Member ticket business management system and method thereof

Abstract

The invention discloses a member ticket business management system and a method thereof, and relates to the technical field of ticket business management. According to the method, access permissions are set for users of different roles, so that the multi-level security of the system is ensured, and unauthorized access is prevented; meanwhile, real-time analysis is carried out in combination with login and transaction behavior data of the user, abnormal behaviors can be rapidly identified, fraud and account embezzlement are effectively prevented, and information security and transaction security of the user are guaranteed; according to the method, the unique ticket ID is generated by adopting the Hash algorithm, once the repeated use behavior is detected, the ticket is immediately prevented from being further used, and the uniqueness and legality of the ticket are guaranteed; according to the method, an administrator is helped to make an optimization decision through big data analysis and chart display; in combination with a potential fraud user identification model of a support vector machine, potential fraud users are automatically identified and processed, the risk control capability of the system is improved, malicious behaviors are effectively prevented, and loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of ticket management, in particular to a member ticket management system and method thereof. Background Art

[0002] With the popularization of the Internet, online ticketing platforms have gradually become an important channel for users to purchase various tickets for performances, events, travel, etc. The core task of the ticket management system is to ensure the accuracy of ticket information, the security of transactions, and the privacy protection of user data. However, with the continuous development of the market, traditional ticket management systems are facing a series of challenges.

[0003] Currently, many ticketing systems on the market focus mainly on ticket issuance and payment functions, often lacking in-depth analysis of user behavior data and real-time identification of potential fraudulent behaviors. Such systems are usually unable to effectively identify and prevent fraudulent behaviors through false registration, malicious ticket swiping, abuse of discounts, etc. In addition, traditional risk control measures mostly rely on manual review, which has problems of low efficiency and high false alarm rate, resulting in poor user experience and affecting the security and reliability of the system.

[0004] Therefore, it is necessary to propose a member ticket management system and method thereof to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to solve the problems existing in the background technology and to provide a member ticket management system and method thereof.

[0006] The purpose of the present invention can be achieved through the following technical solutions: In a first aspect, the present invention provides a member ticket management system, including a user management module, a payment gateway module, a ticket issuance module, a data analysis and reporting module, a risk control and anti-fraud module and a database.

[0007] The user management module is responsible for user registration and authority management. Registration is completed by collecting basic user information and assigning different authorities according to the user's role to ensure the security of the system and the standardization of user management; behavioral data is collected by accessing user behavior logs to provide data support for subsequent risk control and behavior analysis.

[0008] Whenever a new user applies for registration, the user's basic information, including mobile phone number, email address and user role, is used for registration. After registration, a unique user identifier is generated for each user based on the basic information. All user identifiers obtained through registration are stored in the database, converted into structured data and encrypted for storage.

[0009] As a preferred embodiment of the present invention, different permissions are set according to the user role of each user, including: For ordinary users, the user is allowed to browse public ticket information, purchase ordinary tickets, and view his or her historical transaction records, but the user is restricted from accessing system management functions and other user data; For ordinary members: allow the user to purchase public tickets and enjoy ticket discounts; allow the user to view their transaction records, apply for refunds and order modifications; restrict the user from accessing system management functions and other user data; For VIP members: Allow the user to view and purchase public tickets, VIP exclusive tickets and limited-time rush tickets, and enjoy privileges such as priority purchase and exclusive discounts; allow the user to view his or her transaction records, apply for refunds and order modifications; restrict the user from accessing system management functions and other user data; For administrators: Allow this user to view, modify, and disable all user accounts; allow this user to publish, modify, and delete ticket information, and adjust ticket prices.

[0010] As a preferred method of the present invention, the data log of each user is accessed to obtain the characteristic behavior data therein, including the user's login characteristic behavior data and transaction characteristic behavior data.

