Data analysis system for e-commerce platform and method executed by same

Abstract

The invention provides a data analysis system for an e-commerce platform and a method executed by the data analysis system. According to one embodiment, a data analysis system includes a processor and a memory. The memory stores a database and program instructions. When the program instruction is executed by the processor, the data analysis system executes the following operations: acquiring account information of each participating user of each pre-panic buying commodity in an application time period, and screening each qualified user; counting the number of qualified users in real time, and determining an operation instruction about commodity panic buying; the advance payment state of each qualified user is obtained, and an operation instruction about panic buying of the pre-panic buying commodity is determined; and carrying out statistics on each user who succeeds in panic buying, analyzing the remaining tail payment state of each user who succeeds in panic buying, and determining an operation instruction about processing of the panic buying commodity.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and more particularly to a data analysis system for an e-commerce platform and a method executed thereby. Background Art

[0002] With the rapid development of e-commerce, the number and variety of e-commerce platforms are increasing. This has provided numerous conveniences for consumers' daily shopping and significantly promoted social and economic development. To increase the visibility and activity of e-commerce platforms, as well as to enhance their reach and influence, many platforms now regularly or irregularly organize group buying events. These events attract more users to the e-commerce platforms and enhance their influence. Summary of the Invention

[0003] This section is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This section is not intended to identify essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter.

[0004] One of the objectives of the present disclosure is to provide an improved data analysis system for e-commerce platforms based on big data analysis and a method implemented thereby. Specifically, one of the technical problems addressed by the present disclosure is that, in existing e-commerce platforms' flash sales activities, flash sales management often requires manual review of the information of participating users and effective intervention in the operation of the flash sales. This approach, which relies solely on manual operations, is labor-intensive, complex, and lacks flexibility. It cannot meet the complexity, scale, and timeliness requirements of e-commerce platform operations, thereby affecting the expected operational quality of flash sales activities conducted by e-commerce platforms. Another technical problem addressed by the present disclosure is that the handling of flash sales activities on existing e-commerce platforms lacks specificity. There are no targeted comfort measures for users who fail to purchase a product, and users who have participated multiple times but failed are not identified. For these users, failing to purchase a product once or twice is disappointing, but multiple failures directly undermine their enthusiasm for participating in flash sales, causing them to develop a negative attitude towards flash sales activities on e-commerce platforms, believing that they are just a gimmick to attract traffic, thereby adversely affecting the development of e-commerce platforms.

[0005] According to a first aspect of the present disclosure, a data analysis system for an e-commerce platform is provided. The data analysis system includes at least one processor and at least one memory. The at least one memory stores a database and program instructions. When executed by the at least one processor, the program instructions cause the data analysis system to perform the following operations: obtain account information of each user participating in the group purchase of each pre-purchased product on the e-commerce platform within the registration period, and screen each qualified user participating in the group purchase of each pre-purchased product on the e-commerce platform within the registration period; count the number of qualified users participating in the group purchase corresponding to each pre-purchased product on the e-commerce platform in real time, and determine operation instructions for the purchase of products based on the real-time count of the number of qualified users participating in the group purchase; obtain the prepayment payment status of each qualified user participating in the group purchase corresponding to each pre-purchased product on the e-commerce platform, and determine operation instructions for the purchase of pre-purchased products based on the prepayment payment status of each qualified user participating in the group purchase; and count the number of successful users corresponding to each purchased product on the e-commerce platform, analyze the remaining balance payment status of each successful user corresponding to each purchased product on the e-commerce platform, and determine operation instructions for the processing of the purchased products based on the remaining balance payment status of each successful user.

[0006] According to the first aspect above, by obtaining the account information of each user who participated in the group purchase for each pre-purchased product on the e-commerce platform during the registration period and screening each qualified user who participated in the group purchase for each pre-purchased product during the registration period, manual intervention is avoided, which helps to improve the automation level of the e-commerce platform and reduce the manual management costs of the e-commerce platform. Since the operation instructions for the purchase of the corresponding pre-purchased product are determined based on the number of qualified users who participated in the group purchase corresponding to each pre-purchased product and the prepayment payment status of each qualified user who participated in the group purchase, the flexibility of the e-commerce platform's product purchase management can be effectively improved, meeting the complexity, scale, and timeliness requirements of the e-commerce platform's operations, thereby improving the expected operational quality of the e-commerce platform's product purchase activities. Since the number of successful purchasers corresponding to each purchased product on the e-commerce platform is counted and the operation instructions for handling the corresponding purchased product are determined based on the remaining balance payment status of each successful purchaser, targeted management of product purchase activities on the e-commerce platform can be achieved, improving the operational efficiency of the purchase of products on the e-commerce platform, and helping to increase the economic benefits of the e-commerce platform.

[0007] In one embodiment of the present disclosure, the program instructions, when executed by the at least one processor, cause the data analysis system to perform the following operations: count the users who failed to purchase each purchased item on the e-commerce platform and determine an operation instruction for returning the prepayment; obtain the account history data of each user who failed to purchase each purchased item on the e-commerce platform; and determine the compensation value of each user who failed to purchase each purchased item on the e-commerce platform based on the obtained account history data of each user who failed to purchase. According to this embodiment, by counting the users who failed to purchase each purchased item on the e-commerce platform and analyzing the compensation value of each user based on the account history data of each user who failed to purchase, the corresponding prepayment of the purchased item and the corresponding compensation value can be distributed to the account of the corresponding user who failed to purchase, thereby achieving targeted management of the purchase activities of the e-commerce platform, avoiding the problem that users' enthusiasm for participating in the purchase activities is dampened due to multiple purchase failures, offsetting users' negative psychology towards the purchase activities of the e-commerce platform, and further promoting the development of the e-commerce platform.

[0008] In one embodiment of the present disclosure, when the program instructions are executed by the at least one processor, the data analysis system screens the users who are eligible to participate in the group purchase by performing the following operations: extracting the account identification (ID) of each user who participates in the group purchase of each pre-purchased product in the e-commerce platform within the registration period; extracting the account ID corresponding to each defaulting user stored in the database, and screening the users who are eligible to participate in the group purchase of each pre-purchased product in the e-commerce platform within the registration period; extracting the preset start time of each pre-purchased product in the e-commerce platform, removing the users who are eligible to participate in the group purchase corresponding to multiple pre-purchased products that participate in the same preset start time at the same time, and obtaining the users who are eligible to participate in the group purchase of each pre-purchased product in the e-commerce platform within the registration period.

[0009] In one embodiment of the present disclosure, when the program instructions are executed by the at least one processor, the data analysis system determines the operation instructions for the product rush purchase based on the real-time statistics of the number of users who are qualified to participate in the group rush purchase by performing the following operations: comparing the number of users who are qualified to participate in the group rush purchase of each pre-snatched product in the e-commerce platform within the registration period with a preset maximum user number threshold; if the number of users who are qualified to participate in the group rush purchase of a pre-snatched product within the registration period is greater than or equal to the preset maximum user number threshold, then determining the operation instructions for starting the pre-snatched product in advance; if the number of users who are qualified to participate in the group rush purchase of the pre-snatched product within the registration period is less than the preset maximum user number threshold, If the number of users is less than the minimum preset user number threshold of the corresponding pre-purchase product, an operation instruction for canceling the pre-purchase product is determined; if the number of users who are qualified to participate in the group purchase of the pre-purchase product at the deadline of the registration time is greater than or equal to the minimum preset user number threshold of the corresponding pre-purchase product, an operation instruction for canceling the pre-purchase product is determined.

