Ticket anti-counterfeiting method and system

Abstract

The present invention relates to the field of information security technology, specifically to a ticket anti-counterfeiting method and system, comprising the following steps: using blockchain technology to record the transaction information and verification data of the ticket, and encrypting and storing the ticket information through an encryption algorithm. In the present invention, blockchain technology, image recognition, radio spectrum fingerprint, Internet of Things technology, and physical random number technologies are combined to ensure the authenticity of the ticket at multiple levels and in an all-round manner, thereby improving the anti-counterfeiting ability of the ticket. Through the Internet of Things technology, the location and status information of the ticket can be tracked and recorded in real time, thereby improving the anti-counterfeiting efficiency. Through blockchain technology, each ticket transaction information and verification data are stored to ensure the integrity and traceability of the data. Through cloud computing and big data analysis, the risks in the process of using the ticket are evaluated in real time, and a visual risk assessment result is formed, thereby improving the real-time and accuracy of risk prevention.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a ticket anti-counterfeiting method and system. Background Art

[0002] Information security technology is a field that studies and applies various technical means to protect the security, integrity and availability of information systems and data. It covers many sub-fields, such as encryption algorithms, access control, identity authentication, anti-counterfeiting technology, etc., aiming to protect information from threats such as unauthorized access, tampering and forgery. Ticket anti-counterfeiting method is a technical means applied in the field of tickets, aiming to protect tickets from counterfeiting and ensure their authenticity and legitimacy. These tickets may include admission tickets, bills, certificates, vouchers, etc. Their authenticity and legitimacy are of great significance to ensure fair transactions, information security and prevent fraud.

[0003] The purpose of ticket anti-counterfeiting methods is to ensure the authenticity and legality of tickets by adopting different technical means and anti-counterfeiting measures, and to provide certain anti-counterfeiting capabilities, identify or authenticate tickets, and prevent fraud such as forgery, copying or tampering. It aims to provide users and relevant institutions with reliable ticket verification methods and enhance the anti-counterfeiting effect and security of tickets. In order to achieve this goal, ticket anti-counterfeiting methods usually adopt a variety of means and technologies. These include the use of special anti-counterfeiting materials or labels, such as watermarks, magnetic materials, textures, etc., to increase the ability to identify the authenticity of tickets. In addition, image recognition, optical color change, anti-copying technology and other means can be used in combination with information technologies such as digital signatures, QR codes, and RFID to achieve unique identification and traceability of tickets. Through these means, tickets can be authenticated, anti-copying, anti-tampering, and anti-counterfeiting traceable, thereby ensuring the security and reliability of tickets.

[0004] Among the existing ticket anti-counterfeiting methods, the existing ticket anti-counterfeiting methods usually only use a single anti-counterfeiting technology, such as physical anti-counterfeiting or digital anti-counterfeiting, and lack the comprehensive use of multiple anti-counterfeiting technologies, so the anti-counterfeiting ability is limited. Existing methods are often unable to track the use and location changes of tickets in real time, which increases the difficulty of anti-counterfeiting and may result in the failure to promptly detect and investigate the behavior of counterfeiting tickets. Current ticket verification mostly relies on a single method, such as barcode or QR code scanning, which cannot guarantee its uniqueness, is easily copied or forged, and the information verification is not accurate and timely enough. Existing ticket anti-counterfeiting methods usually do not design and implement risk assessment systems, making it difficult to promptly and effectively discover and warn of potential risks and problems. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a ticket anti-counterfeiting method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a ticket anti-counterfeiting method, comprising the following steps:

[0007] S1: Use blockchain technology to record ticket transaction information and verification data, encrypt and store ticket information through encryption algorithms, and use smart contracts to verify and control ticket transactions, generating a blockchain-based ticket record and authentication system;

[0008] S2: The blockchain-based ticket recording and authentication system uses a deep learning algorithm to identify and analyze ticket image data to ensure the uniqueness of the ticket and form a ticket image recognition model;

[0009] S3: Based on the ticket image recognition model, the radio spectrum fingerprint technology is used to authenticate the ticket to ensure the uniqueness of the ticket, thereby forming a radio spectrum fingerprint ticket authentication system;

[0010] S4: The ticket authentication system based on the radio spectrum fingerprint uses the Internet of Things technology to track and record the location and status information of the ticket in real time, forming an Internet of Things ticket tracking system;

[0011] S5: Based on the IoT ticket tracking system, a physical random number generator is used to add a unique identifier to the ticket to generate a ticket with a random number identifier;

[0012] S6: Based on the ticket with the random number identifier, embed an optical signal on the ticket using visible light communication technology, and perform reading verification to form a ticket verification system based on optical communication technology;

[0013] S7: The ticket verification system based on optical communication technology utilizes cloud computing and big data analysis to evaluate the risks in the process of ticket use in real time, thereby forming a ticket risk assessment system.

