A membership ticket management system and its method

Abstract

The present invention discloses a membership ticketing management system and its method, which relates to the technical field of ticketing management; by setting access permissions for users of different roles, the present invention ensures the multi-level security of the system and prevents unauthorized access; at the same time, by combining the login and transaction behavior data of users for real-time analysis, it can quickly identify abnormal behaviors, effectively prevent fraud and account theft, and ensure the information security and transaction security of users; the present invention uses a hash algorithm to generate a unique ticket ID, and once a repeated use behavior is detected, the further use of the ticket is immediately blocked to ensure the uniqueness and legality of the ticket; the present invention helps administrators make optimized decisions through big data analysis and chart display; combined with a potential fraud user identification model based on support vector machines, it automatically identifies and processes potential fraud users, improves the risk control ability of the system, effectively prevents malicious behaviors, and reduces losses.

Description

Technical Field

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

[0002] With the popularization of the Internet, online ticket platforms have gradually become an important channel for users to purchase various tickets such as performances, sports events, and tourism. The core tasks of a ticket management system are 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 ticket systems on the market mainly focus on ticket issuance and payment functions, and often lack in-depth analysis of user behavior data and real-time identification of potential fraud behaviors. Such systems usually cannot effectively identify and prevent fraud behaviors such as false registration, malicious ticket scalping, and abuse of discounts. In addition, most traditional risk control measures 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 membership 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 art, and to propose a membership ticket management system and method thereof.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] In a first aspect, the present invention provides a membership 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.

[0008] The user management module is responsible for user registration and permission management. It completes registration by collecting basic user information, and assigns different permissions according to the user's role to ensure the security of the system and the standardization of user management; it collects behavior data by accessing user behavior logs, providing data support for subsequent risk control and behavior analysis.

[0009] Whenever a registration application is received from a new user, it registers through the basic user information including mobile phone number, email, and user role; after completing registration, it generates a unique user identifier for each user according to the basic user information. It stores all the user identifiers obtained through registration in the database, converts them into structured data and encrypts and saves them.

[0010] As a preferred embodiment of the present invention, different permissions are set according to the user roles of each user, including:

[0011] For ordinary users, the user is allowed to view public ticket information, purchase ordinary tickets, and view their own historical transaction records, and is restricted from accessing system management functions and other users' data;

[0012] For ordinary members: The user is allowed to purchase public tickets and enjoy ticket discount activities; the user is allowed to view their own transaction records, apply for refunds and order modifications; the user is restricted from accessing system management functions and other users' data;

[0013] For VIP members: The user is allowed to view and purchase public tickets, VIP-exclusive tickets, and time-limited rush-purchase tickets, and enjoys privileges such as priority purchase and exclusive discounts; the user is allowed to view their own transaction records, apply for refunds and order modifications; the user is restricted from accessing system management functions and other users' data;

[0014] For administrators: The user is allowed to view, modify, and disable the accounts of all users; the user is allowed to publish, modify, and delete ticket information, and adjust ticket prices.

[0015] As a preferred embodiment of the present invention, access the data logs of each user to obtain the characteristic behavior data therein, including the user's login characteristic behavior data and transaction characteristic behavior data.

[0016] Obtain the user's login characteristic behavior data, access the login data logs of each user i, 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;

[0017] Obtain the transaction characteristic behavior data of user i, access the transaction data logs of each user i, and obtain the specific time timestamp_purchase_(i,j) when all ticket purchase behaviors of user i occur; obtain 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; where the transaction result status symbol represents the result of each ticket purchase behavior, including success, failure, pending payment, and transaction anomaly. Where j is the sequence number of each transaction. j = 1, 2,..., m; where m is the number of all ticket purchase behaviors recorded in the transaction data log of user i; where i is the user number;

[0018] Send the collected login feature behavior data and transaction feature behavior data of each user to the risk control and anti-fraud module.

[0019] The payment gateway module is responsible for providing the payment function for ticket transactions, including online payment and offline payment methods. Generate a transaction record for each ticket transaction and store it in the database, and provide a human-computer interaction interface for querying the payment status of ticket transactions;

[0020] Provide multiple payment interfaces, including support for credit cards, debit cards, Alipay, WeChat, and virtual currency, and implement the encryption, decryption, and transmission of payment information through transaction encryption algorithms. The specific process is as follows:

[0021] Whenever a payment behavior occurs, use the AES symmetric encryption algorithm to encrypt the payment information of all users, including payment accounts, payment amounts, and payment times, and generate a session key for data encryption and decryption. Key management is carried out through the public key infrastructure PKI.