[0010] In one embodiment of the present disclosure, the prepayment payment status includes a prepayment paid status and a prepayment unpaid status. When executed by the at least one processor, the program instructions cause the data analysis system to determine an operation instruction for rushing to purchase a pre-purchased product based on the prepayment payment status of each qualified group purchase user by performing the following operations: if the prepayment payment status of each qualified group purchase user corresponding to a pre-purchased product on the e-commerce platform is all in the paid state, then determine an operation instruction for rushing to purchase the pre-purchased product; if the prepayment payment status of one or more qualified group purchase users corresponding to a pre-purchased product on the e-commerce platform is unpaid, then determine an operation instruction for notifying the one or more qualified group purchase users to pay the prepayment for the corresponding pre-purchased product within a specified time, and an operation instruction for rushing to purchase the pre-purchased product after the specified time arrives.

[0011] In one embodiment of the present disclosure, when the program instructions are executed by the at least one processor, the data analysis system performs the following operations: obtaining the rush purchase quantity of each pre-sale product in the e-commerce platform; and setting the corresponding rush purchase quantity for each pre-sale product in the e-commerce platform.

[0012] In one embodiment of the present disclosure, the number of eligible group purchase users corresponding to each pre-purchased product in the e-commerce platform is marked as x. j , where j = 1, 2, ..., m. When the program instructions are executed by the at least one processor, the data analysis system is caused to obtain the rush purchase quantity of each pre-sale product in the e-commerce platform by performing the following operations: extracting the number of users participating in the group rush purchase corresponding to a preset single pre-sale product x 单 , determine the rush purchase quantity Y of each pre-snatched product in the e-commerce platform j , where the analysis formula for the rush purchase quantity of each pre-snatched product in the e-commerce platform is: Indicates the minimum purchase quantity.

[0013] In one embodiment of the present disclosure, when the program instructions are executed by the at least one processor, the data analysis system analyzes the remaining balance payment status of each successful purchase user and determines the operation instructions for processing the purchased goods by performing the following operations: extracting the sales amount of each purchased goods on the e-commerce platform, wherein the sales amount of each purchased goods on the e-commerce platform is marked as R j , where j = 1, 2, ..., m; obtaining the prepayment amount corresponding to each purchased product on the e-commerce platform, wherein the prepayment amount corresponding to each purchased product on the e-commerce platform is marked as R′ j ; Determine the remaining balance amount ΔR of each purchased product on the e-commerce platform j , where the analysis formula for the remaining balance of each purchased product on the e-commerce platform is ΔR j =R j -R′ j ; If the remaining balance amount of a purchased commodity in the e-commerce platform is equal to zero, indicating that the remaining balance payment status of each successful purchaser corresponding to the purchased commodity in the e-commerce platform is no payment required, then determine the operation instructions for sequentially shipping the corresponding purchased commodity to each successful purchaser corresponding to the purchased commodity; if the remaining balance amount of a purchased commodity in the e-commerce platform is greater than zero, indicating that the remaining balance payment status of each successful purchaser corresponding to the purchased commodity in the e-commerce platform is payment required, then determine the operation instructions for sequentially notifying each successful purchaser corresponding to the purchased commodity to pay the remaining balance amount of the corresponding purchased commodity.

[0014] In one embodiment of the present disclosure, when the program instructions are executed by the at least one processor, the data analysis system performs the following operations: recording the remaining balance payment time of each successful purchaser corresponding to each purchased product on the e-commerce platform; comparing the remaining balance payment time of each successful purchaser corresponding to each purchased product on the e-commerce platform with a set time threshold; if the remaining balance payment time of one or more successful purchasers corresponding to a purchased product on the e-commerce platform is less than or equal to the set time threshold, determining an operation instruction for shipping the corresponding purchased product to the one or more successful purchasers corresponding to the purchased product; if the remaining balance payment time of one or more successful purchasers corresponding to a purchased product on the e-commerce platform is greater than the set time threshold, marking the one or more successful purchasers corresponding to the purchased product as a default user, and sending the account ID corresponding to the default user to the database.

[0015] In one embodiment of the present disclosure, when the program instructions are executed by the at least one processor, the data analysis system determines the operational instructions regarding the return of the prepayment by performing the following operations: obtaining the account of each user who failed to purchase each purchased item on the e-commerce platform; extracting the prepayment amount corresponding to each purchased item on the e-commerce platform, and determining the operational instructions for returning the prepayment of each purchased item to the corresponding account wallet of each user who failed to purchase.

[0016] In one embodiment of the present disclosure, when the program instructions are executed by the at least one processor, the data analysis system obtains the account history data of each user who failed to purchase a product by performing the following operations: obtaining the number of times the user participated in the purchase in the account history data of each user who failed to purchase a product corresponding to each product that has been purchased on the e-commerce platform, wherein the number of times the user participated in the purchase in the account history data of each user who failed to purchase a product corresponding to each product that has been purchased on the e-commerce platform is marked as w j a v , where v = 1, 2, ..., u; obtain the target number of failed purchases in the account history data of each user who failed to purchase each purchased product on the e-commerce platform, where the target number of failed purchases is the number of times the user participated in the purchase after the most recent successful purchase, and mark the target number of failed purchases in the account history data of each user who failed to purchase each purchased product on the e-commerce platform as w′ j a′ v .

[0017] In one embodiment of the present disclosure, when the program instructions are executed by the at least one processor, the data analysis system determines the compensation value of each user who failed to purchase each purchased product by performing the following operations: extracting the total number of purchase events held on the e-commerce platform, wherein the total number of purchase events held on the e-commerce platform is marked as W. 总 ; The number of times w participated in the rush purchase in the account history data of each failed user corresponding to each rushed purchase product in the e-commerce platform j a v , target purchase failure times w′ j a′ v Substitute the compensation value analysis formula Obtain the compensation value R″ of each failed purchase user corresponding to each purchased product on the e-commerce platform j a v , where α represents a fixed percentage and is positive, R′ j represents the prepaid amount corresponding to each purchased product on the e-commerce platform, w′ 预 Indicates the preset purchase failure threshold.

[0018] According to a second aspect of the present disclosure, a method performed by a data analysis system for an e-commerce platform is provided. The method includes obtaining account information of each user participating in a group purchase for each pre-purchased product on the e-commerce platform within a registration period, and screening each qualified user participating in the group purchase for each pre-purchased product on the e-commerce platform within the registration period. The method also includes counting the number of qualified users participating in the group purchase corresponding to each pre-purchased product on the e-commerce platform in real time, and determining operational instructions for the purchase of the product based on the real-time count of qualified users participating in the group purchase. The method also includes obtaining the prepayment payment status of each qualified user participating in the group purchase corresponding to each pre-purchased product on the e-commerce platform, and determining operational instructions for the purchase of the pre-purchased product based on the prepayment payment status of each qualified user participating in the group purchase. The method also includes counting the number of users who successfully purchased each pre-purchased product on the e-commerce platform, analyzing the remaining balance payment status of each successful user corresponding to each pre-purchased product on the e-commerce platform, and determining operational instructions for handling the pre-purchased product based on the remaining balance payment status of each successful user.