[0014] As a further solution of the present invention, blockchain technology is used to record the transaction information and verification data of tickets, the ticket information is encrypted and stored through an encryption algorithm, and smart contracts are used to verify and control ticket transactions. The steps of generating a blockchain-based ticket record and authentication system are specifically as follows:

[0015] S101: sorting the transaction data of the ticket using the Merkle tree structure to generate a ticket transaction data structure;

[0016] S102: Based on the ticket transaction data structure, a secure hash algorithm is used to perform hash processing on the data to generate a ticket transaction hash value;

[0017] S103: Using public key encryption technology to encrypt the ticket transaction hash value to generate encrypted ticket transaction information;

[0018] S104: Based on the encrypted ticket transaction information, the transaction is verified and controlled using a smart contract to generate a blockchain-based ticket record and authentication system.

[0019] As a further solution of the present invention, the blockchain-based ticket recording and authentication system uses a deep learning algorithm to identify and analyze ticket image data to ensure the uniqueness of the ticket. The steps of forming a ticket image recognition model are specifically as follows:

[0020] S201: extracting features from the ticket image using a convolutional neural network to obtain a ticket image feature set;

[0021] S202: Based on the ticket image feature set, a support vector machine is used to perform classification training to generate a ticket image classification model;

[0022] S203: Based on the ticket image classification model, a new ticket image is identified using the ticket image classification model to generate a ticket image classification result;

[0023] S204: Perform deep learning analysis on the ticket image classification results to determine the uniqueness of the ticket and form a ticket image recognition model.

[0024] As a further solution of the present invention, based on the ticket image recognition model, radio spectrum fingerprint technology is used to authenticate the ticket to ensure the uniqueness of the ticket. The steps of forming a radio spectrum fingerprint ticket authentication system are specifically as follows:

[0025] S301: collecting the amplitude, phase and frequency information of the radio signal to obtain the original spectrum data of the ticket;

[0026] S302: Based on the original spectrum data of the ticket, a spectrum analysis is performed using a fast Fourier transform to generate a spectrum fingerprint of the ticket;

[0027] S303: performing pattern matching and verification on the ticket spectrum fingerprint to generate a ticket spectrum verification result;

[0028] S304: Based on the ticket spectrum verification result, the uniqueness of the ticket is ensured, and a ticket authentication system of radio spectrum fingerprint is formed.

[0029] As a further solution of the present invention, the ticket authentication system based on the radio spectrum fingerprint uses the Internet of Things technology to track and record the location and status information of the ticket in real time, and the steps of forming the Internet of Things ticket tracking system are specifically as follows:

[0030] S401: Installing a GPS module and a sensor on the ticket to collect real-time location and status information of the ticket and generate real-time location and status information data;

[0031] S402: Based on the real-time location and status information data, use ad hoc wireless communication technology to transmit the data to a central server to form real-time data received by the central server;

[0032] S403: The central server processes and analyzes the real-time data, optimizes the accuracy of the data using an optimal filtering method based on state estimation, and generates optimized real-time position and state information;

[0033] S404: The optimized real-time location and status information is updated to the ticket tracking system in real time via the Internet to form an Internet of Things ticket tracking system.

[0034] As a further solution of the present invention, based on the IoT ticket tracking system, a physical random number generator is used to add a unique identifier to the ticket, and the steps of generating a ticket with a random number identifier are specifically as follows:

[0035] S501: Generate a random number using a physical random number generator;

[0036] S502: Generate a unique identifier using a hash function based on the random number;

[0037] S503: Binding the unique identifier with the real-time location and status information of the IoT ticket tracking system to generate bound ticket information;

[0038] S504: The bound ticket information is stored in the blockchain to ensure that the data cannot be tampered with, and a ticket with a random number identifier is generated.

[0039] As a further solution of the present invention, based on the ticket with the random number identifier, the steps of embedding an optical signal on the ticket using visible light communication technology and performing reading verification to form a ticket verification system based on optical communication technology are specifically as follows:

[0040] S601: converting the information of the ticket with the random number identifier into an optical signal to generate optical signal information;

[0041] S602: Based on the optical signal information, embed the optical signal on the ticket using visible light communication technology to generate a ticket embedded with the optical signal;

[0042] S603: using an optical signal reader to read and verify the ticket embedded with the optical signal to obtain a verification result of the ticket;

[0043] S604: Determine the validity of the ticket based on the verification result of the ticket, and form a ticket verification system based on optical communication technology.

