A data monitoring management method and system for an e-commerce platform

Abstract

This invention provides a data monitoring and management method and system for e-commerce platforms, belonging to the field of data processing technology. Specifically, it includes: determining the monitoring and analysis method for user association data of products based on the association of products of different potentially busy merchants; determining the matching status of server ports in potentially busy merchants based on the monitoring and analysis results of user association data of each product in potentially busy merchants; and determining the products that need to be optimized and updated based on the matching status and the products of potentially busy merchants, thereby improving the user experience of merchants.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a data monitoring and management method and system for e-commerce platforms. Background Technology

[0002] In B2B e-commerce platforms, there are often multiple merchants selling the same product. In existing technical solutions, the number of server ports configured for different merchants is usually fixed. However, since the products of the merchants are different, once a certain product becomes too popular, it will inevitably lead to lag or other issues when browsing the merchant's store, which will in turn worsen the user experience.

[0003] Therefore, determining how to analyze and process user data related to a merchant's products based on their association with other merchants' products, in order to identify the popularity of a merchant's products in a timely and effective manner, and thus provide data support for the optimization of the merchant's server ports, has become an urgent technical problem to be solved.

[0004] Specifically, there is an urgent need for a data monitoring and management method and system for e-commerce platforms. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted:

[0006] Specifically, this application provides a data monitoring and management method for e-commerce platforms, which includes:

[0007] S1 determines the associated merchants of a merchant in different products based on the association of the merchant's products on the e-commerce platform. Based on the associated merchant data in different products, it determines the potential busy merchants among the merchants. Based on the server port configuration data of different potential busy merchants, it determines when it is necessary to monitor and analyze the user association data of the products, and then proceeds to the next step.

[0008] S2 determines the method for monitoring and analyzing the user association data of the products based on the association of products of different potentially busy merchants;

[0009] S3 determines the matching status of server ports in potentially busy merchants by analyzing the user association data of each product in the potential busy merchants. Based on the matching status and the products of the potential busy merchants, it identifies the products that need to be optimized and updated in terms of the monitoring analysis method.

[0010] The beneficial effects of this invention are as follows:

[0011] Based on the association between products of different potentially busy merchants, the method for monitoring and analyzing user association data of products was determined. This enabled the determination of the method for monitoring and analyzing user association data of products from the composition of products in potentially busy merchants and the association between products and potential busy merchants. While ensuring that the data pressure on the server can meet the requirements, the monitoring and analysis of user association data of products of potentially busy merchants was realized, and the foundation was laid for further determination of server port configuration scheme.

[0012] Based on the matching results and the products of potentially busy merchants, we can identify products that require optimization and updates to the monitoring and analysis methods. This allows for timely optimization and updates to the monitoring and analysis methods for potentially busy merchants' products, even when there are many users matching the user profiles of their products and few products requiring monitoring and analysis. This also enables us to more clearly determine the server port requirements of potentially busy merchants, laying the foundation for improving the user browsing experience.

[0013] Furthermore, the association of the products is determined based on the merchant data of the products belonging to the merchant.

[0014] Furthermore, the associated merchants of the merchants in different products are the merchants that have the products.

[0015] Furthermore, the method for determining the potentially busy merchants among the merchants is as follows:

[0016] Based on the associated merchant data in the products, identify the associated merchants in different products;

[0017] Based on the associated merchants in different products, identify products that do not have associated merchants;

[0018] Based on the products of merchants that are not associated with other merchants, determine whether the merchant is a potentially busy merchant.

[0019] Furthermore, the method for determining the regulatory analysis method for the user-related data of the aforementioned products is as follows:

[0020] Based on the association of the products of the potential busy merchants, identify the potential busy merchants that have the products.

[0021] Based on the products of different potentially busy merchants that do not have associated merchants, determine the total number of products of different potentially busy merchants that do not have associated merchants, and use this as the total number of preset types. Based on the total number of preset types of potentially busy merchants that have the products, determine the demand weight coefficient of the potentially busy merchants that have the products.

[0022] Based on the demand weight coefficients of different potential busy merchants for the products, the regulatory analysis method for user-related data of the products is determined.