[0019] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, wherein program instructions are stored on the computer-readable storage medium, and when the program instructions are executed by at least one processor, the at least one processor executes the method according to the second aspect.

[0020] According to a fourth aspect of the present disclosure, a data analysis system for an e-commerce platform is provided. The data analysis system includes: a group purchase user screening module, a group purchase user number counting module, a pre-purchased product analysis module, a purchased product analysis module, and a database for the e-commerce platform. The group purchase user screening module is configured to obtain the account information of each group purchase user for each pre-purchased product on the e-commerce platform within the registration period, and to screen each qualified group purchase user for each pre-purchased product on the e-commerce platform within the registration period. The group purchase user number counting module is configured to count the number of qualified group purchase users corresponding to each pre-purchased product on the e-commerce platform in real time, and determine operational instructions for the purchase of the product based on the real-time count of qualified group purchase users. The pre-purchased product analysis module is configured to obtain the prepayment payment status of each qualified group purchase user corresponding to each pre-purchased product on the e-commerce platform, and determine operational instructions for the purchase of the pre-purchased product based on the prepayment payment status of each qualified group purchase user. The purchased goods analysis module is configured to count the successful users corresponding to each purchased goods in the e-commerce platform, analyze the remaining balance payment status of each successful user corresponding to each purchased goods in the e-commerce platform, and determine the operation instructions for the processing of the purchased goods based on the remaining balance payment status of each successful user.

[0021] In one embodiment of the present disclosure, the data analysis system further includes: a module for counting users who failed to purchase, a module for acquiring user account history data, and a module for analyzing compensation values. The module for counting users who failed to purchase corresponding to each purchased product on the e-commerce platform is configured to count the number of users who failed to purchase corresponding to each purchased product on the e-commerce platform, and to determine an operation instruction for returning the prepayment. The module for acquiring user account history data is configured to acquire the account history data of each user who failed to purchase corresponding to each purchased product on the e-commerce platform. The module for analyzing compensation values is configured to determine the compensation value of each user who failed to purchase corresponding to each purchased product on the e-commerce platform based on the acquired account history data of each user.

[0022] In one embodiment of the present disclosure, the group purchase user screening module screens eligible group purchase users by performing the following operations: extracting the account identification (ID) of each group purchase user for each pre-purchased product in the e-commerce platform within the registration period; extracting the account ID corresponding to each defaulting user stored in the database, and screening each eligible group purchase user for each pre-purchased product in the e-commerce platform within the registration period; extracting the preset start time of each pre-purchased product in the e-commerce platform, removing each eligible group purchase user corresponding to multiple pre-purchased products that participate in the same preset start time at the same time, and obtaining each eligible group purchase user for each pre-purchased product in the e-commerce platform within the registration period.

[0023] In one embodiment of the present disclosure, the module for counting the number of users participating in the group purchase determines the operation instructions for the commodity purchase based on the real-time statistics of the number of qualified users participating in the group purchase by performing the following operations: comparing the number of qualified users participating in the group purchase of each pre-purchase commodity in the e-commerce platform within the registration period with a preset maximum user number threshold; if the number of qualified users participating in the group purchase of a pre-purchase commodity within the registration period is greater than or equal to the preset maximum user number threshold, then determining the operation instructions for starting the pre-purchase commodity in advance; if the number of qualified users participating in the group purchase of the pre-purchase commodity within the registration period is less than the preset maximum user number threshold, , then count the number of users who are qualified to participate in the group purchase of the pre-purchase product at the deadline of the registration time; compare the number of users who are qualified to participate in the group purchase of the pre-purchase product at the deadline of the registration time with the minimum preset user number threshold of the corresponding pre-purchase product; if the number of users who are qualified to participate in the group purchase of the pre-purchase product at the deadline of the registration time is less than the minimum preset user number threshold of the corresponding pre-purchase product, determine the operation instruction for canceling the purchase of the pre-purchase product; if the number of users who are qualified to participate in the group purchase of the pre-purchase product at the deadline of the registration time is greater than or equal to the minimum preset user number threshold of the corresponding pre-purchase product, determine the operation instruction for the purchase of the pre-purchase product.

[0024] In one embodiment of the present disclosure, the prepayment payment status includes a prepayment paid status and a prepayment unpaid status. The pre-purchase product analysis module determines an operation instruction for rushing to purchase the pre-purchased product based on the prepayment payment status of each qualified group purchase user by performing the following operations: if the prepayment payment status of each qualified group purchase user corresponding to a pre-purchased product on the e-commerce platform is all in a paid state, then determine an operation instruction for rushing to purchase the pre-purchased product; if the prepayment payment status of one or more qualified group purchase users corresponding to a pre-purchased product on the e-commerce platform is unpaid, then determine an operation instruction for notifying the one or more qualified group purchase users to pay the prepayment for the corresponding pre-purchased product within a specified time, and an operation instruction for rushing to purchase the pre-purchased product after the specified time arrives.

[0025] In one embodiment of the present disclosure, the pre-purchase product analysis module includes a pre-purchase product purchase quantity acquisition unit, which is configured to obtain the purchase quantity of each pre-purchase product in the e-commerce platform and set the corresponding purchase quantity for each pre-purchase product in the e-commerce platform.

[0026] In one embodiment of the present disclosure, the number of eligible group purchase users corresponding to each pre-purchased product in the e-commerce platform is marked as x. j , where j = 1, 2, ..., m. The pre-purchased goods rush purchase quantity acquisition unit acquires the rush purchase quantity of each pre-purchased goods in the e-commerce platform by performing the following operations: extracting the number of users participating in the group rush purchase corresponding to a preset single pre-purchased goods x 单 , determine the rush purchase quantity Y of each pre-snatched product in the e-commerce platform j , where the analysis formula for the rush purchase quantity of each pre-snatched product in the e-commerce platform is: Indicates the minimum purchase quantity.

[0027] In one embodiment of the present disclosure, the purchased goods analysis module analyzes the remaining balance payment status of each successful purchase user and determines the operation instructions for processing the purchased goods by performing the following operations: extracting the sales amount of each purchased goods in the e-commerce platform, wherein the sales amount of each purchased goods in the e-commerce platform is marked as R j , where j = 1, 2, ..., m; obtaining the prepayment amount corresponding to each purchased product on the e-commerce platform, wherein the prepayment amount corresponding to each purchased product on the e-commerce platform is marked as R′ j ; Determine the remaining balance amount ΔR of each purchased product on the e-commerce platform j, where the analysis formula for the remaining balance of each purchased product on the e-commerce platform is ΔR j =R j -R′ j ; If the remaining balance amount of a purchased commodity in the e-commerce platform is equal to zero, indicating that the remaining balance payment status of each successful purchaser corresponding to the purchased commodity in the e-commerce platform is no payment required, then determine the operation instructions for sequentially shipping the corresponding purchased commodity to each successful purchaser corresponding to the purchased commodity; if the remaining balance amount of a purchased commodity in the e-commerce platform is greater than zero, indicating that the remaining balance payment status of each successful purchaser corresponding to the purchased commodity in the e-commerce platform is payment required, then determine the operation instructions for sequentially notifying each successful purchaser corresponding to the purchased commodity to pay the remaining balance amount of the corresponding purchased commodity.

