Random acquisition-based e-commerce platform sales scheme generation method and system

Abstract

The invention discloses an e-commerce platform sales scheme generation method and system based on random acquisition, and relates to the technical field of e-commerce sales, and the method comprises the steps: generating a first data identifier and a first strategy parameter through a merchant client by employing a random parameter generation algorithm, and generating a data acquisition instruction based on the first data identifier and the first strategy parameter, and transmitting the data acquisition instruction to an authentication server; the authentication server verifies the instruction and then sends the instruction to the data acquisition server; the data acquisition server randomly acquires product sales data from a plurality of e-commerce platforms according to the instruction, determines a second data identifier and a second strategy parameter, encrypts the second data identifier and the second strategy parameter, and sends the second data identifier and the second strategy parameter to the merchant client; the merchant client generates a plurality of sales schemes in combination with the first and second data identifiers and the strategy parameters, and supports personalized adjustments, the invention further comprises a system, a computer device and a computer readable storage medium for implementing the method, and the technical scheme randomly collects and analyzes the e-commerce platform data, combines the personalized needs of merchants, and improves the sales performance of the e-commerce platform. An optimized and compliant sales scheme is generated, and the e-commerce sales efficiency and effect are improved.

Description

Technical Field

[0001] The present invention relates to the field of e-commerce sales technology, and more specifically, to a method and system for generating e-commerce platform sales plans based on random sampling. Background Art

[0002] With the rapid development of e-commerce platforms, merchants are facing an increasingly complex market environment when formulating sales strategies. Traditional sales plan generation methods usually rely on fixed rules or historical data, which are difficult to adapt to rapidly changing market demands. In existing technologies, merchants often generate sales plans through manual analysis or simple algorithms. This approach has the following problems: First, the scope of data collection is limited, usually relying only on a single platform or fixed data source, and cannot fully cover market dynamics; second, the data processing method lacks flexibility, and it is impossible to dynamically adjust strategies according to real-time market changes; third, data security and privacy protection are insufficient, especially in the process of cross-platform data collection and transmission, which can easily lead to data leakage or tampering. In addition, the sales plans generated by existing methods often lack personalized adjustment capabilities, making it difficult to meet the diverse needs of merchants in different market environments.

[0003] Existing technologies have the following problems in the process of data collection, processing and solution generation: limited data collection scope, lack of flexibility in processing methods, insufficient data security and privacy protection, and lack of personalized adjustment capabilities in solution generation. Summary of the Invention

[0004] In order to overcome the problems of limited data collection scope, lack of flexibility in processing methods, insufficient data security and privacy protection, and lack of personalized adjustment capabilities in solution generation in the existing technology, the present invention discloses an e-commerce platform sales solution generation method and system based on random collection, which can effectively solve the above technical problems.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows: The e-commerce platform sales plan generation method based on random collection is applied to the e-commerce intelligent service system, including the following steps: The merchant client in the e-commerce intelligent service system uses a random parameter generation algorithm to generate a first data identifier and a first strategy parameter; The merchant client generates a data collection instruction based on the first data identifier and the first policy parameter, and sends the data collection instruction to the authentication server; The authentication server verifies the data collection instruction, and if the verification is successful, sends the data collection instruction to the data collection server; The data collection server randomly collects product sales data from multiple e-commerce platforms based on the data collection instruction, determines a second data identifier and a second policy parameter, and sends the second data identifier and the encrypted second policy parameter to the merchant client; The merchant client determines a comprehensive data identifier based on the first data identifier and the second data identifier, and determines a comprehensive policy parameter based on the first policy parameter and the encrypted second policy parameter; A plurality of product sales plans are generated according to the comprehensive data identifier and the comprehensive strategy parameters. The merchant client supports the merchant to make personalized adjustments to the generated sales plans and stores the adjusted plans in a plan library.

[0006] Preferably, the merchant client in the e-commerce intelligent service system generates the first data identifier and the first strategy parameter using a random parameter generation algorithm, including: The merchant client generates a random seed value based on product category and market positioning information; Based on the random seed value, the first data identifier and the first strategy parameter are calculated by a preset random parameter generation algorithm, wherein the first data identifier includes product keyword features and target user portrait features, and the first strategy parameter includes basic pricing rules and a promotion activity framework.

[0007] Preferably, the merchant client generates a data collection instruction based on the first data identifier and the first policy parameter, and sends the data collection instruction to the authentication server, including: The merchant client digitally signs the merchant account identifier and the first data identifier using the first policy parameter to obtain first signature information; The merchant client encrypts the merchant account identifier and the first data identifier using the public key of the authentication server to generate first encrypted data; The merchant client generates the data collection instruction based on the first signature information and the first encrypted data, and sends it to the authentication server.