[0011] Obtain the user's login characteristic behavior data, access each user i's login data log, and obtain the average login time interval timestamp_login_i of the user in all login history records and the IP address change frequency login_ipchange_i of each user i in all login history records; where i is the user number; Get the transaction characteristic behavior data of user i, access the transaction data log of each user i, and obtain the specific time timestamp_purchase_(i, j) of all ticket purchase behaviors of user i; get the ticket number ticket_id(i, j), quantity ticket_quantity(i, j) and transaction result status symbol payment_status(i, j) involved in each ticket purchase behavior; the transaction result status symbol represents the result of each ticket purchase behavior, including success, failure, pending payment and transaction abnormality. j is the sequential number of each transaction. j=1, 2, ..., m; m is the number of all ticket purchase behaviors recorded in the transaction data log of user i; i is the user number; The collected login characteristic behavior data and transaction characteristic behavior data of each user are sent to the risk control and anti-fraud module.

[0012] The payment gateway module is responsible for providing payment functions for ticket transactions, including online and offline payment methods. It generates transaction records for each ticket transaction and stores them in the database, and provides a human-computer interaction interface for querying the payment status of ticket transactions; Provides a variety of payment interfaces, including support for credit cards, debit cards, Alipay, WeChat and virtual currencies, and realizes encryption, decryption and transmission of payment information through transaction encryption algorithms. The specific process is: Whenever a payment occurs, the AES symmetric encryption algorithm is used to encrypt all users' payment information, including payment account number, payment amount and payment time, and a session key is generated for data encryption and decryption. Key management is performed through the public key infrastructure PKI.

[0013] The encrypted payment information is transmitted to the ticket issuance module through the TLS encrypted channel.

[0014] The ticket issuance module is responsible for issuing virtual tickets and provides a batch operation interface for large-scale virtual ticket distribution. The uniqueness of the ticket ID is ensured through a hash algorithm, ensuring that each ticket can only be used once.

[0015] Whenever encrypted payment information is received, the public key is obtained from the public key infrastructure (PKI) to verify the authenticity of the encrypted payment information. If the verification is correct, virtual ticket allocation and ticket ID generation are performed.

[0016] For each ticket, obtain the user identifier of the user who purchased the ticket, ticket issuance time, ticket type and transaction serial number, and use the SHA-256 hash algorithm to concatenate the user identifier of the user who purchased the ticket, ticket issuance time, ticket type and transaction serial number to generate a unique ticket ID.

[0017] All generated ticket IDs will be stored in the database. Each ticket ID will remain unique in the database and its usage status will be marked as "unused".

[0018] Track the usage status of all tickets. Whenever a user is detected using a ticket through ticket activation or ticket inspection, the usage status of the ticket ID is automatically updated to "used".

[0019] For a ticket ID whose usage status is updated to "used", whenever a user is detected trying to use the ticket corresponding to the ticket ID, further use of the ticket is blocked, an error message "ticket has been used" is returned, and the user's ticket usage behavior is marked as "abnormal ticket use".

[0020] The data analysis and reporting module provides data reports on ticket sales, payment method usage, etc. by performing statistical analysis on historical transaction data in the system.

[0021] As a preferred method of the present invention, the total ticket sales of each month are counted, the proportion of payment method usage, the average ticket price trend and the number of ticket usage behaviors labeled "abnormal ticket usage" are counted, and big data analysis and chart display are used to help administrators optimize management decisions, generate statistical charts of total ticket sales, pie charts of payment method usage proportions, schematic diagrams of average ticket price trends and statistical charts of the number of abnormal ticket usage occurrences.

[0022] The risk control and anti-fraud module identifies potential fraud users through comprehensive analysis of each user's login characteristic behavior data and transaction characteristic behavior data, and performs risk control based on the potential fraud user identification results.

[0023] Generate a high-dimensional feature vector for each user i: Xi = {P, timestamp, role, timestamp_login_i, login_ipchange_i, timestamp_purchase_(i,1), ticket_id(i,1), ticket_quantity(i,1), payment_status(i,1), timestamp_purchase_(i,2), ticket_id(i,2), ticket_quantity(i,2), payment_status(i,2), ..., timestamp_purchase_(i,m), ticket_id(i,m), ticket_quantity(i,1m), payment_status(i,m)}; The high-dimensional feature vector corresponding to each user i is input into the potential fraud user identification model based on support vector machine.

