A user rights upgrade authentication method and system for a network platform

Abstract

The present invention relates to the field of user authentication technology, specifically to a user authority upgrade authentication method and system for a network platform, comprising the following steps: the network platform receives a user's authority upgrade request, obtains a user identifier through login information provided by the user, and indexes the associated social network data according to the user account to obtain a record of the number of user friends. In the present invention, the social activity and trust of the user are comprehensively evaluated through the index of the number of friends, the login frequency and the interaction situation are investigated, and the grasp of the user behavior pattern is further refined. Through the application of classifiers and Markov decision processes, the user's behavior changes can be predicted and adapted more dynamically, making the authority upgrade process more accurate and personalized, while improving the user experience, it also enhances the security of the system, and can adjust and optimize the authority review strategy in a targeted manner, while maintaining the security of the system, to maximize the satisfaction of user needs.

Description

Technical Field

[0001] The present invention relates to the technical field of user authentication, and in particular to a method and system for authenticating user rights upgrade on a network platform. Background Art

[0002] The field of user authentication technology involves a variety of methods and technologies used to verify and confirm user identities to ensure the security of network platforms or systems. This ranges from simple username and password authentication to more complex multi-factor authentication (MFA), such as SMS verification codes, biometrics, and hardware tokens. User authentication technology is an important part of information security and network security, designed to prevent unauthorized access and ensure data integrity and privacy protection. With the development of technology, the field of user authentication is also constantly introducing advanced technologies such as artificial intelligence and machine learning to improve the intelligence and automation of the authentication process.

[0003] Among them, the user rights upgrade authentication method of the network platform refers to enhancing the user account's rights or access level through additional verification steps or standards on the basis of the existing user authentication. The purpose of this method is mainly to more finely control the user's access rights to sensitive data or operations, and is used in scenarios that require a higher level of security, such as financial services and internal enterprise data management. Through this upgraded authentication method, data leakage and abuse can be effectively prevented, ensuring the security of the system and data.

[0004] Although existing technologies have achieved multi-level user authentication and permission control, they lack sufficient adaptability and personalization. Existing multi-factor authentication methods mainly rely on fixed authentication standards, such as passwords, biometrics, and hardware tokens, and in some cases cannot effectively evaluate and respond to user behavior and environmental changes. For example, in the face of social engineering attacks or internal data leaks, traditional authentication methods cannot fully identify risks because they do not take into account abnormal patterns of user behavior. The lack of dynamic analysis of user behavior trends has led to overly rigid permission management and the inability to flexibly respond to various complex and changing security threats. This static authentication model limits the system's ability to respond to emerging security challenges, resulting in insufficient response to real security threats or excessive restrictions on user operations, affecting user experience and operational efficiency. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a user authority upgrade authentication method and system for a network platform.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a method for authenticating user rights upgrade on a network platform, comprising the following steps:

[0007] S1: The network platform receives the user's permission upgrade request, obtains the user ID through the login information provided by the user, indexes the associated social network data according to the user account, and obtains the number of the user's friends;

[0008] S2: Compare the number of friends of the user with the preset permission upgrade threshold, use conditional query to determine whether the number of friends reaches the threshold, and if so, mark it and generate an initial permission review status;

[0009] S3: Analyze the user's behavior data using the initial permission review status, record the user's login frequency, posting and interaction, input the behavior data into a classifier, and generate a behavior pattern classification result;

[0010] S4: Input the behavior pattern classification result into the Markov decision process, use the real-time status to estimate the probability of status change within the next week, decide whether to recommend permission upgrade based on the estimated value, and generate a permission review strategy;

[0011] S5: Implement the permission audit strategy, perform policy evaluation, compare user behavior data with expected results, analyze deviations and adjust the permission audit strategy to match user behavior changes and generate policy optimization results;

[0012] S6: According to the policy optimization result, if the conditions are met, the user rights are updated, the new permission status is written into the user configuration file and the user session is reloaded, the permission is changed, and a permission update log is generated.

[0013] As a further solution of the present invention, the user friend number record includes data items recording the number of friends of each user account, the user's unique identifier and the number of associated friends; the initialization permission review status includes the pending review mark of the permission review, the review result and the status of whether the upgrade conditions are met; the behavior pattern classification result includes the user's behavior type, interaction mode and user group positioning; the permission review strategy includes a permission upgrade plan based on user behavior prediction, predicted user behavior change trends and potential risk assessment; the strategy optimization result includes adjusted decision parameters, optimized user classification logic and adjusted risk management measures; the permission update log includes the timestamp of the update operation, the user's identification information and the permission change information.

