the store's sales revenue, sales volume, number of categories, store opening time, number of followers, store evaluation points, etc. From the chart, you can clearly understand the strengths and weaknesses of the current store, so that targeted measures can be taken for development or prevention.

[0067] Based on this, when the current user adds a new store, the new store binding information uploaded by the mini program is received. After the new store binding information is verified, the new store is bound to the user's mobile phone number. Thus, the new store is added to the list of registered stores.

[0068] When the current user unbinds a store, the store unbinding information uploaded by the mini program is received, and the unbound store is unbound from the user's mobile phone number, thereby deleting the unbound store from the registered store list.

[0069] It should be noted that when deleting or unbinding a store, the store data will not be deleted, only the binding relationship will be released.

[0070] In addition, the competitiveness index data of each store in the registered store list is scored, and a competitiveness analysis radar chart of each store is generated based on the competitiveness index data of each store. The competitiveness analysis radar chart of each store is sent to the mini program.

[0071] For example, the system generates a six-dimensional graph: sales (7 points / 10 points), sales volume (6 points), number of categories (8 points), store opening time (3 years or more = 5 points), number of followers (10,000 or more = 7 points), and store rating (4.8 or more = 8 points). The graph shows that the number of categories and store rating are advantages, while the store opening time is a clear weakness. Based on this analysis, users can strengthen their engagement with existing customers and increase repurchase rates.

[0072] In some embodiments of the present application, the report management module is used by operators to view / download store operating status reports, which can be divided into monthly reports, quarterly reports, and annual reports.

[0073] It should be noted that this function depends on whether the user has enabled the subscription (only when the user manually enables the subscription will the application platform allow the mini program to push messages). When the user has not enabled the report subscription, the page will prompt the user to enable it. After enabling it, the system will automatically set up a scheduled task to query and summarize the store's operating data, generate a structured report, and regularly push messages to the operator through social applications. When the user clicks on the message, the mini program will be entered and the details page of the report will be opened. The report list page will query all the store's operating reports by default, and you can switch to filter monthly, quarterly, and annual reports. Click on the report to view details and export the report.

[0074] Based on this, receive the notification of opening store subscription report uploaded by the mini program.

[0075] Based on the scheduled tasks, generate an operation report for the target store and return the operation report to the mini program.

[0076] Call the social app notification API to send an operation report notification. The operation report notification includes the report title and mini-program path. Additionally, the operation report notification may include the report ID.

[0077] For example, Zhang San activated his report subscription in early July. On August 5, he received a notification from the social app: "Zhang San Clothing Store's July 2024 operating report is out! Click to view details." He clicked the notification and directly entered the mini-program to open the PDF monthly report, which detailed July's sales, customer base, and market comparisons.

[0078] In some embodiments of the present application, the intelligent assistance module provides operators with certain market trend forecasts and business suggestions by accessing the AI ​​big model and combining factors such as store sales data and public e-commerce data in the market.

[0079] This module also depends on whether the user turns on this function. After the function is turned on, the system will use a large model to analyze market sales data and other environmental factors to intelligently generate a market sales trend forecast for the future (for example, 1-3 months), and combine store sales data to provide operators with store operating situation analysis and business plan recommendations.

[0080] Based on this, the store that receives the upload from the mini program starts the intelligent assistance request.

[0081] Within a preset period, auxiliary structured data of the target store is obtained. Auxiliary structured data includes store data, market public data of the store's category, and industry public opinion data.

[0082] The auxiliary structured data and preset prompt words are input into the large language model to obtain an auxiliary report.

[0083] It should be noted that the auxiliary reports include market trend forecasts, store status analysis, and specific business suggestions.

[0084] For example, the prompt is as follows: If you are a professional e-commerce data analyst, please analyze based on the following information:

[0085] 1) The store's sales and volume data for the past 12 months (data table...), 2) The current top 20 items in the category (women's clothing) and their sales trends (data...), 3) Trending elements mentioned in recent industry reports (the popularity of dopamine-inducing outfits is rising), 4) Holidays scheduled for the next three months.

[0086] Please analyze the core strengths and weaknesses of your current store, predict the sales trends in the women's clothing market (such as popular categories, styles, and price ranges) for the next 1-3 months, and provide specific and actionable operational optimization suggestions for your current store (at least 3).

[0087] In some embodiments of the present application, the system front end is developed based on the social application applet framework, the language includes vue3+uniapp, and plug-ins such as echart are used to complete data visualization. The back end is developed based on springBoot and is connected to the AI ​​big model to realize intelligent analysis and prediction of e-commerce data.