[0024] The expression of the potential fraud user identification model based on support vector machine is:

[0025] in Output function of the model for identifying potential fraudulent users; in is the output value of the potential fraud user identification model, representing the identification result; the output value of the potential fraud user identification model based on support vector machine 1 or -1; represents the result of judging whether the user i is a potential fraud user based on the high-dimensional feature vector Xi. -1 means that user i is judged as a potential fraud user; -1 means that user i is judged as a normal user; Where w is the normal vector of the hyperplane, defining the decision boundary; Where b is the bias term, which defines the position of the decision hyperplane; Where sign is the sign function, and the calculation formula is: .

[0026] in The optimization objective function of the potential fraud user identification model represents maximizing the classification boundary by optimizing the following objective function and calculating the specific value of the normal vector w of the hyperplane; in It is a slack variable used to handle classification errors and allows some data points to not meet the classification requirements.

[0027] Where C is the regularization parameter, which is used to balance the complexity of the model and the penalty for classification errors. Smaller C values ​​allow more misclassifications, while larger C values ​​make the classification more stringent and reduce the number of misclassifications. By adjusting the regularization parameter C, the model can strike a balance between accuracy and generalization ability.

[0028] in The constraints of the model for identifying potential fraudulent users represent the process of ensuring that each data point is either correctly classified or tolerated with a small error control during model training, and calculating the slack variable and the specific value of the bias term b; in is the artificial label given to the high-dimensional feature vector Xi during model training. 1 represents a normal user. A value of -1 indicates a potential fraudulent user.

[0029] Once the system identifies a user i as a potential fraud user, it takes the following measures against the user: Send notification: Send a warning notification to the user and administrator to inform them that the user's account has abnormal behavior.

[0030] Manual review: Notify the administrator to manually review the identified potential fraud users to see if they are false positives; Take security measures: If the administrator manually reviews and confirms that the identification result of the fraudulent user is not a false positive, security measures including account freezing, transaction prohibition and forced account password reset will be taken.

[0031] The database is responsible for storing all user identifiers, transaction records for each ticket transaction, and all ticket IDs.

[0032] In a second aspect, the present invention provides a member ticket management method, comprising the following steps: Step 1: User registration and permission setting; Users complete registration by using their phone number, email address, and role information, which generates a unique user identifier and stores it in the database. Access rights are assigned based on user roles, including regular users, members, VIPs, and administrators.

[0033] Step 2: Behavioral data collection; Collect users' login characteristic behavior data and transaction characteristic behavior data for subsequent analysis and potential fraud identification.

[0034] Step 3: Encrypt and transmit payment information; The payment information is encrypted using the AES symmetric encryption algorithm and securely transmitted to the ticket issuance module through the TLS encrypted channel.

[0035] Step 4: Ticket issuance and ID generation; The ticket issuance module generates a unique ticket ID based on the encrypted payment information, ensures the uniqueness of each ticket ID in the system, and updates the ticket usage status.

[0036] Step 5: Potential fraud identification; The potential fraud user identification model based on support vector machine is used to analyze the user's login characteristic behavior data and transaction characteristic behavior data to determine whether the user is a potential fraud user.

[0037] Step 6: Fraud handling and security measures implementation; Taking action against identified potentially fraudulent users, including sending notifications, manual review, and enforcing one or a combination of account freezes and transaction disabling.

[0038] Step 7: Data analysis and reporting; By analyzing historical transaction data, visual data views and reports are generated to support administrators' decision-making and optimize management.