[0008] Preferably, the authentication server verifies the data collection instruction, and if the verification is successful, sends the data collection instruction to the data collection server, including: The authentication server decrypts the first encrypted data using the private key of the authentication server to obtain the merchant account identifier and the first data identifier; The authentication server verifies the first signature information using a hash algorithm using the first data identifier and the merchant account identifier; If the verification is successful, the authentication server sends the merchant account identifier and the first data identifier to the data collection server.

[0009] Preferably, the data collection server randomly collects product sales data from multiple e-commerce platforms based on the data collection instruction, determines the second data identifier and the second policy parameter, and sends the second data identifier and the encrypted second policy parameter to the merchant client, including: The data collection server randomly selects similar products from multiple e-commerce platforms based on the merchant account identifier and the first data identifier; Collecting images, price ranges, sales volumes, user reviews, and promotional activity data for the selected product, and analyzing and extracting the second data identifier and the second strategy parameter; The second policy parameter is encrypted using the second data identifier, and the second data identifier and the encrypted second policy parameter are sent to the merchant client.

[0010] Preferably, before determining the comprehensive policy parameter based on the first policy parameter and the encrypted second policy parameter, the method includes: The merchant client decrypts the encrypted second policy parameter using the second data identifier to obtain the second policy parameter.

[0011] Preferably, it also includes: The plan review server verifies the generated multiple product sales plans. If the verification passes, the sales plan is pushed to the merchant client; if the verification fails, it returns to regenerate the sales plan; The scheme review server verifies the generated multiple product sales schemes including: The plan review server uses the comprehensive data identifier and preset compliance rules to perform a legality check on the price setting and promotional activity content in the sales plan; The solution review server uses historical sales data and market trend models to evaluate the expected effects of the sales solution.

[0012] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described above when executing the computer program.

[0013] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0014] The e-commerce platform sales plan generation system based on random collection includes: The merchant client is configured to generate a first data identifier and a first strategy parameter using a random parameter generation algorithm, generate a data collection instruction based on the first data identifier and the first strategy parameter, receive and generate a sales plan based on the data, and support personalized adjustments; The authentication server is used to verify the data collection instructions sent by the merchant client. If the verification is successful, the instructions are forwarded to the data collection server; A data collection server, configured to randomly collect product sales data from multiple e-commerce platforms based on the data collection instruction, determine a second data identifier and a second policy parameter, and send the data to the merchant client; The plan review server is used to verify the generated product sales plan and push the verified plan to the merchant client.

[0015] Compared with the prior art, the present invention has the following advantages: a data collection server randomly collects product sales data from multiple e-commerce platforms, and combines it with random parameters generated by merchant clients to ensure the diversity and breadth of data sources, thereby breaking through the bottleneck of limited data collection scope in traditional methods. Random collection can cover market dynamics of different platforms and provide comprehensive data support for sales plan generation; the merchant client dynamically generates data identifiers and policy parameters based on a random parameter generation algorithm, and generates comprehensive data identifiers and comprehensive policy parameters based on the second data identifiers and policy parameters returned by the data collection server. This dynamic generation enables the sales plan to be flexibly adjusted according to real-time market changes, avoiding the limitations of fixed rules in traditional methods; the security of data collection instructions and policy parameters is ensured through digital signatures, encrypted transmission, and verification mechanisms of the authentication server. The data collection server encrypts the second policy parameters when returning them, and the merchant client obtains the data through decryption, thereby ensuring the privacy and integrity of the data and effectively preventing data leakage and tampering; the merchant client supports personalized adjustment of the generated sales plan and stores the adjusted plan in a plan library, which not only meets the diverse needs of merchants in different market environments, but also provides an optimization basis for subsequent plan generation through the accumulation of the plan library, thereby improving the practicality and adaptability of the plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the implementation methods or the description of the prior art. Obviously, the drawings described below are merely exemplary. For ordinary technicians in this field, other implementation drawings can be derived based on the provided drawings without any creative work.

