Automatic processing and tracking method, system and device for open platform capability subscription and storage medium

Abstract

The invention relates to an automatic processing and tracking method, system and device for open platform capability subscription and a storage medium. The automatic processing and tracking method comprises the following steps: analyzing preference settings in a capability subscription request submitted by a user side, and generating a personalized service resource list; performing permission verification on the user by adopting a multi-factor authentication mechanism to obtain a permission verification result and a request risk level; generating subscription detail confirmation information based on the permission verification result according to the subscription process and the request risk level; continuously tracking the subscription state and the service use condition of the user by using a real-time monitoring technology, and generating an optimized service configuration scheme; and tracking the optimized service configuration scheme and the implementation process thereof, and generating a data report supporting a platform operation decision. According to the technical scheme provided by the invention, personalized service resource recommendation can be provided, and the security and management efficiency of the platform are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of services or facility technologies specifically applicable to wireless communication networks, and in particular to a method, system, device and storage medium for automatically processing and tracking open platform capability subscriptions. Background Art

[0002] With the rapid development of the internet and cloud computing, open platforms have become an important channel for businesses and individuals to access a variety of services. Users submit subscription requests through the platform, and the platform needs to provide personalized service resources based on the user's preferences.

[0003] Currently, several platforms on the market offer similar service subscription features, primarily using the following approaches: The platform recommends a fixed list of service resources based on preset rules or simple user input. While simple, this approach lacks personalization and flexibility, and cannot meet diverse user needs. The platform uses only a single authentication method (such as username and password) to authenticate users, making it vulnerable to attacks and insecure. The platform relies on manual operations to manage and adjust user subscription status and service usage, which is inefficient, error-prone, and difficult to respond to in-real-time changes in user needs.

[0004] Although existing solutions have met basic service subscription needs to a certain extent, they still have the following significant defects: existing service recommendation mechanisms are mostly based on fixed rules and cannot provide personalized service resources based on users' actual needs, resulting in low user satisfaction; the single authentication mechanism is easily cracked and cannot effectively prevent unauthorized access and malicious behavior, increasing the security risks of the platform; manual resource adjustment methods are time-consuming and labor-intensive, making it difficult to achieve real-time monitoring and dynamic adjustment of user subscription status and service usage, affecting the platform's operational efficiency and user service quality; existing solutions lack in-depth analysis and mining of user service usage, and cannot generate data reports to support platform operational decisions, limiting the platform's intelligent management level. Summary of the Invention

[0005] This invention addresses the problem of poor intelligent management of subscription platforms in the prior art and proposes a method, system, device, and storage medium for automated processing and tracking of subscriptions to open platform capabilities. The specific technical solution is as follows:

[0006] In a first aspect, the present invention provides a method for automatically processing and tracking open platform capability subscriptions, comprising the following steps:

[0007] S100: parsing the preference settings in the capability subscription request submitted by the user, screening the available service resources on the platform according to the specific requirements, and generating a personalized service resource list;

[0008] S200: Based on the personalized service resource list and user identity information, a multi-factor authentication mechanism is used to verify the user's permissions. At the same time, a risk assessment algorithm is applied to assess the risk level of the capability subscription request to obtain a permission verification result and a request risk level.

[0009] S300: establishing a subscription relationship between the user and the service resource selected by the user based on the permission verification result, recording the subscription process, and generating subscription detail confirmation information based on the subscription process and the request risk level;

[0010] S400: Utilizing real-time monitoring technology to continuously track the user's subscription status and service usage, predicting future changes in the user's service needs through machine learning models, and dynamically adjusting the user's service resource configuration based on the subscription details and service usage to generate an optimized service configuration plan.

[0011] S500: Track the optimized service configuration plan and its implementation process, mine patterns in the service usage, and generate data reports to support platform operation decisions.

[0012] In a second aspect, the present invention provides an automated processing and tracking system for open platform capability subscriptions, comprising:

[0013] A parsing module is used to parse the preference settings in the capability subscription request submitted by the user, filter the available service resources on the platform according to the specific requirements, and generate a personalized service resource list;

[0014] An evaluation module is used to verify the user's authority using a multi-factor authentication mechanism based on the personalized service resource list and user identity information, and to apply a risk assessment algorithm to evaluate the risk level of the capability subscription request to obtain an authority verification result and a request risk level;

[0015] A recording module, configured to automatically establish a subscription relationship between the user and the selected service resource based on the permission verification result and the request risk level, record the subscription process and risk level, and generate subscription details confirmation information;

[0016] A prediction module, which uses real-time monitoring technology to continuously track users' subscription status and service usage, predicts future changes in users' service needs through machine learning models, and dynamically adjusts users' service resource configuration based on the subscription details and service usage to generate an optimized service configuration plan;

[0017] The tracking module is used to track the optimized service configuration plan and its implementation process, mine patterns in the service usage, and generate data reports to support platform operation decisions.

[0018] In a third aspect, the present invention provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an automated processing and tracking method for open platform capability subscriptions as described in the first aspect.

[0019] In a fourth aspect, the present invention provides a computer storage medium storing a computer program, which, when executed by a computer, implements the method for automated processing and tracking of open platform capability subscriptions as described in the first aspect.

[0020] The present invention parses the preference settings in the capability subscription request submitted by the user end, filters the available service resources on the platform according to the user's specific needs, and generates a personalized service resource list. This not only improves the efficiency of users finding suitable services, but also enhances the user experience. The generation of a personalized service resource list enables the service resources on the platform to be utilized more efficiently, reducing idle resources and waste. A multi-factor authentication mechanism is used to verify the user's permissions, ensuring the authenticity and legitimacy of the user's identity, effectively preventing unauthorized access and use, and improving the security of the platform. A risk assessment algorithm is applied to comprehensively analyze each subscription request, assess the risk level, and generate a detailed request risk assessment report. This helps the platform to promptly discover and handle potential security threats and ensure the stable operation of the platform. Based on the permission verification results, a subscription relationship between the user and the selected service resource is automatically established, and the subscription process is recorded. This simplifies the subscription process and improves management efficiency. It generates subscription details confirmation information, records the subscription process and request risk level, and provides detailed data support for subsequent management and auditing. It uses real-time monitoring technology to continuously track users' subscription status and service usage, collects users' actual usage data, and generates service usage data. It uses machine learning models to predict changes in users' future service needs, and dynamically adjusts users' service resource configuration based on subscription details confirmation information and service usage, generating optimized service configuration plans. This not only improves resource utilization, but also enhances users' service experience. It tracks optimized service configuration plans and their implementation process, explores patterns in service usage, and generates data reports that support platform operational decisions. Data reports provide a scientific basis for the platform's operational decisions, helping the platform better understand user needs, optimize service configuration, and improve overall operational efficiency and user satisfaction.