[0014] As a further solution of the present invention, the network platform receives a user's permission upgrade request, obtains a user identifier through login information provided by the user, and indexes the associated social network data according to the user account to obtain a record of the number of user friends, specifically the following steps:

[0015] S101: The network platform receives a permission upgrade request submitted by a user, collects the user's login information, including the user name and password, verifies the legitimacy of the user's identity through identity verification, and generates a user identity verification result;

[0016] S102: Based on the user identity authentication result, using the user's login information as a key index, retrieving social network data associated with the user account, identifying a friend list, and generating a social network data snapshot;

[0017] S103: Calculate the total number of the user's friends based on the social network data snapshot, record the user's social interaction frequency and friend activity, evaluate the user's influence and connectivity in the social network, and generate a record of the number of the user's friends.

[0018] As a further solution of the present invention, the user's friend number record is compared with a preset permission upgrade threshold, and a conditional query is used to determine whether the number of friends reaches the threshold. If so, it is marked, and the steps of generating the initialization permission review status are specifically as follows:

[0019] S201: comparing the user's friend number record with a preset permission upgrade threshold, where the permission upgrade threshold includes the minimum number of friends required, using conditional query to determine whether the number of friends exceeds the threshold, and generating a threshold comparison result;

[0020] S202: Based on the threshold comparison result, if the number of friends of the user reaches and exceeds the preset threshold, the user is marked as meeting the conditions; if the number does not exceed the threshold, the user is marked as not meeting the conditions, and a permission upgrade mark is generated;

[0021] S203: Based on the permission upgrade mark, the user does not need to pay the permission upgrade certification fee, the user's permission review process is identified, whether the user meets the permission upgrade requirements is recorded, the user's permission authentication process and processing flow are updated and tracked in real time, and the initialization permission review status is generated.

[0022] As a further solution of the present invention, the steps of using the initial permission review state to analyze the user's behavior data, record the user's login frequency, posting and interaction, input the behavior data into the classifier, and generate the behavior pattern classification result are specifically as follows:

[0023] S301: According to the initial permission review status, monitor and collect user login, posting and interaction data, the collected data includes login times, posting frequency and interaction type and times, and generate user activity records;

[0024] S302: Based on the user activity records, organize and classify the data, identify key behavior characteristics, including login periodicity, posting content preference and interaction preference, and generate a behavior characteristic summary;

[0025] S303: Based on the behavior feature summary, cluster analysis technology is used to perform pattern recognition and grouping of user behaviors, group user behaviors according to the features, calculate the weighted Euclidean distance of user behavior features, and generate a behavior pattern classification result.

[0026] As a further solution of the present invention, the formula of the cluster analysis technology is as follows:

[0027]

[0028] in, is the weighted Euclidean distance, Representative User in Behavioral characteristics, Representative The performance of a user on the same behavioral feature, Representative The standard deviation of the behavioral characteristics, It is The weight coefficient of each behavior feature, is the total number of behavioral characteristics.

[0029] As a further solution of the present invention, the behavior pattern classification result is input into the Markov decision process, and the probability of state change within the next week is estimated using the real-time state. According to the estimated value, it is decided whether to recommend the permission upgrade. The steps of generating the permission audit strategy are specifically as follows:

[0030] S401: According to the behavior pattern classification result, real-time status data and real-time status data within the next week are collected, and data integration and screening are performed using time series analysis, and status data are grouped to obtain status data accumulation;

[0031] S402: using the state data accumulation, by calculating the frequency and duration between the differentiated state groups, recording the transition frequency, calculating the state-to-state transition probability, and generating a state transition probability table;

[0032] S403: Using the state transition probability table, evaluate the probability that the differentiated state reaches the recommended permission upgrade condition, and obtain the permission review strategy by comparing the probability with the set threshold.

[0033] As a further solution of the present invention, the steps of implementing the permission audit strategy, performing policy evaluation, comparing user behavior data with expected results, analyzing deviations and adjusting the permission audit strategy to match user behavior changes and generating policy optimization results are specifically as follows:

[0034] S501: Implement the authority audit strategy, capture behavior changes under the influence of the strategy, synchronize data to the network platform, and record behavior pattern changes to obtain monitoring behavior data;

[0035] S502: Based on the monitoring behavior data, using statistical comparative analysis, compare the real-time user behavior with the expected results, calculate the behavior deviation, evaluate the real-time effect and deviation degree of the strategy, and generate a strategy evaluation result;

[0036] S503: Based on the strategy evaluation result, analyze the cause of the deviation, adjust the strategy to match the real-time changes in user behavior, and update the strategy parameters and execution logic to obtain a strategy optimization result.

