E-commerce platform sales scheme generation method and system based on random collection

Abstract

The application discloses a random collection-based e-commerce platform sales scheme generation method and system, relates to the technical field of e-commerce sales, and generates a first data identifier and a first strategy parameter by using a random parameter generation algorithm through a merchant client, and generates a data collection instruction based on the same and sends the same to an authentication server; the authentication server sends the instruction to a data collection server after verification; the data collection server randomly collects product sales data from multiple e-commerce platforms according to the instruction, determines a second data identifier and a second strategy parameter, and sends the second data identifier and the second strategy parameter to the merchant client after encryption; the merchant client generates multiple sales schemes in combination with the first and second data identifiers and the strategy parameters, and supports individualized adjustment. The application also includes a system, a computer device and a computer readable storage medium for implementing the method. The technical scheme randomly collects and analyzes e-commerce platform data, combines individualized needs of merchants, generates optimized and compliant sales schemes, and improves e-commerce sales efficiency and effect.

Description

A Method and System for Generating Sales Plans for E-commerce Platforms Based on Random Data Collection Technical Field

[0001] This invention relates to the field of e-commerce sales technology, and more specifically, to a method and system for generating sales plans for e-commerce platforms based on random data collection. Background Technology

[0002] With the rapid development of e-commerce platforms, merchants face an increasingly complex market environment when formulating sales strategies. Traditional methods for generating sales plans often rely on fixed rules or historical data, making it 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 on a single platform or fixed data source, which cannot fully cover market dynamics. Second, the data processing methods lack flexibility and cannot dynamically adjust strategies according to real-time market changes. Third, data security and privacy protection are insufficient, especially during cross-platform data collection and transmission, which can easily lead to data leakage or tampering. In addition, 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 scope of data collection, 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 capability in scheme generation in existing technologies, this invention discloses a method and system for generating sales schemes for e-commerce platforms based on random data collection, which can effectively solve the above-mentioned technical problems.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A method for generating sales plans based on randomly collected data from e-commerce platforms, applied to an e-commerce intelligent service system, includes the following steps:

[0007] The merchant client in the e-commerce intelligent service system uses a random parameter generation algorithm to generate the first data identifier and the first strategy parameter;

[0008] 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;

[0009] The authentication server verifies the data collection command. If the verification is successful, the data collection command is sent to the data collection server.

[0010] Based on the data collection instruction, the data collection server randomly collects product sales data from multiple e-commerce platforms, determines a second data identifier and a second strategy parameter, and sends the second data identifier and the encrypted second strategy parameter to the merchant client.

[0011] The merchant client determines the comprehensive data identifier based on the first data identifier and the second data identifier, and determines the comprehensive strategy parameter based on the first strategy parameter and the encrypted second strategy parameter;

[0012] Multiple product sales plans are generated based on the comprehensive data identifier and comprehensive strategy parameters. The merchant client allows merchants to personalize the generated sales plans and store the adjusted plans in the plan library.

[0013] Preferably, the merchant client in the e-commerce intelligent service system generates the first data identifier and the first strategy parameters using a random parameter generation algorithm, including:

[0014] The merchant client generates a random seed value based on product category and market positioning information;

[0015] Based on the random seed value, the first data identifier and the first strategy parameter are calculated by a preset random parameter generation algorithm. The first data identifier includes product keyword features and target user profile features, and the first strategy parameter includes basic pricing rules and promotional activity framework.

[0016] 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:

[0017] The merchant client uses the first strategy parameter to digitally sign the merchant account identifier and the first data identifier to obtain the first signature information;

[0018] The merchant client 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;

[0019] 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.

[0020] Preferably, the authentication server verifies the data collection command, and if the verification is successful, sends the data collection command to the data collection server, including:

[0021] The authentication server uses its private key to decrypt the first encrypted data to obtain the merchant account identifier and the first data identifier.

[0022] The authentication server uses the first data identifier and the merchant account identifier to verify the first signature information using a hash algorithm;

[0023] If the verification is successful, the authentication server will send the merchant account identifier and the first data identifier to the data collection server.