[0044] As a further solution of the present invention, the ticket verification system based on optical communication technology uses cloud computing and big data analysis to evaluate the risks in the process of using tickets in real time. The steps of forming a ticket risk assessment system are specifically as follows:

[0045] S701: The ticket verification system based on optical communication technology collects data during the ticket usage process and generates a ticket usage data set;

[0046] S702: Based on the ticket usage data set, big data analysis technology is used to process and analyze the data to obtain a ticket usage risk analysis result;

[0047] S703: Uploading the ticket usage risk analysis result to the cloud server to generate a cloud-based risk analysis result;

[0048] S704: Based on the risk analysis results in the cloud, the ticket risk assessment system is updated in real time to form a ticket risk assessment system.

[0049] A ticket anti-counterfeiting system is used to execute the above-mentioned ticket anti-counterfeiting method, and the ticket anti-counterfeiting system is composed of a blockchain recording module, an image recognition module, a spectrum fingerprint authentication module, an Internet of Things tracking module, a random number identification module, and an optical communication verification module;

[0050] The blockchain recording module uses a Merkle tree structure to organize the transaction data of the ticket, and uses a secure hash algorithm to perform hash processing, and after obtaining the hash value, performs public key encryption, uses smart contracts for verification and control, and generates a blockchain-based ticket record and authentication system;

[0051] The image recognition module is based on the ticket record and authentication system of the blockchain, uses a convolutional neural network to extract features from the ticket image, uses a support vector machine for classification training based on the obtained feature set, recognizes new ticket images, and generates a ticket image recognition model;

[0052] The spectrum fingerprint authentication module collects the amplitude, phase and frequency information of the radio signal based on the ticket image recognition model, uses fast Fourier transform to perform spectrum analysis, and performs pattern matching and verification on the obtained ticket spectrum fingerprint to generate a ticket authentication system of radio spectrum fingerprint;

[0053] The IoT tracking module is based on a spectrum fingerprint authentication system, with GPS modules and sensors installed on the ticket, and collects and transmits real-time location and status information to the central server through ad hoc wireless communication technology, which is then updated to the ticket tracking system in real time after optimization to generate an IoT ticket tracking system;

[0054] The random number identification module is based on the Internet of Things ticket tracking system, uses a physical random number generator to generate a random number, and uses a hash function to generate a unique identifier, binds the unique identifier with the real-time location and status information of the Internet of Things ticket tracking system, stores it in the blockchain, and generates a ticket with a random number identifier;

[0055] The optical communication verification module converts the ticket information into an optical signal based on the ticket with a random number identifier and embeds it into the ticket, uses an optical signal reader to read and verify, determines the validity of the ticket based on the verification result, and generates a ticket verification system based on optical communication technology.

[0056] As a further solution of the present invention, the blockchain recording module includes a transaction data sorting submodule, a hash processing submodule, and a public key encryption submodule;

[0057] The image recognition module includes a feature extraction submodule, a classification training submodule, and an image recognition submodule;

[0058] The spectrum fingerprint authentication module includes a signal acquisition submodule, a spectrum analysis submodule, and a pattern matching submodule;

[0059] The IoT tracking module includes a GPS positioning submodule, a wireless transmission submodule, and a state optimization submodule;

[0060] The random number identification module includes a random number generation submodule, an identification generation submodule, and a state binding submodule;

[0061] The optical communication verification module includes an optical signal conversion submodule, an optical signal embedding submodule, and an optical signal reading submodule.

[0062] Compared with the prior art, the advantages and positive effects of the present invention are:

[0063] In the present invention, blockchain technology, image recognition, radio spectrum fingerprint, Internet of Things technology, and physical random number technologies are combined to ensure the authenticity of tickets in a multi-level and comprehensive manner and enhance the anti-counterfeiting ability of tickets. Through Internet of Things technology, the location and status information of tickets can be tracked and recorded in real time, and the tickets can be managed and controlled in real time, thereby improving the anti-counterfeiting efficiency. Through blockchain technology, each ticket transaction information and verification data can be stored to ensure the integrity and traceability of the data and enhance the security of the tickets. Through cloud computing and big data analysis, the risks in the use of tickets can be evaluated in real time, and visual risk assessment results can be formed, so that regulators can learn about the use of tickets faster and more accurately, thereby improving the real-time and accuracy of risk prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1It is a schematic diagram of the workflow of the present invention;

