Internet-based e-commerce commodity management and control method and system

Abstract

The application relates to an Internet-based e-commerce commodity management and control method and system, which comprises the following steps: an Internet-based monitoring module acquires commodity purchase history data of a current e-commerce consumer subject in current e-commerce consumption platform consumption, and generates current actual purchase commodity data; the commodity selection time and commodity display data usage rate of the e-commerce commodities that have been purchased in the current actual purchase commodity data are generated, effective data weight values are generated, seller quality evaluation data corresponding to the e-commerce commodities that have been purchased are acquired, buyer quality evaluation data are acquired, commodity quality evaluation weight values are generated, commodity shopping consumption satisfaction values of the current e-commerce consumption platform are generated according to the effective data weight values and the commodity quality evaluation weight values of the e-commerce commodities that have been purchased, and management and control are performed according to the commodity shopping consumption satisfaction values. The application realizes accurate commodity management and control and adjustment in line with the actual use habits of the current e-commerce consumer subject.

Description

Technical Field

[0001] This application relates to the field of Internet technology, and in particular to an Internet-based e-commerce product management method and system. Background Technology

[0002] The Internet, also known as the international network, refers to a vast network of interconnected networks linked by a set of common protocols, forming a logically single, enormous international network. E-commerce shopping has emerged based on the Internet.

[0003] Currently, internet technology has been gradually applied to the management of e-commerce products. For example, the invention patent with application number CN201710772839.0 discloses an internet-based shopping method, device, and storage medium, which relates to the field of terminal technology. The method includes: when a user operates on the keyboard, determining the target key triggered by the user, comparing the target key with at least one set of pre-set shopping shortcut keys to determine whether there is a shopping shortcut key that matches the target key in the at least one set of shopping shortcut keys, and when there is a matching shopping shortcut key, determining the product information bound to the matching shopping shortcut key, and purchasing the product corresponding to the product information based on the product information.

[0004] Although the aforementioned patent documents enable quick purchase of designated goods, they only achieve the effect of the purchase end and cannot achieve automatic adjustment of goods. Specifically, they cannot control and adjust goods according to the user's usage habits. Summary of the Invention

[0005] Therefore, it is necessary to provide an internet-based e-commerce product management method and system that can accurately manage and adjust products in accordance with users' actual usage habits, addressing the aforementioned technical issues.

[0006] The technical solution of this invention is as follows: An internet-based e-commerce product management method, the method comprising: The system acquires historical purchase data of goods consumed by current e-commerce consumers on the current e-commerce platform based on an internet monitoring module. Data extraction is performed on this historical data to generate current actual purchase data. Data analysis is then conducted based on this current actual purchase data to generate product selection time and product display data usage rate for purchased e-commerce products. Effective data weights are generated based on these product selection time and product display data usage rate, with one effective data weight corresponding to each purchased e-commerce product. Seller quality assessment data corresponding to each purchased e-commerce product is obtained from the e-commerce supplier. Buyer quality assessment data for the purchased e-commerce products is also obtained from the current e-commerce consumer. Product quality assessment weights are generated based on the seller quality assessment data and the buyer quality assessment data, with one product quality assessment weight corresponding to each purchased e-commerce product. Finally, a product shopping satisfaction score is generated for the current e-commerce platform based on the effective data weights and product quality assessment weights for each purchased e-commerce product. The product shopping satisfaction score is then used to manage and control the products on the current e-commerce platform.

[0007] Specifically, data analysis is performed based on the current actual purchase data to generate the product selection time and product display data usage rate of the purchased e-commerce products in the current actual purchase data, and effective data weights are generated based on the product selection time and product display data usage rate, wherein one purchased e-commerce product corresponds to one effective data weight; specifically including: Based on the current actual purchase data, the system extracts and obtains purchased e-commerce products according to preset product names, wherein the number of purchased e-commerce products is multiple; it obtains the product start trigger time for each purchased e-commerce product by the current e-commerce consumer, and the product purchase trigger time for the current e-commerce consumer confirming the purchase; it generates a product selection time based on the product start trigger time and the product purchase trigger time, wherein each purchased e-commerce product corresponds to one product selection time; it obtains the actual product browsing data of the current e-commerce consumer on the purchased e-commerce products within the product selection time; it obtains the total product display data of the purchased e-commerce products based on the actual product browsing data, wherein the total product display data is preset, and one total product display data corresponds to one purchased e-commerce product; it generates a product display data utilization rate based on the actual product browsing data and the total product display data; and it generates effective data weights based on preset proportional weights, the product selection time, and the product display data utilization rate.