[0024] Preferably, the data acquisition server, based on the data acquisition instruction, randomly collects product sales data from multiple e-commerce platforms, determines a second data identifier and a second strategy parameter, and sends the second data identifier and the encrypted second strategy parameter to the merchant client, including:

[0025] The data acquisition server randomly selects similar products from multiple e-commerce platforms based on the merchant account identifier and the first data identifier.

[0026] Collect image materials, price range, sales volume, user reviews and promotional activity data of the selected products, and analyze and extract the second data identifier and the second strategy parameters;

[0027] The second strategy parameter is encrypted using the second data identifier, and the second data identifier and the encrypted second strategy parameter are sent to the merchant client.

[0028] Preferably, before determining the comprehensive strategy parameters based on the first strategy parameters and the encrypted second strategy parameters, the following steps are included:

[0029] The merchant client uses the second data identifier to decrypt the encrypted second policy parameter to obtain the second policy parameter.

[0030] Preferably, it further includes:

[0031] The scheme review server verifies multiple product sales schemes generated. If the verification passes, the sales scheme is pushed to the merchant's client; if the verification fails, the server returns to regenerate the sales scheme.

[0032] The scheme review server verifies multiple generated product sales schemes, including:

[0033] The scheme review server uses the comprehensive data identifier and preset compliance rules to check the legality of the price settings and promotional activities in the sales scheme;

[0034] The proposed plan review server uses historical sales data and market trend models to evaluate the expected results of the sales plan.

[0035] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0036] A computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the method described above.

[0037] A sales plan generation system for e-commerce platforms based on random data collection includes:

[0038] The merchant client is used 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;

[0039] The authentication server is used to verify the data collection commands sent by the merchant's client. If the verification is successful, the command is forwarded to the data collection server.

[0040] The data acquisition server is used to randomly collect product sales data from multiple e-commerce platforms based on data acquisition instructions, determine the second data identifier and the second strategy parameters, and send them to the merchant client.

[0041] The solution review server is used to verify the generated product sales solutions and push the verified solutions to the merchant's client.

[0042] Compared with existing technologies, the beneficial effects of this invention are as follows: By randomly collecting product sales data from multiple e-commerce platforms through a data acquisition server and combining it with random parameters generated by the merchant client, the diversity and breadth of data sources are ensured, thus breaking through the bottleneck of limited data collection scope in traditional methods. Random collection can cover market dynamics on different platforms, providing comprehensive data support for sales plan generation. The merchant client dynamically generates data identifiers and strategy parameters based on a random parameter generation algorithm, and combines them with the second data identifier and strategy parameters returned by the data acquisition server to generate comprehensive data identifiers and comprehensive strategy parameters. This dynamic generation allows sales plans to be flexibly adjusted according to real-time market changes, avoiding the limitations of fixed rules in traditional methods. Through digital signatures, encrypted transmission, and authentication server verification mechanisms, the security of data acquisition instructions and strategy parameters is ensured. The data acquisition server encrypts the returned second strategy parameters, and the merchant client obtains the data by decryption, ensuring data privacy and integrity, and effectively preventing data leakage and tampering. The merchant client supports personalized adjustments to the generated sales plans and stores the adjusted plans in a plan library. This not only meets the diverse needs of merchants in different market environments but also provides an optimization foundation for subsequent plan generation through the accumulation of the plan library, improving the practicality and adaptability of the plans. Attached Figure Description

[0043] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other embodiments can be derived from the provided drawings without creative effort.

[0044] Figure 1 is a flowchart of the method steps of the present invention;

[0045] Figure 2 is a system structure diagram of the present invention. Detailed Implementation

[0046] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent.

[0047] To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions;

[0048] It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0049] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments. Embodiment 1

[0050] A method for generating sales plans based on randomly collected data from e-commerce platforms, applied to an e-commerce intelligent service system, includes the following steps:

[0051] The merchant client in the e-commerce intelligent service system uses a random parameter generation algorithm to generate the first data identifier and the first strategy parameter;

[0052] 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;

[0053] The authentication server verifies the data collection command. If the verification is successful, the data collection command is sent to the data collection server.