[0022] Transmit the encrypted payment information to the ticket issuance module through the TLS encrypted channel.

[0023] The ticket issuance module is responsible for issuing virtual tickets and providing a batch operation interface for large-scale virtual ticket distribution. Ensure the uniqueness of ticket IDs through the hash algorithm to ensure that each ticket can only be used once.

[0024] Whenever the encrypted payment information is received, obtain the public key from the public key infrastructure PKI, verify the authenticity of the encrypted payment information. If the verification is correct, perform virtual ticket distribution and ticket ID generation.

[0025] For each ticket, obtain the user identifier, ticket issuance time, ticket type, and transaction serial number of the user who purchased the ticket, and use the SHA-256 hash algorithm to splice the user identifier, ticket issuance time, ticket type, and transaction serial number of the user who purchased the ticket to generate a unique ticket ID.

[0026] Store all generated ticket IDs in the database. Each ticket ID will remain unique in the database, and its usage status will be marked as "unused".

[0027] Track the usage status of all tickets. Whenever it is detected that a user uses a ticket by activating or checking the ticket, automatically update the usage status of the ticket ID to "used".

[0028] For ticket IDs with the usage status updated to "used", whenever it is detected that a user attempts to use the ticket corresponding to the ticket ID, further use of the ticket is blocked, an error message "The ticket has been used" is returned, and the user's current ticket usage behavior is marked as "abnormal ticket usage".

[0029] The data analysis and reporting module provides data reports such as ticket sales and payment method usage by statistically analyzing the historical transaction data in the system.

[0030] As a preferred embodiment of the present invention, the total monthly ticket sales amount is statistically analyzed, the proportion of payment method usage, the trend of the average ticket price, and the occurrence times of ticket usage behaviors labeled as "abnormal ticket usage" are statistically analyzed. Through big data analysis and chart display, it helps the administrator optimize management decisions and generates a statistical chart of the total ticket sales amount, a pie chart of the proportion of payment method usage, a schematic diagram of the trend of the average ticket price, and a statistical chart of the occurrence times of abnormal ticket usage.

[0031] The risk control and anti-fraud module identifies potential fraudulent users through comprehensive analysis of the login characteristic behavior data and transaction characteristic behavior data of each user. And risk control is executed according to the results of potential fraudulent user identification.

[0032] 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)};

[0033] Input the high-dimensional feature vectors corresponding to each user i into the potential fraudulent user identification model based on the support vector machine.

[0034] The expression of the potential fraudulent user identification model based on the support vector machine is:

[0035]

[0036] where is the output function of the potential fraudulent user identification model;

[0037] where 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 the support vector machine is 1 or -1; it represents the result of determining whether user i is a potential fraud user based on the high-dimensional feature vector Xi of the user, being -1 means that user i is determined to be a potential fraud user; being 1 means that user i is determined to be a normal user;

[0038] where w is the normal vector of the hyperplane, defining the decision boundary;

[0039] where b is the bias term, defining the position of the decision hyperplane;

[0040] where sign is the sign function, and the calculation formula is:

[0041] .

[0042] where is the optimization objective function of the potential fraud user identification model, representing maximizing the classification boundary by optimizing the following objective function and calculating the specific value of the normal vector w of the hyperplane;

[0043] where is the slack variable, used to handle classification errors and allow some data points not to meet the classification requirements.

[0044] where C is the 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 strict and reduces the number of misclassifications. By adjusting the regularization parameter C, the model can balance between accuracy and generalization ability.

[0045] where is the constraint condition of the potential fraud user identification model, representing ensuring that each data point is either correctly classified or tolerated with a small error control during the model training process, and calculating the specific values of the slack variable and the bias term b;

[0046] where is the artificial label assigned to the high-dimensional feature vector Xi during the model training process, being 1 represents a normal user, being -1 represents a potential fraud user.

[0047] Once the system identifies a certain user i as a potential fraud user, the following measures will be taken for this user:

[0048] Send notifications: Send warning notifications to the user and the administrator, informing that there is abnormal behavior in the account of user i.