[0065] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0066] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0067] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0068] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0069] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0070] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0071] Figure 8 This is a detailed flow chart of S7 of the present invention;

[0072] Fig. 9 is a system flow chart of the present invention;

[0073] Fig.10 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0076] Embodiment 1

[0077] See also Figure 1 The present invention provides a technical solution: a ticket anti-counterfeiting method, comprising the following steps:

[0078] S1: Use blockchain technology to record ticket transaction information and verification data, encrypt and store ticket information through encryption algorithms, and use smart contracts to verify and control ticket transactions, generating a blockchain-based ticket record and authentication system;

[0079] S2: The blockchain-based ticket recording and authentication system uses deep learning algorithms to identify and analyze ticket image data to ensure the uniqueness of the ticket and form a ticket image recognition model;

[0080] S3: Based on the ticket image recognition model, the radio spectrum fingerprint technology is used to authenticate the ticket to ensure the uniqueness of the ticket, forming a radio spectrum fingerprint ticket authentication system;

[0081] S4: A ticket authentication system based on radio spectrum fingerprinting, which uses IoT technology to track and record the location and status information of tickets in real time, forming an IoT ticket tracking system;

[0082] S5: Based on the IoT ticket tracking system, a physical random number generator is used to add a unique identifier to the ticket, generating a ticket with a random number identifier;

[0083] S6: Based on the ticket with the random number identifier, use visible light communication technology to embed an optical signal on the ticket, and perform reading verification to form a ticket verification system based on optical communication technology;

[0084] S7: A ticket verification system based on optical communication technology uses cloud computing and big data analysis to assess the risks in the use of tickets in real time, forming a ticket risk assessment system.

[0085] First, by recording the ticket transaction information and verification data in the blockchain, encrypting and storing them, and using smart contracts for verification and control, the transaction information security and immutability of the ticket are ensured. Secondly, deep learning algorithms are used to identify and analyze ticket images to ensure the uniqueness of each ticket and effectively prevent forgery and copying. In addition, radio spectrum fingerprint technology is used to authenticate tickets, and the authenticity and uniqueness of the tickets are verified by collecting the radio spectrum fingerprint of the tickets to improve the security of the tickets. At the same time, the location and status information of the tickets are tracked and recorded in real time using the Internet of Things technology to ensure the security and traceability of the tickets. A physical random number generator is used to add a unique identifier to the ticket to further increase the uniqueness and anti-counterfeiting of the ticket. Visible light communication technology is used to embed optical signals into the ticket and read and verify them, increasing the security and anti-counterfeiting of the ticket. Finally, cloud computing and big data analysis are combined to evaluate the risks in the use of tickets in real time, so that potential risks can be discovered and responded to in a timely manner to ensure the security of the ticket. In summary, this comprehensive ticket anti-counterfeiting method can effectively improve the security, credibility and traceability of tickets, prevent counterfeiting, and assess risks in real time to ensure the security and reliability of tickets during use.

[0086] See also Figure 2 , using blockchain technology to record ticket transaction information and verification data, encrypting and storing ticket information through encryption algorithms, and using smart contracts to verify and control ticket transactions. The specific steps for generating a blockchain-based ticket record and authentication system are as follows:

[0087] S101: sorting the transaction data of the ticket using the Merkle tree structure to generate a ticket transaction data structure;

[0088] S102: Based on the ticket transaction data structure, a secure hash algorithm is used to perform hash processing on the data to generate a ticket transaction hash value;

[0089] S103: Using public key encryption technology to encrypt the ticket transaction hash value to generate encrypted ticket transaction information;

[0090] S104: Based on the encrypted ticket transaction information, the transaction is verified and controlled using smart contracts to generate a blockchain-based ticket record and authentication system.

[0091] First, the Merkle tree structure is used to organize the transaction data of tickets, which can effectively organize and manage ticket transaction information and improve the readability and verifiability of data. Secondly, the ticket transaction data is hashed through a secure hash algorithm to generate a unique ticket transaction hash value to ensure the integrity and tamper-proofness of the ticket transaction. At the same time, the public key encryption technology is used to encrypt the ticket transaction hash value to protect the privacy and security of the ticket transaction information.

[0092] Smart contracts are used to verify and control ticket transactions, and a blockchain-based ticket record and authentication system is built. This system can provide transparent and tamper-proof ticket records and authentication methods to ensure the credibility and authenticity of ticket transactions. At the same time, the automatic execution characteristics of smart contracts can reduce trust issues and human errors in the middle links, and improve the efficiency and accuracy of ticket transactions.