[0054] Based on the data collection instruction, the data collection server randomly collects product sales data from multiple e-commerce platforms, determines a second data identifier and a second strategy parameter, and sends the second data identifier and the encrypted second strategy parameter to the merchant client.

[0055] The merchant client determines the comprehensive data identifier based on the first data identifier and the second data identifier, and determines the comprehensive strategy parameter based on the first strategy parameter and the encrypted second strategy parameter;

[0056] Multiple product sales plans are generated based on the comprehensive data identifier and comprehensive strategy parameters. The merchant client allows merchants to personalize the generated sales plans and store the adjusted plans in the plan library.

[0057] The merchant client in the e-commerce intelligent service system uses a random parameter generation algorithm to generate the first data identifier and the first strategy parameters, including:

[0058] The merchant client generates a random seed value based on product category and market positioning information;

[0059] Based on the random seed value, the first data identifier and the first strategy parameter are calculated by a preset random parameter generation algorithm. The first data identifier includes product keyword features and target user profile features, and the first strategy parameter includes basic pricing rules and promotional activity framework.

[0060] 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:

[0061] The merchant client uses the first strategy parameter to digitally sign the merchant account identifier and the first data identifier to obtain the first signature information;

[0062] The merchant client 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;

[0063] 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.

[0064] The authentication server verifies the data collection command. If the verification is successful, sending the data collection command to the data collection server includes:

[0065] The authentication server uses its private key to decrypt the first encrypted data to obtain the merchant account identifier and the first data identifier.

[0066] The authentication server uses the first data identifier and the merchant account identifier to verify the first signature information using a hash algorithm;

[0067] If the verification is successful, the authentication server will send the merchant account identifier and the first data identifier to the data collection server.

[0068] The data acquisition server, based on the data acquisition instruction, randomly collects product sales data from multiple e-commerce platforms, determines a second data identifier and a second strategy parameter, and sends the second data identifier and the encrypted second strategy parameter to the merchant client, including:

[0069] The data acquisition server randomly selects similar products from multiple e-commerce platforms based on the merchant account identifier and the first data identifier.

[0070] Collect image materials, price range, sales volume, user reviews and promotional activity data of the selected products, and analyze and extract the second data identifier and the second strategy parameters;

[0071] The second strategy parameter is encrypted using the second data identifier, and the second data identifier and the encrypted second strategy parameter are sent to the merchant client.

[0072] Before determining the comprehensive strategy parameters based on the first strategy parameters and the encrypted second strategy parameters, the following steps are included:

[0073] The merchant client uses the second data identifier to decrypt the encrypted second policy parameter to obtain the second policy parameter.

[0074] Also includes:

[0075] The scheme review server verifies multiple product sales schemes generated. If the verification passes, the sales scheme is pushed to the merchant's client; if the verification fails, the server returns to regenerate the sales scheme.

[0076] The scheme review server verifies multiple generated product sales schemes, including:

[0077] The scheme review server uses the comprehensive data identifier and preset compliance rules to check the legality of the price settings and promotional activities in the sales scheme;

[0078] The proposed plan review server uses historical sales data and market trend models to evaluate the expected results of the sales plan.

[0079] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0080] A computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the method described above.

[0081] In practical implementation, please refer to Figure 1. Suppose that an e-commerce merchant A, which mainly sells fashionable women's clothing, is developing a sales plan for the upcoming summer new dresses.

[0082] The merchant client generates a random seed value based on the product category "Fashion Women's Clothing - 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 this random seed value, a first data identifier is calculated through a preset random parameter generation algorithm. This identifier includes product keyword characteristics, such as French retro dress, slimming and figure-flattering, chiffon material, and target user profile characteristics, such as young women who pay attention to fashion trends and have a certain level of purchasing power. At the same time, the first strategy parameters are derived, including basic pricing rules, such as a suggested retail price in the range of 300-800 yuan, and promotional activity frameworks, such as a discount of 50 yuan for purchases over 300 yuan, and bundled sales discounts.

[0083] The merchant client uses the first policy parameter to digitally sign the merchant account identifier (the unique identifier of merchant A, SHOP_A_001) and the first data identifier to obtain the first signature information; then, it 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, it generates a data collection instruction based on the first signature information and the first encrypted data and sends it to the authentication server.