[0008] Specifically, based on the purchased e-commerce products, seller quality assessment data corresponding to the purchased e-commerce products is obtained from the e-commerce supply entity; buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products is obtained; and a product quality assessment weight is generated based on the seller quality assessment data and the buyer quality assessment data, wherein one purchased e-commerce product corresponds to one product quality assessment weight; specifically including: Based on the purchased e-commerce products, seller quality assessment data corresponding to the purchased e-commerce products is obtained from the e-commerce supply entity. Each purchased e-commerce product corresponds to one seller quality assessment data point, and each seller quality assessment data point includes multiple seller quality assessment parameters. The seller quality assessment parameters are compared with pre-stored standard assessment parameters, and seller quality assessment parameters that meet the standard assessment parameters are selected. The number of selected seller quality assessment parameters is counted and recorded as the actual qualified quantity. Each purchased e-commerce product corresponds to one actual qualified quantity. Purchased e-commerce products with values ​​greater than or equal to the standard qualified quantity are set as currently qualified products. Buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products is obtained, and buyer quality assessment data is extracted based on the currently qualified products to generate buyer qualified product evaluation data. Buyer quality assessment values ​​are generated based on the buyer qualified product evaluation data, and seller quality assessment values ​​are generated based on the seller quality assessment parameters. A product quality assessment weight is generated based on the buyer quality assessment value and the seller quality assessment value using the following formula: ; Where K represents the product quality assessment weight, s represents the actual qualified quantity, Ui represents the seller's quality assessment value, and Vi represents the buyer's quality assessment value.

[0009] Specifically, based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, a shopping satisfaction score for the current e-commerce platform is generated, and the products on the current e-commerce platform are controlled based on the shopping satisfaction score; this includes: Based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, the current e-commerce platform's product shopping satisfaction score is generated using the following formula: ; Where p represents the consumer satisfaction value, k0 represents the basic satisfaction value, kn represents the product quality assessment weight of the nth purchased e-commerce product, tn represents the effective data weight of the nth purchased e-commerce product, and q represents the error coefficient; it is determined whether the consumer satisfaction value meets the acceptable satisfaction range, wherein the acceptable satisfaction range is preset by the current e-commerce consumer; if the consumer satisfaction value meets the acceptable satisfaction range, no adjustment is made to the products on the current e-commerce platform; if the consumer satisfaction value does not meet the acceptable satisfaction range, the current satisfaction difference value is obtained, and the corresponding satisfaction gap level is obtained based on the satisfaction difference value; the product adjustment plan corresponding to the satisfaction gap level is retrieved based on the satisfaction gap level, and the products on the current e-commerce platform are controlled based on the retrieved product adjustment plan.

[0010] Specifically, an internet-based e-commerce product management system includes: The product data purchase module is used to obtain the historical purchase data of the current e-commerce consumer on the current e-commerce platform based on the Internet monitoring module, and to extract the historical purchase data and generate the current actual purchase data after the data extraction is completed. The effective data generation module is used to perform data analysis based on the current actual purchased goods data and generate the product selection time and product display data usage rate of the purchased e-commerce goods in the current actual purchased goods data, and generate effective data weights based on the product selection time and product display data usage rate, wherein one purchased e-commerce goods corresponds to one effective data weight; The evaluation data generation module is used to obtain seller quality evaluation data corresponding to each purchased e-commerce product from the e-commerce supply entity, obtain buyer quality evaluation data of the current e-commerce consumer entity for the purchased e-commerce products, and generate product quality evaluation weights based on the seller quality evaluation data and the buyer quality evaluation data, wherein one purchased e-commerce product corresponds to one product quality evaluation weight. The product control and adjustment module is used to generate a product shopping satisfaction value for the current e-commerce platform based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, and to control the products on the current e-commerce platform based on the product shopping satisfaction value.

[0011] Specifically, the effective data generation module is also used for: Based on the current actual purchase data, the system extracts and obtains purchased e-commerce products according to preset product names, wherein the number of purchased e-commerce products is multiple; it obtains the product start trigger time for each purchased e-commerce product by the current e-commerce consumer, and the product purchase trigger time for the current e-commerce consumer confirming the purchase; it generates a product selection time based on the product start trigger time and the product purchase trigger time, wherein each purchased e-commerce product corresponds to one product selection time; it obtains the actual product browsing data of the current e-commerce consumer on the purchased e-commerce products within the product selection time; it obtains the total product display data of the purchased e-commerce products based on the actual product browsing data, wherein the total product display data is preset, and one total product display data corresponds to one purchased e-commerce product; it generates a product display data utilization rate based on the actual product browsing data and the total product display data; and it generates effective data weights based on preset proportional weights, the product selection time, and the product display data utilization rate.

[0012] Specifically, the evaluation data generation module is also used for: Based on the purchased e-commerce products, seller quality assessment data corresponding to the purchased e-commerce products is obtained from the e-commerce supply entity. Each purchased e-commerce product corresponds to one seller quality assessment data point, and each seller quality assessment data point includes multiple seller quality assessment parameters. The seller quality assessment parameters are compared with pre-stored standard assessment parameters, and seller quality assessment parameters that meet the standard assessment parameters are selected. The number of selected seller quality assessment parameters is counted and recorded as the actual qualified quantity. Each purchased e-commerce product corresponds to one actual qualified quantity. Purchased e-commerce products with values ​​greater than or equal to the standard qualified quantity are set as currently qualified products. Buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products is obtained, and buyer quality assessment data is extracted based on the currently qualified products to generate buyer qualified product evaluation data. Buyer quality assessment values ​​are generated based on the buyer qualified product evaluation data, and seller quality assessment values ​​are generated based on the seller quality assessment parameters. A product quality assessment weight is generated based on the buyer quality assessment value and the seller quality assessment value using the following formula: ; Where K represents the product quality assessment weight, s represents the actual qualified quantity, Ui represents the seller's quality assessment value, and Vi represents the buyer's quality assessment value.