[0023] Furthermore, the method for determining the commodities requiring optimization and updating of regulatory analysis methods is as follows:

[0024] Based on the matching results, determine the ratio of the number of users matching the user profiles of each product in the potentially busy merchants to the configured number of server ports of the potentially busy merchants within the most recent preset time period, and use this ratio as the set matching ratio.

[0025] Based on the regulatory analysis of user association data of the goods of the aforementioned potentially busy merchants, the goods for each regulatory analysis method are identified;

[0026] Based on the matching ratios of each product and the products for each regulatory analysis method, identify the products that require optimization and updates to the regulatory analysis methods.

[0027] Furthermore, the most recent preset time period is determined based on the most recent preset duration, which in one possible embodiment is the most recent week.

[0028] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the aforementioned data monitoring and management method for an e-commerce platform when running the computer program.

[0029] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0031] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings;

[0032] Figure 1 This is a flowchart of a data monitoring and management method for e-commerce platforms;

[0033] Figure 2 This is a flowchart illustrating the method for identifying potentially busy merchants among the merchants;

[0034] Figure 3 This is a flowchart for determining which user-related data for products needs to be monitored and analyzed;

[0035] Figure 4 This is a flowchart illustrating the method for determining the regulatory analysis approach for user-related data of goods;

[0036] Figure 5 This is a flowchart illustrating the methods for identifying products that require optimization and updates to regulatory analysis approaches. Detailed Implementation

[0037] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0038] Example 1

[0039] like Figure 1 As shown, this application provides a data monitoring and management method for e-commerce platforms, specifically including:

[0040] S1 determines the associated merchants of a merchant in different products based on the association of the merchant's products on the e-commerce platform. Based on the associated merchant data in different products, it determines the potential busy merchants among the merchants. Based on the server port configuration data of different potential busy merchants, it determines when it is necessary to monitor and analyze the user association data of the products, and then proceeds to the next step.

[0041] Furthermore, the association of the products is determined based on the merchant data of the products belonging to the merchant.

[0042] Furthermore, the associated merchants of the merchants in different products are the merchants that have the products.

[0043] Specifically, such as Figure 2 As shown, the method for determining potentially busy merchants among the merchants is as follows:

[0044] S11 Based on the associated merchant data in the said products, determine the associated merchants in different products;

[0045] In one possible specific embodiment, the system invokes the supplier recommendation database of the B2B platform. This database records other suppliers that the platform intelligently recommends as alternatives or complements when a buyer browses a product. For example, the associated merchants for the product "[A Steel Group] Q235B hot-rolled steel coil" are: B Steel Trading Company and C Metal Materials Co., Ltd., and the associated merchants for the product "[D Chemical Company] Epoxy Resin EP-128" are: E Polymer Materials Factory and F Imported Chemical Agency.

[0046] S12 determines the products that do not have associated merchants based on the associated merchants among different products;

[0047] In one possible specific embodiment, the system comprehensively scans all product catalogs on the platform, compares them with the recommendation relationship database, and filters out products that have no associated merchant records. When buyers browse these products, the platform does not provide any alternative suppliers. As a result, the system found that the following products have no associated merchants: "[G Precision Components Factory] Customized Non-standard Gears", "[G Precision Components Factory] High-Precision Linear Guides", "[G Precision Components Factory] Special Alloy Bearings", "[H Mining Company] Rare Earth Oxides (Limited Stock)".

[0048] S13 determines whether the merchant is a potentially busy merchant based on the products of the merchant that are not associated with it.

[0049] It should be noted that when a merchant has multiple products that are not associated with other merchants, the merchant is identified as a potentially busy merchant.

[0050] In one possible specific embodiment, the system determines a merchant as a potentially busy merchant based on the rule: "When a merchant has multiple products without associated merchants, the merchant is identified as a potentially busy merchant." It counts the number of such "isolated products" under each merchant's name. Merchant "G Precision Components Factory": has 3 products without associated merchants; Merchant "H Mining Company": has only 1 such product. Decision: The system determines "G Precision Components Factory" as a potentially busy merchant. "H Mining Company" is excluded due to its small number of "isolated products."

[0051] This method intelligently identifies high-quality suppliers that are in a "busy" or "scarce" state due to technological barriers, saturated production capacity, or monopolistic resources by analyzing the "data gaps" in the recommendation system. This transforms the platform from a passive information display provider into a proactive supply chain resource coordinator, greatly enhancing the platform's intelligence level and core value.