[0028] In one embodiment of the present disclosure, the purchased goods analysis module also includes a remaining balance payment time recording unit, which is configured to record the remaining balance payment time of each successful purchase user corresponding to each purchased goods in the e-commerce platform, and compare the remaining balance payment time of each successful purchase user corresponding to each purchased goods in the e-commerce platform with a set time threshold. If the remaining balance payment time of one or more successful purchase users corresponding to a purchased goods in the e-commerce platform is less than or equal to the set time threshold, then determine an operation instruction for shipping the corresponding purchased goods to the one or more successful purchase users corresponding to the purchased goods. If the remaining balance payment time of one or more successful purchase users corresponding to a purchased goods in the e-commerce platform is greater than the set time threshold, then mark the one or more successful purchase users corresponding to the purchased goods as default users, and send the account ID corresponding to the default user to the database.

[0029] In one embodiment of the present disclosure, the failed purchase user statistics module determines the operational instructions for returning the prepayment by performing the following operations: obtaining the account of each failed purchase user corresponding to each purchased product in the e-commerce platform; extracting the prepayment amount corresponding to each purchased product in the e-commerce platform, and determining the operational instructions for returning the prepayment of each purchased product to the corresponding account wallet of each failed purchase user.

[0030] In one embodiment of the present disclosure, the user account history data acquisition module acquires the account history data of each user who failed to purchase a product by performing the following operations: acquiring the number of times the user participated in the purchase in the account history data of each user who failed to purchase a product corresponding to the product that has been purchased on the e-commerce platform, wherein the number of times the user participated in the purchase in the account history data of each user who failed to purchase a product corresponding to the product that has been purchased on the e-commerce platform is marked as w j a v , where v = 1, 2, ..., u; obtain the target number of failed purchases in the account history data of each user who failed to purchase each purchased product on the e-commerce platform, where the target number of failed purchases is the number of times the user participated in the purchase after the most recent successful purchase, and mark the target number of failed purchases in the account history data of each user who failed to purchase each purchased product on the e-commerce platform as w′ j a′ v .

[0031] In one embodiment of the present disclosure, the compensation value analysis module determines the compensation value of each user who failed to purchase each purchased product by performing the following operations: extracting the total number of purchase events held on the e-commerce platform, wherein the total number of purchase events held on the e-commerce platform is marked as W. 总 ; The number of times w participated in the rush purchase in the account history data of each failed user corresponding to each rushed purchase product in the e-commerce platform j a v , target purchase failure times w′ j a′ v Substitute the compensation value analysis formula Obtain the compensation value R″ of each failed purchase user corresponding to each purchased product on the e-commerce platform j a v , where α represents a fixed percentage and is positive, R′ j represents the prepaid amount corresponding to each purchased product on the e-commerce platform, w′ 预 Indicates the preset purchase failure threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] To more clearly illustrate the technical solutions of the present disclosure, the following is a brief introduction to the accompanying drawings of the embodiments. Obviously, the structural diagrams in the following drawings are not necessarily drawn to scale, but rather present various features in a simplified form. Furthermore, the following drawings merely relate to some embodiments of the present disclosure and are not intended to limit the present disclosure.

[0033] Figure 1A and Figure 1B are flowcharts each illustrating a method performed by a data analysis system for an e-commerce platform according to different embodiments of the present disclosure;

[0034] Figure 2 Is used to illustrate Figure 1A and Figure 1B a flowchart of a method;

[0035] Figure 3 Is used to illustrate Figure 1A and Figure 1B a flowchart of a method;

[0036] Figure 4 Is used to illustrate Figure 1A and Figure 1B a flowchart of a method;

[0037] Figure 5 It is used to illustrate that Figure 1A and Figure 1B A flowchart of additional steps added to the method;

[0038] Figure 6 Is used to illustrate Figure 5 Flowchart of additional steps;

[0039] Figure 7 Is used to illustrate Figure 1A and Figure 1B a flowchart of a method;

[0040] Figure 8 It is used to illustrate that Figure 1A and Figure 1B A flowchart of additional steps added to the method;

[0041] Figure 9 Is used to illustrate Figure 1B a flowchart of a method;

[0042] Figure 10 Is used to illustrate Figure 1B a flowchart of a method;

[0043] Figure 11 Is used to illustrate Figure 1B a flowchart of a method;

[0044] Figure 12 is a block diagram illustrating a data analysis system for an e-commerce platform according to one embodiment of the present disclosure; and

[0045] Figure 13 is a block diagram illustrating a data analysis system for an e-commerce platform according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0046] For purposes of explanation, certain details are set forth in the following description in order to provide a thorough understanding of the disclosed embodiments. However, it is apparent to one skilled in the art that the embodiments can be practiced without these specific details or with an equivalent configuration.

[0047] Current e-commerce platforms' flash sales events have, to a certain extent, satisfied users' demand for products they purchase. However, these events still present the following challenges. One issue is that flash sales management often requires manual review of participating users' information and effective intervention in the operation of purchased products. This reliance solely on manual operations is labor-intensive, complex, and lacks flexibility. It cannot meet the complexity, scale, and timeliness requirements of e-commerce platform operations, thus impacting the expected operational quality of flash sales events on e-commerce platforms.

[0048] Another issue is that current e-commerce platforms' handling of flash sales events lacks specificity. There's no targeted consolation for users who fail to secure a purchase, and there's no way to identify users who have failed multiple times. For these users, one or two failed attempts are merely disappointing, but repeated failures can dampen their enthusiasm for participating in flash sales, leading to a negative perception of these events and the perception that they're merely a traffic-driving gimmick, negatively impacting the platform's growth.

[0049] The present disclosure provides an improved data analysis system for an e-commerce platform based on big data analysis and a method executed thereby. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0050] Figure 1A is a flowchart illustrating a method performed by a data analysis system for an e-commerce platform according to an embodiment of the present disclosure. For example, the data analysis system may be implemented as a device on dedicated hardware, or as a software instance running on dedicated hardware, or as a virtualized function instantiated on an appropriate platform (e.g., on a cloud infrastructure). In step 102, the data analysis system obtains the account information of each user who participates in the group purchase of each pre-purchased product in the e-commerce platform within the registration period, and screens each qualified user who participates in the group purchase of each pre-purchased product in the e-commerce platform within the registration period. The account information of the users who participate in the group purchase includes, but is not limited to: user name, user account identification (ID), user contact information, and user delivery address. For example, the screening operation in step 102 may be implemented as Figure 2 Steps 216 to 220.

[0051] In step 216, the account IDs of each user participating in the group purchase for each pre-purchased product on the e-commerce platform during the registration period are retrieved. In step 218, the account IDs corresponding to each defaulting user stored in the database are retrieved, and users eligible to participate in the group purchase for each pre-purchased product on the e-commerce platform during the registration period are screened. For example, the account IDs of each user participating in the group purchase for each pre-purchased product on the e-commerce platform during the registration period can be compared with the account IDs corresponding to each defaulting user. If the account IDs of a user participating in the group purchase for a pre-purchased product on the e-commerce platform during the registration period do not match the account IDs corresponding to each defaulting user, it indicates that the user participating in the group purchase for the pre-purchased product during the registration period is eligible, and the user is recorded as the eligible user. In step 220, the preset start time for each pre-purchased product on the e-commerce platform is retrieved, and the eligible users corresponding to multiple pre-purchased products that simultaneously participate in the same preset start time are removed, thereby obtaining the eligible users participating in the group purchase for each pre-purchased product on the e-commerce platform during the registration period.

