Telecommunication system service authorization method

Abstract

The invention discloses a telecommunication system service authorization method, and relates to the technical field of optical communication, and the system comprises the following components: S100, a user behavior data acquisition and analysis module, S200, an AI learning engine, S300, a personalized authorization strategy generation module, S400, a dynamic adjustment and feedback mechanism, and S500, a safety guarantee mechanism. According to the method, the diversified behavior data of the user is collected and analyzed in real time, the accurate user behavior portrait is constructed, and the prediction capability of the AI learning engine is combined, so that the future service demand of the user can be intelligently predicted, and a personalized service authorization strategy is generated according to the future service demand; according to the method and the system, telecommunication services which better meet the actual requirements of users can be provided for the users, the user experience is improved, the accuracy of service authorization can be ensured, unnecessary resource waste is reduced, and the service efficiency of operators and the customer satisfaction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of telecommunication services, and in particular to a telecommunication system service authorization method. Background Art

[0002] With the rapid development of information technology, the telecommunications service sector is facing unprecedented challenges and opportunities. In the context of the big data era, the massive growth of user behavior data has provided a rich source of information for telecommunications services, but it has also put forward higher requirements for data processing and analysis.

[0003] Although some telecommunications systems have introduced AI learning engines to optimize service authorization, traditional technologies still have obvious shortcomings. They cannot analyze and predict user behavior based on problem characteristics and data features, and cannot combine users' current behavior patterns and external environmental factors to predict possible changes in users' service needs in the future. Therefore, it is particularly important to develop a telecommunications system service authorization method. Summary of the invention

[0004] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a telecommunication system service authorization method, which optimizes resource allocation and improves user experience through intelligent means, and minimizes energy consumption while ensuring communication quality and information security.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a telecommunication system service authorization method, the specific steps of the method are: S100, user behavior data collection and analysis module: collects users' communication records, location information, and service usage records in real time through telecommunications networks, mobile applications, and third-party data sources, cleans, integrates, and preliminarily analyzes these data to build user behavior profiles; S200, AI learning engine: selects features that have a significant impact on building user profiles and predicting service needs from the cleaned and integrated user behavior data, transforms these features according to business needs, and divides the pre-processed data into training sets, validation sets, and test sets; Select algorithms based on problem characteristics and data features. For user behavior analysis and prediction, use training set data to train the selected algorithms. By iteratively optimizing algorithm parameters, the model can accurately learn users' communication habits, service preferences, and potential security needs from user behavior data. Adopt online learning or incremental learning methods to enable the model to continuously receive new user data or data on user behavior changes, and perform real-time updates and training, thereby continuously optimizing the accuracy of user portraits. Based on the model training results, the user portraits are updated as needed to reflect the user's latest behavior patterns and preferences. Based on the trained user portrait model, a model is built to predict the user's future service needs. The prediction model is used to combine the user's current behavior pattern and external environmental factors to predict the possible changes in the user's service needs in the future. Use validation sets and test sets to evaluate the trained model. Evaluation indicators include accuracy, recall, F1 score, and area under the ROC curve (AUC) to verify the model's prediction and generalization capabilities. Establish a user feedback channel to collect user satisfaction evaluations on authorization strategies and service recommendations, and use these feedback data to further optimize and adjust the model. S300, personalized authorization policy generation module: Based on the output of the AI ​​learning engine, combined with telecommunications business rules and cost-benefit analysis, it intelligently generates personalized service authorization policies. For privacy-conscious users, it recommends encrypted communication services and follows the principle of least privilege to limit access rights to non-essential services. S400, dynamic adjustment and feedback mechanism: It has real-time monitoring and dynamic adjustment capabilities, and can adjust authorization policies in a timely manner according to changes in user behavior and feedback. At the same time, it can establish user feedback channels to collect users' evaluations and suggestions on authorization policies to further optimize system performance. S500, security assurance mechanism: In the process of data processing and authorization policy generation, strictly abide by data protection regulations, use encryption technology to protect user privacy, and at the same time, conduct security audits on authorization policies to strengthen user information and communication security.

[0006] Furthermore, the S100 collects diversified data such as user's communication records, location information, and service usage records in real time through telecommunication networks, mobile applications, and third-party data sources.