[0093] See also Figure 3 The blockchain-based ticket recording and authentication system uses a deep learning algorithm to identify and analyze ticket image data to ensure the uniqueness of the ticket. The steps to form a ticket image recognition model are as follows:

[0094] S201: extracting features from the ticket image using a convolutional neural network to obtain a ticket image feature set;

[0095] S202: Based on the ticket image feature set, a support vector machine is used to perform classification training to generate a ticket image classification model;

[0096] S203: Based on the ticket image classification model, the new ticket image is identified using the ticket image classification model to generate a ticket image classification result;

[0097] S204: Perform deep learning analysis on the ticket image classification results to determine the uniqueness of the ticket and form a ticket image recognition model.

[0098] First, a convolutional neural network is used to extract features from ticket images, which can effectively capture the visual information in the ticket images and extract a feature set with distinguishing properties. This ensures the uniqueness of each ticket, thereby preventing forgery and duplication and increasing the anti-counterfeiting properties of the ticket.

[0099] Secondly, by using support vector machines for classification training, a classification model can be built based on the feature set of ticket images. The model can learn the association between the features and categories of ticket images, so that new ticket images can be classified quickly and accurately. This ensures accurate recognition of ticket images and improves the credibility and efficiency of ticket authentication.

[0100] Based on the ticket image classification results, deep learning analysis is performed to further determine the uniqueness of the ticket. By deeply analyzing the ticket image classification results, potential patterns and associations can be discovered, further improving the precision and accuracy of ticket image recognition. This can better ensure the identity authentication and anti-counterfeiting functions of the ticket.

[0101] See also Figure 4Based on the ticket image recognition model, the radio spectrum fingerprint technology is used to authenticate the ticket to ensure the uniqueness of the ticket. The steps of forming the radio spectrum fingerprint ticket authentication system are as follows:

[0102] S301: collecting the amplitude, phase and frequency information of the radio signal to obtain the original spectrum data of the ticket;

[0103] S302: Based on the original spectrum data of the ticket, use fast Fourier transform to perform spectrum analysis to generate a spectrum fingerprint of the ticket;

[0104] S303: Perform pattern matching and verification on the ticket spectrum fingerprint to generate a ticket spectrum verification result;

[0105] S304: Based on the ticket spectrum verification result, the uniqueness of the ticket is ensured, and a ticket authentication system of radio spectrum fingerprint is formed.

[0106] First, by collecting the amplitude, phase and frequency information of the radio signal, the original spectrum data of the ticket can be obtained. These data can reflect the radio signal characteristics of the ticket and provide a basis for subsequent analysis and authentication.

[0107] Secondly, based on the original spectrum data of the ticket, fast Fourier transform is used to perform spectrum analysis to generate the spectrum fingerprint of the ticket. The spectrum fingerprint is a unique identifier generated based on the characteristics of the ticket on the radio spectrum, which can effectively distinguish different tickets. This ensures the uniqueness of the ticket and prevents forgery and copying.

[0108] Then, the spectral fingerprint of the ticket is pattern matched and verified to achieve ticket authentication. By comparing and verifying the spectral fingerprint of the ticket with the existing authentication model, the authenticity and legality of the ticket can be determined. This can ensure the accuracy and reliability of the ticket authentication process.

[0109] Based on the ticket spectrum verification results, the uniqueness of the ticket is ensured, forming a ticket authentication system with radio spectrum fingerprint. The system can effectively identify and verify the spectrum fingerprint of the ticket, thereby ensuring the uniqueness of each ticket and further enhancing the anti-counterfeiting and security of the ticket.

[0110] See also Figure 5 The ticket authentication system based on radio spectrum fingerprint uses IoT technology to track and record the location and status information of tickets in real time. The steps to form the IoT ticket tracking system are as follows:

[0111] S401: Installing a GPS module and a sensor on the ticket to collect real-time location and status information of the ticket and generate real-time location and status information data;

[0112] S402: Based on the real-time location and status information data, the data is transmitted to the central server using the ad hoc network wireless communication technology to form real-time data received by the central server;

[0113] S403: The central server processes and analyzes the real-time data, optimizes the accuracy of the data using an optimal filtering method based on state estimation, and generates optimized real-time position and state information;

[0114] S404: The optimized real-time location and status information is updated to the ticket tracking system in real time via the Internet to form an Internet of Things ticket tracking system.

[0115] First, by collecting the amplitude, phase and frequency information of the radio signal, the original spectrum data of the ticket can be obtained. These data can reflect the radio signal characteristics of the ticket and provide a basis for subsequent analysis and authentication.