[0037] As a further solution of the present invention, according to the policy optimization result, the user rights are updated when the conditions are met, the new permission status is written into the user configuration file and the user session is reloaded to change the permission, and the steps of generating the permission update log are specifically as follows:

[0038] S601: Based on the policy optimization result, check whether the user's real-time permissions are consistent with the permissions recommended by the policy. When the update condition is met, modify the permission status in the user configuration file to obtain a configuration status modification record;

[0039] S602: Using the configuration status modification record, write the new permission status into the user configuration file, restart the user permission service, reload the user session, verify the response speed of the permission adjustment, and generate a session reload record;

[0040] S603: Monitor and record the user activity data after the permission is updated through the session reload record, check the consistency of the permission change, update and synchronize the user permissions in real time, and generate a permission update log.

[0041] A user rights upgrade authentication system for a network platform, the user rights upgrade authentication system for the network platform is used to execute the user rights upgrade authentication method for the network platform, the system comprises:

[0042] The request receiving module collects the permission upgrade request submitted by the user, extracts the user's login information and identifies the user ID, retrieves the social network data associated with the user account based on the ID, and generates a record of the number of friends;

[0043] The threshold comparison module compares the friend quantity record with the set permission upgrade threshold to evaluate whether the upgrade conditions are met. If so, the user is marked and the initialization review status is generated;

[0044] The behavior data analysis module uses the initial audit status to collect the user's login frequency, posting and interaction data, inputs the behavior data into the classifier, classifies the user's behavior pattern, and generates a behavior pattern classification result;

[0045] The decision-making module applies the Markov decision process based on the behavior pattern classification results to evaluate the probability of changes in the user's behavior status in the next week, decides whether to recommend user permission upgrade based on the probability, and generates a permission review strategy;

[0046] The policy evaluation module implements the permission audit policy, compares the user's real-time behavior data with the expected behavior, analyzes the deviation between the two, adjusts the permission audit policy to match the user's behavior changes, updates the user's permissions, writes the real-time permission status to the user configuration file and reloads the user session, and generates a permission update log.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, the context sensitivity of user authentication is improved by integrating the analysis of user social network data into the decision-making process of permission upgrade. The introduction of social data makes the verification of user behavior more comprehensive and accurate. It is not limited to a single password or biometric feature, but comprehensively evaluates the user's social activity and trust through indicators such as the number of friends. In-depth analysis of behavioral data, such as the examination of login frequency and interaction, further refines the grasp of user behavior patterns. Through the application of classifiers and Markov decision processes, it is possible to more dynamically predict and adapt to user behavior changes, making the permission upgrade process more accurate and personalized. While improving the user experience, it also enhances the security of the system, and can adjust and optimize the permission review strategy in a targeted manner to maximize user satisfaction while maintaining system security. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a user rights upgrade authentication method for a network platform, comprising the following steps:

[0060] S1: The network platform receives the user's permission upgrade request, obtains the user ID through the login information provided by the user, indexes the associated social network data according to the user account, and obtains the number of the user's friends;

[0061] S2: Compare the number of friends of the user with the preset permission upgrade threshold, use conditional query to determine whether the number of friends reaches the threshold, and if so, mark the user as meeting the conditions and generate the initial permission review status;

[0062] S3: Initialize the permission review status, analyze the user's behavior data, record the user's login frequency, posting and interaction, input the behavior data into the classifier, and the classifier classifies according to the pre-defined behavior pattern to generate the behavior pattern classification result;

[0063] S4: Input the behavior pattern classification results into the Markov decision process, use the real-time status to estimate the probability of status change within the next week, decide whether to recommend permission upgrade based on the estimated value, and generate a permission review strategy;

[0064] S5: Implement the permission audit strategy, perform policy evaluation, compare the current user behavior data with the expected results, analyze the deviation and adjust the permission audit strategy to match the user's behavior changes and generate policy optimization results;

[0065] S6: Based on the policy optimization results, the network platform updates the user permissions when the conditions are met, writes the new permission status into the user profile and reloads the user session to make permission changes and generate a permission update log.

[0066] The record of the number of user friends includes data items recording the number of friends of each user account, the user's unique identifier and the number of associated friends. The initialization permission review status includes the pending review mark of the permission review, the review result and the status of whether the upgrade conditions are met. The behavior pattern classification results include the user's behavior type, interaction mode and user group positioning. The permission review strategy includes the permission upgrade plan based on user behavior prediction, predicted user behavior change trends and potential risk assessment. The strategy optimization results include adjusted decision parameters, optimized user classification logic and adjusted risk management measures. The permission update log includes the timestamp of the update operation, the user's identification information and the permission change information.