[0007] Furthermore, the S200 selects an algorithm according to the characteristics of the problem and the data characteristics to analyze and predict the user behavior: ,in, is a nonlinear activation function used to transform the intermediate output of the model into the final predicted value. Is related to each feature The relevant weights are used to adjust the influence of different features on the results. is the hyperbolic tangent function, which can increase the nonlinear expression ability of the model. It is a feature A preprocessing function of is a random error term used to represent the uncertainty factors that cannot be fully explained by the model.

[0008] Furthermore, the S200 constructs a model for predicting the user's future service needs based on the trained user portrait model: ,in, is an activation function, and is the corresponding weight coefficient, Behavioral characteristics Related frequency adjustment parameters, This term takes into account the impact of cyclical or fluctuating nature of behavior patterns on service demand.

[0009] Furthermore, the S300 combines telecommunication service rules with cost-benefit analysis to intelligently generate personalized service authorization strategies.

[0010] Furthermore, for users who value privacy, S300 recommends encrypted communication services and limits access rights to non-essential services in accordance with the principle of least privilege.

[0011] Furthermore, S400 timely adjusts the authorization strategy according to the changes in user behavior and feedback, establishes a user feedback channel, and collects user evaluations and suggestions on the authorization strategy.

[0012] Furthermore, the S500 uses encryption technology to protect user privacy, conducts security audits on authorization policies, and enhances user information and communication security.

[0013] Compared with the prior art, a telecommunication system service authorization method has the following beneficial effects: 1. The present invention collects and analyzes users' diversified behavior data in real time to build accurate user behavior portraits. Combined with the predictive ability of the AI ​​learning engine, the method can intelligently predict users' future service needs and generate personalized service authorization strategies based on them. This can not only provide users with telecommunications services that are more in line with their actual needs and improve user experience, but also ensure the accuracy of service authorization, reduce unnecessary waste of resources, and improve the service efficiency and customer satisfaction of operators.

[0014] 2. The present invention strictly abides by data protection laws and regulations during data processing and authorization policy generation, and adopts encryption technology to protect user privacy. At the same time, by building a security guarantee mechanism, security audits are conducted on authorization policies to ensure user information and communication security. This comprehensive data protection measure can effectively prevent user data leakage and abuse, enhance users' trust in telecommunications systems, and provide strong guarantees for the healthy development of telecommunications services. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 An operation flow chart of a telecommunication system service authorization method. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1 Identify and access telecommunications networks, mobile applications, and reliable third-party data sources, including but not limited to user communication logs, location service APIs, and mobile application usage records; configure data capture interfaces to ensure real-time and stability; set reasonable data collection frequencies and transmission protocols; collect user communication records from telecommunications networks, including voice calls, text messages, and data traffic usage; obtain user application usage records, interactive behaviors, and dwell time data from mobile applications; obtain user location information, consumption habits, and diversified social network data through third-party data sources; pre-process the collected raw data, including removing duplicate data, processing missing values, correcting erroneous data, identifying and removing noise data, unifying and standardizing the data format, ensuring data consistency and comparability; and integrating data from different sources and formats into a unified data warehouse or data lake. , establish the correlation between data, classify and mark the data, prepare for subsequent feature selection and analysis, conduct basic data statistical analysis, identify the patterns and laws of user behavior, analyze the time series characteristics of user behavior, and based on the results of preliminary analysis, extract the features that have an important impact on the construction of user portraits. Use algorithms and models to weight and combine the features to form quantitative indicators of user behavior portraits. Based on the quantitative indicators, construct user behavior portraits with personalized characteristics, including multiple dimensions such as user basic attributes, behavioral characteristics, and service needs. Use the validation set to verify the constructed user behavior portraits and evaluate their accuracy and reliability. According to the verification results, adjust and optimize the portrait construction process to improve the accuracy and practicality of the portraits. Regularly update user behavior data, and rebuild or update user behavior portraits to reflect changes and trends in user behavior.