[0021] Preferably, the S200 includes the following process:

[0022] S210: Filter available service resources on the platform using the preference settings in the capability subscription request submitted by the user, and generate a personalized service resource list;

[0023] S220: Verify the user's identity using a multi-factor authentication mechanism based on the personalized service resource list and the identity information provided by the user to obtain a user identity verification result;

[0024] S230: Based on the user identity authentication result, apply a risk assessment algorithm to comprehensively analyze the capability subscription request, assess the risk level of the capability subscription request, and generate a request risk assessment report;

[0025] S240: Using the request risk assessment report and combining it with the user identity verification result, a comprehensive judgment is made on the user's subscription eligibility to obtain the permission verification result and the final request risk level.

[0026] By parsing the preference settings in the capability subscription requests submitted by users, a personalized service resource list is generated, which improves the efficiency of users finding suitable services and enhances the user experience; a multi-factor authentication mechanism is used to verify user identity to ensure the authenticity and legitimacy of the user identity, effectively prevent unauthorized access and use, and improve the security of the platform; a risk assessment algorithm is applied to conduct a comprehensive analysis of each capability subscription request and generate a detailed request risk assessment report, which helps the platform to promptly discover and deal with potential security threats and ensure the stable operation of the platform; based on the permission verification results, a subscription relationship is automatically established between the user and the selected service resources, and the subscription process is recorded, and subscription details confirmation information is generated, which simplifies the subscription process and improves management efficiency; by comprehensively judging the user's subscription qualifications, generating permission verification results and the final request risk level, it provides a scientific basis for the platform's operational decisions and improves the overall operation level and user service quality of the platform.

[0027] Preferably, the S400 includes the following process:

[0028] S410: Utilizing real-time monitoring technology, continuously tracking the user's subscription status and service usage, collecting the user's actual usage data, and generating service usage data;

[0029] S420: Building a user service usage profile based on the service usage data and the user information recorded in the subscription details confirmation information;

[0030] S430: Based on the user service usage profile, using a pre-trained machine learning model, predict the user's future service demand changes and generate a user's future service demand prediction result;

[0031] S440: Utilizing the user's future service demand prediction result and combining it with the current service usage, dynamically adjusting the user's service resource configuration to generate an optimized service configuration plan.

[0032] Through real-time monitoring technology, users' subscription status and service usage are continuously tracked, and actual user usage data is collected to generate service usage data, ensuring that the platform can keep abreast of users' latest usage and improving the timeliness and accuracy of the data. Based on service usage data and subscription details confirmation information, user service usage profiles are constructed, providing a detailed data foundation for subsequent analysis and prediction, and improving the integrity and reliability of the data. Pre-trained machine learning models are used to predict future changes in users' service needs and generate prediction results for future user service needs, improving the accuracy and foresight of the predictions and helping the platform prepare resources in advance. Using the prediction results for future user service needs, combined with current service usage, users' service resource allocation is dynamically adjusted to generate optimized service configuration plans, improving resource utilization and user service experience. Through the above process, optimized service configuration plans and their implementation processes are generated, patterns in service usage are discovered, and data reports supporting platform operational decisions are generated, providing a scientific basis for the platform's operational decisions and improving the platform's intelligent management level and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 This is a flow chart of a method for automatically processing and tracking open platform capability subscriptions according to the present invention.

[0035] Figure 2 This is a structural diagram of an automated processing and tracking system for open platform capability subscriptions according to the present invention.

[0036] Figure 3 The figure is a schematic structural diagram of a computing device according to the present invention. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention.

[0038] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as S100, S200, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0040] Example 1:

[0041] like Figure 1 As shown, a method for automatically processing and tracking open platform capability subscriptions includes:

[0042] S100: parsing the preference settings in the capability subscription request submitted by the user, screening the available service resources on the platform according to the specific requirements, and generating a personalized service resource list;

[0043] In this step, preferences refer to the personal preferences and requirements provided by users when submitting a capability subscription request, such as the desired service type, functional requirements, and performance indicators. Available service resources refer to all service resources available to users on the platform, including but not limited to API interfaces, data services, and computing resources. The personalized service resource list is a collection of service resources that meet the user's specific needs, selected from the available service resources based on the user's preferences.

[0044] A brief explanation of the solution process: Users submit a capability subscription request through the platform's front-end interface, including detailed preference settings. Upon receiving the request, the platform's back-end parses the preference information. Based on the parsed results, it selects eligible service resources from the platform's available service resource library. These selected service resources are then compiled into a personalized service resource list for the user to select.

[0045] S200: Based on the personalized service resource list and user identity information, a multi-factor authentication mechanism is used to verify the user's permissions. At the same time, a risk assessment algorithm is applied to assess the risk level of the capability subscription request to obtain a permission verification result and a request risk level.

[0046] In this step, multi-factor authentication refers to an enhanced identity verification method that requires users to provide two or more authentication credentials, such as a password, mobile phone verification code, or fingerprint. The permission verification result is the result of verifying the user's identity using the multi-factor authentication mechanism, indicating whether the user has permission to access specific service resources. The risk assessment algorithm is used to assess the risk level of the subscription request, taking into account factors such as the user's identity verification results, historical user behavior, and the characteristics of the requested service resources.

[0047] A brief explanation of the solution process: The user selects a service resource from the personalized service resource list and submits a subscription request. The platform activates a multi-factor authentication mechanism to verify the user's identity. Simultaneously, a risk assessment algorithm is applied to evaluate the risk level of the capability subscription request. Based on the results of the permission verification and risk assessment, a decision is made on whether to approve the user's subscription request.

[0048] The S200 specifically includes the following processes:

[0049] S260: Using the personalized service resource list and the identity information provided by the user, a multi-factor authentication mechanism is activated to verify the user's identity and obtain a user identity verification result;

[0050] S220: Based on the user identity authentication result, a preset risk assessment algorithm is applied, combined with various aspects of the user's information, to perform a comprehensive risk assessment on the capability subscription request to obtain a total risk assessment score;

[0051] S280: Based on the total risk assessment score and the user identity verification result, a comprehensive judgment is made on the user's subscription eligibility to generate an authorization verification result;

[0052] S290: Using the permission verification result, a final assessment is made on the risk level of the capability subscription request, and the total risk assessment score and the user identity verification result are comprehensively considered to generate a request risk level.

[0053] Suppose user A submits a capability subscription request for a premium service. The specific implementation steps are as follows: A personalized service resource list, including premium service resources, and user A's identity information, including username, password, and mobile phone number, is generated. A multi-factor authentication mechanism is activated, and a text message verification code is sent to user A's phone. User A enters the verification code to complete the authentication process. The user authentication result is passed. The user authentication result is passed, user A has no negative historical records, and the service resource information is a premium service with high sensitivity. Environmental information includes: access time is during the day on a weekday, and the device used is a company computer. A pre-defined risk assessment algorithm is applied to conduct a comprehensive risk assessment of user A's subscription request, calculating a total risk assessment score of 0.3. The total risk assessment score is 0.3, and the user authentication result is "passed." A comprehensive assessment of user A's subscription eligibility is made based on the total risk assessment score and the user authentication result. The permission verification result is "passed," with a total risk assessment score of 0.3. Taking into account the permission verification result and the total risk assessment score, a final risk assessment is made for user A's subscription request, assigning it a "low" risk level. Through the above embodiment, the system successfully completes the permission verification and risk assessment of user A's subscription request, ensuring the security of the platform and the high quality of services.