[0067] See also Figure 2 The network platform receives the user's permission upgrade request, obtains the user ID through the login information provided by the user, and indexes the associated social network data according to the user account to obtain the number of user friends. The specific steps are:

[0068] S101: The network platform receives the permission upgrade request submitted by the user, collects the user's login information, including the user name and password, verifies the legitimacy of the user's identity through identity verification, and generates the user identity verification result. The execution process is as follows;

[0069] When the network platform receives a permission upgrade request submitted by a user, it verifies the login information submitted by the user, including the user name and password. The security verification of the data involves a standard identity verification process. The identity verification process includes a security check on the password and a matching verification of the username to ensure the uniqueness and legitimacy of the username and password combination and prevent unauthorized access and account fraud. The verification process uses specific algorithms such as hash functions to compare the encrypted information stored in the database to ensure the accuracy and security of the information. The verification result generates the user identity verification result.

[0070] S102: Based on the user identity authentication result, the user's login information is used as a key index to retrieve the social network data associated with the user account, identify the friend list, and generate a snapshot of the social network data. The execution process is as follows;

[0071] Based on the user authentication results, the system uses the user's login information as a key index to retrieve social network data associated with the user account through database query operations. The query process involves index matching of social network information stored in the database, locating and accessing the user's social media account information based on the combination of username and password, identifying and listing all friend lists associated with the account, including the extraction and analysis of friend data, and generating a social network data snapshot.

[0072] S103: Calculate the total number of the user's friends based on the social network data snapshot, record the user's social interaction frequency and friend activity, evaluate the user's influence and connectivity in the social network, and generate the user's friend number record. The execution process is as follows;

[0073] Calculate the total number of user's friends according to the formula , calculate the total number of friends. In the formula, Represents the total number of friends, Represents the total number of entries in the friend list. Formula explanation and formula calculation derivation process: Suppose a user's friend list includes friends. Each friend in the list is counted as 1 (existence). Then the formula The calculation process is , so the total number of friends of the user is 8. This approach ensures that every entry is counted, giving the exact number of actual friends in the user's social network.

[0074] See also Figure 3 , compare the user's friend count record with the preset permission upgrade threshold, use conditional query to determine whether the number of friends reaches the threshold, and mark it if it reaches it. The specific steps to generate the initial permission review status are:

[0075] S201: Compare the user's friend number record with the preset permission upgrade threshold, the permission upgrade threshold includes the minimum number of friends required, use conditional query to determine whether the number of friends exceeds the threshold, and generate the threshold comparison result. The execution process is as follows;

[0076] Record the user's social interaction frequency and friend activity, according to the formula , calculate the frequency of social interaction. In the formula, Represents the frequency of social interaction, Representative Number of interactions with friends, Represents the friend's activity index. is the total number of friends. Detailed explanation of the formula and the process of formula calculation: Assume that a user has 5 friends, and the number of interactions with each friend is The activity indexes are Then the formula Calculation process , that is, the average social interaction frequency of the user is 2.4.

[0077] S202: Based on the threshold comparison result, if the number of friends of the user reaches and exceeds the preset threshold, the user is marked as qualified; if it does not exceed the threshold, the user is marked as unqualified. The execution process of generating the permission upgrade mark is as follows;

[0078] When comparing the user's friend number record with the preset permission upgrade threshold, a conditional query is used to determine whether the number of friends exceeds the threshold. A database query language such as SQL is used for conditional judgment. The database query statement will be filtered according to the user's friend number record. If the number of friends displayed in the record exceeds the preset minimum friend number threshold, the query result will indicate that the user meets the permission upgrade requirements. Otherwise, a non-compliant result will be returned, reflecting whether the user has sufficient social foundation to support permission upgrades and generating a threshold comparison result.

[0079] S203: Based on the permission upgrade mark, the user does not need to pay the permission upgrade certification fee, identifies the user's permission review process, records whether the user meets the permission upgrade requirements, updates and tracks the user's permission certification process and processing flow in real time, and generates the execution flow of initializing the permission review state as follows;

[0080] Based on the permission upgrade mark, the system will not require users to pay for permission upgrade certification fees. The system automatically identifies and records whether the user meets the permission upgrade requirements, updates and tracks the user's permission certification process and processing flow in real time, and records the review status and required administrative processing of each step through the internal management system, ensuring the fairness and transparency of the permission upgrade and generating the initial permission review status.

[0081] See also Figure 4 , using the initial permission review status, analyzing the user's behavior data, recording the user's login frequency, posting and interaction, inputting the behavior data into the classifier, and generating the behavior pattern classification results are as follows:

[0082] S301: According to the initial permission review status, the user login, posting and interaction data are monitored and collected. The collected data includes the number of logins, posting frequency, and interaction type and number. The execution process of generating user activity records is as follows;

[0083] Based on the initial permission review status, the system monitors and collects the user's login, posting and interaction data in detail. The process includes regularly recording the user's login times, posting frequency, and the type and number of interactions, and capturing and storing activity data through automated data collection tools such as log analysis software to ensure the comprehensiveness and accuracy of the data, reflecting in detail the user's behavior patterns on the platform, including active time, content preferences and interaction habits, providing basic data for subsequent behavior analysis and generating user activity records.