[0019] From the cleaned and integrated user behavior data, select features that have an important impact on building user portraits and predicting service needs. According to business needs, transform these features to improve the training efficiency and prediction accuracy of the model. Divide the preprocessed data into training sets, validation sets, and test sets for subsequent model training and evaluation. According to the characteristics of the problem and data characteristics, select algorithms for user behavior analysis and prediction. ,in, is a nonlinear activation function used to transform the intermediate output of the model into the final predicted value. Is related to each feature The relevant weights are used to adjust the influence of different features on the results. is the hyperbolic tangent function, which can increase the nonlinear expression ability of the model. It is a feature A preprocessing function of It is a random error term, which is used to represent the uncertainty factors that cannot be fully explained by the model. The selected algorithm is trained using the training set data. By iteratively optimizing the algorithm parameters, the model can accurately learn the user's communication habits, service preferences and potential security needs from the user behavior data. The online learning or incremental learning method is adopted to enable the model to continuously receive new user data or data on user behavior changes, and perform real-time updates and training, thereby continuously optimizing the accuracy of the user portrait. According to the model training results, the user portrait is updated according to the needs to reflect the user's latest behavior patterns and preferences. Based on the trained user portrait model, a model for predicting the user's future service needs is constructed. ,in, is an activation function, and is the corresponding weight coefficient, Behavioral characteristics Related frequency adjustment parameters, This item takes into account the impact of the periodicity or fluctuation of behavioral patterns on service demand. It uses the prediction model, combined with the user's current behavior pattern and external environmental factors, to predict the possible changes in the user's service demand in the future. The trained model is evaluated using the validation set and test set. The evaluation indicators include accuracy, recall rate, F1 score, and area under the ROC curve (AUC) to verify the model's predictive and generalization capabilities. A user feedback channel is established to collect user satisfaction evaluations on authorization strategies and service recommendations, and use these feedback data to further optimize and adjust the model.

[0020] Obtain the trained and optimized user portrait model and its prediction results from the AI ​​learning engine. These results include the user's communication habits, service preferences, potential security needs, and possible changes in service needs in the future. Compare and integrate the output results of the AI ​​learning engine with the telecommunications business rules. This includes considering the activation conditions, restrictions, and fee structures of different telecommunications services. According to the business rules, select telecommunications services that meet the user's current needs and future predicted needs. Perform a cost-benefit analysis on the selected telecommunications services, evaluate the cost and expected benefits of each service, comprehensively consider costs and benefits, and determine the optimal telecommunications service combination. Based on the above analysis, intelligently generate personalized service authorization strategies that should be able to maximize user satisfaction. The authorization policy should clearly list the telecommunications services accessible to users, the rights to use the services, restrictions, and related fee information. It should identify users who are particularly concerned about privacy. This can be determined through the privacy preference characteristics in the user portrait or the privacy protection behavior in the user's historical behavior. For these users, encrypted communication services are recommended to ensure that their communication content is protected. The principle of least privilege is followed to restrict these users from accessing non-essential services to reduce potential risks of privacy leakage. The generated personalized authorization policy should be output to the user and informed through the user interface or notification. A user feedback channel should be established to collect users' evaluation and suggestions on the authorization policy. These feedbacks will be used for subsequent strategy optimization and adjustment.

[0021] The user behavior data collection and analysis module is used to continuously collect users' real-time communication records, location information, and diversified data of service usage records, and the data is cleaned and integrated in real time to ensure the accuracy and timeliness of the data. The AI ​​learning engine is used to analyze user behavior data in real time, update the user portrait model, reflect the user's latest behavior patterns and preferences, compare the user's historical behavior data with the newly collected behavior data, identify significant changes in user behavior patterns, and analyze the reasons for these changes, such as whether they are due to changes in user needs, environmental changes, or external events. According to the type and degree of user behavior changes, combined with telecommunications business rules and cost-benefit analysis, the adjustment direction of the authorization policy is preliminarily determined. The personalized authorization policy generation module is used to intelligently generate new authorization policies to ensure that the policies meet user needs and comply with business rules and cost-effectiveness. Risk assessment and verification are performed on the new authorization policy to ensure its security and feasibility. The adjusted authorization policy is pushed to the user, and the authorization policy configuration in the system is updated at the same time. The user behavior changes after the implementation of the new policy are monitored to ensure To ensure that the policy adjustment achieves the expected effect, user feedback channels are established through user interface, customer service hotline, and online surveys. Satisfaction questionnaires are regularly pushed to users to collect users' satisfaction evaluations on authorization policies and service recommendations. A special user feedback processing team is established to respond to and handle user feedback in a timely manner to ensure that user questions and suggestions are properly handled. User feedback data, including satisfaction evaluations, specific issues, and improvement suggestions, is collected and analyzed. Valuable information and opinions are extracted and the analysis results are fed back to relevant departments for further optimization and adjustment of system performance, including user behavior data collection and analysis modules, AI learning engines, and personalized authorization policy generation modules. Based on user feedback and system operation data, system performance is regularly evaluated to identify existing problems and room for improvement, optimization plans are formulated, optimization goals and measures are clearly defined, and they are assigned to relevant departments and teams for continuous optimization of user behavior data analysis models, AI learning engine algorithms, and personalized authorization policy generation logic to enhance the overall intelligence level and user experience of the system.