[0049] Manual review: Notify the administrator to conduct a manual review of the identified potential fraud users to check if it is a false alarm;

[0050] Take security measures: If, after the administrator's manual review, it is confirmed that the identification result of the fraud user is not a false alarm, then take security measures including account freezing, prohibiting transactions, and forced account password reset.

[0051] The database is responsible for storing all user identifiers, transaction records of each ticket transaction, and all ticket IDs.

[0052] In a second aspect, the present invention provides a membership ticket management method, including the following steps:

[0053] Step 1: User registration and permission setting;

[0054] The user completes registration through mobile phone number, email, and role information, generates a unique user identifier, and stores it in the database. Access permissions are assigned according to user roles including ordinary users, members, VIPs, and administrators.

[0055] Step 2: Behavior data collection;

[0056] Collect the login feature behavior data and transaction feature behavior data of the user for subsequent analysis and potential fraud identification.

[0057] Step 3: Payment information encryption and transmission;

[0058] Use the AES symmetric encryption algorithm to encrypt the payment information and securely transmit it to the ticket issuance module through a TLS encrypted channel.

[0059] Step 4: Ticket issuance and ID generation;

[0060] 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.

[0061] Step 5: Potential fraud identification;

[0062] Analyze the login feature behavior data and transaction feature behavior data of the user through a potential fraud user identification model based on support vector machines to determine if it is a potential fraud user.

[0063] Step 6: Fraud handling and security measure execution;

[0064] Take measures against the identified potential fraudulent users, including sending notifications, manual review, and implementing one or more combinations of account freezing and transaction disabling.

[0065] Step Seven: Data Analysis and Reporting;

[0066] Generate visual data views and reports by analyzing historical transaction data to support administrator decision-making and optimize management.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] 1. By setting access permissions for users of different roles, the present invention ensures the multi-level security of the system, prevents unauthorized access. At the same time, by combining real-time analysis of users' login and transaction behavior data, it can quickly identify abnormal behaviors, effectively prevent fraud and account theft, and ensure the information security and transaction security of users;

[0069] 2. The present invention uses a hash algorithm to generate unique ticket IDs and, through the status tracking function, ensures that each ticket can only be used once. The system monitors in real time during the ticket usage process. Once a repeated usage behavior is detected, the further use of the ticket is immediately blocked, ensuring the uniqueness and legality of the ticket;

[0070] 3. Through big data analysis and chart display, the present invention provides data reports such as ticket sales and payment method usage, helping administrators make optimized decisions. Combined with a potential fraud user identification model based on support vector machines, it automatically identifies and processes potential fraud users, enhances the risk control ability of the system, effectively prevents malicious behaviors, and reduces losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings:

[0072] Figure 1 It is the system block diagram of the present invention;

[0073] Figure 2 It is the statistical chart of the total ticket sales amount proposed in the embodiment of the present invention;

[0074] Figure 3 It is the pie chart of the usage proportion of payment methods proposed in the embodiment of the present invention;

[0075] Figure 4 It is the schematic diagram of the trend of the average ticket price proposed in the embodiment of the present invention;

[0076] Figure 5 It is the statistical chart of the occurrence times of abnormally used tickets proposed in the embodiment of the present invention;

[0077] Figure 6This is the flowchart of the method of the present invention. Detailed implementation manners

[0078] 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 a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0079] Please refer to Figure 1 As shown, a membership ticketing 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.

[0080] The user management module obtains user information and conducts registration. Different permissions are set according to the roles of users to ensure the multi-level security of the system. Access the data logs of each user to obtain the characteristic behavior data therein, including the login characteristic behavior data and transaction characteristic behavior data of the user, and collect the original data for the real-time analysis of user behavior and the identification of abnormal behavior.

[0081] Whenever a registration application sent by a new user is received, registration is performed through the basic user information including the mobile phone number, email, and user role; after the registration is completed, a unique user identifier Ui = {P, timestamp, role} is generated for each user; where i is the user number, timestamp is the user registration timestamp, that is, the registration moment of the user i; where role is the user role identifier corresponding to the user role of the user i, including ordinary users, ordinary members, VIP members, and administrators.

[0082] All the user identifiers obtained through registration are stored in the database, converted into structured data, and encrypted and saved.