[0039] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention ensures the multi-level security of the system by setting access rights for users of different roles to prevent unauthorized access. At the same time, it combines the user's login and transaction behavior data for real-time analysis, can quickly identify abnormal behavior, effectively prevent fraud and account theft, and ensure the user's information security and transaction security; 2. The present invention uses a hash algorithm to generate a unique ticket ID, and through the status tracking function, ensures that each ticket can only be used once. The system monitors the use of the ticket in real time, and once repeated use is detected, the ticket is immediately blocked from further use to ensure the uniqueness and legitimacy of the ticket; 3. The present invention provides data reports on ticket sales, payment method usage, etc. through big data analysis and chart display to help administrators make optimization decisions. Combined with the potential fraud user identification model of the support vector machine, it automatically identifies and processes potential fraud users, improves the risk control ability of the system, effectively prevents malicious behavior, and reduces losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings: Figure 1 is a system block diagram of the present invention; Figure 2 A statistical chart of the total ticket sales amount proposed in the embodiment of the present invention; Figure 3 A pie chart showing the percentage of payment methods used in the embodiments of the present invention; Figure 4 A schematic diagram of the average ticket price trend proposed in the embodiment of the present invention; Figure 5 A statistical diagram of the number of occurrences of abnormal ticket usage proposed in the embodiment of the present invention; Figure 6 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1 As shown, a member ticket management system includes a user management module, a payment gateway module, a ticket issuance module, a data analysis and reporting module, a risk control and anti-fraud module and a database.

[0043] The user management module obtains user information and registers users. Different permissions are set according to the user's role to ensure the multi-level security of the system. Access the data logs of each user to obtain the characteristic behavior data, including the user's login characteristic behavior data and transaction characteristic behavior data, and collect raw data for real-time analysis of user behavior and identification of abnormal behavior.

[0044] Whenever a registration application is received from a new user, the user is registered using basic information including mobile phone number, email address and user role. After registration is completed, a unique user identifier Ui={P, timestamp, role} is generated for each user. i is the user ID, timestamp is the user registration timestamp, i.e. the registration time of user i. role is the user role identifier, corresponding to the user role of user i, including ordinary user, ordinary member, VIP member and administrator.

[0045] All registered user identifiers are stored in the database, converted into structured data and saved in encrypted form.

[0046] Furthermore, different permissions are set according to the user role of each user i, including: For ordinary users, the user is allowed to browse public ticket information, purchase ordinary tickets, and view his or her historical transaction records, but the user is restricted from accessing system management functions and other user data; For ordinary members: allow the user to purchase public tickets and enjoy ticket discounts; allow the user to view their transaction records, apply for refunds and order modifications; restrict the user from accessing system management functions and other user data; For VIP members: Allow the user to view and purchase public tickets, VIP exclusive tickets and limited-time rush tickets, and enjoy privileges such as priority purchase and exclusive discounts; allow the user to view his or her transaction records, apply for refunds and order modifications; restrict the user from accessing system management functions and other user data; For administrators: Allow this user to view, modify, and disable all user accounts; allow this user to publish, modify, and delete ticket information, and adjust ticket prices.

[0047] Furthermore, the data log of each user i is accessed to obtain characteristic behavior data therein, including login characteristic behavior data and transaction characteristic behavior data of user i.

[0048] Obtain the login characteristic behavior data of user i, access the login data log of each user i, and obtain the average login time interval timestamp_login_i of user i in all login history records and the IP address change frequency login_ipchange_i of each user i in all login history records; Get the transaction characteristic behavior data of user i, access the transaction data log of each user i, and obtain the specific time timestamp_purchase_(i, j) of all ticket purchase behaviors of user i; get the ticket number ticket_id(i, j), quantity ticket_quantity(i, j) and transaction result status symbol payment_status(i, j) involved in each ticket purchase behavior; the transaction result status symbol represents the result of each ticket purchase behavior, including success, failure, pending payment and transaction abnormality. j is the sequential number of each transaction. j=1, 2, ..., m; m is the number of all ticket purchase behaviors recorded in the transaction data log of user i.

[0049] The collected login characteristic behavior data and transaction characteristic behavior data of each user are sent to the risk control and anti-fraud module.