[0054] S300: establishing a subscription relationship between the user and the service resource selected by the user based on the permission verification result, recording the subscription process, and generating subscription detail confirmation information based on the subscription process and the request risk level;

[0055] In this step, the subscription relationship is the binding between the user and the selected service resource. Once established, the user can use the service resource. Subscription details confirmation information contains key information from the subscription process, such as user identity, selected service resource, subscription time, and risk level, which is used for subsequent management and auditing.

[0056] Brief explanation of the solution process: If permission verification passes and the risk assessment results are within an acceptable range, the platform establishes a subscription relationship between the user and the selected service resource. Detailed information about the subscription process is recorded, including user identity, selected service resource, subscription time, and risk level. A subscription confirmation message is generated and sent to the user for confirmation.

[0057] S400: Utilizing real-time monitoring technology to continuously track the user's subscription status and service usage, predicting future changes in the user's service needs through machine learning models, and dynamically adjusting the user's service resource configuration based on the subscription details and service usage to generate an optimized service configuration plan.

[0058] In this step, real-time monitoring technology refers to technologies used to continuously monitor user subscription status and service usage, such as log analysis and traffic monitoring. Machine learning models are used to predict future changes in user service needs and are trained using historical data. The optimized service configuration plan is generated by dynamically adjusting the user's service resource configuration based on the predicted results and service usage.

[0059] Brief explanation of the solution process: Leveraging real-time monitoring technology, we continuously track users' subscription status and service usage. Applying machine learning models, we validate information and service usage based on subscription details and predict future changes in users' service needs. Based on these predictions, we dynamically adjust the user's service resource allocation and generate an optimized service configuration plan. This optimized service configuration plan is then applied to the user's account, improving the service experience.

[0060] The S400 specifically includes the following processes:

[0061] S410: Utilizing real-time monitoring technology, continuously tracking the user's subscription status and service usage, collecting the user's actual usage data, and generating service usage data;

[0062] S420: Building a user service usage profile based on the service usage data and the user information recorded in the subscription details confirmation information;

[0063] S430: Based on the user service usage profile, using a pre-trained machine learning model, predict the user's future service demand changes and generate a user's future service demand prediction result;

[0064] S440: Utilizing the user's future service demand prediction result and combining it with the current service usage, dynamically adjusting the user's service resource configuration to generate an optimized service configuration plan.

[0065] Assume that user Li Hua wants to subscribe to a premium membership service on an online video streaming platform, which provides features such as ad-free viewing, high-definition image quality, and priority downloading. The platform uses real-time monitoring technology to continuously track Li Hua's subscription status and service usage, and collects Li Hua's actual usage data, including: 8:00 to 10:00 p.m. every day; an average of 2 hours each time; 3 times a week; and approximately 2GB of viewing each time. The platform organizes the collected data into service usage data for subsequent analysis and prediction.

[0066] User basic information: LiHua; 13812345678; Premium member; Premium service level; January 1, 2024; 8:00 to 10:00 p.m. every day; an average of 2 hours each time; 3 times a week; approximately 2GB of video viewed each time; The platform constructed a service usage profile for Li Hua based on the above information; The platform used a pre-trained machine learning model to predict future changes in Li Hua's service needs. The model takes into account Li Hua's historical usage data, viewing habits, download frequency, and other factors; the machine learning model predicts that Li Hua's service demand will change as follows in the next three months: remain unchanged, from 8 to 10 pm every day; increase slightly, to an average of 2.5 hours each time; increase to 4 times a week; each viewing is about 2.5GB; based on Li Hua's current service usage, the platform finds that Li Hua's viewing time and download frequency have an increasing trend; based on the prediction results, the platform dynamically adjusts Li Hua's service resource allocation: allocates more network bandwidth to Li Hua to support longer HD viewing and more frequent downloads; caches popular videos in advance during Li Hua's frequently used viewing time periods to reduce loading time; improves server performance during Li Hua's peak viewing hours to ensure a smooth viewing experience; the platform generates an optimized service configuration plan, including: increasing bandwidth by 20%; caching popular videos in advance; and improving server performance between 8 and 10 pm; the platform applies the optimized service configuration plan to Li Hua's account to ensure that Li Hua receives a better service experience; the platform continues to use real-time monitoring technology to track Li Hua's service usage and record the implementation results. By comparing the data before and after optimization, the effectiveness of the optimization plan is evaluated; based on the implementation results, the platform generates data reports to support the platform's operational decisions, providing a scientific basis for the platform's operational decisions; based on the feedback in the data report, the platform continuously optimizes the service configuration plan to further improve user satisfaction and resource utilization.

[0067] Through the above embodiments, the platform can not only monitor the user's subscription status and service usage in real time, but also predict the user's future service demand changes through machine learning models, and dynamically adjust the service resource configuration based on the prediction results to generate an optimized service configuration plan, thereby improving resource utilization and user service experience.

[0068] The S430 specifically includes the following process:

[0069] S431: Using the service usage data and user information in the user service usage file, pre-process the data to ensure data consistency and accuracy, and obtain a standardized service usage data set;

[0070] S432: Extracting features related to service requirements based on the standardized service usage data set, combining and transforming the features related to service requirements to generate a user service usage feature vector;

[0071] S433: Based on the user service usage feature vector, a pre-trained machine learning model is used to predict the user's future service demand changes to obtain a preliminary future service demand prediction value;

[0072] S434: Using the preliminary future service demand prediction value and combining it with the user's subscription details confirmation information, the prediction result is refined and corrected to generate the user's future service demand prediction result.

[0073] Suppose user Zhang wishes to subscribe to a premium virtual private cloud (VPC) service on an enterprise-class cloud computing platform. The platform utilizes real-time monitoring technology to continuously track Zhang's subscription status and service usage, collecting actual usage data, including: daily usage between 8:00 PM and 10:00 PM; an average of 2 hours per session; approximately 2GB of usage per session; and no security incidents in the past month. The platform compiles this collected data into service usage data for subsequent analysis and forecasting.