[0022] Only the minimum user data necessary to achieve business purposes is collected. Before collecting any personal data, users are clearly informed of how their data will be used and their explicit consent is obtained. During data transmission, SSL / TLS encryption technology is used to ensure the security of data at the transmission layer. The collected data is stored in an encrypted database to ensure that even if the database is illegally accessed, the data is difficult to decrypt. Strict access control policies are implemented to ensure that only authorized personnel or systems can access and process user data. Sensitive data is desensitized to protect user privacy. Data access and modification records are recorded to track and audit data usage. The AI ​​learning engine model is encrypted to prevent the model from being stolen or tampered with. When user data is divided into training sets, validation sets, and test sets, ensure that the data in these partitions are appropriately protected during storage and transmission. Differential hidden Private and federated learning privacy protection algorithms reduce the exposure of user privacy during model training. The generated authorization policy itself can be encrypted and stored to prevent unauthorized access. The generated authorization policy is audited to ensure that it complies with the company's security policies and regulatory requirements. When generating authorization policies, follow the principle of least privilege and only authorize necessary service access rights. Establish a real-time monitoring mechanism to monitor the execution of authorization policies, promptly discover and handle abnormal situations, and dynamically adjust authorization policies based on changes in user behavior and feedback. Ensure that these adjustment processes also comply with security specifications. Establish a secure user feedback channel to ensure that user privacy is protected during the collection, processing and response of user feedback. Strictly comply with relevant data protection laws and regulations to ensure the legality of data processing and authorization policies. Communicate regularly with legal advisors to ensure that business operations and technical implementations comply with the latest laws and regulations.

[0023] Embodiment 2 In addition to collecting employees’ communication records through telecommunications networks, we also integrate the data interfaces of the company’s internal mobile applications and third-party travel platforms to capture employees’ itinerary information and diversified data on service usage habits in real time. We use data cleaning technology to remove duplicate, erroneous and irrelevant data entries to ensure the accuracy and completeness of the data. Subsequently, we integrate the cleaned data to form a unified employee behavior data warehouse. We use data analysis and mining technology to conduct in-depth analysis of employee behavior data to construct a personalized behavior portrait of each employee, including communication habits, work preferences, and frequency of business trips.

[0024] Select features from employee behavior portraits that have an important impact on predicting future service needs and perform conversion processing to improve the model's training efficiency and prediction accuracy. Select a machine learning algorithm for model training based on problem characteristics and data features. Iteratively optimize algorithm parameters so that the model can accurately capture employees' communication habits, service preferences, and potential security needs. Use online learning or incremental learning methods so that the model can continuously receive data on new employees or data on changes in existing employees' behavior, and perform real-time updates and training to ensure the timeliness and accuracy of user portraits. Based on the trained user portrait model, build a model to predict employees' future service needs. ,in, is an activation function, and is the corresponding weight coefficient, Indicates some characteristic or information related to an employee. Behavioral characteristics Related frequency adjustment parameters, This item takes into account the impact of the cyclical or fluctuating characteristics of behavioral patterns on service demand, and combines the current behavioral patterns of employees with external environmental factors to predict possible changes in their service demand in the future.

[0025] Based on the output results of the AI ​​learning engine, combined with the company's communication policies, business rules and cost-benefit analysis, a customized service authorization strategy is intelligently generated for each employee, with special attention paid to employee privacy protection. For employees who value privacy, encrypted communication services are recommended, and access rights to non-essential services are restricted in accordance with the principle of least privilege to ensure the security of employees' personal information.

[0026] Establish a real-time monitoring mechanism and pay close attention to changes in employee behavior patterns in order to adjust service authorization strategies in a timely manner. Set up a dedicated user feedback channel to encourage employees to evaluate and make suggestions on authorization strategies. The collected feedback will serve as an important basis for optimizing system performance. Dynamically adjust authorization strategies based on changes in employee behavior and feedback to ensure the effectiveness and practicality of the strategies.