[0052] Specifically, such as Figure 3 As shown, it is determined that monitoring and analysis of user-related data for products is necessary, specifically including:

[0053] S21 uses the configuration data of the server ports of different potentially busy merchants to determine the ratio of the number of access users that the server port of a potentially busy merchant can support to the number of goods that do not have an associated merchant, and determines the setting matching ratio of the server port of the potentially busy merchant.

[0054] S21: Calculate the matching ratio for potential busy merchants. Server port configuration data: A data table recording the server package specifications purchased by each merchant. Matching ratio: A key indicator used to quantify the degree of matching between a merchant's service capabilities and the uniqueness of its products.

[0055] This step aims to quantitatively assess the server resource pressure on potentially busy merchants. A higher ratio indicates that each exclusive product has more abundant server resources and a stronger ability to withstand access pressure; a lower ratio means that server resources are relatively scarce, and under high demand, there is a risk of page lag, unresponsive customer service, and other issues.

[0056] Example

[0057] The system retrieves the configuration data of all potentially busy merchants and calculates their configuration matching ratio. Merchant G, Precision Components Factory: Capable of supporting 100 users, number of products without associated merchants: 3, Configuration matching ratio = 100 / 3 ≈ 33.3; Merchant J, Special Materials Co., Ltd.: Capable of supporting 50 users, number of products without associated merchants: 10, Configuration matching ratio = 50 / 10 = 5

[0058] S22 determines potential busy merchants whose server port settings do not meet the requirements based on the matching ratio of the potential busy merchants.

[0059] The above steps are used to identify potentially busy merchants whose matching ratio does not meet the requirements. A matching ratio that does not meet the requirements specifically refers to merchants who have a large number of products without associated merchants (high demand-side pressure) but whose server ports can only handle a small number of users (weak supply-side capacity). This is directly reflected in their matching ratio being lower than a certain threshold.

[0060] This step is crucial for the precise identification of high-risk merchants. It further segments all potentially busy merchants, identifying those with the highest IT infrastructure risk and the greatest likelihood of experience crashes due to a surge in traffic, thus providing a clear target list for the platform's priority interventions.

[0061] In one possible specific implementation: the platform sets the critical ratio to 10. The system determines that merchant G's ratio of 33.3 is greater than 10, meeting the requirement, while merchant J's ratio of 5 is less than 10, not meeting the requirement. Therefore, the system determines that merchant J, Special Materials Co., Ltd., is a potentially busy merchant whose matching ratio does not meet the requirement.

[0062] S23 determines whether monitoring and analysis of user-related data for products is necessary for potential busy merchants whose matching ratio does not meet the requirements.

[0063] In the above steps, determine whether user-related data monitoring and analysis is needed. The preset range is a quantity threshold range, which is used to judge whether the scale of the problematic merchants has reached the level that requires the initiation of global and in-depth analysis.

[0064] This step involves intelligent scheduling decisions for platform resources. Its significance lies in ensuring that the platform's expensive big data analytics resources are only triggered when a problem reaches a certain scale and economic value. If only a very few merchants have issues, case-by-case handling is possible; however, if the number of problematic merchants exceeds expectations, macro-level analysis must be initiated to find the root cause and systemic solutions.

[0065] Example: Statistics: The number of this type of merchant is 1 (i.e., merchant J). Determination: Is the quantity 1 outside the preset range [0, 20]? Determination result: No. 1 is within the preset range. According to the rules, the system determines that there is currently no need to monitor and analyze the user association data of the product.

[0066] Specifically, the potential busy merchants whose matching ratio does not meet the requirements are those with a large number of products without associated merchants and whose server ports can only support a small number of users.

[0067] It is understandable that for potentially busy merchants whose matching ratio does not meet the requirements, it is necessary to determine whether monitoring and analysis of user association data for their products is required. This specifically includes:

[0068] When the number of potentially busy merchants whose matching ratio does not meet the requirements is not within the preset range, it is necessary to monitor and analyze the user association data of the products to identify users who are interested in different products. Then, targeted server port configuration suggestions can be provided to potentially busy merchants to improve the user experience of users accessing the products.