[0083] Furthermore, different permissions are set according to the user roles of each user i, including:

[0084] For ordinary users, the user is allowed to browse public ticket information, purchase ordinary tickets, and view their own historical transaction records, and the user is restricted from accessing system management functions and other user data;

[0085] For ordinary members: The user is allowed to purchase public tickets and enjoy ticket discount activities; the user is allowed to view their own transaction records, apply for refunds and order modifications; the user is restricted from accessing system management functions and other user data;

[0086] 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 their own transaction records, apply for refunds and order modifications; restrict the user from accessing system management functions and other users' data;

[0087] For administrators: Allow the user to view, modify, and disable all users' accounts; allow the user to publish, modify, and delete ticket information, and adjust ticket prices.

[0088] Furthermore, access the data logs of each user i to obtain the characteristic behavior data therein, including the login characteristic behavior data and transaction characteristic behavior data of user i.

[0089] Obtain the login characteristic behavior data of user i, access the login data logs 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;

[0090] Obtain the transaction characteristic behavior data of user i, access the transaction data logs of each user i, and obtain the specific time timestamp_purchase_(i,j) when all ticket purchase behaviors of user i occur; obtain 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; where the transaction result status symbol represents the result of each ticket purchase behavior, including success, failure, pending payment, and transaction anomaly. Where j is the sequence number of each transaction. j = 1, 2,..., m; where m is the number of all ticket purchase behaviors recorded in the transaction data log of user i.

[0091] Send the collected login characteristic behavior data and transaction characteristic behavior data of each user to the risk control and anti-fraud module.

[0092] The payment gateway module is responsible for providing the payment function for ticket transactions, including online payment and offline payment methods. Generate the transaction record of each ticket transaction and store it in the database, and provide a human-computer interaction interface for querying the payment status of ticket transactions;

[0093] Provide multiple payment interfaces, including support for credit cards, debit cards, Alipay, WeChat, and virtual currency, and implement the encryption, decryption, and transmission of payment information through transaction encryption algorithms. The specific process is as follows:

[0094] Whenever a payment event occurs, use the AES symmetric encryption algorithm to encrypt all users' payment information including payment accounts, payment amounts, and payment times, and generate a session key for data encryption and decryption. Key management is performed through the Public Key Infrastructure (PKI).

[0095] Transmit the encrypted payment information to the ticket issuance module through a TLS encrypted channel.

[0096] The ticket issuance module is responsible for issuing virtual tickets and providing a batch operation interface for large-scale virtual ticket distribution. Ensure the uniqueness of ticket IDs through a hashing algorithm to ensure that each ticket can only be used once.

[0097] Whenever the encrypted payment information is received, obtain the public key from the Public Key Infrastructure (PKI) to verify the authenticity of the encrypted payment information. If the verification is correct, perform virtual ticket distribution and ticket ID generation.

[0098] For each ticket, obtain the user identifier, ticket issuance time, ticket type, and transaction serial number of the user who purchased the ticket, and use the SHA-256 hashing algorithm to concatenate the user identifier, ticket issuance time, ticket type, and transaction serial number of the user who purchased the ticket to generate a unique ticket ID.

[0099] Store all generated ticket IDs in the database. Each ticket ID will remain unique in the database, and its usage status will be marked as "unused".

[0100] Track the usage status of all tickets. Whenever it is detected that a user uses a ticket by activating or checking the ticket, automatically update the usage status of the ticket ID to "used".

[0101] For ticket IDs whose usage status is updated to "used", whenever it is detected that a user attempts to use the ticket corresponding to the ticket ID, prevent further use of the ticket, return an error prompt message "The ticket has been used", and mark the user's current ticket usage behavior as "abnormal ticket usage".

[0102] The data analysis and reporting module provides data reports such as ticket sales and payment method usage by statistically analyzing the historical transaction data in the system.

[0103] Please refer to Figures 2 - 5As shown, the total ticket sales amount for each month is counted, the usage proportion of payment methods, the trend of average ticket prices, and the occurrence times of ticket usage behaviors labeled as "abnormal ticket usage" are counted. Through big data analysis and chart display, it helps administrators optimize management decisions, generating a statistical chart of total ticket sales amount, a pie chart of the usage proportion of payment methods, a schematic diagram of the trend of average ticket prices, and a statistical chart of the occurrence times of abnormal ticket usage.

[0104] The risk control and anti-fraud module identifies potential fraud users through comprehensive analysis of the login characteristic behavior data and transaction characteristic behavior data of each user. And risk control is executed according to the identification results of potential fraud users.