[0013] Specifically, the commodity control and adjustment module is also used for: Based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, the current e-commerce platform's product shopping satisfaction score is generated using the following formula: ; Where p represents the consumer satisfaction value, k0 represents the basic satisfaction value, kn represents the product quality assessment weight of the nth purchased e-commerce product, tn represents the effective data weight of the nth purchased e-commerce product, and q represents the error coefficient; it is determined whether the consumer satisfaction value meets the acceptable satisfaction range, wherein the acceptable satisfaction range is preset by the current e-commerce consumer; if the consumer satisfaction value meets the acceptable satisfaction range, no adjustment is made to the products on the current e-commerce platform; if the consumer satisfaction value does not meet the acceptable satisfaction range, the current satisfaction difference value is obtained, and the corresponding satisfaction gap level is obtained based on the satisfaction difference value; the product adjustment plan corresponding to the satisfaction gap level is retrieved based on the satisfaction gap level, and the products on the current e-commerce platform are controlled based on the retrieved product adjustment plan.

[0014] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps described in the above-mentioned Internet-based e-commerce product management method.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps described in the Internet-based e-commerce product management method.

[0016] The technical effects achieved by this invention are as follows: The aforementioned internet-based e-commerce product management method and system sequentially acquires historical purchase data of goods consumed by the current e-commerce consumer on the current e-commerce platform through an internet-based monitoring module, extracts data from the historical purchase data, and generates current actual purchase data after data extraction; performs data analysis on the current actual purchase data and generates product selection time and product display data usage rate for the purchased e-commerce products in the current actual purchase data, and generates effective data weights based on the product selection time and product display data usage rate, wherein each purchased e-commerce product corresponds to one effective data weight; obtains seller quality assessment data corresponding to each purchased e-commerce product from the e-commerce supplier, obtains buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products, and generates product quality assessment data based on the seller quality assessment data and the buyer quality assessment data. The invention employs a product quality assessment weighting system, where each purchased e-commerce product corresponds to a product quality assessment weighting. Based on the valid data weighting corresponding to each purchased e-commerce product and the product quality assessment weighting, a product shopping satisfaction value is generated for the current e-commerce platform. This satisfaction value is then used to manage and control the products on the current e-commerce platform. Specifically, this invention uses product quality assessment to generate a product shopping satisfaction value for the current e-commerce platform, based on the valid data weighting corresponding to each purchased e-commerce product and the product quality assessment weighting. This allows for the management and control of products on the current e-commerce platform using the product shopping satisfaction value. This achieves the generation of a final satisfaction value based on various data generated during user e-commerce shopping that aligns with the user's actual usage habits, thereby realizing accurate and consistent product management and adjustment that reflects the actual usage habits of the current e-commerce consumer. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an internet-based e-commerce product management method in one embodiment. Figure 2 This is a structural block diagram of an internet-based e-commerce product management system in one embodiment; Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] In one embodiment, a terminal is provided, the terminal being configured to: acquire historical purchase data of goods consumed by a current e-commerce consumer on a current e-commerce platform based on an internet monitoring module, extract data from the historical purchase data, and generate current actual purchase data after data extraction; perform data analysis based on the current actual purchase data and generate the product selection time and product display data usage rate of the purchased e-commerce goods in the current actual purchase data, and generate effective data weights based on the product selection time and product display data usage rate, wherein one purchased e-commerce goods corresponds to one effective data weight; according to Each purchased e-commerce product obtains seller quality assessment data corresponding to the purchased e-commerce product from the e-commerce supplier, obtains buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce product, and generates a product quality assessment weight based on the seller quality assessment data and the buyer quality assessment data, wherein one purchased e-commerce product corresponds to one product quality assessment weight; generates a product shopping satisfaction value for the current e-commerce consumption platform based on the valid data weight corresponding to each purchased e-commerce product and the product quality assessment weight, and manages the products on the current e-commerce consumption platform based on the product shopping satisfaction value.

[0020] The terminal may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.

[0021] In one embodiment, such as Figure 1 As shown, an internet-based e-commerce product management method is provided, the method comprising: Step S100: Based on the Internet monitoring module, obtain the purchase history data of the current e-commerce consumer on the current e-commerce platform, extract the purchase history data, and generate the current actual purchase data after the data extraction is completed; In one embodiment, the internet monitoring module is an intelligent module based on internet technology used to monitor the current e-commerce consumer's consumption on the current e-commerce platform. The current e-commerce platform includes, but is not limited to, various applications such as Taobao and Pinduoduo, as well as various browser-based internet platforms. The product purchase history data comprises all records generated by the current e-commerce consumer during their shopping activities. To ensure greater data accuracy, data is extracted from the product purchase history data, and current actual purchase data is generated after extraction. This current actual purchase data is data related to the purchased e-commerce products, such as the purchase time, browsing time, and details of the clicked product information.