[0050] The payment gateway module is responsible for providing payment functions for ticket transactions, including online and offline payment methods. It generates transaction records for each ticket transaction and stores them in the database, and provides a human-computer interaction interface for querying the payment status of ticket transactions; Provides a variety of payment interfaces, including support for credit cards, debit cards, Alipay, WeChat and virtual currencies, and realizes encryption, decryption and transmission of payment information through transaction encryption algorithms. The specific process is: Whenever a payment occurs, the AES symmetric encryption algorithm is used to encrypt all users' payment information, including payment account number, payment amount and payment time, and a session key is generated for data encryption and decryption. Key management is performed through the public key infrastructure PKI.

[0051] The encrypted payment information is transmitted to the ticket issuance module through the TLS encrypted channel.

[0052] The ticket issuance module is responsible for issuing virtual tickets and provides a batch operation interface for large-scale virtual ticket distribution. The uniqueness of the ticket ID is ensured through a hash algorithm, ensuring that each ticket can only be used once.

[0053] Whenever encrypted payment information is received, the public key is obtained from the public key infrastructure (PKI) to verify the authenticity of the encrypted payment information. If the verification is correct, virtual ticket allocation and ticket ID generation are performed.

[0054] For each ticket, obtain the user identifier of the user who purchased the ticket, ticket issuance time, ticket type and transaction serial number, and use the SHA-256 hash algorithm to concatenate the user identifier of the user who purchased the ticket, ticket issuance time, ticket type and transaction serial number to generate a unique ticket ID.

[0055] All generated ticket IDs will be stored in the database. Each ticket ID will remain unique in the database and its usage status will be marked as "unused".

[0056] Track the usage status of all tickets. Whenever a user is detected using a ticket through ticket activation or ticket inspection, the usage status of the ticket ID is automatically updated to "used".

[0057] For a ticket ID whose usage status is updated to "used", whenever a user is detected trying to use the ticket corresponding to the ticket ID, further use of the ticket is blocked, an error message "ticket has been used" is returned, and the user's ticket usage behavior is marked as "abnormal ticket use".

[0058] The data analysis and reporting module provides data reports on ticket sales, payment method usage, etc. by performing statistical analysis on historical transaction data in the system.

[0059] See also Figure 2-Figure 5 As shown, the total ticket sales of each month are counted, the proportion of payment methods used, the average ticket price trend and the number of ticket usage behaviors labeled "abnormal ticket usage" are counted, and big data analysis and chart display are used to help administrators optimize management decisions, generate statistical charts of total ticket sales, pie charts of payment method usage proportions, schematic diagrams of average ticket price trends and statistical charts of the number of abnormal ticket usage.

[0060] The risk control and anti-fraud module identifies potential fraud users through comprehensive analysis of each user's login characteristic behavior data and transaction characteristic behavior data, and performs risk control based on the potential fraud user identification results.

[0061] Generate a high-dimensional feature vector for each user i: Xi = {P, timestamp, role, timestamp_login_i, login_ipchange_i, timestamp_purchase_(i,1), ticket_id(i,1), ticket_quantity(i,1), payment_status(i,1), timestamp_purchase_(i,2), ticket_id(i,2), ticket_quantity(i,2), payment_status(i,2), ..., timestamp_purchase_(i,m), ticket_id(i,m), ticket_quantity(i,1m), payment_status(i,m)}; The high-dimensional feature vector corresponding to each user i is input into the potential fraud user identification model based on support vector machine.

[0062] The expression of the potential fraud user identification model based on support vector machine is:

[0063] in Output function of the model for identifying potential fraudulent users; in The output value of the potential fraud user identification model represents the identification result; Specifically, the output value of the potential fraud user identification model based on support vector machine 1 or -1; represents the result of judging whether the user i is a potential fraud user based on the high-dimensional feature vector Xi. -1 means that user i is judged as a potential fraud user; -1 means that user i is judged as a normal user; Where w is the normal vector of the hyperplane, defining the decision boundary; Where b is the bias term, which defines the position of the decision hyperplane; Where sign is the sign function, and the calculation formula is: .