[0069] In another embodiment, if the number of such merchants is 25, which exceeds the preset range, the system will determine that it is necessary to monitor and analyze the user-related data of the products. The platform can provide merchants with data-driven server port configuration suggestions to avoid customer churn. Through the above intervention, the user experience of potentially busy merchants can be improved, and the smoothness and stability of platform transactions can be guaranteed.

[0070] S2 determines the method for monitoring and analyzing the user association data of the products based on the association of products of different potentially busy merchants;

[0071] Specifically, the user-related data for the product includes the user's browsing data, search data, and purchase data.

[0072] Specifically, such as Figure 4 As shown, the method for determining the regulatory analysis method for the user-related data of the product is as follows:

[0073] S31 determines the potential busy merchants that have the products based on the association information of the products of the potential busy merchants;

[0074] S31: Identify potential busy merchants selling this product and the product association among them: This step records data on the correspondence between potential busy merchants and the products they sell. It aims to pinpoint the target. The goal is to identify all potential busy merchants selling this specific product (e.g., "high-precision linear guides"), as these merchants are the core focus of subsequent regulatory analysis.

[0075] In a possible implementation:

[0076] The system performed a query targeting the product "high-precision linear guide rail". From the associated product data of potentially busy merchants, the system identified the following potential busy merchants for this product: G Precision Components Factory and K Automation Company.

[0077] S32 determines the total number of goods without associated merchants in different potentially busy merchants based on the goods without associated merchants in different potentially busy merchants, and uses this as the total number of preset types. Based on the total number of preset types of potentially busy merchants with the goods, the demand weight coefficient of the potentially busy merchants with the goods is determined.

[0078] In the above steps, the total quantity of preset types refers to the total number of "isolated goods" owned by a potentially busy merchant. This quantity reflects the uniqueness and scarcity of the merchant's business. The demand weight coefficient is calculated as 1 / total quantity of preset types. This coefficient reflects "the weight of this product among all the scarce goods in the merchant's portfolio".

[0079] This step aims to quantify the importance of a product to each merchant. If a merchant has only one isolated product, then that product is its entirety, with a weight coefficient of 1, indicating it is very important. If a merchant has 10 isolated products, then the importance of one of the products is relatively low, with a weight coefficient of 0.1. The higher the coefficient, the more core the product is to the merchant, and the higher the priority for monitoring it.

[0080] Example: Determine the total quantity of preset types: G Precision Components Factory has 3 products without associated merchants (including "High Precision Linear Guides"). K Automation Company has 5 products without associated merchants (including "High Precision Linear Guides"). Calculate the demand weighting coefficient: For G Precision Components Factory, coefficient = 1 / 3 ≈ 0.33; for K Automation Company, coefficient = 1 / 5 = 0.20.

[0081] S33 determines the regulatory analysis method for user-related data of the product based on the demand weight coefficients of different potential busy merchants with the product.

[0082] In step S33 above, case 1: If the sum of the demand weight coefficients of the potential busy merchants of the product is greater than the preset weight coefficient threshold, then the monitoring and analysis method of the user association data of the product is determined to be that the monitoring and analysis of the user association data of the product is carried out whenever there is an update of the user association data of the product, so as to realize the evaluation of the popularity of the product more quickly, thereby laying the foundation for determining the server port configuration suggestions for merchants.

[0083] In the above steps, the term "weight coefficient sum greater than threshold (real-time monitoring)" is explained as follows: Preset weight coefficient threshold: This is the threshold that triggers the highest priority monitoring mode. When the sum of the weight coefficients is very large, it indicates that the product is extremely important to these potentially busy merchants (i.e., these merchants have very few scarce product types, and this product is their lifeline). Therefore, it is essential to monitor user behavior data for this product in real time to quickly identify changes in market demand and provide merchants with the most timely operational advice.

[0084] Example (hypothesis): Assume that only G Precision Components Factory sells this product, and its demand weighting coefficient is 0.33. The sum of the weighting coefficients is 0.33. If 0.33 < 0.6, then condition 1 is not met, and real-time monitoring will be performed.

[0085] Scenario 2: If the sum of the demand weight coefficients of the potential busy merchants of the product is not greater than the preset weight coefficient threshold, then the regulatory analysis method for the user association data of the product is determined to be that regulatory analysis of the user association data of the product is only required when regulatory analysis of the user association data of the product of the potential busy merchants of the product is performed and the merchant has user association data that has not yet been processed by regulatory analysis.