[0022] Step S200: Perform data analysis based on the current actual purchased goods data and generate the product selection time and product display data usage rate of the purchased e-commerce goods in the current actual purchased goods data, and generate effective data weights based on the product selection time and product display data usage rate, wherein one purchased e-commerce goods corresponds to one effective data weight; Step S300: Obtain seller quality assessment data corresponding to each purchased e-commerce product from the e-commerce supply entity, obtain buyer quality assessment data of the current e-commerce consumer entity for the purchased e-commerce products, and generate product quality assessment weights based on the seller quality assessment data and the buyer quality assessment data, wherein one purchased e-commerce product corresponds to one product quality assessment weight. Step S400: Generate the shopping satisfaction value of the current e-commerce platform based on the valid data weight and product quality assessment weight corresponding to each purchased e-commerce product, and control the products on the current e-commerce platform based on the shopping satisfaction value.

[0023] In one embodiment, to ensure the accuracy of e-commerce product control data by considering multiple dimensions, the following measures are taken: First, the product selection time and product display data usage rate are considered. Specifically, data analysis is performed on the currently purchased product data to generate the product selection time and product display data usage rate of the purchased e-commerce products. The product display data usage rate is the proportion of the data of the current e-commerce consumer browsing the purchased e-commerce products to the total display data of the purchased e-commerce products. Effective data weights are generated based on the product selection time and product display data usage rate. Second, product quality assessment is used. Specifically, the product shopping satisfaction value of the current e-commerce platform is generated based on the effective data weights and product quality assessment weights corresponding to each purchased e-commerce product. This allows for the control of products on the current e-commerce platform through the product shopping satisfaction value. This achieves the generation of a final satisfaction value based on various data generated by users when shopping for e-commerce products, which conforms to the usage habits of the current e-commerce consumer. Consequently, accurate and consistent product control and adjustment are achieved.

[0024] In one embodiment, step S200: Perform data analysis based on the current actual purchased goods data and generate the product selection time and product display data usage rate of the purchased e-commerce goods in the current actual purchased goods data; and generate effective data weights based on the product selection time and product display data usage rate, wherein one purchased e-commerce goods corresponds to one effective data weight; specifically including: Step S210: Based on the current actual purchased goods data, extract the purchased goods according to the preset goods names and obtain the purchased e-commerce goods, wherein the number of purchased e-commerce goods is multiple; Step S220: Obtain the product start trigger time for each of the purchased e-commerce products by the current e-commerce consumer, and obtain the product purchase trigger time for the current e-commerce consumer to confirm the purchase of the product by the current e-commerce consumer. Step S230: Generate a product selection time based on the product start trigger time and the product purchase trigger time, wherein one purchased e-commerce product corresponds to one product selection time; In this embodiment, considering that the longer the selection time for a product is, the more the user likes or needs the product, it is necessary to obtain the product selection time. Specifically, the product start trigger time of each of the purchased e-commerce products is obtained first, and the purchase trigger time of the current e-commerce consumer confirming the purchase of the product is obtained. Then, the difference between the purchase trigger time and the product start trigger time is calculated to generate the product selection time.

[0025] Step S240: Obtain the actual product browsing data of the current e-commerce consumer on the purchased e-commerce products within the product selection time; Step S250: Obtain the total product display data of the purchased e-commerce products based on the actual product browsing data, wherein the total product display data is preset, and one total product display data corresponds to one purchased e-commerce product; Step S260: Generate product display data utilization rate based on the actual product browsing data and the total product display data; Step S270: Generate effective data weights based on preset proportional weights, the product selection time, and the product display data usage rate.

[0026] In this embodiment, the utilization rate of user data is considered. The higher the utilization rate of product display data, the more useful the data displayed for the current product is. This involves first obtaining the actual product browsing data of the current e-commerce consumer on the purchased e-commerce products within the product selection time; then, obtaining the total product display data of the purchased e-commerce products based on the actual product browsing data; next, calculating the proportion of the actual product browsing data to the total product display data to generate the product display data utilization rate; and then generating effective data weights based on preset proportion weights, the product selection time, and the product display data utilization rate. The proportion weights include a first weight and a second weight, which correspond to the product selection time and the product display data utilization rate, respectively. During calculation, the first weight is multiplied by the product selection time, and the second weight is multiplied by the product display data utilization rate, thereby generating the effective data weights.

[0027] In one embodiment, step S300 involves: obtaining seller quality assessment data corresponding to each purchased e-commerce product from the e-commerce supplier, obtaining buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products, and generating a product quality assessment weight based on the seller quality assessment data and the buyer quality assessment data, wherein one purchased e-commerce product corresponds to one product quality assessment weight; specifically including: Step S310: Obtain seller quality assessment data corresponding to each purchased e-commerce product from the e-commerce supplier, wherein each purchased e-commerce product corresponds to one seller quality assessment data, and each seller quality assessment data includes multiple seller quality assessment parameters. Step S320: Compare the seller quality assessment parameters with the pre-stored standard assessment parameters and filter out the seller quality assessment parameters that meet the standard assessment parameters, count the number of the filtered seller quality assessment parameters, and record them as the actual qualified quantity, wherein one purchased e-commerce product corresponds to one actual qualified quantity. Step S330: Set the purchased e-commerce products corresponding to the actual qualified quantity whose value is greater than or equal to the standard qualified quantity as the current qualified products; Step S340: Obtain the buyer quality assessment data of the current e-commerce consumer on the purchased e-commerce goods, and extract the buyer quality assessment data based on the current qualified products to generate buyer qualified product evaluation data; Step S350: Generate a buyer quality assessment value based on the buyer's qualified product evaluation data, and generate a seller quality assessment value based on the seller's quality assessment parameters; Step S360: Generate a product quality assessment weight based on the buyer's quality assessment value and the seller's quality assessment value using the following formula: ; Where K represents the product quality assessment weight, s represents the actual qualified quantity, Ui represents the seller's quality assessment value, and Vi represents the buyer's quality assessment value.