[0116] Secondly, based on the original spectrum data of the ticket, fast Fourier transform is used to perform spectrum analysis to generate the spectrum fingerprint of the ticket. The spectrum fingerprint is a unique identifier generated based on the characteristics of the ticket on the radio spectrum, which can effectively distinguish different tickets. This ensures the uniqueness of the ticket and prevents forgery and copying.

[0117] Then, the spectral fingerprint of the ticket is pattern matched and verified to achieve ticket authentication. By comparing and verifying the spectral fingerprint of the ticket with the existing authentication model, the authenticity and legality of the ticket can be determined. This can ensure the accuracy and reliability of the ticket authentication process.

[0118] Based on the ticket spectrum verification results, the uniqueness of the ticket is ensured, forming a ticket authentication system with radio spectrum fingerprint. The system can effectively identify and verify the spectrum fingerprint of the ticket, thereby ensuring the uniqueness of each ticket and further enhancing the anti-counterfeiting and security of the ticket.

[0119] See also Figure 6 Based on the IoT ticket tracking system, a physical random number generator is used to add a unique identifier to the ticket. The specific steps for generating a ticket with a random number identifier are:

[0120] S501: Generate a random number using a physical random number generator;

[0121] S502: Generate a unique identifier using a hash function based on a random number;

[0122] S503: Binding the unique identifier with the real-time location and status information of the IoT ticket tracking system to generate bound ticket information;

[0123] S504: The bound ticket information is stored in the blockchain to ensure that the data cannot be tampered with, and a ticket with a random number identifier is generated.

[0124] First, a random number is generated through a physical random number generator to ensure uniqueness and randomness. This ensures that each ticket has a unique identification to prevent conflicts and duplications.

[0125] Secondly, based on the generated random number, a hash function is used to generate a unique identification for the ticket. The hash function processes the random number as input and generates a hash value of fixed length as a unique identification. This ensures the stability and consistency of the identification. By binding with the real-time location and status information of the IoT ticket tracking system, the ticket information can be associated with the actual situation.

[0126] The bound ticket information is stored in the blockchain to ensure that the data cannot be tampered with. Blockchain technology provides a distributed and decentralized storage method, so that the ticket information is shared and verified by multiple nodes and protects the data from tampering. This ensures the integrity and authenticity of the ticket information.

[0127] See also Figure 7 Based on the ticket with random number identification, visible light communication technology is used to embed optical signals on the ticket, and read and verify. The steps of forming a ticket verification system based on optical communication technology are as follows:

[0128] S601: converting the information of the ticket with the random number identifier into an optical signal to generate optical signal information;

[0129] S602: Based on the optical signal information, embed the optical signal on the ticket using visible light communication technology to generate a ticket embedded with the optical signal;

[0130] S603: Using an optical signal reader to read and verify the ticket embedded with the optical signal, and obtaining a verification result of the ticket;

[0131] S604: Determine the validity of the ticket based on the ticket verification result, and form a ticket verification system based on optical communication technology.

[0132] First, the ticket information with a random number identifier is converted into an optical signal to generate optical signal information. This process can convert the ticket information into a visible light signal through coding and modulation technology to ensure the accuracy and transmittability of the information.

[0133] Secondly, based on the optical signal information, visible light communication technology is used to embed the optical signal into the ticket to generate a ticket with embedded optical signal. This method uses the visible light transmittance on the surface of the ticket to embed the optical signal information directly into the ticket. In this way, the ticket itself becomes a carrier of information, preventing the information from being tampered with or forged.

[0134] Then, the optical signal reader is used to read and verify the ticket embedded with the optical signal. The reader can analyze the optical signal on the ticket and restore it to the original data to verify and interpret the information on the ticket. Through the reader's recognition and decoding, the verification result of the ticket can be obtained to determine the validity and authenticity of the ticket.

[0135] Based on the verification results based on the ticket, the validity of the ticket is determined, forming a ticket verification system based on optical communication technology. This system uses visible light communication technology to build a fast, reliable and anti-counterfeiting ticket verification method. It can read and verify ticket information in real time, improving the accuracy and efficiency of verification.

[0136] See also Figure 8 The ticket verification system based on optical communication technology uses cloud computing and big data analysis to evaluate the risks in the use of tickets in real time. The steps to form a ticket risk assessment system are as follows:

[0137] S701: A ticket verification system based on optical communication technology collects data during the use of tickets and generates a ticket use data set;

[0138] S702: Based on the ticket usage data set, the data is processed and analyzed using big data analysis technology to obtain a ticket usage risk analysis result;

[0139] S703: Upload the ticket usage risk analysis result to the cloud server to generate a cloud-based risk analysis result;

[0140] S704: Based on the risk analysis results in the cloud, the ticket risk assessment system is updated in real time to form a ticket risk assessment system.