[0084] 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, using the first data identifier and the merchant account identifier, it uses a hash algorithm to verify the first signature information. Since the verification is successful, the authentication server sends the merchant account identifier and the first data identifier to the data collection server.

[0085] The data acquisition server randomly selects similar fashionable women's dress products from multiple e-commerce platforms such as Taobao, JD.com, and Pinduoduo based on the merchant account identifier and the first data identifier. It collects image materials, price range (150-1200 yuan), sales volume, user reviews, and promotional activity data such as limited-time discounts and free gifts for the selected products. The server then analyzes and extracts the second data identifier, such as summer best-selling dresses, Instagram-style designs, and second strategy parameters, such as limited-time 70% off and buy-two-get-one-free offers. The second strategy parameters are encrypted using the second data identifier and then sent to the merchant's client.

[0086] The merchant client uses the second data identifier to decrypt the encrypted second strategy parameter to obtain the second strategy parameter; then, it determines the comprehensive data identifier based on the first data identifier and the second data identifier, and determines the comprehensive strategy parameter based on the first strategy parameter and the second strategy parameter.

[0087] Multiple product sales plans are generated based on comprehensive data identification and comprehensive strategy parameters. For example, Plan 1: Price the new dress at 599 yuan and use a "spend 500 yuan and get 80 yuan off" promotion; Plan 2: Price it at 699 yuan and carry out 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 "spend 500 yuan and get 100 yuan off", and store the adjusted plan in the plan library.

[0088] The solution review server verifies multiple generated product sales solutions. First, it uses comprehensive data identification and preset compliance rules to check the legality of price settings and promotional activities in the sales solutions, confirming the absence of price fraud or other violations. Then, it uses historical sales data and market trend models to evaluate the expected effects of the sales solutions, determining their feasibility and potential revenue. If the verification passes, the sales solution is pushed to the merchant's client; if the verification fails, the server returns to regenerate the sales solution. Example 2

[0089] A sales plan generation system for e-commerce platforms based on random data collection includes:

[0090] The merchant client is used 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;

[0091] The authentication server is used to verify the data collection commands sent by the merchant's client. If the verification is successful, the command is forwarded to the data collection server.

[0092] The data acquisition server is used to randomly collect product sales data from multiple e-commerce platforms based on data acquisition instructions, determine the second data identifier and the second strategy parameters, and send them to the merchant client.

[0093] The solution review server is used to verify the generated product sales solutions and push the verified solutions to the merchant's client.

[0094] Please refer to Figure 2. The merchant client provides merchants with an operation interface and functional modules. In the case of the fashion women's clothing merchant A mentioned above, merchant A logs in to the merchant client. Based on the product category and market positioning information entered by the merchant, the client automatically generates the first data identifier and the first strategy parameters using a random parameter generation algorithm. Based on these parameters, it generates and sends a data collection command. Subsequently, it 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 and promotional activity formats.

[0095] The authentication server is responsible for verifying the data collection instructions sent by the merchant client. After receiving the data collection instructions from 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 a hash algorithm to verify the first signature information. After the verification is successful, the merchant account identifier and the first data identifier are forwarded to the data collection server.

[0096] The data collection server randomly collects similar product data from 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 collection, it analyzes and extracts the second data identifier and the second strategy parameters, encrypts them, and sends them to the merchant's client.

[0097] The solution review server verifies the product sales plan generated by the merchant's client, using comprehensive data identification and preset compliance rules to check the legality of the sales plan; it also evaluates the expected effect of the plan using historical sales data and market trend models. If the verification passes, the sales plan is pushed to the merchant's client; if it fails, the merchant's client is notified to regenerate the plan. Through the collaborative work of these components, a complete process for generating sales plans based on randomly collected e-commerce platform data is achieved.

[0098] The same or similar labels correspond to the same or similar parts;

[0099] The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent.