[0074] Username: zhangsan; Contact: 13812345678; Advanced Virtual Private Cloud (VPC); Advanced service level; January 1, 2024; 8 to 10 pm every day; 2 hours per session on average; approximately 2GB of data is used each time; no security incidents in the past month; the platform built a service usage profile for Zhang San based on the above information; the platform used a pre-trained machine learning model to predict future changes in Zhang San's service needs. The model takes into account many factors such as Zhang San's historical usage data, usage frequency, and network traffic; the machine learning model predicts that Zhang San's service demand changes in the next three months will be as follows: remain unchanged, from 8 to 10 pm every day; increase slightly, an average of 2.5 hours each time; approximately 2.5GB of data is used each time; based on Zhang San's current service usage, the platform found that Zhang San's usage time and network traffic have an increasing trend; based on the prediction results, the platform dynamically adjusts Zhang San's service resource configuration; allocates more network bandwidth to Zhang San to support longer usage and larger network traffic; strengthens security monitoring during Zhang San's usage period to ensure data security; during Zhang San's peak usage period, increases Server performance ensures a smooth user experience. The platform generates an optimized service configuration plan, including: a 20% increase in bandwidth; enhanced security monitoring; improved server performance between 8:00 PM and 10:00 PM; successful user identity verification; successful user identity validation; stable usage with no negative records in the past year; normal behavior pattern analysis results; premium VPC service, all-day usage; normal anomaly assessment results; premium VPC service, high sensitivity and high importance; high sensitivity assessment results. The overall risk assessment score (RS) is calculated by combining the user identity validation results, user behavior pattern analysis results, request anomaly assessment results, and service resource sensitivity assessment results. The overall risk assessment score (RS) is 0.7. Based on the preset threshold, RS < 0.7 indicates low risk, 0.7 ≤ RS < 1.0 indicates medium risk, and RS ≥ 1.0 indicates high risk. Therefore, the risk level of Zhang San's subscription request is "medium." The platform generates a detailed request risk assessment report, including the analysis results of each risk assessment dimension and the final risk level. The platform applies the optimized service configuration plan to Zhang San's account to ensure that Zhang San receives a better service experience. The platform continues to use real-time monitoring technology to track Zhang San's service usage and record the implementation results. By comparing the data before and after optimization, the effectiveness of the optimization plan is evaluated. Based on the implementation results, the platform generates a data report to support the platform's operational decisions, providing a scientific basis for the platform's operational decisions. Based on the feedback in the data report, the platform continues to optimize the service configuration plan to further improve user satisfaction and resource utilization.

[0075] Through the above-mentioned implementation, the platform not only monitors users' subscription status and service usage in real time, but also uses machine learning models to predict future changes in users' service needs. Based on these predictions, the platform dynamically adjusts service resource allocation and generates optimized service configuration plans. Furthermore, the platform ensures the security and legitimacy of user subscription requests through risk assessment, improving its overall operational efficiency and user satisfaction.

[0076] This invention recognizes that accurately predicting future user service demands on open platforms is key to improving resource utilization and user satisfaction. Traditional prediction methods often rely on simple statistical models, which struggle to capture complex variations in service demands. Therefore, designing a nonlinear prediction model that comprehensively considers multiple factors is particularly important.

[0077] The calculation formula for the user's future service demand prediction result in S430 is as follows:

[0078]

[0079] Among them, FSDP is the prediction result of the user's future service demand; UUC is the user usage score, UUC∈[0,1]; USU is the user service usage frequency score, USU∈[0,1]; SSI is the service subscription information score, SSI∈[0,1]; A, B, C, D, E, F, T are model parameters used to adjust the contribution of each factor; W1, W2, W3, W4 are weight parameters used to adjust the sensitivity of nonlinear terms;

[0080] Among them, A, B, C, D, E, and F are the weights of each factor, which are used to adjust the contribution in the prediction model; T is a tuning parameter used to control the periodic influence of nonlinear terms;

[0081] in, K1, K2, K3, and K4 are constants for adjusting weight sensitivity.

[0082] This model aims to predict future service demand by comprehensively considering factors such as user history, service usage frequency, and subscription information, using pre-trained machine learning models. This allows businesses to prepare in advance, rationally allocate resources, and promptly respond to changing user needs, thereby improving service efficiency and satisfaction.

[0083] The reasons for each sub-item design are as follows:

[0084] User usage score UUC: reflects the user's usage of existing services, using a logarithmic function To measure, it emphasizes the impact of user activity on the prediction results.

[0085] User Service Usage Frequency Score (USU): Indicates how frequently users use services, using the square root function. It is expressed as , indicating that the higher the frequency of use, the greater the positive impact on demand forecast.

[0086] Service Subscription Information SSI: Considers the type and number of services subscribed by the user and uses the Sigmoid function This reflects the importance of service diversity to demand forecasting.

[0087] Nonlinear interaction terms: such as sin(T·(UUC+USU+SSI)), and Nonlinear relationships were introduced to simulate the complex interactions between different factors and improve the prediction accuracy of the model.

[0088] The weight parameters W1, W2, W3, and W4 are: These parameters are defined by the Sigmoid function, which allows the model to dynamically adjust the importance of each factor according to the actual situation, increasing the flexibility and adaptability of the model.

[0089] Parameters A, B, C, D, E, F, K1, K2, K3, and K4 are typically derived through training on large amounts of historical data using machine learning algorithms, such as gradient descent or random forests. The training process aims to minimize prediction error, enabling the model to predict future user needs as accurately as possible.

[0090] UUC, USU, SSI: These scores can be obtained through internal data sources such as the company's CRM system and user behavior tracking tools, and can also be supplemented by external market research data.

[0091] Assume that in the cloud computing field, user A has subscribed to a premium service, and the platform needs to continuously monitor and dynamically adjust it. The specific implementation steps are as follows:

[0092] The user usage score (UUC) is set to 0.8; the user service usage frequency score (USU) is set to 0.7; the service subscription information score (SSI) is set to 0.9; K1 = 2; K2 = 1.5; K3 = 1.2; K4 = 1.3; A = 0.5; B = 0.6; C = 0.7; D = 0.8; E = 0.9; F = 1.0; T = 1.1

[0093] Calculate the weight parameters:

[0094]

[0095] Calculate the forecast results of users' future service needs:

[0096]

[0097] Based on the above calculation, the predicted future service demand FSDP for user A is 2.5545. Based on the preset threshold, we can determine the future changes in user A's service demand. For example, if FSDP < 2.0 indicates low demand, 2.0 ≤ FSDP < 3.0 indicates medium demand, and FSDP ≥ 3.0 indicates high demand, then this prediction indicates that user A's future service demand will be medium.

[0098] Through this nonlinear prediction model, the platform can more comprehensively and accurately predict users' future service needs, thereby dynamically adjusting service resource allocation, improving resource utilization and users' service experience.

[0099] S500: Track the optimized service configuration plan and its implementation process, mine patterns in the service usage, and generate data reports to support platform operation decisions.

[0100] In this step, the implementation process of the optimized service configuration plan refers to the actual application process of the optimization plan in the user account, including the comparison data before and after the adjustment.

[0101] Patterns in service usage refer to the regularities and trends mined from user service usage data.

[0102] Data reports refer to analytical reports that include the implementation effects of optimization plans and service usage patterns, and are used to support platform operation decisions.