[0017] Figure 1 It is a step diagram of the method of the present invention; Figure 2This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0018] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent; In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size; It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0019] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments. Example 1

[0020] The e-commerce platform sales plan generation method based on random collection is applied to the e-commerce intelligent service system, including the following steps: The merchant client in the e-commerce intelligent service system uses a random parameter generation algorithm to generate a first data identifier and a first strategy parameter; The merchant client generates a data collection instruction based on the first data identifier and the first policy parameter, and sends the data collection instruction to the authentication server; The authentication server verifies the data collection instruction, and if the verification is successful, sends the data collection instruction to the data collection server; The data collection server randomly collects product sales data from multiple e-commerce platforms based on the data collection instruction, determines a second data identifier and a second policy parameter, and sends the second data identifier and the encrypted second policy parameter to the merchant client; The merchant client determines a comprehensive data identifier based on the first data identifier and the second data identifier, and determines a comprehensive policy parameter based on the first policy parameter and the encrypted second policy parameter; A plurality of product sales plans are generated according to the comprehensive data identifier and the comprehensive strategy parameters. The merchant client supports the merchant to make personalized adjustments to the generated sales plans and stores the adjusted plans in a plan library.

[0021] The merchant client in the e-commerce intelligent service system uses a random parameter generation algorithm to generate a first data identifier and a first strategy parameter, including: The merchant client generates a random seed value based on product category and market positioning information; Based on the random seed value, the first data identifier and the first strategy parameter are calculated by a preset random parameter generation algorithm, wherein the first data identifier includes product keyword features and target user portrait features, and the first strategy parameter includes basic pricing rules and a promotion activity framework.

[0022] The merchant client generates a data collection instruction based on the first data identifier and the first policy parameter, and sends the data collection instruction to the authentication server, including: The merchant client digitally signs the merchant account identifier and the first data identifier using the first policy parameter to obtain first signature information; The merchant client encrypts the merchant account identifier and the first data identifier using the public key of the authentication server to generate first encrypted data; The merchant client generates the data collection instruction based on the first signature information and the first encrypted data, and sends it to the authentication server.

[0023] The authentication server verifies the data collection instruction, and if the verification is successful, sends the data collection instruction to the data collection server, including: The authentication server decrypts the first encrypted data using the private key of the authentication server to obtain the merchant account identifier and the first data identifier; The authentication server verifies the first signature information using a hash algorithm using the first data identifier and the merchant account identifier; If the verification is successful, the authentication server sends the merchant account identifier and the first data identifier to the data collection server.

[0024] The data collection server randomly collects product sales data from multiple e-commerce platforms based on the data collection instruction, determines a second data identifier and a second policy parameter, and sends the second data identifier and the encrypted second policy parameter to the merchant client, including: The data collection server randomly selects similar products from multiple e-commerce platforms based on the merchant account identifier and the first data identifier; Collecting images, price ranges, sales volumes, user reviews, and promotional activity data for the selected product, and analyzing and extracting the second data identifier and the second strategy parameter; The second policy parameter is encrypted using the second data identifier, and the second data identifier and the encrypted second policy parameter are sent to the merchant client.

[0025] Before determining the comprehensive policy parameter based on the first policy parameter and the encrypted second policy parameter, the method includes: The merchant client decrypts the encrypted second policy parameter using the second data identifier to obtain the second policy parameter.

[0026] Also includes: The plan review server verifies the generated multiple product sales plans. If the verification passes, the sales plan is pushed to the merchant client; if the verification fails, it returns to regenerate the sales plan; The scheme review server verifies the generated multiple product sales schemes including: The plan review server uses the comprehensive data identifier and preset compliance rules to perform a legality check on the price setting and promotional activity content in the sales plan; The solution review server uses historical sales data and market trend models to evaluate the expected effects of the sales solution.

[0027] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described above when executing the computer program.

[0028] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0029] In the specific implementation, please refer to Figure 1 , let's assume that an e-commerce merchant A, which mainly sells fashionable women's clothing, is developing a sales plan for the upcoming new summer dresses. The merchant client generates a random seed value based on the product category "Fashion Women's Wear - Dresses" and market positioning information, targeting young women aged 18-35, focusing on the mid-to-high-end market, emphasizing design and quality. Based on the random seed value, the merchant client calculates the first data identifier through a preset random parameter generation algorithm, which includes product keyword features, such as French retro dress, slim fit, chiffon material, and target user portrait features, such as young women who pay attention to fashion trends and have a certain consumption capacity; at the same time, the first strategy parameters are obtained, including basic pricing rules, such as a suggested retail price in the range of 300-800 yuan, a promotional activity framework, such as 50 yuan off for purchases over 300 yuan, and matching sales discounts.

[0030] The merchant client uses the first policy parameter to digitally sign the merchant account identifier (merchant A's unique identification code SHOP_A_001) and the first data identifier to obtain the first signature information; then uses the public key of the authentication server to encrypt the merchant account identifier and the first data identifier to generate the first encrypted data; finally, based on the first signature information and the first encrypted data, a data collection instruction is generated and sent to the authentication server.