[0105] 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)};

[0106] Input the high-dimensional feature vectors corresponding to each user i into the potential fraud user identification model based on support vector machine.

[0107] The expression of the potential fraud user identification model based on support vector machine is:

[0108]

[0109] where is the output function of the potential fraud user identification model;

[0110] where is the output value of the potential fraud user identification model, representing the identification result;

[0111] Specifically, the output value of the potential fraud user identification model based on support vector machine is 1 or -1; it represents the result of determining whether user i is a potential fraud user according to the high-dimensional feature vector Xi of user i. being -1 represents that user i is determined to be a potential fraud user; -1 indicates that user i is determined to be a normal user;

[0112] where w is the normal vector of the hyperplane, defining the decision boundary;

[0113] where b is the bias term, defining the position of the decision hyperplane;

[0114] where sign is the sign function, and the calculation formula is:

[0115] .

[0116] where is the optimization objective function of the potential fraud user identification model, representing maximizing the classification boundary by optimizing the following objective function and calculating the specific value of the normal vector w of the hyperplane;

[0117] where is the slack variable, used to handle classification errors and allowing some data points not to meet the classification requirements.

[0118] where C is the regularization parameter, used to balance the model complexity and the penalty for classification errors. A smaller C value allows more misclassifications, while a larger C value makes the classification more strict and reduces the number of misclassifications. By adjusting the regularization parameter C, the model can balance between accuracy and generalization ability.

[0119] where is the constraint condition of the potential fraud user identification model, representing ensuring that each data point is either correctly classified or tolerated with a small error control during the model training process and calculating the specific values of the slack variable and the bias term b;

[0120] where is the artificial label assigned to the high-dimensional feature vector Xi during the model training process, being 1 represents a normal user, being -1 represents a potential fraud user.

[0121] Once the system identifies a certain user i as a potential fraud user, the following measures will be taken for this user:

[0122] Send a notice: Send a warning notice to this user and the administrator, informing that there are abnormal behaviors in the account of user i.

[0123] Manual review: Notify the administrator to conduct a manual review of the identified potential fraud user to check if it is a false alarm;

[0124] Take security measures: If, after manual review by the administrator, it is confirmed 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 shall be taken.

[0125] The database is responsible for storing all user identifiers, transaction records of each ticket transaction, and all ticket IDs.

[0126] Please refer to Figure 6 As shown, a membership ticket management method includes the following steps:

[0127] Step 1: User registration and permission setting;

[0128] The user completes registration through mobile phone number, email, and role information, generates a unique user identifier, and stores it in the database. Access permissions are assigned according to user roles including ordinary users, members, VIPs, and administrators.

[0129] Step 2: Behavioral data collection;

[0130] Collect the user's login characteristic behavior data and transaction characteristic behavior data for subsequent analysis and potential fraud identification.

[0131] Step 3: Payment information encryption and transmission;

[0132] Use the AES symmetric encryption algorithm to encrypt the payment information and securely transmit it to the ticket issuance module through a TLS encrypted channel.

[0133] Step 4: Ticket issuance and ID generation;

[0134] 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.

[0135] Step 5: Potential fraud identification;

[0136] Analyze the user's login characteristic behavior data and transaction characteristic behavior data through a potential fraud user identification model based on support vector machines to determine whether it is a potential fraud user.

[0137] Step 6: Fraud handling and security measure execution;

[0138] Take measures against the identified potential fraud users, including sending notifications, manual review, and implementing one or more combinations of account freezing and transaction disabling.

[0139] Step 7: Data analysis and reporting;

[0140] Generate visual data views and reports by analyzing historical transaction data to support the administrator's decision-making and optimize management.

[0141] It should be understood that the terms "comprising" and "including" as used in the specification and claims of this disclosure indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or their groups.