[0028] In one embodiment, each purchased e-commerce product corresponds to a product quality assessment weight. The generation of the product quality assessment weight considers both the actual number of qualified products and their corresponding quality assessment values. By considering two factors—the buyer's quality assessment value and the seller's quality assessment value—the system first compares the seller's quality assessment parameters with pre-stored standard assessment parameters, then filters out those that meet the standard parameters, counts the number of filtered seller quality assessment parameters, and records them as the actual number of qualified products. Then, the purchased e-commerce products corresponding to the actual number of qualified products with values ​​greater than or equal to the standard number of qualified products are set as the currently qualified products. This further... The process involves screening qualified products, specifically obtaining buyer quality assessment data of the currently purchased e-commerce goods from the e-commerce consumer. This data is then used to extract buyer quality assessment data and generate buyer-qualified product evaluation data. A buyer quality assessment value is generated based on this data, and a seller quality assessment value is generated based on the seller quality assessment parameters. Specifically, the buyer-qualified product evaluation data includes multiple evaluations, each corresponding to a different score. These scores are summed to obtain the buyer quality assessment value. Similarly, a corresponding score is generated based on the seller quality assessment parameters, and these scores are then combined to generate the seller quality assessment value. This process achieves comprehensive data generation.

[0029] In one embodiment, step S400 involves generating a consumer satisfaction score for the current e-commerce platform based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, and then controlling the products on the current e-commerce platform based on the consumer satisfaction score; specifically including: Step S410: Based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, generate the current e-commerce platform's product shopping satisfaction score using the following formula: ; Where p represents the satisfaction value of shopping, k0 represents the basic satisfaction value, kn represents the quality assessment weight of the nth purchased e-commerce product, tn represents the effective data weight of the nth purchased e-commerce product, and q represents the error coefficient. In this step, by comprehensively considering the basic satisfaction value, the product quality assessment weight of the purchased e-commerce goods, the effective data weight of the purchased e-commerce goods, and the error coefficient, the generated product shopping satisfaction value is comprehensive.

[0030] Step S420: Determine whether the satisfaction value of the shopping experience of the product meets the acceptable satisfaction range, wherein the acceptable satisfaction range is preset by the current e-commerce consumer. Step S430: If it is determined that the satisfaction value of the shopping experience of the product meets the qualified satisfaction range, then no adjustment will be made to the products on the current e-commerce platform; Step S440: If it is determined that the satisfaction value of the shopping consumption of the product does not meet the qualified satisfaction range, then obtain the current satisfaction difference value, and obtain the corresponding satisfaction gap level based on the satisfaction difference value; Step S450: Retrieve the product adjustment plan corresponding to the satisfaction gap level, and manage the products on the current e-commerce platform according to the retrieved product adjustment plan.

[0031] In one embodiment, to ensure the accuracy of the acquired data, the following steps are taken: First, a satisfaction score for the current e-commerce platform is generated. Then, it is determined whether the satisfaction score falls within a acceptable satisfaction range, which is pre-set by the current e-commerce consumer. Next, if the satisfaction score falls within the acceptable satisfaction range, no adjustments are made to the products on the current e-commerce platform. Then, if the satisfaction score does not fall within the acceptable satisfaction range, a current satisfaction difference value is obtained, and a corresponding satisfaction gap level is determined based on this value. Finally, a product adjustment plan corresponding to the satisfaction gap level is retrieved, and the products on the current e-commerce platform are managed according to the retrieved product adjustment plan. This overall management and the pre-set product adjustment plan enable timely adjustment and control of e-commerce products.

[0032] In one embodiment, such as Figure 2 As shown, an internet-based e-commerce product management system is also provided, the system comprising: The product data purchase module is used to obtain the historical purchase data of the current e-commerce consumer on the current e-commerce platform based on the Internet monitoring module, and to extract the historical purchase data and generate the current actual purchase data after the data extraction is completed. The effective data generation module is used to perform data analysis based on the current actual purchased goods data and generate the product selection time and product display data usage rate of the purchased e-commerce goods in the current actual purchased goods data, and generate effective data weights based on the product selection time and product display data usage rate, wherein one purchased e-commerce goods corresponds to one effective data weight; The evaluation data generation module is used to obtain seller quality evaluation data corresponding to each purchased e-commerce product from the e-commerce supply entity, obtain buyer quality evaluation data of the current e-commerce consumer entity for the purchased e-commerce products, and generate product quality evaluation weights based on the seller quality evaluation data and the buyer quality evaluation data, wherein one purchased e-commerce product corresponds to one product quality evaluation weight. The product control and adjustment module is used to generate a product shopping satisfaction value for the current e-commerce platform based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, and to control the products on the current e-commerce platform based on the product shopping satisfaction value.