[0103] Brief explanation of the solution process: Continuously track the implementation of the optimized service configuration plan, recording service usage data before and after adjustments. Use data analysis tools to identify patterns and trends in service usage. Generate data reports to summarize the optimization plan's effectiveness and analyze user behavior patterns to inform platform operations decisions. Provide feedback to the platform operations team for further service optimization and user experience enhancements.

[0104] like Figure 2 As shown, an automated processing and tracking system for open platform capability subscriptions includes:

[0105] The parsing module 21 is used to parse the preference settings in the capability subscription request submitted by the user, filter the available service resources on the platform according to the specific requirements, and generate a personalized service resource list;

[0106] An evaluation module 22 is configured to verify the user's permissions using a multi-factor authentication mechanism based on the personalized service resource list and user identity information, and to apply a risk assessment algorithm to evaluate the risk level of the capability subscription request to obtain a permission verification result and a request risk level;

[0107] A recording module 23 is configured to automatically establish a subscription relationship between the user and the selected service resource based on the permission verification result and the request risk level, record the subscription process and risk level, and generate subscription details confirmation information;

[0108] Prediction module 24, configured to continuously track a user's subscription status and service usage using real-time monitoring technology, predict future changes in a user's service needs through machine learning models, dynamically adjust the user's service resource configuration based on the subscription details and service usage, and generate an optimized service configuration plan;

[0109] The tracking module 25 is used to track the optimized service configuration plan and its implementation process, mine patterns in the service usage, and generate data reports to support platform operation decisions.

[0110] Figure 2 The automated processing and tracking system for open platform capability subscription can execute Figure 1 The implementation principles and technical effects of the automated processing and tracking method for open platform capability subscriptions are not described in detail here. The specific manner in which each module and unit performs operations in the automated processing and tracking system for open platform capability subscriptions in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0111] In one possible design, Figure 2 The automated processing and tracking system for open platform capability subscription shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0112] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0113] The processing component 32 is used to: parse the preference settings in the capability subscription request submitted by the user end, screen the available service resources on the platform according to the specific needs, and generate a personalized service resource list; based on the personalized service resource list and user identity information, use a multi-factor authentication mechanism to verify the user's permissions, and apply a risk assessment algorithm to evaluate the risk level of the capability subscription request to obtain a permission verification result and a request risk level; based on the permission verification result, establish a subscription relationship between the user and the service resource selected by the user, record the subscription process, and generate subscription detail confirmation information based on the subscription process and the request risk level; use real-time monitoring technology to continuously track the user's subscription status and service usage, predict the user's future service demand changes through a machine learning model, and dynamically adjust the user's service resource configuration based on the subscription detail confirmation information and service usage to generate an optimized service configuration plan; track the optimized service configuration plan and its implementation process, explore patterns in the service usage, and generate data reports to support platform operation decisions.

[0114] The processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0115] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0116] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0117] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0118] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0119] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0120] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 The illustrated embodiment provides a method for automated processing and tracking of open platform capability subscriptions.

[0121] Example 2:

[0122] The difference between Example 2 and Example 1 lies in the specific process of S200 in the method for automated processing and tracking of open platform capability subscriptions, and the rest are the same.

[0123] The S200 specifically includes the following processes:

[0124] S210: Filter available service resources on the platform using the preference settings in the capability subscription request submitted by the user, and generate a personalized service resource list;

[0125] S220: Verify the user's identity using a multi-factor authentication mechanism based on the personalized service resource list and the identity information provided by the user to obtain a user identity verification result;

[0126] S230: Based on the user identity authentication result, apply a risk assessment algorithm to comprehensively analyze the capability subscription request, assess the risk level of the capability subscription request, and generate a request risk assessment report;

[0127] S240: Using the request risk assessment report and combining it with the user identity verification result, a comprehensive judgment is made on the user's subscription eligibility to obtain the permission verification result and the final request risk level.

[0128] User Li Si wants to subscribe to a premium course package on an online education platform. This package includes advanced courses in multiple subjects and is suitable for students preparing for professional exams. When submitting the subscription request, Li Si specified the following preferences: premium course package; advanced; daily from 8:00 to 10:00 p.m.; Based on Li Si's preferences, the platform selects premium course packages that meet the requirements from all available course resources and generates a personalized service resource list. These course packages include advanced courses in subjects such as mathematics, English, and computer science.; Li Si provided the following identity information: Username: LiSi123; Password: ComplexPassword123; Mobile phone number: 13812345678; The platform activated the multi-factor authentication mechanism and sent a text message containing a verification code to Li Si's mobile phone. After receiving the text message, Li Si entered the verification code and submitted it. The platform verified the username, password, and verification code provided by Li Si and confirmed that Li Si's identity verification result was "passed." The user identity verification result was passed. The platform checked Li Si's historical behavior records and found that Li Si had no bad record in the past year, had subscribed to multiple courses, and completed course assignments on time. The course package was of medium sensitivity and high importance. The access time was 8 pm on a weekday, and the device used was a personal computer. The platform applied a preset risk assessment algorithm and, based on the above information, conducted a comprehensive risk assessment of Li Si's subscription request. The total risk assessment score was calculated as follows:

[0129] User identity verification result score IVR: 0.9; User historical behavior record score HBR: 0.8; Requested service resource feature score SRP: 0.7; Total risk assessment score RS: 0.65; According to the preset threshold, RS < 0.7 indicates low risk, 0.7 ≤ RS < 1.0 indicates medium risk, and RS ≥ 1.0 indicates high risk; Therefore, the risk level of Li Si's subscription request is "low"; The platform uses the risk assessment report and Li Si's identity verification result "passed" to make a comprehensive judgment on Li Si's subscription eligibility, and the result of the permission verification is "passed"; The final request risk level is low; The platform decides to approve Li Si's subscription request and provide Li Si with the required advanced course package; The platform establishes a subscription between Li Si and the selected advanced course package The platform establishes a subscription relationship and records the subscription process; it generates detailed subscription confirmation information, including a list of subscribed courses, usage time, subscription fees, etc., to provide data support for subsequent management and auditing; the platform uses real-time monitoring technology to continuously track Li Si's subscription status and service usage, and collects Li Si's actual usage data; based on the collected data, the platform generates Li Si's service usage data, including course completion rate, learning time, etc.; the platform uses a pre-trained machine learning model to predict Li Si's future service demand changes and generate a user's future service demand forecast; based on the forecast results and current service usage, the platform dynamically adjusts Li Si's service resource configuration and generates an optimized service configuration plan. For example, if the forecast results show that Li Si may need more English courses in the future, the platform may recommend relevant courses to Li Si in advance; the platform tracks the optimized service configuration plan and its implementation process, and records the implementation results; the platform mines patterns in service usage and generates data reports that support platform operational decisions, providing a scientific basis for the platform's operational decisions.