[0052] In this way, by obtaining the account information of each user participating in the group purchase for each pre-purchased product on the e-commerce platform during the registration period, and screening each qualified user participating in the group purchase for each pre-purchased product during the registration period, manual intervention is avoided, which helps to improve the automation level of the e-commerce platform and reduce the manual management costs of the e-commerce platform. By screening each qualified user participating in the group purchase for each pre-purchased product on the e-commerce platform during the registration period, it is effectively prevented that defaulting users occupy the purchase quota, and at the same time prevents users corresponding to multiple pre-purchased products with the same opening time from occupying the purchase quota, thereby increasing the opportunities for other users on the e-commerce platform to participate in the group purchase.

[0053] In step 104, the data analysis system counts in real time the number of qualified users participating in the group purchase corresponding to each pre-purchased product on the e-commerce platform, and determines the operation instructions for the product purchase based on the real-time count of qualified users participating in the group purchase. The number of qualified users participating in the group purchase corresponding to each pre-purchased product on the e-commerce platform can be marked as x j , where j = 1, 2, ..., m. The operation instruction for rushing to buy goods can trigger the corresponding module in the e-commerce platform to perform the corresponding operation. For example, the determination operation of step 104 can be implemented as Figure 3 Steps 322 to 332 of the preceding text.

[0054] In step 322, the number of users who qualified for the group purchase of each pre-purchased product on the e-commerce platform during the registration period is compared with a preset maximum user number threshold. If the number of users who qualified for the group purchase of a pre-purchased product during the registration period is greater than or equal to the preset maximum user number threshold, then in step 324, an operation instruction is determined to advance the pre-purchase of the pre-purchased product. If the number of users who qualified for the group purchase of the pre-purchased product during the registration period is less than the preset maximum user number threshold, then in step 326, the number of users who qualified for the group purchase of the pre-purchased product at the end of the registration period is counted. In step 328, the number of users who qualified for the group purchase of the pre-purchased product at the end of the registration period is compared with a preset minimum user number threshold for the corresponding pre-purchased product. If the number of users who qualified for the group purchase of the pre-purchased product at the end of the registration period is less than the preset minimum user number threshold for the corresponding pre-purchased product, then in step 330, an operation instruction is determined to cancel the pre-purchase of the pre-purchased product. If the number of users who are eligible to participate in the group purchase of the pre-purchased product is greater than or equal to the minimum preset user number threshold of the corresponding pre-purchased product when the registration time expires, the operation instruction for purchasing the pre-purchased product is determined in step 332.

[0055] In step 106, the data analysis system obtains the prepayment payment status of each qualified group purchase user corresponding to each pre-purchased product on the e-commerce platform, and determines the operation instructions for the pre-purchased product based on the prepayment payment status of each qualified group purchase user. For example, the prepayment payment status includes the prepayment paid status and the prepayment unpaid status. The operation instructions for the pre-purchased product can trigger the corresponding module in the e-commerce platform to perform the corresponding operation. The determination operation in step 106 can be implemented as follows: Figure 4 Steps 434 to 436.

[0056] In step 434, if the prepayment payment status of each qualified group purchase user corresponding to a pre-purchased product on the e-commerce platform is paid, then an operational instruction for purchasing the pre-purchased product is determined. In step 436, if the prepayment payment status of one or more qualified group purchase users corresponding to a pre-purchased product on the e-commerce platform is unpaid, then an operational instruction for notifying the one or more qualified group purchase users to pay the prepayment for the corresponding pre-purchased product within a specified time, as well as an operational instruction for purchasing the pre-purchased product after the specified time has arrived, is determined. It should be noted that if the qualified group purchase user fails to pay the prepayment for the corresponding pre-purchased product within the specified time, the qualified group purchase user may be marked as a defaulting user, and the defaulting user's corresponding account ID may be sent to the database used for the e-commerce platform. The defaulting user will then be unable to purchase the pre-purchased product.

[0057] In this way, since the corresponding pre-purchased goods are purchased according to the number of qualified group-buying users corresponding to each pre-purchased product and the prepayment status of each qualified group-buying user, the flexibility of the e-commerce platform's product purchase management is effectively improved, and the complexity, scale and timeliness requirements of the e-commerce platform's operations are met, thereby improving the expected operational quality of the e-commerce platform's product purchase activities.

[0058] Optionally, Figure 1A The method may include additional steps 538 to 540. In step 538, the data analysis system obtains the rush purchase quantity of each pre-sale product in the e-commerce platform. For example, step 538 may be implemented as Figure 6 Steps 642 to 644. In step 642, the number of users x participating in the group purchase corresponding to the preset single pre-purchased product is extracted. 单 In step 644, the rush purchase quantity Y of each pre-rush purchase commodity in the e-commerce platform is determined. j , where the analysis formula for the rush purchase quantity of each pre-snatched product in the e-commerce platform is: In step 540, the data analysis system sets a corresponding rush purchase quantity for each pre-rush purchase commodity in the e-commerce platform.

[0059] In step 108, the data analysis system counts the number of successful users corresponding to each purchased product on the e-commerce platform, analyzes the remaining balance payment status of each successful user corresponding to each purchased product on the e-commerce platform, and determines the operation instructions for processing the purchased product based on the remaining balance payment status of each successful user. The operation instructions for processing the purchased product can trigger the corresponding module in the e-commerce platform to perform the corresponding operation. For example, the analysis and determination operations in step 108 can be implemented as Figure 7 Steps 746 to 754.

[0060] In step 746, the sales amount of each purchased product in the e-commerce platform is extracted. The sales amount of each purchased product in the e-commerce platform can be marked as R j , where j = 1, 2, ..., m. In step 748, the prepayment amount corresponding to each purchased product on the e-commerce platform is obtained. The prepayment amount corresponding to each purchased product on the e-commerce platform can be marked as R j '. For example, the sales amount of each purchased product on the e-commerce platform can be extracted and compared with the maximum prepayment amount preset by the e-commerce platform. If the sales amount of a purchased product on the e-commerce platform is less than or equal to the maximum prepayment amount preset by the e-commerce platform, the prepayment amount of the purchased product is the sales amount. If the sales amount of a purchased product on the e-commerce platform is greater than the maximum prepayment amount preset by the e-commerce platform, the prepayment amount of the purchased product is the preset maximum prepayment amount.

[0061] In step 750, the remaining balance amount ΔR of each purchased product on the e-commerce platform is determined. j The analysis formula for the remaining balance of each purchased product on the e-commerce platform is ΔR j =R j -R j'. In step 752, if the remaining balance amount of a purchased commodity in the e-commerce platform is equal to zero, indicating that the remaining balance payment status of each successful purchaser corresponding to the purchased commodity in the e-commerce platform is not in a payment-required state, then an operation instruction is determined for sequentially shipping the corresponding purchased commodity to each successful purchaser corresponding to the purchased commodity. For example, the account information of each successful purchaser corresponding to the purchased commodity can be filtered, and the name, delivery address, and contact information in the account information of each successful purchaser corresponding to the purchased commodity can be extracted, and the corresponding purchase processing of the purchased commodity can be performed on each successful purchaser corresponding to the purchased commodity in turn. In step 754, if the remaining balance amount of a purchased commodity in the e-commerce platform is greater than zero, indicating that the remaining balance payment status of each successful purchaser corresponding to the purchased commodity in the e-commerce platform is in a payment-required state, then an operation instruction is determined for sequentially notifying each successful purchaser corresponding to the purchased commodity to pay the remaining balance amount of the corresponding purchased commodity.