[0086] In the above steps, the sum of the weighting coefficients does not exceed a threshold (on-demand monitoring). When the sum of the weighting coefficients is small, it indicates that although the product is important, the merchant possesses relatively few scarce products, and fluctuations in a single product have a relatively limited impact on the merchant as a whole. Therefore, a resource-saving monitoring approach can be adopted, triggering analysis only when the system performs routine checks on the merchant and discovers new data for the product.

[0087] In a possible specific implementation: the weighting coefficients are calculated as follows: G Precision Components Factory (0.33) + K Automation Company (0.20) = 0.53, which is less than 0.6. The final decision is: the system determines that the user-related data monitoring and analysis method for the product "High-Precision Linear Guide Rail" is Case 2, i.e.:

[0088] Only when monitoring and analyzing the user association data of potential busy merchants (G factory or K company) for the product, and discovering that there is user association data for "high-precision linear guide rail" that has not yet been processed by monitoring and analysis, is it necessary to conduct special monitoring and analysis for the product.

[0089] S3 determines the matching status of server ports in potentially busy merchants by analyzing the user association data of each product in the potential busy merchants. Based on the matching status and the products of the potential busy merchants, it identifies the products that need to be optimized and updated in terms of the monitoring analysis method.

[0090] Furthermore, the matching status of server ports among the potential busy merchants is determined based on the matching status of the configured number of server ports with the user data of the matching user profiles of each product in the potential busy merchants.

[0091] The above embodiments have the following beneficial effects:

[0092] 1. For platform operators: Achieving "on-demand allocation" and "cost control" of computing resources.

[0093] Significance: The platform's computing resources (server computing power, data analysis bandwidth) are limited and expensive. This method acts like a "smart constant-temperature air conditioner" rather than a "heater running at full power continuously," avoiding resource waste: When merchant load is low and monitoring is already in place (as in the main implementation), the system maintains the status quo, avoiding unnecessary computing consumption and proactively releasing resources; when merchant load is found to be consistently low (S432 path), the system will proactively reduce the monitoring level of some products from "real-time" to "on-demand," "turning off unnecessary lights," and using the saved resources for other more needed business operations, ensuring critical business operations; when merchant load is detected to surge into a risky area (S433 path), the system will unhesitatingly invest maximum resources in monitoring, "ensuring all spotlights illuminate the ships in the storm," preventing transaction failures and user experience degradation due to server crashes.

[0094] 2. For merchants (suppliers): Gain "forward-looking" and "data-driven" operational support, which transforms the platform from a passive tool into a proactive "business partner".

[0095] Risk Warning: The highest level of monitoring means the platform can quickly detect abnormal demand for a product (sudden surge in popularity or decline in demand) and notify merchants immediately. This provides merchants with valuable lead time to adjust inventory, production plans, or marketing strategies.

[0096] Precise Decision Support: The ultimate goal of optimized monitoring is to provide more accurate server configuration recommendations. The platform can tell merchants, "Your 'Linear Guide' has been searched by 200 new customers in the past week, and the peak concurrent access has reached 90% of your server's capacity. We recommend an immediate 50% expansion." This makes merchants' IT investments more data-driven, avoiding blind or insufficient investment.

[0097] 3. For purchasing users: To ensure a "smooth and stable" purchasing experience, all backend technical optimizations will ultimately be reflected in the frontend user experience.

[0098] Eliminating lag and crashes: By proactively identifying high-load merchants and guiding them to upgrade their configurations, the platform fundamentally avoids the poor experience buyers encounter when browsing products and inquiring about inquiries, such as slow page loading and unresponsive customer service, thus improving matching efficiency: Stable technical services are the foundation of business matching. A smooth experience allows buyers to find their desired products and complete transactions more quickly, improving the overall business efficiency of the platform.

[0099] 4. For the platform ecosystem: Building a virtuous cycle of "self-optimization" – once this method is embedded in the platform, it forms a "digital ecosystem" with self-regulating capabilities.