[0130] Suppose user Wang Wu wishes to subscribe to a premium virtual private cloud (VPC) service on an enterprise-class cloud computing platform. When submitting his subscription request, he specifies the following preferences: premium virtual private cloud (VPC); high-level; all-day. Based on Wang Wu's preferences, the platform selects eligible premium VPC services from all available VPC resources and generates a personalized list of service resources. These VPC services include high bandwidth, high availability, and advanced security features. Wang Wu also provides the following identity information:

[0131] Username: wangwu; Password: SecurePassword2023; Mobile number: 13912345678

[0132] The platform activated its multi-factor authentication mechanism and sent a text message containing a verification code to Wang Wu's phone. Wang Wu entered the verification code and submitted it. The platform verified Wang Wu's username, password, and verification code, confirming his identity verification as "passed."

[0133] The user's identity verification result is passed. The platform queries Wang Wu's historical behavior records and finds that Wang Wu has no bad record in the past year, has subscribed to multiple cloud services, and has stable usage. The premium VPC service is highly sensitive and important. The access time is 9 pm on a weekday, and the device used is a company computer. The platform applies a preset risk assessment algorithm and, based on the above information, conducts a comprehensive risk assessment of Wang Wu's subscription request. Calculate the total risk assessment score:

[0134] User authentication result score IVR: 0.9; User historical behavior record score HBR: 0.8; Requested service resource feature score SRP: 0.9; Total risk assessment score RS: 0.85;

[0135] According to the preset threshold, RS < 0.7 indicates low risk, 0.7 ≤ RS < 1.0 indicates medium risk, and RS ≥ 1.0 indicates high risk. Therefore, the risk level of Wang Wu's subscription request is "medium"; the platform uses the risk assessment report and Wang Wu's identity verification result of "pass" to make a comprehensive judgment on Wang Wu's subscription eligibility, and concludes that the permission verification result is "pass"; the final request risk level is medium; the platform decides to approve Wang Wu's subscription request, but recommends that Wang Wu strengthen security measures during use and conduct regular security checks; the platform establishes a subscription relationship between Wang Wu and the selected advanced VPC service and records the subscription process; the platform generates detailed subscription details confirmation information, including the subscribed service content, usage time, subscription fee, etc., to provide data support for subsequent management and auditing; the platform uses real-time monitoring technology to continuously track Wang Wu's subscription status and service usage, and collects Wang Wu's actual usage data; based on the collected data, the platform generates Wang Wu's service usage data, including network traffic, security incidents, etc.; the platform uses a pre-trained machine learning model to predict Wang Wu's future service demand changes and generate user future service demand forecast results; based on the prediction results and current service usage, the platform dynamically adjusts Wang Wu's service resource configuration to generate an optimized service configuration plan. For example, if the prediction results show that Wang Wu may need higher network bandwidth in the future, the platform may allocate more bandwidth resources to Wang Wu in advance; the platform tracks the optimized service configuration plan and its implementation process, and records the implementation effect; the platform explores patterns in service usage, generates data reports to support platform operational decisions, and provides a scientific basis for the platform's operational decisions.

[0136] Through the above-mentioned embodiments, the platform not only ensures the authenticity and legitimacy of user identities, but also effectively assesses the risks of subscription requests, ensuring platform security and high-quality services. Furthermore, by dynamically adjusting service resource allocation, it improves resource utilization and user service experience.

[0137] This invention takes into account that on an open platform, user-submitted capability subscription requests must undergo rigorous permission verification and risk assessment to ensure platform security and high-quality services. Traditional risk assessment methods often rely on simple rules and linear models, which are difficult to fully and accurately reflect complex risk factors. Therefore, it is particularly important to design a nonlinear risk assessment model that comprehensively considers multiple factors.

[0138] In S230, the total risk assessment score is calculated as follows:

[0139]

[0140] Where RS is the final risk assessment score; IVR is the score of the user authentication result, IVR∈[0,1]; HBR is the score of the user's historical behavior record, HBR∈[0,1]; SRP is the score of the requested service resource characteristics, SRP∈[0,1]; α, β, γ, δ, θ are model parameters used to adjust the contribution of each factor; w1 and w3 are weight parameters used to adjust the sensitivity of the nonlinear term;

[0141] in, k1 and k3 are constants for adjusting weight sensitivity;

[0142] in, k α ,k β ,k γ ,k θ ,k θ is a constant that adjusts the sensitivity of the model parameters.

[0143] The overall design goal of this formula is to create a flexible and accurate risk assessment mechanism that can adjust the assessment criteria according to different application scenarios. By setting different weights and parameters for different factors, the impact of various factors can be effectively balanced, thereby achieving an accurate assessment of request risk.

[0144] The reasons for each sub-item design are as follows:

[0145] User authentication result IVR: This is the first line of defense to assess the risk of the request. Good authentication can effectively reduce the possibility of illegal access. Using logarithmic function To calculate its contribution value, it means that when IVR approaches 1, the marginal effect of the increase gradually decreases, reflecting the phenomenon that the importance of identity verification results to risk assessment increases with its improvement but the growth rate slows down.

[0146] User historical behavior record HBR: Historical behavior is one of the important bases for judging user credibility. Use square root function This is because for lower HBR values, the impact is relatively small; for higher HBR values, the growth rate is faster, reflecting the significant role of good historical behavior in reducing risks.

[0147] Requested Service Resource Characteristics SRP: Different service resources may face different security threats, so the risk assessment criteria need to be adjusted according to the characteristics of the resource. Here, the Sigmoid function is used. It can handle smooth transitions from low to high well and is suitable for describing the impact of resource characteristics on risk.

[0148] Comprehensive factor: The last term, sin(θ·(IVR+HBR+SRP), is a periodic function used to introduce nonlinear changes and simulate the impact of some unforeseen factors on risk assessment in actual scenarios.

[0149] In the above formula, the parameters k1, k3, k α ,k β ,k γ ,k δ ,k θ It is usually trained through machine learning methods based on a large amount of historical data to ensure that the model can adapt to the needs of specific fields.

[0150] IVR, HBR, SRP: These three scores can be obtained through specific business logic calculations, such as feedback from the identity authentication system, historical data statistics from the user behavior analysis system, and configuration information from the service resource management system.

[0151] Assume that user A submits a capability subscription request with the following information:

[0152] The user authentication result IVR is set to 0.8; the user historical behavior record HBR is set to 0.7; the requested service resource feature SRP is set to 0.9;

[0153] The adjustment constants are set as follows:

[0154] k1=2;k3=1.5;k α =1;k β =1.2; k γ =1.3; k δ =1.1;k θ =1.4

[0155] Calculate the weight parameters:

[0156]

[0157] Calculate model parameters:

[0158]

[0159] Calculate the total risk assessment score:

[0160]

[0161] RS=0.711·log(1+1.676)+0.747·0.837+0.762·0.638+0.726·sin(1.8672)

[0162] RS=0.711·0.515+0.747·0.837+0.762·0.638+0.726·0.978

[0163] RS=0.366+0.626+0.487+0.710

[0164] RS≈2.190

[0165] Based on the above calculation, the total risk assessment score RS for User A's subscription request is 2.190. The risk level of a capability subscription request can be determined based on preset thresholds. For example, if RS < 2.0 indicates low risk, 2.0 ≤ RS < 3.0 indicates medium risk, and RS ≥ 3.0 indicates high risk, then the risk level of the capability subscription request is medium risk.