[0084] S302: Based on the user activity records, the data is sorted and classified to identify key behavior characteristics, including login periodicity, posting content preference and interaction preference. The execution process of generating a behavior characteristic summary is as follows;

[0085] Based on user activity records, data analysis will organize and classify the collected data, and identify the key behavioral characteristics of users by applying data mining technology and pattern recognition methods, including analyzing login data to determine the user's active cycle, for example, identifying high-frequency and low-frequency login users. Post content analysis helps understand users' preferred topics, and interactive preference analysis reveals how and how often users interact with members in the community. This information is crucial for the platform to optimize user experience and content recommendations, and generate a behavioral characteristic summary.

[0086] S303: Based on the behavior feature summary, cluster analysis technology is used to perform pattern recognition and grouping of user behaviors, group user behaviors according to the features, calculate the weighted Euclidean distance of user behavior features, and generate the behavior pattern classification results. The execution process is as follows;

[0087] The formula for cluster analysis technique is as follows:

[0088]

[0089] in, is the weighted Euclidean distance, Representative User in Behavioral characteristics, Representative The performance of a user on the same behavioral feature, Representative The standard deviation of the behavioral characteristics, It is The weight coefficient of each behavior feature, is the total number of behavioral characteristics.

[0090] The formula is used to calculate the user With users The weighted Euclidean distance between them is used for user behavior pattern recognition in cluster analysis. and Respectively represent users and In behavioral characteristics The value on is the standard deviation of the behavioral characteristic, is the weight coefficient of the feature, is the total number of behavioral characteristics.

[0091] Set up two existing users and , which includes two behavioral characteristics and , the specific values ​​are: , : The value of the first line feature; , : The value of the second behavior feature; : standard deviation of the first feature; : The standard deviation of the second feature; : The weight of the first feature; : The weight of the second feature.

[0092] Weight coefficient The basis for setting is the importance of the features in distinguishing user behavior patterns. In real-world settings, the weights are set based on previous research or the advice of domain experts, and feature 1 is considered more critical and therefore has a higher weight.

[0093] The specific calculation process of the formula is:

[0094] 1. Calculate features The weighted terms of:

[0095]

[0096] The weighted value is .

[0097] 2. Calculate features The weighted terms of:

[0098]

[0099] The weighted value is .

[0100] 3. Add the two weighted terms and take the square root:

[0101]

[0102] Calculation results Indicates user and users The smaller the distance under the weighted Euclidean distance metric, the more similar the behaviors of the two users are. This value is used in the clustering algorithm to decide whether to assign the two users to the same category. The numerical results show that after considering the weights of each feature and normalization, the behavior patterns of the two users have relatively small differences and tend to be classified into the same behavior pattern category.

[0103] See also Figure 5 , input the behavior pattern classification results into the Markov decision process, use the real-time status to estimate the probability of status change in the next week, and decide whether to recommend permission upgrade based on the estimated value. The specific steps for generating permission review strategy are as follows:

[0104] S401: According to the behavior pattern classification results, real-time status data and real-time status data within the next week are collected, and the data is integrated and screened using time series analysis, and the status data is grouped to obtain the execution process of status data accumulation as follows;

[0105] Data is collected based on the results of behavioral pattern classification, involving real-time and next week's data collection. Data integration and screening are performed through time series analysis. Based on the collected data, various types of status data are clustered using time series methods, and then orderly integration of data is achieved. The grouped and screened data not only includes numerical status indicators, but also involves sequences of time points, ensuring the accuracy and real-time nature of data integration. The corresponding status data is accumulated through comparative analysis and clustering results. The integrated data will be used for further analysis to generate status data accumulation.

[0106] S402: Using state data accumulation, by calculating the frequency and duration between differentiated state groups, recording the transition frequency, calculating the state-to-state transition probability, and generating a state transition probability table, the execution flow is as follows;

[0107] Using state data accumulation, by calculating the frequency and duration between differentiated state groups, according to the formula , calculate the transition probability from state to state. In the formula, Represents the state Transfer to state The probability of Represents the state Transfer to state The number of monitoring times, Represents in state Detailed explanation of the formula and the derivation process of the formula calculation: Consider a simplified practical scenario. If the transition from state 1 to state 2 is observed 30 times at 100 time points, and state 1 appears 50 times in total, then the transition probability for The computational examples intuitively demonstrate how to obtain the probability of state transitions from real data, and through this process can be further applied to prediction and decision making.