[0064] in The optimization objective function of the potential fraud user identification model represents maximizing the classification boundary by optimizing the following objective function and calculating the specific value of the normal vector w of the hyperplane; in It is a slack variable used to handle classification errors and allows some data points to not meet the classification requirements.

[0065] Where C is the regularization parameter, which is used to balance the complexity of the model and the penalty for classification errors. Smaller C values ​​allow more misclassifications, while larger C values ​​make the classification more stringent and reduce the number of misclassifications. By adjusting the regularization parameter C, the model can strike a balance between accuracy and generalization ability.

[0066] in The constraints of the model for identifying potential fraudulent users represent the process of ensuring that each data point is either correctly classified or tolerated with a small error control during model training, and calculating the slack variable and the specific value of the bias term b; in is the artificial label given to the high-dimensional feature vector Xi during model training. 1 represents a normal user. A value of -1 indicates a potential fraudulent user.

[0067] Once the system identifies a user i as a potential fraud user, it takes the following measures against the user: Send notification: Send a warning notification to the user and administrator to inform them that the user's account has abnormal behavior.

[0068] Manual review: Notify the administrator to manually review the identified potential fraud users to see if they are false positives; Take security measures: If the administrator manually reviews and confirms that the identification result of the fraudulent user is not a false positive, security measures including account freezing, transaction prohibition and forced account password reset will be taken.

[0069] The database is responsible for storing all user identifiers, transaction records for each ticket transaction, and all ticket IDs.

[0070] See also Figure 6 As shown, a member ticket management method includes the following steps: Step 1: User registration and permission setting; Users complete registration by using their phone number, email address, and role information, which generates a unique user identifier and stores it in the database. Access rights are assigned based on user roles, including regular users, members, VIPs, and administrators.

[0071] Step 2: Behavioral data collection; Collect users' login characteristic behavior data and transaction characteristic behavior data for subsequent analysis and potential fraud identification.

[0072] Step 3: Encrypt and transmit payment information; The payment information is encrypted using the AES symmetric encryption algorithm and securely transmitted to the ticket issuance module through the TLS encrypted channel.

[0073] Step 4: Ticket issuance and ID generation; The ticket issuance module generates a unique ticket ID based on the encrypted payment information, ensures the uniqueness of each ticket ID in the system, and updates the ticket usage status.

[0074] Step 5: Potential fraud identification; The potential fraud user identification model based on support vector machine is used to analyze the user's login characteristic behavior data and transaction characteristic behavior data to determine whether the user is a potential fraud user.

[0075] Step 6: Fraud handling and security measures implementation; Taking action against identified potentially fraudulent users, including sending notifications, manual review, and enforcing one or a combination of account freezes and transaction disabling.

[0076] Step 7: Data analysis and reporting; By analyzing historical transaction data, visual data views and reports are generated to support administrators' decision-making and optimize management.

[0077] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0078] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations; The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A member ticket management system, comprising a user management module, a payment gateway module, a ticket issuance module and a risk control and fraud prevention module, characterized in that: The user management module is responsible for user registration and permission management. It completes registration by collecting basic user information and assigns different permissions based on the user's role. It collects behavior data by accessing user behavior logs to provide data support for subsequent risk control and behavior analysis. The payment gateway module provides secure payment functions, including online and offline payments. It ensures the security of payment information through encryption algorithms, and ensures the encryption and transmission security of transaction data through public key infrastructure and encryption channels. Each transaction in the payment process will generate a transaction record for query and subsequent ticket issuance. The ticket issuance module is responsible for issuing virtual tickets. It ensures the uniqueness of the ticket ID through a hash algorithm and generates a unique ticket ID for each ticket. The module also tracks the usage status of the ticket to ensure that each ticket can only be used once to avoid reuse or abuse. It updates the ticket status in real time to ensure that once a ticket is used, it is marked as "used" to prevent illegal use. The risk control and anti-fraud module is responsible for identifying potential fraudulent behavior by analyzing the user's login characteristic behavior data and transaction characteristic behavior data; using the potential fraud user identification model based on support vector machine to model and analyze the behavioral characteristics of each user to determine whether there is abnormal or fraudulent behavior; once a potential fraudulent user is identified, the system will take security measures to ensure the overall security of the system.