[0166] Through this nonlinear risk assessment model, the platform can more comprehensively and accurately evaluate users' subscription requests, ensuring the security of the platform and the high quality of its services.

[0167] The S230 includes the following specific processes:

[0168] S231: Using the user identity verification result, confirm the authenticity and legitimacy of the user identity to obtain a user identity legitimacy confirmation result;

[0169] S232: Analyze and process the user's behavior pattern based on the user identity legitimacy confirmation result and the user's historical behavior record to obtain a user behavior pattern analysis result;

[0170] S233: Based on the user behavior pattern analysis result, perform abnormality evaluation on the specific content of the capability subscription request to obtain a request abnormality evaluation result;

[0171] S234: Using the request abnormality evaluation result, evaluate the sensitivity and importance of the selected service resource to obtain a service resource sensitivity evaluation result;

[0172] S235: Based on the user behavior pattern analysis results, the request abnormality degree assessment results and the service resource sensitivity assessment results, a preset risk assessment algorithm is applied to comprehensively analyze the capability subscription request, calculate the total risk assessment score of the capability subscription request, and generate a request risk assessment report.

[0173] In this step, the user identity verification result refers to the result obtained after verifying the user identity through the multi-factor authentication mechanism, including the authenticity and legitimacy of the user identity.

[0174] The result of user identity legitimacy confirmation refers to confirming whether the user identity is real and legal based on the user identity verification result to prevent impersonation and fraud.

[0175] A user's historical behavior records refer to all of the user's past behavior data on the platform, including login records, operation records, service usage records, etc.

[0176] The results of user behavior pattern analysis refer to identifying the user's behavior habits and patterns through analysis of the user's historical behavior records, and judging whether the current behavior is in line with the norm.

[0177] The request anomaly assessment result refers to the assessment of whether there are any anomalies in the current capability subscription request based on the analysis results of user behavior patterns, such as whether the request frequency, request time, request content, etc. deviate from the normal range.

[0178] The service resource sensitivity assessment result refers to the sensitivity and importance of the service resources requested by the user, including data sensitivity, operation permission level, etc.

[0179] The risk assessment algorithm comprehensively considers the results of user identity legitimacy confirmation, user behavior pattern analysis, request anomaly degree assessment, and service resource sensitivity assessment to calculate the total risk assessment score of the capacity subscription request.

[0180] A brief explanation of the solution process: First, when a user submits a capability subscription request, the platform activates a multi-factor authentication mechanism to verify the user's identity. The verification result is used to confirm the authenticity and legitimacy of the user's identity and generate a user identity legitimacy confirmation result.

[0181] Secondly, based on the results of the user's identity verification and combined with the user's historical behavior records, the user's behavior patterns are analyzed. The user's behavior habits and patterns are identified, and whether the current behavior conforms to the norm is determined, generating user behavior pattern analysis results.

[0182] Furthermore, based on the results of the user behavior pattern analysis, the abnormality of the current capability subscription request is evaluated. The request frequency, request time, and request content are checked to see if they deviate from the normal range, generating a request abnormality assessment result. The sensitivity and importance of the service resources requested by the user are evaluated, including data sensitivity and operation permission level, generating a service resource sensitivity assessment result.

[0183] Finally, the system combines the user behavior pattern analysis results, the request anomaly assessment results, and the service resource sensitivity assessment results with a preset risk assessment algorithm to calculate the total risk assessment score for the capacity subscription request. This generates a request risk assessment report containing the total risk assessment score and detailed risk analysis results.

[0184] Assume that an online payment platform wants to improve the security and accuracy of its subscription requests and can adopt the above risk assessment scheme.

[0185] First, the user submits a subscription request through the platform's front-end, requesting services such as payment gateway access and transaction monitoring. The platform activates a multi-factor authentication mechanism, requiring the user to provide a password and a mobile phone verification code for identity verification. The verification results confirm the user's authenticity and legitimacy, generating a user identity verification result.

[0186] Next, the platform extracts the user's login and transaction history from the database. Analyzing the user's login frequency, transaction amount, and transaction time patterns, the platform finds that the user typically conducts small transactions during the day on weekdays. The current request occurs in the early morning hours and is for large-value transaction permissions, generating a user behavior pattern analysis result.

[0187] Furthermore, based on the user behavior pattern analysis results, the abnormality of the current request is evaluated. It is found that the request time (early morning) and the request content (large transaction permissions) both deviate from the user's normal behavior, and the request abnormality assessment result is generated.

[0188] Furthermore, the sensitivity and importance of the large-value transaction permissions requested by the user are evaluated. Large-value transaction permissions involve large capital flows and high risks, generating a service resource sensitivity assessment result.

[0189] Finally, the system combines the user behavior pattern analysis results, the request anomaly assessment results, and the service resource sensitivity assessment results, and applies a preset risk assessment algorithm to calculate the total risk assessment score for the capacity subscription request. This generates a request risk assessment report containing the total risk assessment score and detailed risk analysis results, such as "The request time is unusual, the request content involves high-risk service resources, and a second manual review is recommended."

[0190] Through this series of steps, the platform can more accurately assess the risks of each capability subscription request, ensuring the security of the platform and the interests of users.

[0191] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0192] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0193] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for automated processing and tracking of open platform capability subscriptions, characterized in that: The process includes the following: S100: parsing the preference settings in the capability subscription request submitted by the user, screening the available service resources on the platform according to the specific requirements, and generating a personalized service resource list; S200: Based on the personalized service resource list and user identity information, a multi-factor authentication mechanism is used to verify the user's permissions. At the same time, a risk assessment algorithm is applied to assess the risk level of the capability subscription request to obtain a permission verification result and a request risk level. S300: establishing a subscription relationship between the user and the service resource selected by the user based on the permission verification result, recording the subscription process, and generating subscription detail confirmation information based on the subscription process and the request risk level; S400: Utilizing real-time monitoring technology to continuously track the user's subscription status and service usage, predicting future changes in the user's service needs through machine learning models, and dynamically adjusting the user's service resource configuration based on the subscription details and service usage to generate an optimized service configuration plan. S5 00: Track the optimized service configuration plan and its implementation process, explore patterns in the service usage, and generate data reports to support platform operation decisions.