[0108] S403: Using the state transition probability table, evaluate the probability that the differentiated state reaches the recommended permission upgrade condition, and by comparing the probability with the set threshold, obtain the execution process of the permission review strategy as follows;

[0109] The state transition probability table is used to evaluate the probability of differentiated states reaching the recommended permission upgrade conditions. By comparing with the preset threshold, a permission review strategy is formulated. The process includes analyzing the transition probability table, identifying state transitions with probabilities exceeding specific thresholds, and adjusting strategies or implementing new measures based on the data. In this way, the performance and response of the system can be effectively managed and optimized. The use of the transition probability table not only improves the scientific nature of decision-making, but also enhances the accuracy of operations to respond to different state changes and obtain permission review strategies.

[0110] See also Figure 6 , implement the permission audit strategy, perform policy evaluation, compare user behavior data with expected results, analyze deviations and adjust the permission audit strategy to match user behavior changes, and generate policy optimization results in the following steps:

[0111] S501: Implement the authority audit strategy, capture the behavior changes under the influence of the strategy, synchronize the data to the network platform, and record the behavior pattern changes. The execution process of obtaining the monitoring behavior data is as follows;

[0112] After the implementation of the permission review strategy, the system begins to capture behavioral changes under the influence of the strategy, synchronizes data to the network platform, and records changes in behavioral patterns. The entire process is not just a simple recording of data, but also includes formatting and standardizing the data to ensure data availability and consistency. After the data is synchronized to the network platform, preliminary data collation is performed through specific analysis tools. The data will serve as the basis for subsequent analysis. In this way, the implementation of the strategy can be monitored in real time, and changes in behavioral patterns can be responded to quickly to obtain monitoring behavior data.

[0113] S502: Based on the monitored behavior data, using statistical comparative analysis, the real-time user behavior is compared with the expected results, the behavior deviation is calculated, the real-time effect and deviation degree of the strategy are evaluated, and the execution process of generating the strategy evaluation result is as follows;

[0114] Compare real-time user behavior with expected results, according to the formula , calculate the behavior deviation. In the formula, represents the sum of behavioral deviations, Represents observed user behavior data, Represents the expected user behavior data. Detailed explanation of the formula and the process of formula calculation: For example, if the expected user behavior data is , and the observed user behavior data is , the behavior deviation is calculated as This calculation shows how to calculate the deviation of behavior from actual and expected data to evaluate the real-time effect and deviation of the strategy.

[0115] S503: Based on the policy evaluation results, the causes of deviation are analyzed, the policy is adjusted to match the real-time changes in user behavior, and the policy parameters and execution logic are updated. The execution process of obtaining the policy optimization results is as follows;

[0116] Based on the policy evaluation results, the causes of deviations are analyzed and the policies are adjusted to match the real-time changes in user behavior, and the policy parameters and execution logic are updated. The policy adjustment is not only based on the numerical analysis of the deviations, but also includes an in-depth understanding of the causes of the deviations. By adjusting policy parameters, such as modifying thresholds or adjusting decision tree logic, it can better adapt to changes in user behavior. This method can effectively optimize the policy to make it more accurate and adaptable, directly affecting the overall performance of the system and user satisfaction, and obtaining policy optimization results.

[0117] See also Figure 7 , according to the policy optimization results, update the user permissions when the conditions are met, write the new permission status to the user configuration file and reload the user session to change the permissions. The specific steps to generate the permission update log are:

[0118] S601: Based on the policy optimization result, check whether the user's real-time permissions are consistent with the permissions recommended by the policy. When the update condition is met, modify the permission status in the user configuration file. The execution process of obtaining the configuration status modification record is as follows;

[0119] Based on the policy optimization results, check whether the user's real-time permissions are consistent with the permissions recommended by the policy. The process includes reading and analyzing the user's current configuration file, querying the user's permission status through automated scripts or management tools, and comparing the query results with the permissions recommended by the policy. If inconsistency is detected or the update conditions are met, the system will automatically modify the permission status in the user configuration file. The modification of the permission status not only reflects the adjustment capability of the system, but also reflects the direct results of policy optimization. In this way, timely updating of user permissions can be ensured to meet security and operational requirements, and configuration status modification records can be generated.

[0120] S602: Using the configuration status modification record, write the new permission status into the user configuration file, restart the user permission service, reload the user session, verify the response speed of the permission adjustment, and generate the session reload record. The execution process is as follows;

[0121] Write the new permission status to the user configuration file and restart the user permission service according to the formula , calculate the session reload time. Where, Represents the total time of session reload, The time required to stop the service. Represents the time required to restart the service. Formula explanation and formula calculation process: It takes 2 seconds to stop the user permission service and 3 seconds to start the service. The total time for session reload is calculated as The example shows how to calculate the entire session reload time from the service stop and start time, which is a key indicator for evaluating the response speed of permission adjustment and directly affects user experience and system performance.