[0027] Strictly abide by the data protection laws of various countries and regions, ensure the legal collection, processing and storage of employee data, use advanced encryption technology to protect the confidentiality and integrity of employee communications, prevent data leakage and tampering, conduct regular security audits of authorization policies to ensure policy compliance and security, and at the same time, strengthen the protection of employee information and improve the overall level of information security.

[0028] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A telecommunication system service authorization method, characterized in that: The specific steps of this method are: S100, user behavior data collection and analysis module: collects users' communication records, location information, and service usage records in real time through telecommunications networks, mobile applications, and third-party data sources, cleans, integrates, and preliminarily analyzes these data to build user behavior profiles; S200, AI learning engine: selects features that have a significant impact on building user profiles and predicting service needs from the cleaned and integrated user behavior data, transforms these features according to business needs, and divides the pre-processed data into training sets, validation sets, and test sets; Select algorithms based on problem characteristics and data features. For user behavior analysis and prediction, use training set data to train the selected algorithms. By iteratively optimizing algorithm parameters, the model can accurately learn users' communication habits, service preferences, and potential security needs from user behavior data. Adopt online learning or incremental learning methods to enable the model to continuously receive new user data or data on user behavior changes, and perform real-time updates and training, thereby continuously optimizing the accuracy of user portraits. Based on the model training results, the user portraits are updated as needed to reflect the user's latest behavior patterns and preferences. Based on the trained user portrait model, a model is built to predict the user's future service needs. The prediction model is used to combine the user's current behavior pattern and external environmental factors to predict the possible changes in the user's service needs in the future. Use validation sets and test sets to evaluate the trained model. Evaluation indicators include accuracy, recall, F1 score, and area under the ROC curve (AUC) to verify the model's prediction and generalization capabilities. Establish a user feedback channel to collect user satisfaction evaluations on authorization strategies and service recommendations, and use these feedback data to further optimize and adjust the model. S300, personalized authorization policy generation module: Based on the output of the AI ​​learning engine, combined with telecommunications business rules and cost-benefit analysis, it intelligently generates personalized service authorization policies. For privacy-conscious users, it recommends encrypted communication services and follows the principle of least privilege to limit access rights to non-essential services. S400, dynamic adjustment and feedback mechanism: It has real-time monitoring and dynamic adjustment capabilities, and can adjust authorization policies in a timely manner according to changes in user behavior and feedback. At the same time, it can establish user feedback channels to collect users' evaluations and suggestions on authorization policies to further optimize system performance. S500, security assurance mechanism: In the process of data processing and authorization policy generation, strictly abide by data protection regulations, use encryption technology to protect user privacy, and at the same time, conduct security audits on authorization policies to strengthen user information and communication security.

2. A telecommunication system service authorization method according to claim 1, characterized in that: The S100 collects diversified data such as user's communication records, location information, and service usage records in real time through telecommunication networks, mobile applications, and third-party data sources.

3. A telecommunication system service authorization method according to claim 1, characterized in that: The S200 selects an algorithm based on the characteristics of the problem and the data to analyze and predict user behavior: ,in, is a nonlinear activation function used to transform the intermediate output of the model into the final predicted value. With each feature The relevant weights are used to adjust the influence of different features on the results. is the hyperbolic tangent function, which can increase the nonlinear expression ability of the model. It is a feature A preprocessing function of is a random error term used to represent the uncertainty factors that cannot be fully explained by the model.

4. A telecommunication system service authorization method according to claim 1, characterized in that: The S200 builds a model for predicting users' future service needs based on the trained user portrait model: ,in, is an activation function, and is the corresponding weight coefficient, Behavioral characteristics Related frequency adjustment parameters, This term takes into account the impact of cyclical or fluctuating nature of behavior patterns on service demand.

5. A telecommunication system service authorization method according to claim 1, characterized in that: The S300 combines telecommunication service rules with cost-benefit analysis to intelligently generate personalized service authorization strategies.

6. A telecommunication system service authorization method according to claim 1, characterized in that: For users who value privacy, S300 recommends encrypted communication services and limits access rights to non-essential services in accordance with the principle of least privilege.

7. A telecommunication system service authorization method according to claim 1, characterized in that: The S400 timely adjusts the authorization strategy according to the changes in user behavior and feedback, establishes a user feedback channel, and collects user evaluations and suggestions on the authorization strategy.

8. A telecommunication system service authorization method according to claim 1, characterized in that: The S500 uses encryption technology to protect user privacy, conduct security audits on authorization policies, and enhance user information and communication security.

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