[0031] The authentication server uses its own private key to decrypt the first encrypted data to obtain the merchant account identifier and the first data identifier; then uses the first data identifier and the merchant account identifier to verify the first signature information using a hash algorithm. Since the verification is successful, the authentication server sends the merchant account identifier and the first data identifier to the data collection server.

[0032] Based on the merchant account identifier and the first data identifier, the data collection server randomly selects similar fashionable women's dress products on multiple e-commerce platforms such as Taobao, JD.com, and Pinduoduo, collects the selected products' pictures, price range (150-1200 yuan), sales quantity, user reviews, and promotional activity data, such as limited-time discounts and giveaways, and analyzes and extracts the second data identifier, such as summer hot-selling dresses, Instagram-style designs, and the second strategy parameters, such as 30% off for a limited time and buy two get one free. The second strategy parameters are encrypted using the second data identifier and the second data identifier and the encrypted second strategy parameters are sent to the merchant client.

[0033] The merchant client uses the second data identifier to decrypt the encrypted second policy parameter to obtain the second policy parameter; then determines the comprehensive data identifier based on the first data identifier and the second data identifier, and determines the comprehensive policy parameter based on the first policy parameter and the second policy parameter.

[0034] Multiple product sales plans are generated based on comprehensive data identifiers and comprehensive strategy parameters. For example, Plan 1: Price the new dress at 599 yuan and use the "80 yuan off for purchases over 500 yuan" promotion; Plan 2: Price the dress at 699 yuan and launch a "buy one get one free (same style)" promotion. Merchant A can personalize the generated sales plans in the merchant client, such as changing the promotion of Plan 1 to "100 yuan off for purchases over 500 yuan" and storing the adjusted plans in the plan library.

[0035] The plan review server verifies the multiple product sales plans generated. First, it uses comprehensive data identification and preset compliance rules to check the legality of the price settings and promotional content in the sales plan to confirm that there are no violations such as price fraud; then it uses historical sales data and market trend models to evaluate the expected effects of the sales plan and judge the feasibility and potential benefits of the plan. If the verification passes, the sales plan will be pushed to the merchant client; if the verification fails, it will return to regenerate the sales plan. Example 2

[0036] The e-commerce platform sales plan generation system based on random collection includes: The merchant client is configured to generate a first data identifier and a first strategy parameter using a random parameter generation algorithm, generate a data collection instruction based on the first data identifier and the first strategy parameter, receive and generate a sales plan based on the data, and support personalized adjustments; The authentication server is used to verify the data collection instructions sent by the merchant client. If the verification is successful, the instructions are forwarded to the data collection server; A data collection server, configured to randomly collect product sales data from multiple e-commerce platforms based on the data collection instruction, determine a second data identifier and a second policy parameter, and send the data to the merchant client; The plan review server is used to verify the generated product sales plan and push the verified plan to the merchant client.

[0037] See also Figure 2 The merchant client provides an operation interface and functional modules for merchants. In the case of the above-mentioned fashion women's clothing merchant A, merchant A logs in to the merchant client. The client automatically uses a random parameter generation algorithm to generate a first data identifier and a first strategy parameter based on the product category and market positioning information input by the merchant A; based on these parameters, it generates and sends a data collection instruction, and subsequently receives the data returned by the data collection server, generates a sales plan based on the data, and supports merchant A to make personalized adjustments to the plan, such as modifying prices, promotion forms, etc.

[0038] The authentication server is responsible for verifying the data collection instructions sent by the merchant client. After receiving the data collection instructions sent by the merchant client, it uses the private key to decrypt the first encrypted data, obtains the merchant account identifier and the first data identifier, and uses the hash algorithm to verify the first signature information. After the verification is passed, the merchant account identifier and the first data identifier are forwarded to the data collection server.

[0039] The data collection server randomly collects similar product data on multiple e-commerce platforms, such as Taobao, JD.com, and Suning.com, based on the merchant account identifier and the first data identifier forwarded by the authentication server. After the collection is completed, the second data identifier and the second policy parameter are analyzed and extracted, and then encrypted and sent to the merchant client.

[0040] The plan review server verifies the product sales plan generated by the merchant client, and uses comprehensive data identification and preset compliance rules to check the legality of the sales plan; it uses historical sales data and market trend models to evaluate the expected effect of the plan. If the verification passes, the sales plan will be pushed to the merchant client; if it fails, the merchant client will be notified to regenerate the plan. Through the collaborative work of these components, the complete process of generating sales plans for e-commerce platforms based on random collection is realized.