[0122] S603: Monitor and record the user activity data after the permission is updated through session reload recording, check the consistency of permission changes, update and synchronize user permissions in real time, and generate the execution process of permission update log as follows;

[0123] Through session reload records, monitor and record user activity data after permission updates, and check the consistency of permission changes, including real-time monitoring of user behavior and permission usage, to ensure that the implementation of permissions is consistent with policy recommendations. The system will also synchronously update user permission settings to ensure that all modifications are correctly recorded and implemented. It not only provides detailed records of permission changes, but also serves as an important basis for subsequent audits and compliance checks. Through this real-time update and synchronization, the system can ensure the accuracy and timeliness of permission management and generate permission update logs.

[0124] See also Figure 8 , a user rights upgrade authentication system for a network platform, the user rights upgrade authentication system for a network platform is used to execute the user rights upgrade authentication method for the network platform, the system includes:

[0125] The request receiving module collects the permission upgrade request submitted by the user, extracts the user's login information and identifies the user ID, retrieves the social network data associated with the user account based on the ID, and generates a record of the number of friends;

[0126] The threshold comparison module compares the friend count record with the set permission upgrade threshold to evaluate whether the upgrade conditions are met. If so, the user is marked and the initial review status is generated;

[0127] The behavior data analysis module uses the initialization audit status to collect the user's login frequency, posting and interaction data, inputs the behavior data into the classifier, classifies the user behavior pattern, and generates the behavior pattern classification result;

[0128] The decision-making module applies the Markov decision process based on the behavior pattern classification results to evaluate the probability of changes in user behavior status in the next week, decides whether to recommend user permission upgrade based on the probability, and generates a permission review strategy;

[0129] The policy assessment module implements the permission audit policy, compares the user's real-time behavior data with the expected behavior, analyzes the deviation between the two, adjusts the permission audit policy to match the user's behavior changes, updates the user's permissions, writes the real-time permission status to the user profile and reloads the user session, and generates a permission update log.

[0130] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A user rights upgrade authentication method for a network platform, characterized in that: The following steps are involved: The network platform receives the user's permission upgrade request, obtains the user ID through the login information provided by the user, indexes the associated social network data based on the user account, and obtains the number of the user's friends; Compare the number of friends of the user with the preset permission upgrade threshold, use conditional query to determine whether the number of friends reaches the threshold, and if so, mark it and generate an initial permission review status; Using the initial permission review status, analyzing the user's behavior data, recording the user's login frequency, posting and interaction, inputting the behavior data into a classifier, and generating a behavior pattern classification result; Input the behavior pattern classification result into the Markov decision process, use the real-time status to estimate the probability of status change within the next week, decide whether to recommend permission upgrade based on the estimated value, and generate a permission review strategy; Implement the permission audit strategy, perform policy evaluation, compare user behavior data with expected results, analyze deviations and adjust the permission audit strategy to match user behavior changes and generate policy optimization results; According to the policy optimization results, if the conditions are met, the user permissions are updated, the new permission status is written into the user configuration file and the user session is reloaded to change the permissions and generate a permission update log; According to the behavior pattern classification results, real-time status data and real-time status data within the next week are collected, and time series analysis is used to integrate and filter the data, group the status data, and obtain status data accumulation; Using the state data accumulation, by calculating the frequency and duration between the differentiated state groups, recording the transition frequency, calculating the state-to-state transition probability, and generating a state transition probability table; Using the state transition probability table, evaluate the probability that the differentiated state reaches the recommended permission upgrade condition, and obtain the permission review strategy by comparing the probability with the set threshold; Implement the permission audit strategy, capture the behavior changes under the influence of the strategy, synchronize the data to the network platform, and record the behavior pattern changes to obtain monitoring behavior data; Based on the monitored behavior data, using statistical comparative analysis, real-time user behavior is compared with expected results, behavioral deviations are calculated, real-time effects and deviations of strategies are evaluated, and strategy evaluation results are generated; Based on the strategy evaluation results, analyze the causes of deviations, adjust the strategy to match the real-time changes in user behavior, and update strategy parameters and execution logic to obtain strategy optimization results; Based on the policy optimization result, check whether the user's real-time permissions are consistent with the permissions recommended by the policy, and when the update condition is met, modify the permission status in the user configuration file to obtain a configuration status modification record; Using the configuration status modification record, write the new permission status into the user configuration file, restart the user permission service, reload the user session, verify the response speed of the permission adjustment, and generate a session reload record; Through the session reload record, the user activity data after the permission is updated is monitored and recorded, the consistency of the permission change is checked, the user permissions are updated and synchronized in real time, and a permission update log is generated.