[0062] In this way, by counting the number of successful users corresponding to each purchased product on the e-commerce platform, the corresponding purchased products are processed according to the remaining balance payment status of each successful user, thereby achieving targeted management of product purchase activities on the e-commerce platform, improving the operational efficiency of purchased products on the e-commerce platform, and helping to increase the economic benefits of the e-commerce platform.

[0063] Optionally, Figure 1A The method may include additional steps 856 to 860. In step 856, the remaining balance payment time of each successful purchaser corresponding to each purchased commodity in the e-commerce platform is recorded, and the remaining balance payment time of each successful purchaser corresponding to each purchased commodity in the e-commerce platform is compared with the set time threshold. In step 858, if the remaining balance payment time of one or more successful purchasers corresponding to a purchased commodity in the e-commerce platform is less than or equal to the set time threshold, then determine the operation instruction for shipping the corresponding purchased commodity to the one or more successful purchasers corresponding to the purchased commodity. In step 860, if the remaining balance payment time of one or more successful purchasers corresponding to a purchased commodity in the e-commerce platform is greater than the set time threshold, then mark the one or more successful purchasers corresponding to the purchased commodity as default users, and send the account ID corresponding to the default user to the database.

[0064] Figure 1B is a flowchart illustrating a method performed by a data analysis system for an e-commerce platform according to another embodiment of the present disclosure. Figure 1B Methods and Figure 1AThe difference between the method and the method is that it also includes steps 110 to 114. In step 110, the data analysis system counts the number of users who failed to purchase the products corresponding to the purchased products on the e-commerce platform and determines the operation instructions for the refund of the prepayment. The operation instructions for the refund of the prepayment can trigger the corresponding module in the e-commerce platform to perform the corresponding operation. For example, the determination operation of step 110 can be implemented as Figure 9 Steps 962 to 964 are performed. In step 962, the account numbers of the users who failed to purchase the products corresponding to the purchased products on the e-commerce platform are obtained. In step 964, the prepaid amount corresponding to the purchased products on the e-commerce platform is extracted, and an operation instruction is determined for returning the prepaid amount for each purchased product to the corresponding account wallet of each user who failed to purchase the products.

[0065] In step 112, the data analysis system obtains the account history data of each user who failed to purchase the product corresponding to each purchased product on the e-commerce platform. For example, step 112 can be implemented as Figure 10 Steps 1066 to 1068. In step 1066, the number of times the user who failed to purchase the product in the e-commerce platform participated in the purchase in the account history data is obtained. The number of times the user who failed to purchase the product in the e-commerce platform participated in the purchase in the account history data is marked as w j a v , where v = 1, 2, ..., u. In step 1068, the target number of failed purchases in the account history data of each user who failed to purchase each purchased product on the e-commerce platform is obtained. The target number of failed purchases is the number of times the user participated in the purchase after the most recent successful purchase. The target number of failed purchases in the account history data of each user who failed to purchase each purchased product on the e-commerce platform can be marked as w′ j a′ v .

[0066] In step 114, the data analysis system determines the compensation value of each failed purchase user corresponding to each purchased product in the e-commerce platform based on the acquired account history data of each failed purchase user. For example, step 114 can be implemented as Figure 11 Steps 1170 to 1172. In step 1170, the total number of rush purchase activities held on the e-commerce platform is extracted. The total number of rush purchase activities held on the e-commerce platform can be marked as W 总 In step 1172, the number of times w in the account history data of each user who failed to purchase the product corresponding to the purchased product in the e-commerce platform is used to calculate the number of times w in the account history data of each user who failed to purchase the product. j a v , target purchase failure times w′ j a′ v Substitute the compensation value analysis formula Get the compensation value R of each failed purchase user corresponding to each purchased product in the e-commerce platform j ″a v , where α represents a fixed percentage and is a positive value, R j ′ represents the prepaid amount corresponding to each purchased product on the e-commerce platform, w′ 预 Indicates the preset threshold for failed purchases. For example, compensation can be distributed to the account of the user who failed to purchase the item in the form of a consolation bonus or cash red envelope.

[0067] In this way, by counting the users who failed to purchase the products corresponding to the products purchased on the e-commerce platform, and analyzing the compensation values of the corresponding users based on the account history data of each user who failed to purchase, the corresponding prepayments for the purchased products and the corresponding compensation values can be issued to the corresponding accounts of the users who failed to purchase, thereby realizing the targeted management of the product purchase activities of the e-commerce platform, avoiding the problem that the enthusiasm of users to participate in the purchase activities is dampened due to multiple purchase failures, offsetting the negative psychology of users towards the product purchase activities of the e-commerce platform, and further promoting the development of the e-commerce platform.

[0068] Figure 12 FIG. 1 is a block diagram illustrating a data analysis system for an e-commerce platform according to an embodiment of the present disclosure. Figure 12 As shown, the data analysis system 1200 includes a processor 1210 and a memory 1220. The number of processors 1210 can be one or more, and the number of memories 1220 can be one or more. The memory 1220 stores a database 1221 for the e-commerce platform, and program instructions 1222. When the program instructions 1222 are executed by the processor 1210, the data analysis system 1200 executes the above-mentioned reference Figures 1A to 11 That is, the embodiments of the present disclosure may be implemented at least in part by computer software executable by the processor 1210, or by hardware, or by a combination of software and hardware.

[0069] The processor 1210 may be of any type suitable for the local technical environment and may include, by way of non-limiting example, one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. The memory 1220 may be of any type suitable for the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory, and removable memory.

[0070] Figure 13FIG. 1 is a block diagram illustrating a data analysis system for an e-commerce platform according to another embodiment of the present disclosure. Figure 13 As shown, the data analysis system 1300 includes a group purchase user screening module 1310, a group purchase user number statistics module 1320, a pre-purchased product analysis module 1330, a purchased product analysis module 1340, and a database for the e-commerce platform 1380. The data analysis system 1300 may also optionally include a user statistics module 1350 for failed purchases, a user account history data acquisition module 1360, and a compensation value analysis module 1370. The group purchase user screening module 1310 is configured to obtain the account information of each user who participated in the group purchase for each pre-purchased product on the e-commerce platform within the registration period, and to screen each qualified group purchase user for each pre-purchased product on the e-commerce platform within the registration period, as described above with respect to step 102. The group purchase user statistics module 1320 is configured to count the number of qualified group purchase users corresponding to each pre-purchased product on the e-commerce platform in real time, and determine operational instructions for product pre-purchase based on the real-time count of qualified group purchase users, as described above with respect to step 104. The pre-purchased product analysis module 1330 is configured to obtain the prepayment payment status of each qualified group purchase user corresponding to each pre-purchased product on the e-commerce platform, and determine operational instructions for pre-purchased products based on the prepayment payment status of each qualified group purchase user, as described above with respect to step 106. The purchased product analysis module 1340 is configured to count the number of successful purchasers corresponding to each pre-purchased product on the e-commerce platform, analyze the remaining balance payment status of each successful purchaser corresponding to each pre-purchased product on the e-commerce platform, and determine operational instructions for handling the purchased product based on the remaining balance payment status of each successful purchaser, as described above with respect to step 108. The failed purchase user statistics module 1350 is configured to count the failed purchase users corresponding to each purchased item on the e-commerce platform and determine the prepayment refund instructions, as described above with respect to step 110. The user account history data acquisition module 1360 is configured to acquire the account history data of each failed purchase user corresponding to each purchased item on the e-commerce platform, as described above with respect to step 112. The compensation value analysis module 1370 is configured to determine the compensation value for each failed purchase user corresponding to each purchased item on the e-commerce platform based on the acquired account history data, as described above with respect to step 114.