[0088] In some embodiments of the present application, as a specific example, the system works in the following mode:

[0089] The system obtains the mobile phone number through user authorization for authorized login and identity authentication, queries registered stores based on the mobile phone number, queries store operating and sales data based on the selected store ID, and queries and analyzes market sales data; users can manage store information, subscribe to operating reports, and enable intelligent assistance functions in the personal center.

[0090] Login authentication: The mini program uses the social app's open API to obtain the user's mobile phone number and retrieves registered store information from the backend. If no registered store is found under the mobile phone number, the user can manually add a store. Otherwise, the user must select the store they want to view.

[0091] Business performance: After the user selects a store, he / she will enter the business performance page by default, where he / she can view the store's multi-dimensional business statistics.

[0092] For example, if the user's currently selected store doesn't have a time range, the default query is for the store's operating data for the latest month in the database. This data is then presented to the user intuitively and clearly through indicator dashboards, visual charts (echart, tables), and other forms. Furthermore, users can switch between time periods and indicator types (cumulative / current month) to ensure a more comprehensive data dimension.

[0093] Market Analysis: On this page, the system provides statistical analysis and visual display of the top 20 stores and product rankings in the current market.

[0094] For example, based on processed public market data, a list of the top 20 stores and product catalogs can be obtained. Statistical indicators include sales revenue, sales volume, year-on-year sales revenue, and year-on-year sales volume, and the indicator type can be cumulative or current month. These dimensional data are also displayed for the current store, allowing operators to more intuitively compare their current store's sales with those of the top stores in the market.

[0095] Personal Center: The personal center page includes three functional modules: store management, report management, and intelligent assistance.

[0096] For example, operators can bind / unbind personal stores in store management and view / maintain basic store information; view reports in report management and turn on / off report generation and push; turn on / off intelligent assistance in the intelligent assistance module. The system uses large models to analyze market sales data and other environmental factors to intelligently generate market sales trend forecasts for the next 1-3 months, and combines store sales data to provide operators with store operating status analysis and business plan recommendations.

[0097] In summary, the data analysis involved in this application is as follows:

[0098] Data collection: Connect to the e-commerce platform API to collect store data (basic information, sales, sales volume), user behavior data (click-through rate, conversion rate, etc.), inventory data and market public data (competitive product sales, hot-selling categories, etc.) in real time.

[0099] Data processing: Calculate and process the collected store sales data and market public data based on data processing algorithms.

[0100] Data visualization: Combine the mini program front-end framework and echart visualization charts to visualize the data.

[0101] AI big model analysis: Use AI machine learning models to analyze product life cycles and historical sales data, predict future product sales, and provide certain market trend forecasts and business recommendations.

[0102] Automated reporting and push notifications: After users subscribe, the system automatically generates structured reports containing the store's current and cumulative sales data, as well as risk analysis and early warnings, and pushes them to users through social application notifications.

[0103] It should be noted that although the embodiments of this application are based on Figure 1 To introduce and explain step S101 to step S105 in sequence, but this does not mean that step S101 to step S105 must be executed in a strict order. Figure 1The order shown in FIG1 is to introduce and explain step S101 to step S105 in order to facilitate those skilled in the art to understand the technical solution of the embodiment of the present application. In other words, in the embodiment of the present application, the order between step S101 to step S105 can be appropriately adjusted according to actual needs.

[0104] pass Figure 1 This online store management assistant app, based on e-commerce data analysis, is primarily designed for online store operators. It helps them better and more conveniently understand their store's operating conditions and provides them with supportive decision-making. It boasts ease of use, rich dimensions, flexibility, and high reliability. It provides online store operators with multi-dimensional data statistical analysis and automated report push. It also incorporates AI models to provide market sales forecasts and store management advice, as detailed below:

[0105] By building an online store operation evaluation applet, mobile phone numbers can be encrypted to prevent them from being intercepted during transmission, complying with privacy regulations such as GDPR and reducing the risk of data leakage.

[0106] Bind the user's mobile phone number with the registered store to automatically load the store list dynamically, solving the need for one operator to manage multiple stores without having to repeatedly log in and switch accounts.

[0107] Under the architecture of mini programs and back-end servers, online store operators can view the operating status of their stores from multiple dimensions on the client anytime and anywhere.

[0108] In addition, you can maintain basic store information in store management, and enable push notifications for monthly, quarterly, and annual business reports in subscription management. This eliminates the need for manual compilation and aggregation, making operations more worry-free and immediate.

[0109] In addition, the system provides a visual display of the current sales situation of the store's category in the market, including product classification, online stores, online sales, online retail volume and other information, so that operators can have a more macro understanding of the current market product sales situation.

[0110] Finally, operators can choose whether to enable the intelligent store management assistance function according to their own wishes. After enabling it, the system can provide operators with certain trend forecasts and business suggestions based on current market sales and other factors, thereby helping operators to better broaden their business horizons and achieve cost reduction and efficiency improvement.