2. A member ticket management system according to claim 1, characterized in that: Also includes data analysis and reporting modules and databases; By collecting statistics and analyzing the historical transaction data of the system, visual data reports are generated to help administrators optimize management decisions and conduct market trend analysis; The database is responsible for storing all user identifiers, transaction records for each ticket transaction, and all ticket IDs.

3. A member ticket management system according to claim 1, characterized in that: The specific process of collecting basic user information to complete registration is as follows: Whenever a new user sends a registration application, register the user through basic information including mobile phone number, email address and user role; after completing the registration, generate a unique user identifier Ui={P, timestamp, role} for each user; Where i is the user ID, timestamp is the user registration timestamp, that is, the registration time of user i; role is the user role identifier, corresponding to the user role of user i, including ordinary user, ordinary member, VIP member and administrator; All registered user identifiers are stored in the database, converted into structured data and saved in encrypted form.

4. A member ticket management system according to claim 1, characterized in that: The specific process of assigning different permissions based on the user's role is as follows: For ordinary users, the user is allowed to browse public ticket information, purchase ordinary tickets, and view his or her historical transaction records, but the user is restricted from accessing system management functions and other user data; For ordinary members: allow the user to purchase public tickets and enjoy ticket discounts; allow the user to view their transaction records, apply for refunds and order modifications; restrict the user from accessing system management functions and other user data; For VIP members: Allow the user to view and purchase public tickets, VIP exclusive tickets and limited-time rush tickets, and enjoy privileges such as priority purchase and exclusive discounts; allow the user to view his or her transaction records, apply for refunds and order modifications; restrict the user from accessing system management functions and other user data; For administrators: Allow this user to view, modify, and disable all user accounts; allow this user to publish, modify, and delete ticket information, and adjust ticket prices.

5. A member ticket management system according to claim 1, characterized in that: The specific process of accessing user behavior logs to collect behavior data is as follows: Obtain the login characteristic behavior data of user i, access the login data log of each user i, and obtain the average login time interval timestamp_login_i of user i in all login history records and the IP address change frequency login_ipchange_i of each user i in all login history records; Get the transaction characteristic behavior data of user i, access the transaction data log of each user i, and obtain the specific time timestamp_purchase_(i, j) of all ticket purchase behaviors of user i; get the ticket number ticket_id(i, j), quantity ticket_quantity(i, j) and transaction result status symbol payment_status(i, j) involved in each ticket purchase behavior; the transaction result status symbol represents the result of each ticket purchase behavior, including success, failure, pending payment and transaction abnormality; j is the sequential number of each transaction; j=1, 2, ..., m; m is the number of all ticket purchase behaviors recorded in the transaction data log of user i.

6. A member ticket management system according to claim 1, characterized in that: The specific process of ensuring the uniqueness of the ticket ID through the hash algorithm is as follows: Whenever receiving encrypted payment information, the public key is obtained from the public key infrastructure (PKI) to verify the authenticity of the encrypted payment information. If the verification is correct, virtual ticket allocation and ticket ID generation are performed; For each ticket, obtain the user identifier of the user who purchased the ticket, the ticket issuance time, the ticket type and the transaction serial number, and use the SHA-256 hash algorithm to concatenate the user identifier of the user who purchased the ticket, the ticket issuance time, the ticket type and the transaction serial number to generate a unique ticket ID; All generated ticket IDs will be stored in the database. Each ticket ID will remain unique in the database and its usage status will be marked as "unused"; Track the usage status of all tickets. Whenever a user is detected using a ticket through ticket activation or ticket checking, the usage status of the ticket ID is automatically updated to "used"; For a ticket ID whose usage status is updated to "used", whenever a user is detected trying to use the ticket corresponding to the ticket ID, further use of the ticket is blocked, an error message "ticket has been used" is returned, and the user's ticket usage behavior is marked as "abnormal ticket use".