[0071] Optionally, the pre-snatched product analysis module 1330 includes a pre-snatched product purchase quantity acquisition unit, which is configured to acquire the purchase quantity of each pre-snatched product in the e-commerce platform and set the corresponding purchase quantity for each pre-snatched product in the e-commerce platform. For example, the number of eligible users participating in the group purchase corresponding to each pre-snatched product in the e-commerce platform can be marked as x. j , where j = 1, 2, ..., m. The pre-snatched goods purchase quantity acquisition unit can acquire the purchase quantity of each pre-snatched goods in the e-commerce platform by performing the following operations: extracting the number of users participating in the group purchase corresponding to a preset single pre-snatched goods x 单 , determine the rush purchase quantity Y of each pre-snatched product in the e-commerce platform j , where the analysis formula for the rush purchase quantity of each pre-snatched product in the e-commerce platform is: Indicates the minimum purchase quantity.

[0072] Optionally, the purchased goods analysis module 1340 also includes a remaining balance payment time recording unit, which is configured to record the remaining balance payment time of each successful purchase user corresponding to each purchased goods in the e-commerce platform, and compare the remaining balance payment time of each successful purchase user corresponding to each purchased goods in the e-commerce platform with a set time threshold. If the remaining balance payment time of one or more successful purchase users corresponding to a purchased goods in the e-commerce platform is less than or equal to the set time threshold, then determine the operation instruction for shipping the corresponding purchased goods to the one or more successful purchase users corresponding to the purchased goods. If the remaining balance payment time of one or more successful purchase users corresponding to a purchased goods in the e-commerce platform is greater than the set time threshold, then mark the one or more successful purchase users corresponding to the purchased goods as default users, and send the account ID corresponding to the default user to the database. The above modules or units can be implemented by hardware, software, or a combination of both.

[0073] Based on the above description, at least one aspect of the present disclosure provides a computer-readable storage medium. Program instructions are stored on the computer-readable storage medium. When the program instructions are executed by at least one processor, the at least one processor executes the above reference Figures 1A to 11 Described method.

[0074] References in this disclosure to "one embodiment," "an embodiment," etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment must include the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. In addition, when a particular feature, structure, or characteristic is described in conjunction with one embodiment, it is within the knowledge of those skilled in the art to implement such feature, structure, or characteristic in conjunction with other embodiments, whether or not explicitly described. It should be noted that two boxes (or steps) shown in succession in the accompanying drawings can actually be executed substantially in parallel, or the boxes (or steps) can sometimes be executed in the reverse order, depending on the functionality involved.

[0075] It should be understood that although the terms "first", "second" etc. can be used in this article to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, the first element can be referred to as the second element, and similarly, the second element can be referred to as the first element, without departing from the scope of this disclosure. In this disclosure, the term "and / or" includes any one and all combinations of one or more of the associated listed terms. It should also be understood that the terms "include", "have" and / or "comprise" when used in this article refer to the existence of stated features, elements and / or components, and do not exclude the existence or addition of one or more other features, elements, components and / or their combinations. The term "connection" used in this article covers the direct and / or indirect connection between two elements.

[0076] The present disclosure includes any novel feature or feature combination disclosed herein, either explicitly or in any generalized form thereof. Based on the above description, various modifications and adaptations to the above exemplary embodiments of the present disclosure will become apparent to those skilled in the art when read in conjunction with the accompanying drawings. However, any and all modifications and adaptations will still fall within the scope of the non-limiting and exemplary embodiments of the present disclosure.

Claims

1. A data analysis system for an e-commerce platform, comprising: at least one processor; as well as at least one memory storing a database and program instructions that, when executed by the at least one processor, cause the data analysis system to: Obtaining account information of each user who participated in the group purchase of each pre-purchased product on the e-commerce platform within the registration period, and screening each user who was qualified to participate in the group purchase of each pre-purchased product on the e-commerce platform within the registration period; real-time statistics on the number of qualified group-buying users corresponding to each pre-buyed product on the e-commerce platform, and determining operational instructions for the product pre-buyed product based on the real-time statistics on the number of qualified group-buying users; Obtaining the prepayment payment status of each qualified group purchase user corresponding to each pre-purchased product on the e-commerce platform, and determining an operation instruction for purchasing the pre-purchased product based on the prepayment payment status of each qualified group purchase user; as well as Count the users who successfully purchased the products on the e-commerce platform, analyze the remaining balance payment status of the users who successfully purchased the products on the e-commerce platform, and determine the operation instructions for processing the purchased products based on the remaining balance payment status of the users who successfully purchased the products.

2. The data analysis system according to claim 1, wherein: The program instructions, when executed by the at least one processor, cause the data analysis system to: Counting the number of users who failed to purchase the products purchased on the e-commerce platform, and determining an operation instruction for returning the prepayment; Obtaining account history data of each user who failed to purchase each purchased product on the e-commerce platform; as well as Based on the acquired account history data of each user who failed in the rush purchase, a compensation value for each user who failed in the rush purchase corresponding to each purchased product in the e-commerce platform is determined.

3. The data analysis system according to claim 1, wherein: When the program instructions are executed by the at least one processor, the data analysis system is caused to screen each user who is eligible to participate in the group purchase by performing the following operations: Extracting the account ID of each user who participated in the group purchase of each pre-purchased product on the e-commerce platform within the registration period; Extracting the account ID corresponding to each defaulting user stored in the database, and screening users who are eligible to participate in the group purchase of each pre-purchased product on the e-commerce platform within the registration period; The preset start time of each pre-sale product in the e-commerce platform is extracted, and the users who are eligible to participate in the group purchase corresponding to multiple pre-sale products that participate in the same preset start time are removed, so as to obtain the users who are eligible to participate in the group purchase of each pre-sale product in the e-commerce platform within the registration period.

4. The data analysis system according to claim 1, wherein: When executed by the at least one processor, the program instructions cause the data analysis system to determine an operation instruction for a product rush purchase based on the real-time statistics of the number of users who are eligible to participate in the group rush purchase by performing the following operations: comparing the number of users who are eligible to participate in the group purchase of each pre-purchased product on the e-commerce platform during the registration period with a preset maximum user number threshold; if the number of users who are eligible to participate in the group purchase of a pre-purchased product during the registration period is greater than or equal to the preset maximum user number threshold, determining an operation instruction for opening the pre-purchased product in advance; if the number of users who are eligible to participate in the group purchase of the pre-purchased product during the registration period is less than the preset maximum user number threshold, counting the number of users who are eligible to participate in the group purchase of the pre-purchased product at the end of the registration period; The number of users who are qualified to participate in the group purchase of the pre-purchase product at the deadline of the registration time is compared with the minimum preset user number threshold of the corresponding pre-purchase product. If the number of users who are qualified to participate in the group purchase of the pre-purchase product at the deadline of the registration time is less than the minimum preset user number threshold of the corresponding pre-purchase product, an operation instruction for canceling the purchase of the pre-purchase product is determined. If the number of users who are qualified to participate in the group purchase of the pre-purchase product at the deadline of the registration time is greater than or equal to the minimum preset user number threshold of the corresponding pre-purchase product, an operation instruction for the purchase of the pre-purchase product is determined.