[0111] More intuitively, Figure 2 A schematic diagram of an online store operation evaluation system based on an e-commerce platform provided in an embodiment of the present application.

[0112] exist Figure 2 In the article, a network store operation evaluation system involving login authentication, business situation analysis, market situation analysis, store management, report management, and intelligent assistance is demonstrated.

[0113] Based on the same idea, some embodiments of the present application also provide devices and non-volatile computer storage media corresponding to the above methods.

[0114] Figure 3 A schematic diagram of a network store operation evaluation device based on an e-commerce platform provided in an embodiment of the present application includes:

[0115] at least one processor; and,

[0116] a memory communicatively connected to the at least one processor; wherein,

[0117] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above-mentioned methods for evaluating online store operations based on an e-commerce platform.

[0118] Some embodiments of the present application provide a non-volatile computer storage medium for evaluating online store operations based on an e-commerce platform, which stores computer-executable instructions capable of executing any of the above-mentioned methods for evaluating online store operations based on an e-commerce platform.

[0119] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0120] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0121] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0122] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0125] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0126] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0127] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0128] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0129] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the technical principles of the present application should fall within the scope of protection of the present application.

Claims

1. A method for evaluating online store operations based on an e-commerce platform, characterized in that: The method comprises: Receive the encrypted mobile phone number uploaded by the mini program, decrypt the encrypted mobile phone number, and obtain the user's mobile phone number; Query the database for a list of registered stores associated with the user's mobile phone number; When the list of registered stores is not empty, return the list of stores to the mini program; Upon receiving a target store query request uploaded by the mini program, searching for business data that meets the query request; the query request includes the target store ID, query time, and front-end return format; The business data is returned to the mini program in accordance with the front-end return format.

2. The method according to claim 1, characterized in that After returning the business data to the mini program in the front-end return format, the method further includes: When a user clicks a time filter on the mini-program, receiving a time range update request uploaded by the mini-program; Search for updated business data that meets the update request within the time range, and return the updated business data to the mini program; display the indicator data in the dashboard in the mini program, display the trend changes of the indicators in the chart, and when the user clicks on a data point in the chart, display the detailed data of the data point in the table.

3. The method according to claim 1, characterized in that The method further comprises: Receiving a market situation analysis request uploaded by the mini program; Querying the public market data of a preset number of top-ranked stores according to the market situation analysis request; the market situation analysis request includes analysis indicators, store categories, and target store IDs; According to the target store ID, query the target market data of the target store under the analysis indicator; The store market public data and the target market data are returned to the mini program.

4. The method according to claim 3, characterized in that After returning the store market public data and the target market data to the mini program, the method further includes: When a user clicks on an indicator filter of the mini program, receiving an indicator update request uploaded by the mini program; Searching for updated store market public data that meets the indicator update request, and querying the updated target market data of the target store under the indicator update request; The updated store market public data and the updated target market data are returned to the mini program.

5. The method according to claim 1, wherein The method further comprises: Receive the new store binding information uploaded by the mini program, and after the new store binding information is verified, bind the new store to the user's mobile phone number; Adding the new store to the registered store list; and / or, Receive the store unbinding information uploaded by the mini program, and unbind the unbound store from the user's mobile phone number; Delete the unbound store from the registered store list.

6. The method according to claim 1, characterized in that The method further comprises: Scoring the competitiveness index data of each store in the registered store list; Generate a competitiveness analysis radar chart for each store based on the competitiveness index data of each store; Send the competitiveness analysis radar chart of each store to the mini program.

7. The method according to claim 1, characterized in that The method further comprises: Receive the notification of opening a store subscription report uploaded by the mini program; Generate an operation report for the target store based on the scheduled task, and return the operation report to the mini program; Call the social application notification API to send operation report notification information; the operation report notification information includes the report title and mini program path.

8. The method according to claim 1, characterized in that The method further comprises: Receive a request for enabling intelligent assistance for a store uploaded by the mini program; Acquire auxiliary structured data of the target store within a preset period; the auxiliary structured data includes store data, market public data of the store's category, and industry public opinion data; The auxiliary structured data and preset prompt words are input into the large language model to obtain an auxiliary report; the auxiliary report includes market trend forecast, store status analysis, and business suggestions.

9. A network store operation evaluation device based on an e-commerce platform, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the online store operation evaluation method based on the e-commerce platform as described in any one of claims 1 to 8.

10. A non-volatile computer storage medium for evaluating online store operations based on an e-commerce platform, storing computer-executable instructions, characterized in that: The computer-executable instructions can execute the online store operation evaluation method based on the e-commerce platform as described in any one of claims 1 to 8.

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