[0033] In one embodiment, the effective data generation module is further configured to: Based on the current actual purchase data, the system extracts and obtains purchased e-commerce products according to preset product names, wherein the number of purchased e-commerce products is multiple; it obtains the product start trigger time for each purchased e-commerce product by the current e-commerce consumer, and the product purchase trigger time for the current e-commerce consumer confirming the purchase; it generates a product selection time based on the product start trigger time and the product purchase trigger time, wherein each purchased e-commerce product corresponds to one product selection time; it obtains the actual product browsing data of the current e-commerce consumer on the purchased e-commerce products within the product selection time; it obtains the total product display data of the purchased e-commerce products based on the actual product browsing data, wherein the total product display data is preset, and one total product display data corresponds to one purchased e-commerce product; it generates a product display data utilization rate based on the actual product browsing data and the total product display data; and it generates effective data weights based on preset proportional weights, the product selection time, and the product display data utilization rate.

[0034] In one embodiment, the evaluation data generation module is further configured to: Based on the purchased e-commerce products, seller quality assessment data corresponding to the purchased e-commerce products is obtained from the e-commerce supply entity. Each purchased e-commerce product corresponds to one seller quality assessment data point, and each seller quality assessment data point includes multiple seller quality assessment parameters. The seller quality assessment parameters are compared with pre-stored standard assessment parameters, and seller quality assessment parameters that meet the standard assessment parameters are selected. The number of selected seller quality assessment parameters is counted and recorded as the actual qualified quantity. Each purchased e-commerce product corresponds to one actual qualified quantity. Purchased e-commerce products with values ​​greater than or equal to the standard qualified quantity are set as currently qualified products. Buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products is obtained, and buyer quality assessment data is extracted based on the currently qualified products to generate buyer qualified product evaluation data. Buyer quality assessment values ​​are generated based on the buyer qualified product evaluation data, and seller quality assessment values ​​are generated based on the seller quality assessment parameters. A product quality assessment weight is generated based on the buyer quality assessment value and the seller quality assessment value using the following formula: ; Where K represents the product quality assessment weight, s represents the actual qualified quantity, Ui represents the seller's quality assessment value, and Vi represents the buyer's quality assessment value.

[0035] In one embodiment, the commodity control adjustment module is further configured to: Based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, the current e-commerce platform's product shopping satisfaction score is generated using the following formula: ; Where p represents the consumer satisfaction value, k0 represents the basic satisfaction value, kn represents the product quality assessment weight of the nth purchased e-commerce product, tn represents the effective data weight of the nth purchased e-commerce product, and q represents the error coefficient; it is determined whether the consumer satisfaction value meets the acceptable satisfaction range, wherein the acceptable satisfaction range is preset by the current e-commerce consumer; if the consumer satisfaction value meets the acceptable satisfaction range, no adjustment is made to the products on the current e-commerce platform; if the consumer satisfaction value does not meet the acceptable satisfaction range, the current satisfaction difference value is obtained, and the corresponding satisfaction gap level is obtained based on the satisfaction difference value; the product adjustment plan corresponding to the satisfaction gap level is retrieved based on the satisfaction gap level, and the products on the current e-commerce platform are controlled based on the retrieved product adjustment plan.

[0036] In one embodiment, such as Figure 3As shown, a computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps described in the above-mentioned Internet-based e-commerce product management method.

[0037] Specifically, in one embodiment, when the processor executes the computer program, it performs the following steps: acquiring historical purchase data of goods consumed by the current e-commerce consumer on the current e-commerce platform based on the internet monitoring module, extracting data from the historical purchase data, and generating current actual purchase data after data extraction; performing data analysis based on the current actual purchase data and generating the product selection time and product display data usage rate of the purchased e-commerce goods in the current actual purchase data, and generating effective data weights based on the product selection time and product display data usage rate, wherein one purchased e-commerce goods corresponds to one effective weight. According to the weights; based on the purchased e-commerce products, obtain the seller quality assessment data corresponding to the purchased e-commerce products from the e-commerce supply entity, obtain the buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products, and generate product quality assessment weights based on the seller quality assessment data and the buyer quality assessment data, wherein one purchased e-commerce product corresponds to one product quality assessment weight; generate the current e-commerce consumer platform's product shopping satisfaction value based on the valid data weights corresponding to each purchased e-commerce product and the product quality assessment weights, and manage the products on the current e-commerce consumer platform based on the product shopping satisfaction value.

[0038] Specifically, in one embodiment, when the processor executes the computer program, it performs the following steps: extracting and obtaining purchased e-commerce products according to the current actual purchased product data and preset product names, wherein the number of purchased e-commerce products is multiple; obtaining the product start trigger time for each of the purchased e-commerce products by the current e-commerce consumer, and obtaining the product purchase trigger time when the current e-commerce consumer confirms the purchase of the product; generating a product selection time based on the product start trigger time and the product purchase trigger time, wherein one purchased e-commerce product corresponds to one product selection time; obtaining actual product browsing data of the purchased e-commerce products by the current e-commerce consumer within the product selection time; obtaining total product display data of the purchased e-commerce products based on the actual product browsing data, wherein the total product display data is preset, and one total product display data corresponds to one purchased e-commerce product; generating a product display data utilization rate based on the actual product browsing data and the total product display data; generating effective data weights based on preset proportional weights, the product selection time, and the product display data utilization rate.