2. The user rights upgrade authentication method of the network platform according to claim 1 is characterized in that: The user friend number record includes data items recording the number of friends of each user account, the user's unique identifier and the number of associated friends; the initial permission review status includes the pending review mark of the permission review, the review result and the status of whether the upgrade conditions are met; the behavior pattern classification result includes the user's behavior type, interaction mode and user group positioning; the permission review strategy includes a permission upgrade plan based on user behavior prediction, predicted user behavior change trends and potential risk assessment; the strategy optimization result includes adjusted decision parameters, optimized user classification logic and adjusted risk management measures; the permission update log includes the timestamp of the update operation, the user's identification information and the permission change information.

3. The user rights upgrade authentication method of the network platform according to claim 1 is characterized in that: The network platform receives the user's permission upgrade request, obtains the user ID through the login information provided by the user, and indexes the associated social network data according to the user account to obtain the number of user friends. The specific steps are: The network platform receives the permission upgrade request submitted by the user, collects the user's login information, including the user name and password, verifies the legitimacy of the user's identity through identity verification, and generates a user identity verification result; Based on the user identity authentication result, using the user's login information as a key index, retrieving social network data associated with the user account, identifying the friend list, and generating a social network data snapshot; According to the social network data snapshot, the total number of the user's friends is calculated, the user's social interaction frequency and friend activity are recorded, the user's influence and connectivity in the social network are evaluated, and a record of the number of the user's friends is generated.

4. The user rights upgrade authentication method of the network platform according to claim 1 is characterized in that: Compare the number of friends of the user with the preset permission upgrade threshold, use conditional query to determine whether the number of friends reaches the threshold, and mark it if it reaches the threshold. The specific steps of generating the initial permission review status are: Compare the user's friend count record with a preset permission upgrade threshold, where the permission upgrade threshold includes the minimum number of friends required, use conditional query to determine whether the number of friends exceeds the threshold, and generate a threshold comparison result; Based on the threshold comparison result, if the number of friends of the user reaches and exceeds the preset threshold, the user is marked as qualified; if it does not exceed the threshold, the user is marked as unqualified and a permission upgrade mark is generated; According to the permission upgrade mark, the user does not need to pay the permission upgrade certification fee, the user's permission review process is identified, whether the user meets the permission upgrade requirements is recorded, the user's permission authentication process and processing flow are updated and tracked in real time, and the initialization permission review status is generated.

5. The user rights upgrade authentication method of the network platform according to claim 1 is characterized in that: The steps of using the initial permission review state, analyzing the user's behavior data, recording the user's login frequency, posting and interaction, inputting the behavior data into the classifier, and generating the behavior pattern classification result are as follows: Based on the initial permission review status, monitor and collect user login, posting and interaction data, including the number of logins, posting frequency, and interaction type and number, to generate user activity records; Based on the user activity records, organize and classify the data, identify key behavioral characteristics, including login periodicity, posting content preferences, and interaction preferences, and generate a behavioral characteristic summary; According to the behavioral feature summary, cluster analysis technology is used to perform pattern recognition and grouping of user behaviors, group user behaviors according to the features, calculate the weighted Euclidean distance of user behavior features, and generate behavioral pattern classification results.

6. The method for escalating user rights on a network platform according to claim 5, characterized in that: The formula for the cluster analysis technique is as follows: Among them, d ij is the weighted Euclidean distance, x ik represents the performance of the i-th user on the k-th behavior feature, x jk represents the performance of the jth user on the same behavioral feature, σ k represents the standard deviation of the kth behavior feature, w k is the weight coefficient of the kth behavior feature, and n is the total number of behavior features.

7. A user rights upgrade authentication system for a network platform, characterized in that: According to the user rights upgrade authentication method of the network platform according to any one of claims 1 to 6, the system comprises: The request receiving module collects the permission upgrade request submitted by the user, extracts the user's login information and identifies the user ID, retrieves the social network data associated with the user account based on the ID, and generates a record of the number of friends; The threshold comparison module compares the friend quantity record with the set permission upgrade threshold to evaluate whether the upgrade conditions are met. If so, the user is marked and the initialization review status is generated; The behavior data analysis module uses the initial audit status to collect the user's login frequency, posting and interaction data, inputs the behavior data into the classifier, classifies the user's behavior pattern, and generates a behavior pattern classification result; The decision-making module applies the Markov decision process based on the behavior pattern classification results to evaluate the probability of changes in the user's behavior status in the next week, decides whether to recommend user permission upgrade based on the probability, and generates a permission review strategy; The policy evaluation module implements the permission audit policy, compares the user's real-time behavior data with the expected behavior, analyzes the deviation between the two, adjusts the permission audit policy to match the user's behavior changes, updates the user's permissions, writes the real-time permission status to the user configuration file and reloads the user session, and generates a permission update log.

Images