[0100] Positive Cycle: Precise monitoring -> Timely suggestions -> Improved merchant experience -> Increased merchant reliance on the platform -> Richer platform data -> More accurate algorithms and improved system health: By continuously optimizing the monitoring strategies for tens of thousands of merchants, the platform's overall resource utilization and system stability have been continuously improved, and its ability to resist risks has been enhanced. This constitutes the core competitive barrier for the platform's long-term development.

[0101] Specifically, such as Figure 5 As shown, the method for determining the products that require optimization and updating of the regulatory analysis method is as follows:

[0102] Overall scenario setting: Platform: B2B industrial products platform; Potential busy merchant: G Precision Components Factory; Products: Products sold by G Precision Components Factory, such as: Product A: High-precision linear guide rail; Product B: Customized non-standard gears; Product C: Special alloy bearings; Matching user profile: Buyers who have browsed, searched, or transacted on the products.

[0103] Preset time period: the past 7 days; number of configured server ports: refers to the number of users the server can support, which is 100 in this example; preset ratio threshold: 60%; preset range: the reasonable range of the overall matching ratio, which is defined as [0, 8] in this example.

[0104] Based on the matching situation, S41 determines the ratio of the number of users matching the user profiles of each product in the potentially busy merchants to the number of server ports configured for the potentially busy merchants within the most recent preset time period, and uses this ratio as the set matching ratio. The sum of the set matching ratios of each product is used as the comprehensive matching ratio.

[0105] S41: Calculate the overall matching ratio. Number of users matching the user profile: The number of independent buyers who browsed, searched, or transacted on a specific product within the statistical period. Set the matching ratio: Number of matching users for a single product / Number of servers configured. This ratio measures the actual user pressure generated by a single product. Total matching ratio: The sum of the set matching ratios for all products under the merchant's name. This ratio measures the total user pressure generated by all products of that merchant.

[0106] This step aims to quantify the actual load pressure on merchant servers from the perspective of actual user access behavior. It reflects the concentration of market demand.

[0107] In one specific embodiment, the data for each product of merchant G over the past 7 days is statistically analyzed: Product A: number of matched users = 30, matching ratio = 30 / 100 = 0.3; Product B: number of matched users = 60, matching ratio = 60 / 100 = 0.6; Product C: number of matched users = 50, matching ratio = 50 / 100 = 0.5; overall matching ratio = 0.3 + 0.6 + 0.5 = 1.4.

[0108] S42 uses the user association data of the goods of the potential busy merchants as a basis for regulatory analysis to determine the goods for each regulatory analysis method, and determines the proportion of goods in the potential busy merchants for regulatory analysis based on the goods data of each regulatory analysis method.

[0109] In the above steps, the proportion of products subject to regulatory analysis is calculated. The regulatory analysis method for user-related data is as described above, which is divided into "real-time monitoring" and "on-demand monitoring". Products subject to regulatory analysis refer to products whose regulatory analysis method is "regulatory analysis is performed whenever product data is updated".

[0110] This step aims to assess the current level of monitoring coverage for the merchant. A high percentage indicates that the platform has invested significant resources in intensive monitoring; a low percentage indicates gaps in current monitoring.

[0111] Example: Assume that according to the previous rules, the following are the characteristics of the products: Product A: The regulatory analysis method is "on-demand regulation" (not a product subject to regulatory analysis); Product B: The regulatory analysis method is "real-time regulation" (a product subject to regulatory analysis); Product C: The regulatory analysis method is "on-demand regulation" (not a product subject to regulatory analysis); and the proportion of products subject to regulatory analysis is approximately 1 / 3 ≈ 33.3%.

[0112] S43 determines the products that need to be optimized and updated in terms of the monitoring analysis method based on the comprehensive matching ratio and the proportion of products in the products of the potential busy merchants.

[0113] Specifically, based on the comprehensive matching ratio and the proportion of products in the product range of the potential busy merchants that are subject to regulatory analysis, the products that require optimization and updates to the regulatory analysis methods are determined, including:

[0114] S431 determines whether the proportion of products in the potentially busy merchant's products that are subject to regulatory analysis is greater than a preset proportion threshold. If yes, it determines that none of the products of the potentially busy merchant are products that need to be optimized and updated in terms of regulatory analysis methods. If no, it proceeds to the next step.