[0039] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps described in the Internet-based e-commerce product management method.

[0040] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0041] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0042] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for managing e-commerce goods based on the internet, characterized in that, The method includes: The system acquires historical purchase data of goods consumed by current e-commerce consumers on the current e-commerce platform based on an internet monitoring module. Data extraction is performed on this historical data to generate current actual purchase data. Data analysis is then conducted based on this current actual purchase data to generate product selection time and product display data usage rate for purchased e-commerce products. Effective data weights are generated based on these product selection time and product display data usage rate, with one effective data weight corresponding to each purchased e-commerce product. Seller quality assessment data corresponding to each purchased e-commerce product is obtained from the e-commerce supplier. Buyer quality assessment data for the purchased e-commerce products is also obtained from the current e-commerce consumer. Product quality assessment weights are generated based on the seller quality assessment data and the buyer quality assessment data, with one product quality assessment weight corresponding to each purchased e-commerce product. Finally, a product shopping satisfaction score is generated for the current e-commerce platform based on the effective data weights and product quality assessment weights for each purchased e-commerce product. The product shopping satisfaction score is then used to manage and control the products on the current e-commerce platform.

2. The method for managing e-commerce goods based on the Internet according to claim 1, characterized in that, Data analysis is performed based on the current actual purchase data to generate the product selection time and product display data usage rate of the purchased e-commerce products in the current actual purchase data. Effective data weights are then generated based on the product selection time and product display data usage rate, wherein each purchased e-commerce product corresponds to one effective data weight. Specifically, this includes: Based on the current actual purchase data, the system extracts and obtains purchased e-commerce products according to preset product names, wherein the number of purchased e-commerce products is multiple; it obtains the product start trigger time for each purchased e-commerce product by the current e-commerce consumer, and the product purchase trigger time for the current e-commerce consumer confirming the purchase; it generates a product selection time based on the product start trigger time and the product purchase trigger time, wherein each purchased e-commerce product corresponds to one product selection time; it obtains the actual product browsing data of the current e-commerce consumer on the purchased e-commerce products within the product selection time; it obtains the total product display data of the purchased e-commerce products based on the actual product browsing data, wherein the total product display data is preset, and one total product display data corresponds to one purchased e-commerce product; it generates a product display data utilization rate based on the actual product browsing data and the total product display data; and it generates effective data weights based on preset proportional weights, the product selection time, and the product display data utilization rate.

3. The method for managing e-commerce goods based on the Internet according to claim 1, characterized in that, Based on the purchased e-commerce products, seller quality assessment data corresponding to the purchased e-commerce products is obtained from the e-commerce supply entity; buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products is obtained; and a product quality assessment weight is generated based on the seller quality assessment data and the buyer quality assessment data, wherein one purchased e-commerce product corresponds to one product quality assessment weight; specifically including: Based on the purchased e-commerce products, seller quality assessment data corresponding to the purchased e-commerce products is obtained from the e-commerce supply entity. Each purchased e-commerce product corresponds to one seller quality assessment data point, and each seller quality assessment data point includes multiple seller quality assessment parameters. The seller quality assessment parameters are compared with pre-stored standard assessment parameters, and seller quality assessment parameters that meet the standard assessment parameters are selected. The number of selected seller quality assessment parameters is counted and recorded as the actual qualified quantity. Each purchased e-commerce product corresponds to one actual qualified quantity. Purchased e-commerce products with values ​​greater than or equal to the standard qualified quantity are set as currently qualified products. Buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products is obtained, and buyer quality assessment data is extracted based on the currently qualified products to generate buyer qualified product evaluation data. Buyer quality assessment values ​​are generated based on the buyer qualified product evaluation data, and seller quality assessment values ​​are generated based on the seller quality assessment parameters. A product quality assessment weight is generated based on the buyer quality assessment value and the seller quality assessment value using the following formula: Where K represents the product quality assessment weight, s represents the actual qualified quantity, Ui represents the seller's quality assessment value, and Vi represents the buyer's quality assessment value.

4. The method for managing e-commerce goods based on the Internet according to claim 1, characterized in that, Based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, a shopping satisfaction score for the current e-commerce platform is generated, and the products on the current e-commerce platform are controlled based on the shopping satisfaction score; specifically including: Based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, the current e-commerce platform's product shopping satisfaction score is generated using the following formula: p=k0+k1t1+...+k n t n +q; Where p represents the consumer satisfaction value, k0 represents the basic satisfaction value, kn represents the product quality assessment weight of the nth purchased e-commerce product, tn represents the effective data weight of the nth purchased e-commerce product, and q represents the error coefficient; it is determined whether the consumer satisfaction value meets the acceptable satisfaction range, wherein the acceptable satisfaction range is preset by the current e-commerce consumer; if the consumer satisfaction value meets the acceptable satisfaction range, no adjustment is made to the products on the current e-commerce platform; if the consumer satisfaction value does not meet the acceptable satisfaction range, the current satisfaction difference value is obtained, and the corresponding satisfaction gap level is obtained based on the satisfaction difference value; the product adjustment plan corresponding to the satisfaction gap level is retrieved based on the satisfaction gap level, and the products on the current e-commerce platform are controlled based on the retrieved product adjustment plan.