[0141] First, the ticket verification system based on optical communication technology collects data during the use of tickets to form a ticket usage data set. This data includes ticket verification records, usage time, location and other information, providing a basis for subsequent risk assessment.

[0142] Secondly, based on the ticket usage data set, big data analysis technology is used to process and analyze the data. By using big data analysis algorithms and technologies, valuable information and patterns can be extracted from the massive ticket usage data, and potential risk factors and patterns can be identified. In this way, the risks of ticket usage can be analyzed and evaluated.

[0143] Then, the ticket usage risk analysis results are uploaded to the cloud server to form the risk analysis results in the cloud. Cloud computing technology provides powerful computing and storage capabilities, which can process and analyze large-scale data and generate risk analysis results. The results are uploaded to the cloud server to achieve real-time updates and sharing, which is convenient for multiple users to access and refer to.

[0144] Based on the risk analysis results in the cloud, the ticket risk assessment system is updated in real time. Combining the risk analysis results in the cloud with the ticket risk assessment system can achieve real-time assessment and monitoring of ticket usage risks. The system can conduct risk assessment based on the updated data, generate corresponding risk reports and warnings, and help users identify and respond to potential risk issues in a timely manner.

[0145] See also Fig. 9 , a ticket anti-counterfeiting system is used to execute the above-mentioned ticket anti-counterfeiting method, the ticket anti-counterfeiting system is composed of a blockchain recording module, an image recognition module, a spectrum fingerprint authentication module, an Internet of Things tracking module, a random number identification module, and an optical communication verification module;

[0146] The blockchain record module uses the Merkle tree structure to organize the transaction data of the ticket, and uses the secure hash algorithm for hash processing. After obtaining the hash value, it performs public key encryption, uses smart contracts for verification and control, and generates a blockchain-based ticket record and authentication system;

[0147] The image recognition module is based on the ticket record and authentication system of the blockchain. It uses a convolutional neural network to extract features from ticket images. Based on the obtained feature set, it uses a support vector machine for classification training to recognize new ticket images and generate a ticket image recognition model.

[0148] The spectrum fingerprint authentication module is based on the ticket image recognition model, collects the amplitude, phase and frequency information of the radio signal, uses fast Fourier transform to perform spectrum analysis, and performs pattern matching and verification on the obtained ticket spectrum fingerprint to generate a ticket authentication system with radio spectrum fingerprint;

[0149] The IoT tracking module is based on the spectrum fingerprint authentication system. GPS modules and sensors are installed on the ticket. The real-time location and status information is collected and transmitted to the central server through ad hoc wireless communication technology. After optimization, it is updated to the ticket tracking system in real time to generate an IoT ticket tracking system.

[0150] The random number identification module is based on the IoT ticket tracking system. It uses a physical random number generator to generate a random number and a hash function to generate a unique identifier. The unique identifier is bound to the real-time location and status information of the IoT ticket tracking system and stored in the blockchain to generate a ticket with a random number identifier.

[0151] The optical communication verification module converts the ticket information into an optical signal based on the ticket with a random number identifier and embeds it into the ticket. The optical signal reader is used for reading and verification. The validity of the ticket is determined based on the verification result, generating a ticket verification system based on optical communication technology.

[0152] First, the blockchain record module uses the Merkle tree structure to organize the transaction data of the ticket and uses a secure hashing algorithm for hashing. This ensures the integrity and immutability of the ticket record, and verifies and controls it through smart contracts to generate a blockchain-based ticket record and authentication system.

[0153] Secondly, the image recognition module is based on the blockchain ticket record and authentication system, using convolutional neural networks to extract features from ticket images and using support vector machines for classification training. This allows new ticket images to be recognized, realizing an automated ticket image recognition model and improving recognition accuracy and efficiency.

[0154] Then, the spectrum fingerprint authentication module collects the amplitude, phase and frequency information of the radio signal based on the ticket image recognition model, and uses fast Fourier transform to perform spectrum analysis. By pattern matching and verifying the ticket spectrum fingerprint, a ticket authentication system of radio spectrum fingerprint is formed. This authentication method can increase the uniqueness and anti-counterfeiting of the ticket and improve the reliability of authentication.

[0155] The IoT tracking module is based on the spectrum fingerprint authentication system. GPS modules and sensors are installed on the ticket. Real-time location and status information is collected through ad hoc wireless communication technology and transmitted to the central server. This enables real-time tracking and monitoring of tickets, which are then updated to the ticket tracking system in real time after optimization. This tracking system can provide accurate monitoring of the location and status of tickets, improving ticket management and anti-counterfeiting effects.