[0115] In the above steps, it is determined whether the regulatory coverage is high enough. The current regulatory ratio (33.3%) is greater than the preset ratio threshold (60%). The result is: No. The process proceeds to S432.

[0116] S432 determines whether the comprehensive matching ratio meets the requirements. If yes, when the comprehensive matching ratio is within the preset range, it is determined that the products of the potential busy merchants are not products that need to be optimized and updated by the monitoring analysis method. If the comprehensive matching ratio is not within the preset range, it is determined that the products of the potential busy users with the preset monitoring analysis method are products that need to be optimized and updated by the monitoring analysis method. If not, proceed to the next step.

[0117] In the above steps, determining whether the overall load and the overall matching ratio meet the requirements requires defining a standard, such as whether it is too high. Assume the reasonable upper limit of the ratio is 10; values ​​below this value are considered "meeting the requirements." Example: Determining if the load meets the requirements: The overall matching ratio (1.4) < 10, therefore it meets the requirements. Determining if the load is within the ideal range: Since the requirements are met, further determine if the ratio (1.4) is within the preset range [0, 8]. The result: Yes, 1.4 is within the range [0, 8].

[0118] Decision: Based on the rules, the system determines that none of the products of this potentially busy merchant (G Precision Components Factory) are products that require optimization and updates through monitoring and analysis.

[0119] Assuming the overall matching ratio is calculated to be 9 (less than the lower limit of the preset range of 1.0), and the load meets the requirements, the decision is as follows: Since the overall matching ratio (9) is not within the preset range [0, 8], the system determines that the products of the potentially busy users with the preset monitoring analysis method are all products that need to be optimized and updated. "Products with the preset monitoring analysis method" refers to those products that have been set to "real-time monitoring". By optimizing and updating the monitoring analysis method, the system determines the real user demand and avoids the occurrence of congestion.

[0120] S433 determines that all the products of the potential busy user are products that require optimization and updates through regulatory analysis.

[0121] Assuming the overall matching ratio is calculated to be 11 (greater than 10), indicating that the load does not meet the requirements, the decision is as follows: the process proceeds to S433. The decision further states that the system determines all products of this potentially busy user (G Precision Components Factory) require optimization and updates through monitoring and analysis. This means that merchant G's server is facing extremely high user access pressure. To quickly grasp market dynamics and provide configuration suggestions, the platform will initiate the highest level of monitoring for all of its products. Specifically, whenever there are updates to user-related data for these products, monitoring and analysis of that product's user-related data will be performed.

[0122] It should be noted that the products for which the regulatory analysis method needs to be optimized and updated are those for which regulatory analysis of user-related data is performed whenever there is an update to the user-related data of the product.

[0123] Specifically, the users matched to the product profile are those who have browsed, searched for, or transacted with the product.

[0124] This "optimized and updated product identification method based on regulatory analysis" is far more than just a technical rule; it's a sophisticated platform operation philosophy. It cleverly connects cold computing resources with fiery business demands, using data-driven decision-making to achieve: cost control and efficiency improvement for the platform, intelligent upgrades and risk mitigation for suppliers, and guaranteed experience for buyers. Ultimately, this fosters a healthy, efficient, and highly self-evolving B2B platform ecosystem. This is a key capability for platforms to achieve refined operation and sustainable development in the digital economy era.

[0125] In one possible specific embodiment, the target merchant: G Precision Components Manufacturer's server configuration: number of users that can be accessed = 100, evaluation period: past 7 days, preset ratio threshold = 60%, reasonable range of comprehensive matching ratio = [0, 8], and alarm limit of comprehensive matching ratio = 10;

[0126] Table 1 Overall Matching Ratio

[0127]

[0128] The table below summarizes the user behavior data and calculation process for G Precision Components Factory's main products over the past 7 days:

[0129] S42: Calculate the current regulatory analysis ratio

[0130] Table 2: Current Regulatory Analysis Methods for Each Commodity

[0131]

[0132] S43: Decision-making process and final identification results, Decision-making process:

[0133] S431 Judgment: Current regulatory ratio (33.3%) > 60%? No, proceed to S432. S432 Judgment: Overall matching ratio (1.4) < 10? Yes, the load "meets requirements". Overall matching ratio (1.4) is within the range [0, 8]? Yes, the load is in the "ideal range".