7. A member ticket management system according to claim 1, characterized in that: The specific process for identifying potential fraud is: Generate a high-dimensional feature vector for each user i: Xi = {P, timestamp, role, timestamp_login_i, login_ipchange_i, timestamp_purchase_(i,1), ticket_id(i,1), ticket_quantity(i,1), payment_status(i,1), timestamp_purchase_(i,2), ticket_id(i,2), ticket_quantity(i,2), payment_status(i,2), ..., timestamp_purchase_(i,m), ticket_id(i,m), ticket_quantity(i,1m), payment_status(i,m)}; The high-dimensional feature vector corresponding to each user i is input into a potential fraud user identification model based on a support vector machine; The expression of the potential fraud user identification model based on support vector machine is: ； in Output function of the model for identifying potential fraudulent users; in The output value of the potential fraud user identification model represents the identification result; Specifically, the output value of the potential fraud user identification model based on support vector machine 1 or -1; represents the result of judging whether the user i is a potential fraud user based on the high-dimensional feature vector Xi. -1 means that user i is judged as a potential fraud user; -1 means that user i is judged as a normal user; Where w is the normal vector of the hyperplane, defining the decision boundary; Where b is the bias term, which defines the position of the decision hyperplane; Where sign is the sign function, and the calculation formula is: ； in The optimization objective function of the potential fraud user identification model represents maximizing the classification boundary by optimizing the following objective function and calculating the specific value of the normal vector w of the hyperplane; in It is a slack variable used to handle classification errors and allows some data points not to meet the classification requirements; C is a regularization parameter used to balance the complexity of the model and the penalty for classification errors. A smaller C value allows more misclassifications, while a larger C value makes the classification more stringent and reduces the number of misclassifications. in The constraints of the model for identifying potential fraudulent users represent the process of ensuring that each data point is either correctly classified or tolerated with a small error control during model training, and calculating the slack variable and the specific value of the bias term b; in is the artificial label given to the high-dimensional feature vector Xi during model training. 1 represents a normal user. -1 represents a potential fraud user; Once the system identifies a user i as a potential fraud user, security measures are taken against the user.

8. A member ticket management system according to claim 7, characterized in that: The safety measures include: Send notification: Send a warning notification to the user and administrator, informing them that the user's account has abnormal behavior; Manual review: Notify the administrator to manually review the identified potential fraud users to see if they are false positives; Take security measures: If the administrator manually reviews and confirms that the identification result of the fraudulent user is not a false positive, security measures including account freezing, transaction prohibition and forced account password reset will be taken.

9. A member ticket management method, characterized in that: The following steps are involved: Step 1: User registration and permission setting; Users complete registration using their mobile phone number, email address, and role information, generating a unique user identifier that is stored in the database; access rights are assigned based on user roles, including ordinary users, members, VIPs, and administrators; Step 2: Behavioral data collection; Collect users’ login characteristic behavior data and transaction characteristic behavior data for subsequent analysis and potential fraud identification; Step 3: Encrypt and transmit payment information; The payment information is encrypted using the AES symmetric encryption algorithm and securely transmitted to the ticket issuance module through the TLS encrypted channel; Step 4: Ticket issuance and ID generation; The ticket issuance module generates a unique ticket ID based on the encrypted payment information, ensures the uniqueness of each ticket ID in the system, and updates the ticket usage status; Step 5: Potential fraud identification; Analyze the user's login characteristic behavior data and transaction characteristic behavior data through the potential fraud user identification model based on support vector machine to determine whether the user is a potential fraud user; Step 6: Fraud handling and security measures implementation; Taking action against identified potentially fraudulent users, including sending notifications, manual review, and enforcing one or more combinations of account freezes and transaction disabling; Step 7: Data analysis and reporting; By analyzing historical transaction data, visual data views and reports are generated to support administrators' decision-making and optimize management.

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