[0041] The same or similar reference numerals correspond to the same or similar components; The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent; Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for generating sales plans for an e-commerce platform based on random collection, characterized in that: Applied to e-commerce intelligent service system, including the following steps: The merchant client in the e-commerce intelligent service system uses a random parameter generation algorithm to generate a first data identifier and a first strategy parameter; The merchant client generates a data collection instruction based on the first data identifier and the first policy parameter, and sends the data collection instruction to the authentication server; The authentication server verifies the data collection instruction, and if the verification is successful, sends the data collection instruction to the data collection server; The data collection server randomly collects product sales data from multiple e-commerce platforms based on the data collection instruction, determines a second data identifier and a second policy parameter, and sends the second data identifier and the encrypted second policy parameter to the merchant client; The merchant client determines a comprehensive data identifier based on the first data identifier and the second data identifier, and determines a comprehensive policy parameter based on the first policy parameter and the encrypted second policy parameter; A plurality of product sales plans are generated according to the comprehensive data identifier and the comprehensive strategy parameters. The merchant client supports the merchant to make personalized adjustments to the generated sales plans and stores the adjusted plans in a plan library.

2. The method according to claim 1, characterized in that The merchant client in the e-commerce intelligent service system uses a random parameter generation algorithm to generate a first data identifier and a first strategy parameter, including: The merchant client generates a random seed value based on product category and market positioning information; Based on the random seed value, the first data identifier and the first strategy parameter are calculated by a preset random parameter generation algorithm, wherein the first data identifier includes product keyword features and target user portrait features, and the first strategy parameter includes basic pricing rules and a promotion activity framework.

3. The method according to claim 1, characterized in that The merchant client generates a data collection instruction based on the first data identifier and the first policy parameter, and sends the data collection instruction to the authentication server, including: The merchant client digitally signs the merchant account identifier and the first data identifier using the first policy parameter to obtain first signature information; The merchant client encrypts the merchant account identifier and the first data identifier using the public key of the authentication server to generate first encrypted data; The merchant client generates the data collection instruction based on the first signature information and the first encrypted data, and sends it to the authentication server.

4. The method according to claim 3, characterized in that The authentication server verifies the data collection instruction, and if the verification is successful, sends the data collection instruction to the data collection server, including: The authentication server decrypts the first encrypted data using the private key of the authentication server to obtain the merchant account identifier and the first data identifier; The authentication server verifies the first signature information using a hash algorithm using the first data identifier and the merchant account identifier; If the verification is successful, the authentication server sends the merchant account identifier and the first data identifier to the data collection server.

5. The method according to claim 4, characterized in that The data collection server randomly collects product sales data from multiple e-commerce platforms based on the data collection instruction, determines a second data identifier and a second policy parameter, and sends the second data identifier and the encrypted second policy parameter to the merchant client, including: The data collection server randomly selects similar products from multiple e-commerce platforms based on the merchant account identifier and the first data identifier; Collecting images, price ranges, sales volumes, user reviews, and promotional activity data for the selected product, and analyzing and extracting the second data identifier and the second strategy parameter; The second policy parameter is encrypted using the second data identifier, and the second data identifier and the encrypted second policy parameter are sent to the merchant client.

6. The method according to claim 1, characterized in that Before determining the comprehensive policy parameter based on the first policy parameter and the encrypted second policy parameter, the method includes: The merchant client decrypts the encrypted second policy parameter using the second data identifier to obtain the second policy parameter.

7. The method according to claim 1, characterized in that Also includes: The plan review server verifies the generated multiple product sales plans, and if the verification passes, pushes the sales plan to the merchant client; If the verification fails, return to regenerate the sales plan; The scheme review server verifies the generated multiple product sales schemes including: The plan review server uses the comprehensive data identifier and preset compliance rules to perform a legality check on the price setting and promotional activity content in the sales plan; The solution review server uses historical sales data and market trend models to evaluate the expected effects of the sales solution.

8. An e-commerce platform sales plan generation system based on random collection, which is implemented by the method described in any one of claims 1 to 7, characterized in that: include: The merchant client is configured to generate a first data identifier and a first strategy parameter using a random parameter generation algorithm, generate a data collection instruction based on the first data identifier and the first strategy parameter, receive and generate a sales plan based on the data, and support personalized adjustments; The authentication server is used to verify the data collection instructions sent by the merchant client. If the verification is successful, the instructions are forwarded to the data collection server; A data collection server, configured to randomly collect product sales data from multiple e-commerce platforms based on the data collection instruction, determine a second data identifier and a second policy parameter, and send the data to the merchant client; The plan review server is used to verify the generated product sales plan and push the verified plan to the merchant client.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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