5. The data analysis system according to claim 1, wherein: The prepayment payment status includes a prepayment paid status and a prepayment unpaid status, and wherein the program instructions, when executed by the at least one processor, cause the data analysis system to determine an operation instruction for purchasing the pre-purchased goods based on the prepayment payment status of each qualified group purchase user by performing the following operations: If the prepayment status of each eligible group purchase user corresponding to a pre-purchased product on the e-commerce platform is all paid, determining an operation instruction for purchasing the pre-purchased product; If the prepayment payment status of one or more qualified group purchase users corresponding to a pre-purchase product in the e-commerce platform is unpaid, then determine an operation instruction for notifying the one or more qualified group purchase users to pay the prepayment of the corresponding pre-purchase product within a specified time, and an operation instruction for purchasing the pre-purchase product after the specified time arrives. The data analysis system according to claim 1 , wherein: The program instructions, when executed by the at least one processor, cause the data analysis system to: Obtaining the pre-order quantity of each pre-ordered product on the e-commerce platform; and The corresponding rush purchase quantity is set for each pre-rush purchase commodity in the e-commerce platform.

7. The data analysis system according to claim 6, wherein: The number of eligible group purchase users corresponding to each pre-purchased product on the e-commerce platform is marked as x j , wherein j=1, 2, ..., m, and wherein, when the program instructions are executed by the at least one processor, the data analysis system obtains the rush purchase quantity of each pre-sale product in the e-commerce platform by performing the following operations: Extract the number of users participating in the group purchase corresponding to the preset single pre-purchase product x 单 , determine the rush purchase quantity Y of each pre-snatched product in the e-commerce platform j , where the analysis formula for the rush purchase quantity of each pre-snatched product in the e-commerce platform is: Indicates the minimum purchase quantity.

8. The data analysis system according to claim 1, wherein: When executed by the at least one processor, the program instructions enable the data analysis system to analyze the remaining balance payment status of each successful purchase user and determine an operation instruction for processing the purchased goods by performing the following operations: Extract the sales amount of each purchased commodity on the e-commerce platform, wherein the sales amount of each purchased commodity on the e-commerce platform is marked as R j , where j = 1, 2, ..., m; Get the prepayment amount corresponding to each purchased product on the e-commerce platform, wherein the prepayment amount corresponding to each purchased product on the e-commerce platform is marked as R j '; Determine the remaining balance ΔR for each purchased product on the e-commerce platform j , where the analysis formula for the remaining balance of each purchased product on the e-commerce platform is ΔR j =R j -R j '; If the remaining balance of a purchased product on the e-commerce platform is equal to zero, indicating that the remaining balance payment status of each successful purchaser of the purchased product on the e-commerce platform is not in a payment-required state, determining an operation instruction for sequentially shipping the corresponding purchased product to each successful purchaser of the purchased product; If the remaining balance amount of a purchased product in the e-commerce platform is greater than zero, it indicates that the remaining balance payment status of each successful purchaser corresponding to the purchased product in the e-commerce platform is in a payment-required status, then an operation instruction is determined for notifying each successful purchaser corresponding to the purchased product in turn to pay the remaining balance amount of the corresponding purchased product.

9. The data analysis system according to claim 1, wherein: The program instructions, when executed by the at least one processor, cause the data analysis system to: Record the remaining balance payment time of each successful purchaser corresponding to each purchased product on the e-commerce platform; The remaining balance payment time of each successful purchaser corresponding to each purchased product on the e-commerce platform is compared with a set time threshold. If the remaining balance payment time of one or more successful purchasers corresponding to a purchased product on the e-commerce platform is less than or equal to the set time threshold, an operation instruction for shipping the corresponding purchased product to the one or more successful purchasers corresponding to the purchased product is determined. If the remaining balance payment time of one or more successful purchasers corresponding to a purchased product on the e-commerce platform is greater than the set time threshold, the one or more successful purchasers corresponding to the purchased product are marked as default users, and the account ID corresponding to the default user is sent to the database.

10. The data analysis system according to claim 2, wherein: When executed by the at least one processor, the program instructions cause the data analysis system to determine an operation instruction regarding the return of the advance payment by performing the following operations: Obtaining the account number of each user who failed to purchase each purchased product on the e-commerce platform; Extract the prepaid amount corresponding to each purchased product in the e-commerce platform, and determine the operation instructions for returning the prepaid amount of each purchased product to the account wallet of each corresponding user who failed to purchase the product.

11. The data analysis system according to claim 2, wherein: When executed by the at least one processor, the program instructions cause the data analysis system to obtain the account history data of each user who failed in the rush purchase by performing the following operations: Obtain the number of times each user who failed to purchase a product in the e-commerce platform participated in the rush purchase in the account history data corresponding to each product that has been rushed to purchase, wherein the number of times each user who failed to purchase a product in the e-commerce platform participated in the rush purchase in the account history data corresponding to each product that has been rushed to purchase is marked as w j a v , where v = 1, 2, ..., u; Obtain the target number of failed purchases in the account history data of each user who failed to purchase each purchased product on the e-commerce platform, where the target number of failed purchases is the number of times the user participated in the purchase after the most recent successful purchase. Mark the target number of failed purchases in the account history data of each user who failed to purchase each purchased product on the e-commerce platform as w′ j a v ′.

12. The data analysis system according to claim 2, wherein: When executed by the at least one processor, the program instructions cause the data analysis system to determine the compensation value of each user who failed to purchase each purchased product by performing the following operations: Extract the total number of rush buying activities held in the e-commerce platform, wherein the total number of rush buying activities held in the e-commerce platform is marked as W 总 ; The number of times w that each user who failed to purchase the product in the e-commerce platform participated in the purchase is calculated. j a v , target purchase failure times w′ j a v Substitute into the compensation value analysis formula Get the compensation value R of each failed purchase user corresponding to each purchased product in the e-commerce platform j ″a v , where α represents a fixed percentage and is a positive value, R j ′ represents the prepaid amount corresponding to each purchased product on the e-commerce platform, w′ 预 Indicates the preset purchase failure threshold.

13. A method performed by a data analysis system for an e-commerce platform, comprising: Obtaining account information of each user who participated in the group purchase of each pre-purchased product on the e-commerce platform within the registration period, and screening each user who was qualified to participate in the group purchase of each pre-purchased product on the e-commerce platform within the registration period; real-time statistics on the number of qualified group-buying users corresponding to each pre-buyed product on the e-commerce platform, and determining operational instructions for the product pre-buyed product based on the real-time statistics on the number of qualified group-buying users; Obtaining the prepayment payment status of each qualified group purchase user corresponding to each pre-purchased product on the e-commerce platform, and determining an operation instruction for purchasing the pre-purchased product based on the prepayment payment status of each qualified group purchase user; as well as Count the users who successfully purchased the products on the e-commerce platform, analyze the remaining balance payment status of the users who successfully purchased the products on the e-commerce platform, and determine the operation instructions for processing the purchased products based on the remaining balance payment status of the users who successfully purchased the products.

14. A computer-readable storage medium having program instructions stored thereon, the program instructions, when executed by at least one processor, causing the at least one processor to perform the method according to claim 13.

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