[0134] Table 3: Recognition Results

[0135]

[0136] in conclusion

[0137] Based on the above analysis, the system determines that Merchant G Precision Components Factory's current status is stable. Its user access pressure is within an ideal range. Although monitoring coverage is not high, no load risk has been identified. Therefore, the system decides not to optimize or update the monitoring and analysis methods. The existing hybrid strategy combining "on-demand monitoring" and "real-time monitoring" will continue, thereby meeting business needs while most effectively conserving platform computing resources.

[0138] Example 2

[0139] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the aforementioned data monitoring and management method for an e-commerce platform when running the computer program.

[0140] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0141] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0142] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A data monitoring and management method for e-commerce platforms, characterized in that, Specifically, it includes: Based on the association of merchants' products on the e-commerce platform, identify the associated merchants of the merchants in different products. Based on the associated merchant data in different products, identify the potential busy merchants among the merchants. Based on the server port configuration data of different potential busy merchants, determine when it is necessary to monitor and analyze the user association data of the products, and then proceed to the next step. Based on the association of products among different potentially busy merchants, the method for monitoring and analyzing user association data of the products is determined. By analyzing the user association data of each product in potentially busy merchants, the matching status of server ports in potentially busy merchants is determined. Based on the matching status and the products of potentially busy merchants, products that need to be optimized and updated in terms of monitoring analysis methods are identified. The associated merchants of the merchants in different products are the merchants that have the products; The method for identifying potentially busy merchants among the merchants is as follows: Based on the associated merchant data in the products, identify the associated merchants in different products; Based on the associated merchants in different products, identify products that do not have associated merchants; Based on the products of merchants that do not have associated merchants, determine whether the merchant is a potentially busy merchant; The need to monitor and analyze user-related data for products includes: Based on the configuration data of server ports of different potentially busy merchants, determine the ratio of the number of access users that the server port of a potentially busy merchant can handle to the number of goods of non-associated merchants, and determine the setting matching ratio of the server port of the potentially busy merchant. Based on the matching ratio of the server ports of the potential busy merchants, identify the potential busy merchants whose matching ratio does not meet the requirements. For potentially busy merchants whose matching ratio does not meet the requirements, determine whether monitoring and analysis of user-related data for products is necessary.

2. The data monitoring and management method for e-commerce platforms as described in claim 1, characterized in that, The association of the products is determined based on the merchant data of the products belonging to the merchant.

3. The data monitoring and management method for e-commerce platforms as described in claim 1, characterized in that, When a merchant has multiple products that are not associated with other merchants, the merchant is identified as a potentially busy merchant.

4. The data monitoring and management method for e-commerce platforms as described in claim 1, characterized in that, The matching status of server ports in the potential busy merchants is determined based on the matching status of the number of server ports configured with the user data of the matching user profiles of each product in the potential busy merchants.

5. The data monitoring and management method for e-commerce platforms as described in claim 1, characterized in that, The method for determining the products that require optimization and updating of regulatory analysis methods is as follows: Based on the matching results, determine the ratio of the number of users matching the user profiles of each product in the potentially busy merchants to the configured number of server ports of the potentially busy merchants within the most recent preset time period, and use this ratio as the set matching ratio. Based on the regulatory analysis of user association data of the goods of the aforementioned potentially busy merchants, the goods for each regulatory analysis method are identified; Based on the matching ratios of each product and the products for each regulatory analysis method, identify the products that require optimization and updates to the regulatory analysis methods.

6. The data monitoring and management method for e-commerce platforms as described in claim 5, characterized in that, The most recent preset time period is determined based on the most recent preset duration.

7. The data monitoring and management method for e-commerce platforms as described in claim 5, characterized in that, Based on the matching ratios of each product and the products of the potentially busy merchants, the products that require optimization and updates to the monitoring and analysis methods are identified, specifically including: Based on the matching ratio of each product, determine the sum of the matching ratios of each product. When the sum of the matching ratios of each product does not meet the requirements, i.e., when it exceeds the preset matching ratio threshold, it is determined that all products of the potentially busy merchant belong to products that need to be optimized and updated through regulatory analysis.

8. A computer system, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a data monitoring and management method for an e-commerce platform as described in any one of claims 1-7.

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