[0142] It should also be understood that the terminology used herein in the specification of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used in the specification and claims of this disclosure, unless the context clearly dictates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should be further understood that the term "and / or" as used in the specification and claims of this disclosure refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations;

[0143] The preferred embodiments of the present invention disclosed above are only used to assist in the explanation of the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A membership ticketing management system, comprising a user management module, a payment gateway module, a ticket issuance module, and a risk control and anti-fraud 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 according to the user's role; it collects behavior data by accessing the user behavior log to provide data support for subsequent risk control and behavior analysis; The payment gateway module provides secure payment functions, including online payment and offline payment; 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 encrypted channels; a transaction record is generated for each transaction during the payment process for query and subsequent ticket issuance; The ticket issuance module is responsible for the issuance of virtual tickets and ensures the uniqueness of the ticket ID through a hash algorithm; Whenever the encrypted payment information is received, it obtains the public key from the public key infrastructure PKI to verify the authenticity of the encrypted payment information. If the verification is correct, it proceeds with virtual ticket allocation and ticket ID generation; For each ticket, it obtains the user identifier, ticket issuance time, ticket type, and transaction serial number of the user who purchased the ticket, and concatenates the user identifier, ticket issuance time, ticket type, and transaction serial number of the user who purchased the ticket using the SHA-256 hash algorithm to generate a unique ticket ID; All generated ticket IDs are stored in the database, and each ticket ID remains unique in the database, and its usage status is marked as "unused"; It tracks the usage status of all tickets. Whenever it detects that a user uses a ticket by activating or checking the ticket, it automatically updates the usage status of the ticket ID to "used"; For the ticket ID whose usage status is updated to "used", whenever it detects that a user attempts to use the ticket corresponding to the ticket ID, it prevents further use of the ticket, returns an error message "The ticket has been used", and marks the user's current ticket usage behavior as "abnormal ticket usage"; It generates a unique ticket ID for each ticket; the ticket issuance module also tracks the usage status of tickets to ensure that each ticket can only be used once, avoiding repeated use or abuse; by real-time updating the status of tickets, it ensures 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 fraud behaviors by analyzing the user's login feature behavior data and transaction feature behavior data; it models and analyzes the behavior characteristics of each user using a potential fraud user identification model based on support vector machines to determine whether there are abnormal or fraud behaviors; once a potential fraud user is identified, the system takes security measures to ensure the overall security of the system.

2. The membership ticket management system according to claim 1, wherein It further includes a data analysis and reporting module and a database; By statistically analyzing the historical transaction data of the system, it generates visual data reports to help administrators optimize management decisions and conduct market trend analysis; The database is responsible for storing all user identifiers, transaction records of each ticket transaction, and all ticket IDs.

3. A membership ticket management system according to claim 1, characterized in that, The specific process of collecting users' basic information to complete registration is as follows: Whenever a registration application is received from a new user, registration is carried out with the user's basic information including mobile phone number, email, and user role; after registration is completed, a unique user identifier Ui = {i, timestamp, role} is generated for each user; where i is the user number, timestamp is the user registration timestamp, that is, the registration moment of user i; where role is the user role identifier, corresponding to the user role of user i, including ordinary user, ordinary member, VIP member, and administrator; All user identifiers obtained through registration are stored in the database, converted into structured data, and encrypted for storage.

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

5. A membership ticketing management system according to claim 1, wherein 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 logs 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; Obtain the transaction characteristic behavior data of user i, access the transaction data logs of each user i, and obtain the specific time timestamp_purchase_(i, j) when all ticket purchase behaviors of user i occur; obtain 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; where the transaction result status symbol represents the result of each ticket purchase behavior, including success, failure, pending payment, and transaction exception; where j is the sequence number of each transaction; j = 1, 2,..., m; where m is the number of all ticket purchase behaviors recorded in the transaction data log of user i.