5. An internet-based e-commerce product management system, characterized in that, The system includes: The product data purchase module is used to obtain the historical purchase data of the current e-commerce consumer on the current e-commerce platform based on the Internet monitoring module, and to extract the historical purchase data and generate the current actual purchase data after the data extraction is completed. The effective data generation module is used to perform data analysis based on the current actual purchased goods data and generate the product selection time and product display data usage rate of the purchased e-commerce goods in the current actual purchased goods data, and generate effective data weights based on the product selection time and product display data usage rate, wherein one purchased e-commerce goods corresponds to one effective data weight; The evaluation data generation module is used to obtain seller quality evaluation data corresponding to each purchased e-commerce product from the e-commerce supply entity, obtain buyer quality evaluation data of the current e-commerce consumer entity for the purchased e-commerce products, and generate product quality evaluation weights based on the seller quality evaluation data and the buyer quality evaluation data, wherein one purchased e-commerce product corresponds to one product quality evaluation weight. The product control and adjustment module is used to generate a product shopping satisfaction value for the current e-commerce platform based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, and to control the products on the current e-commerce platform based on the product shopping satisfaction value.

6. The internet-based e-commerce product management system according to claim 5, characterized in that, The effective data generation module is also used for: Based on the current actual purchase data, the system extracts and obtains purchased e-commerce products according to preset product names, wherein the number of purchased e-commerce products is multiple; it obtains the product start trigger time for each purchased e-commerce product by the current e-commerce consumer, and the product purchase trigger time for the current e-commerce consumer confirming the purchase; it generates a product selection time based on the product start trigger time and the product purchase trigger time, wherein each purchased e-commerce product corresponds to one product selection time; it obtains the actual product browsing data of the current e-commerce consumer on the purchased e-commerce products within the product selection time; it obtains the total product display data of the purchased e-commerce products based on the actual product browsing data, wherein the total product display data is preset, and one total product display data corresponds to one purchased e-commerce product; it generates a product display data utilization rate based on the actual product browsing data and the total product display data; and it generates effective data weights based on preset proportional weights, the product selection time, and the product display data utilization rate.

7. The Internet-based e-commerce product management system according to claim 6, characterized in that, The evaluation data generation module is also used for: Based on the purchased e-commerce products, seller quality assessment data corresponding to the purchased e-commerce products is obtained from the e-commerce supply entity. Each purchased e-commerce product corresponds to one seller quality assessment data point, and each seller quality assessment data point includes multiple seller quality assessment parameters. The seller quality assessment parameters are compared with pre-stored standard assessment parameters, and seller quality assessment parameters that meet the standard assessment parameters are selected. The number of selected seller quality assessment parameters is counted and recorded as the actual qualified quantity. Each purchased e-commerce product corresponds to one actual qualified quantity. Purchased e-commerce products with values ​​greater than or equal to the standard qualified quantity are set as currently qualified products. Buyer quality assessment data of the current e-commerce consumer for the purchased e-commerce products is obtained, and buyer quality assessment data is extracted based on the currently qualified products to generate buyer qualified product evaluation data. Buyer quality assessment values ​​are generated based on the buyer qualified product evaluation data, and seller quality assessment values ​​are generated based on the seller quality assessment parameters. A product quality assessment weight is generated based on the buyer quality assessment value and the seller quality assessment value using the following formula: Where K represents the product quality assessment weight, s represents the actual qualified quantity, Ui represents the seller's quality assessment value, and Vi represents the buyer's quality assessment value.

8. The Internet-based e-commerce product management system according to claim 6, characterized in that, The commodity control and adjustment module is also used for: Based on the valid data weights and product quality assessment weights corresponding to each purchased e-commerce product, the current e-commerce platform's product shopping satisfaction score is generated using the following formula: p=k0+k1t1+...+k n t n +q; Where p represents the consumer satisfaction value, k0 represents the basic satisfaction value, kn represents the product quality assessment weight of the nth purchased e-commerce product, tn represents the effective data weight of the nth purchased e-commerce product, and q represents the error coefficient; it is determined whether the consumer satisfaction value meets the acceptable satisfaction range, wherein the acceptable satisfaction range is preset by the current e-commerce consumer; if the consumer satisfaction value meets the acceptable satisfaction range, no adjustment is made to the products on the current e-commerce platform; if the consumer satisfaction value does not meet the acceptable satisfaction range, the current satisfaction difference value is obtained, and the corresponding satisfaction gap level is obtained based on the satisfaction difference value; the product adjustment plan corresponding to the satisfaction gap level is retrieved based on the satisfaction gap level, and the products on the current e-commerce platform are controlled based on the retrieved product adjustment plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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