[0100] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all implementation methods here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A method for generating sales plans on e-commerce platforms based on random data collection, characterized in that, An application in an e-commerce intelligent service system includes the following steps: A merchant client in the e-commerce intelligent service system generates a first data identifier and a first strategy parameter using a random parameter generation algorithm; the merchant client generates a data collection instruction based on the first data identifier and the first strategy parameter, and sends the data collection instruction to an authentication server; wherein, the first data identifier includes product keyword features and target user profile features; the authentication server verifies the data collection instruction, and if the verification is successful, sends the data collection instruction to a data collection server; the data collection server, based on the data collection instruction, randomly collects product sales data from multiple e-commerce platforms, determines a second data identifier and a second strategy parameter, and sends the second data identifier and the encrypted second strategy parameter to the merchant client; including: the data The data collection server randomly selects similar products from multiple e-commerce platforms based on the merchant account identifier and the first data identifier. It collects image materials, price ranges, sales volumes, user reviews, and promotional activity data for the selected products, and analyzes and extracts a second data identifier and a second strategy parameter. The second strategy parameter is encrypted using the second data identifier, and the second data identifier and the encrypted second strategy parameter are sent to the merchant client. The merchant client determines a comprehensive data identifier based on the first and second data identifiers, and determines comprehensive strategy parameters based on the first and encrypted second strategy parameters. Multiple product sales plans are generated based on the comprehensive data identifier and comprehensive strategy parameters. The merchant client allows merchants to personalize 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 generates a first data identifier and a first strategy parameter using a random parameter generation algorithm, including: the merchant client generating a random seed value based on product category and market positioning information; and calculating the first data identifier and the first strategy parameter based on the random seed value using a preset random parameter generation algorithm. The first data identifier includes product keyword features and target user profile features, and the first strategy parameter includes basic pricing rules and a promotional 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, comprising: the merchant client digitally signing the merchant account identifier and the first data identifier using the first policy parameter to obtain first signature information; the merchant client encrypting the merchant account identifier and the first data identifier using the public key of the authentication server to generate first encrypted data; and the merchant client generating the data collection instruction based on the first signature information and the first encrypted data, and sending it to the authentication server.

4. The method according to claim 3, characterized in that, The authentication server verifies the data collection command. If the verification is successful, sending the data collection command to the data collection server includes: the authentication server decrypting the first encrypted data using its private key to obtain the merchant account identifier and the first data identifier; the authentication server using the first data identifier and the merchant account identifier to verify the first signature information using a hash algorithm; if the verification passes, the authentication server sending 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 acquisition server, based on the data acquisition instruction, randomly collects product sales data from multiple e-commerce platforms, determines a second data identifier and a second strategy parameter, and sends the second data identifier and the encrypted second strategy parameter to the merchant client. This includes: the data acquisition server randomly selecting similar products from multiple e-commerce platforms based on the merchant account identifier and the first data identifier; collecting image materials, price ranges, sales quantities, user reviews, and promotional activity data of the selected products, and analyzing and extracting the second data identifier and the second strategy parameter; encrypting the second strategy parameter using the second data identifier, and sending the second data identifier and the encrypted second strategy parameter to the merchant client.

6. The method according to claim 1, characterized in that, Before determining the comprehensive strategy parameter based on the first strategy parameter and the encrypted second strategy parameter, the merchant client uses the second data identifier to decrypt the encrypted second strategy parameter to obtain the second strategy parameter.

7. The method according to claim 1, characterized in that, Also includes: The scheme review server verifies multiple product sales schemes generated. If the verification is successful, the sales scheme is pushed to the merchant's client. If the verification fails, return to regenerate the sales plan; The scheme review server verifies multiple product sales schemes generated, including: using the comprehensive data identifier and preset compliance rules to check the legality of price settings and promotional activities in the sales schemes; and using historical sales data and market trend models to evaluate the expected effects of the sales schemes.

8. A sales plan generation system for e-commerce platforms based on random data collection, used to implement the method described in any one of claims 1-7, characterized in that, include: The merchant client is used 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 commands sent by the merchant's client. If the verification is successful, the command is forwarded to the data collection server. The data acquisition server is used to randomly collect product sales data from multiple e-commerce platforms based on data acquisition instructions, determine the second data identifier and the second strategy parameters, and send them to the merchant client. The solution review server is used to verify the generated product sales solutions and push the verified solutions to the merchant's client.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-7.

Images