6. A membership ticket management system according to claim 1, characterized in that, The specific process of identifying potential fraud behaviors is as follows: Generate a high-dimensional feature vector for each user i: Xi = {i, 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, m), payment_status(i, m)}; Where i is the user ID, timestamp is the user registration timestamp, i.e., the registration moment of user i; where role is the user role identifier, corresponding to the user role of user i, including ordinary user, ordinary member, VIP member, and administrator; where timestamp_login_i is the average login time interval of user i in all login history records; where login_ipchange_i is the IP address change frequency of user i in all login history records; Where timestamp_purchase_(i, 1), ticket_id(i, 1), ticket_quantity(i, 1), and payment_status(i, 1) are respectively the specific time when the ticket purchase behavior occurs, the ticket number involved, the quantity, and the transaction result status symbol in the transaction numbered 1 for user i; Where timestamp_purchase_(i, 2), ticket_id(i, 2), ticket_quantity(i, 2), and payment_status(i, 2) are respectively the specific time when the ticket purchase behavior occurs, the ticket number involved, the quantity, and the transaction result status symbol in the transaction numbered 2 for user i; Where timestamp_purchase_(i, m), ticket_id(i, m), ticket_quantity(i, m), and payment_status(i, m) are respectively the specific time when the ticket purchase behavior occurs, the ticket number involved, the quantity, and the transaction result status symbol in the transaction numbered m for user i; Input the high-dimensional feature vectors corresponding to each user i into the potential fraud user identification model based on support vector machine; The expression of the potential fraud user identification model based on support vector machine is: ； Where n is the total number of users; wherein is the output function of the potential fraud user identification model; Among them is the output value of the potential fraud user identification model, representing the identification result; Specifically, the output value of the potential fraud user identification model based on the support vector machine is 1 or -1; it represents the result of determining whether user i is a potential fraud user based on the high-dimensional feature vector Xi of the user, being -1 represents that user i is determined to be a potential fraud user; being 1 represents that user i is determined to be a normal user; Where w is the normal vector of the hyperplane, defining the decision boundary; Where b is the bias term, defining the position of the decision hyperplane; where sign is the sign function, and its calculation formula is: ； Among them is the optimization objective function of the potential fraud user identification model, which represents maximizing the classification boundary by optimizing the following objective function and calculating the specific value of the normal vector w of the hyperplane; wherein is a slack variable used to handle classification errors and allows some data points not to meet the classification requirements; where C is the regularization parameter, which is 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 strict and reduces the number of misclassifications; Among them is the constraint condition of the potential fraud user identification model, representing that in the model training process, it is ensured that each data point is either correctly classified or tolerated with a small error control, and the specific values of the slack variable and the bias term b are calculated; Among them is the artificial label assigned to the high-dimensional feature vector Xi during the model training process, where 1 represents a normal user, and -1 represents a potential fraudulent user; once the system identifies a user i as a potential fraudulent user, security measures will be taken against this user.

7. The membership ticket management system according to claim 6, characterized in that, The security measures include: Sending notifications: Sending warning notifications to the user and the administrator, informing that there is abnormal behavior in the account of user i; Manual review: Notifying the administrator to conduct a manual review of the identified potential fraud users to check if it is a false alarm; Taking security measures: If, after the administrator's manual review, it is confirmed that the identification result of the fraud user is not a false alarm, then take security measures including account freezing, prohibiting transactions, and forced account password reset.

8. A membership ticket management method, characterized in that It includes the following steps: Step 1: User registration and permission setting; The user completes registration through mobile phone number, email, and role information, generates a unique user identifier, and stores it in the database; access permissions are assigned according to user roles including ordinary users, members, VIPs, and administrators; Step 2: Behavioral data collection; Collect the login feature behavioral data and transaction feature behavioral data of the user for subsequent analysis and potential fraud identification; Step 3: Payment information encryption and transmission; Use the AES symmetric encryption algorithm to encrypt the payment information and securely transmit it to the ticket issuance module through a 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; Whenever the encrypted payment information is received, obtain the public key from the public key infrastructure PKI, verify the authenticity of the encrypted payment information. If the verification is correct, then perform virtual ticket allocation and ticket ID generation; For each ticket, obtain the user identifier, ticket issuance time, ticket type, and transaction serial number of the user who purchased the ticket, and use the SHA-256 hash algorithm to splice the user identifier, ticket issuance time, ticket type, and transaction serial number of the user who purchased the ticket to generate a unique ticket ID; All generated ticket IDs will be stored in the database, and each ticket ID will maintain uniqueness in the database, and its usage status will be marked as "unused"; Track the usage status of all tickets. Whenever it is detected that a user uses a ticket by activating or checking the ticket, automatically update the usage status of the ticket ID to "used"; For the ticket ID whose usage status is updated to "used", whenever it is detected that a user attempts to use the ticket corresponding to the ticket ID, prevent further use of the ticket, return an error prompt message "The ticket has been used", and mark the user's current ticket usage behavior as "abnormal ticket usage"; Step 5: Potential fraud identification; Analyze the login feature behavioral data and transaction feature behavioral data of the user through a potential fraud user identification model based on support vector machines to determine if it is a potential fraud user; Step 6: Fraud handling and security measure execution; Take measures against the identified potential fraudulent users, including sending notifications, manual review, and implementing one or more combinations of account freezing and transaction disabling; Step 7: Data analysis and reporting; Generate visual data views and reports by analyzing historical transaction data to support the administrator's decision-making and optimize management.

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