2. The method for automated processing and tracking of open platform capability subscriptions according to claim 1, characterized in that: The S200 includes the following process: S210: Filter available service resources on the platform using the preference settings in the capability subscription request submitted by the user, and generate a personalized service resource list; S220: Verify the user's identity using a multi-factor authentication mechanism based on the personalized service resource list and the identity information provided by the user to obtain a user identity verification result; S230: Based on the user identity authentication result, a risk assessment algorithm is applied to comprehensively analyze the capability subscription request, assess the risk level of the capability subscription request, and generate a request risk assessment report. The specific process includes the following: Total risk assessment score: Where RS is the final risk assessment score; IVR is the score of the user authentication result, IVR∈[0,1]; HBR is the score of the user's historical behavior record, HBR∈[0,1]; SRP is the score of the requested service resource characteristics, SRP∈[0,1]; α, β, γ, δ, θ are model parameters used to adjust the contribution of each factor; w1 and w3 are weight parameters used to adjust the sensitivity of the nonlinear term; in, k1 and k3 are constants for adjusting weight sensitivity; in, k α ,k β ,k γ ,k δ ,k θ is a constant that adjusts the sensitivity of the model parameters; S240: Using the request risk assessment report and combining it with the user identity verification result, a comprehensive judgment is made on the user's subscription eligibility to obtain the permission verification result and the final request risk level.

3. The method for automated processing and tracking of open platform capability subscriptions according to claim 2, characterized in that: The S230 includes the following process: S231: Using the user identity verification result, confirm the authenticity and legitimacy of the user identity to obtain a user identity legitimacy confirmation result; S232: Analyze and process the user's behavior pattern based on the user identity legitimacy confirmation result and the user's historical behavior record to obtain a user behavior pattern analysis result; S233: Based on the user behavior pattern analysis result, perform abnormality evaluation on the specific content of the capability subscription request to obtain a request abnormality evaluation result; S234: Using the request abnormality evaluation result, evaluate the sensitivity and importance of the selected service resource to obtain a service resource sensitivity evaluation result; S235: Based on the user behavior pattern analysis results, the request abnormality degree assessment results and the service resource sensitivity assessment results, a preset risk assessment algorithm is applied to comprehensively analyze the capability subscription request, calculate the total risk assessment score of the capability subscription request, and generate a request risk assessment report.

4. The method for automated processing and tracking of open platform capability subscriptions according to claim 2, characterized in that: The S240 includes the following specific processes: S241: Using the preference settings in the capability subscription request submitted by the user, matching and screening the available service resources on the platform to generate a personalized service resource list; S242: Verify the user's identity using a multi-factor authentication mechanism based on the personalized service resource list and the identity information provided by the user, and obtain a user identity verification result; S243: Based on the user identity authentication result, a pre-set risk assessment algorithm is applied, combining the user's historical behavior records, the specific content of the current subscription request, and the sensitivity and importance of the selected service resources, to comprehensively analyze the capability subscription request, assess the risk level of the capability subscription request, and generate a request risk assessment report; S244: Utilize the request risk assessment report and the user identity verification result to perform a comprehensive assessment to generate an authority verification result and a final request risk level.

5. The method for automated processing and tracking of open platform capability subscriptions according to claim 1, characterized in that: The S400 includes the following processes: S410: Utilizing real-time monitoring technology, continuously tracking the user's subscription status and service usage, collecting the user's actual usage data, and generating service usage data; S420: Building a user service usage profile based on the service usage data and the user information recorded in the subscription details confirmation information; S430: Based on the user service usage profile, a pre-trained machine learning model is used to predict the user's future service demand changes and generate a user's future service demand prediction result; the user's future service demand prediction result: Among them, FSDP is the prediction result of the user's future service demand; UUC is the user usage score, UUC∈[0,1]; USU is the user service usage frequency score, USU∈[0,1]; SSI is the service subscription information score, SSI∈[0,1]; A, B, C, D, E, F, T are model parameters used to adjust the contribution of each factor; W1, W2, W3, W4 are weight parameters used to adjust the sensitivity of nonlinear terms; Among them, A, B, C, D, E, and F are the weights of each factor, which are used to adjust the contribution in the prediction model; T is a tuning parameter used to control the periodic influence of nonlinear terms; in, K1, K2, K3, and K4 are constants for adjusting weight sensitivity; S440: Utilizing the user's future service demand prediction result and combining it with the current service usage, dynamically adjusting the user's service resource configuration to generate an optimized service configuration plan.

6. The method for automated processing and tracking of open platform capability subscriptions according to claim 5, characterized in that: The S430 includes the following specific processes: S431: Using the service usage data and user information in the user service usage file, pre-process the data to ensure data consistency and accuracy, and obtain a standardized service usage data set; S432: Extracting features related to service requirements based on the standardized service usage data set, combining and transforming the features related to service requirements to generate a user service usage feature vector; S433: Based on the user service usage feature vector, a pre-trained machine learning model is used to predict the user's future service demand changes to obtain a preliminary future service demand prediction value; S434: Using the preliminary future service demand prediction value and combining it with the user's subscription details confirmation information, the prediction result is refined and corrected to generate the user's future service demand prediction result.

7. The method for automated processing and tracking of open platform capability subscriptions according to claim 1, characterized in that: The S200 includes the following specific processes: S260: Using the personalized service resource list and the identity information provided by the user, a multi-factor authentication mechanism is activated to verify the user's identity and obtain a user identity verification result; S270: Based on the user identity authentication result, a preset risk assessment algorithm is applied, combined with various aspects of the user's information, to perform a comprehensive risk assessment on the capability subscription request to obtain a total risk assessment score; S280: Based on the total risk assessment score and the user identity verification result, a comprehensive judgment is made on the user's subscription eligibility to generate an authorization verification result; S290: Using the permission verification result, a final assessment is made on the risk level of the capability subscription request, and the total risk assessment score and the user identity verification result are comprehensively considered to generate a request risk level.

8. An automated processing and tracking system for open platform capability subscriptions, characterized in that: include: A parsing module is used to parse the preference settings in the capability subscription request submitted by the user, filter the available service resources on the platform according to the specific requirements, and generate a personalized service resource list; An evaluation module is used to verify the user's authority using a multi-factor authentication mechanism based on the personalized service resource list and user identity information, and to apply a risk assessment algorithm to evaluate the risk level of the capability subscription request to obtain an authority verification result and a request risk level; A recording module, configured to automatically establish a subscription relationship between the user and the selected service resource based on the permission verification result and the request risk level, record the subscription process and risk level, and generate subscription details confirmation information; A prediction module, which uses real-time monitoring technology to continuously track users' subscription status and service usage, predicts future changes in users' service needs through machine learning models, and dynamically adjusts users' service resource configuration based on the subscription details and service usage to generate an optimized service configuration plan; The tracking module is used to track the optimized service configuration plan and its implementation process, mine patterns in the service usage, and generate data reports to support platform operation decisions.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an automated processing and tracking method for open platform capability subscription as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the method for automatically processing and tracking open platform capability subscriptions according to any one of claims 1 to 7 is implemented.

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