[0156] The random number identification module is based on the IoT ticket tracking system, uses a physical random number generator to generate random numbers, and uses a hash function to generate a unique identifier. The unique identifier is bound to the real-time location and status information of the IoT ticket tracking system and stored in the blockchain to generate a ticket with a random number identifier. This ensures the uniqueness and anti-counterfeiting of each ticket and strengthens the identity authentication and traceability of the ticket.

[0157] Finally, the optical communication verification module converts the ticket information into an optical signal based on the ticket with a random number identifier and embeds it into the ticket. The optical signal reader is used to read and verify the ticket with the embedded optical signal, and the validity of the ticket is determined based on the verification result. This verification method can improve the accuracy and credibility of ticket verification and increase the anti-counterfeiting of the ticket.

[0158] See also Fig.10 ,The blockchain recording module includes a transaction data sorting submodule, a hash processing submodule, and a public key encryption submodule;

[0159] The image recognition module includes a feature extraction submodule, a classification training submodule, and an image recognition submodule;

[0160] The spectrum fingerprint authentication module includes a signal acquisition submodule, a spectrum analysis submodule, and a pattern matching submodule;

[0161] The IoT tracking module includes a GPS positioning submodule, a wireless transmission submodule, and a state optimization submodule;

[0162] The random number identification module includes a random number generation submodule, an identification generation submodule, and a state binding submodule;

[0163] The optical communication verification module includes an optical signal conversion submodule, an optical signal embedding submodule, and an optical signal reading submodule.

[0164] First, the transaction data arrangement submodule of the blockchain recording module can structure the transaction data of the ticket and improve the efficiency of data organization and management. The hash processing submodule processes the ticket data through a secure hash algorithm to ensure the integrity and non-tamperability of the data. The public key encryption submodule increases the security of the data, and only authorized users can access and verify the data. The blockchain recording module formed by integrating these submodules can provide a reliable ticket recording and authentication system.

[0165] Secondly, the feature extraction submodule of the image recognition module can extract useful feature information from the ticket image. The classification training submodule classifies the features through the training algorithm and builds an accurate model. The image recognition submodule uses this model to identify and verify new ticket images, improving the accuracy and efficiency of ticket recognition. This modular design can realize automated ticket image recognition and improve the accuracy and efficiency of ticket verification.

[0166] Then, the signal acquisition submodule of the spectrum fingerprint authentication module can collect the radio signal information of the ticket. The spectrum analysis submodule uses fast Fourier transform to perform spectrum analysis on the signal and extract spectrum fingerprint information. The pattern matching submodule matches and verifies the collected ticket spectrum fingerprint to realize the ticket authentication of radio spectrum fingerprint. This design can increase the uniqueness and anti-counterfeiting of the ticket and improve the authentication reliability of the ticket.

[0167] The GPS positioning submodule of the IoT tracking module can track and record the real-time location information of the ticket. The wireless transmission submodule transmits the location and status information to the central server through ad hoc wireless communication technology to achieve real-time tracking and monitoring. The status optimization submodule uses the collected information for optimization and analysis to achieve real-time update and optimization of the ticket status. This modular design can improve the accuracy and efficiency of ticket tracking and management.

[0168] The random number generation submodule of the random number identification module can generate random numbers to add a unique identification to the ticket. The identification generation submodule generates a unique identification through a hash function and binds it to the real-time location and status information of the IoT tracking system. The status binding submodule stores the bound ticket information in the blockchain to ensure the immutability and security of the data. This design can ensure the uniqueness and anti-counterfeiting of each ticket.

[0169] Finally, the optical signal conversion submodule of the optical communication verification module can convert the ticket information into an optical signal and embed it into the ticket. The optical signal embedding submodule embeds the optical signal into the ticket to realize the function of optical communication verification. The optical signal reading submodule reads and verifies the optical signal through the reader and determines the validity of the ticket based on the verification result. This design can improve the accuracy and credibility of ticket verification and increase the anti-counterfeiting property of the ticket.

[0170] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A ticket anti-counterfeiting method, characterized in that: The following steps are involved: Use blockchain technology to record ticket transaction information and verification data, encrypt and store ticket information through encryption algorithms, and use smart contracts to verify and control ticket transactions, generating a blockchain-based ticket record and authentication system; The blockchain-based ticket recording and authentication system uses a deep learning algorithm to identify and analyze ticket image data, ensure the uniqueness of the ticket, and form a ticket image recognition model; Based on the ticket image recognition model, the radio spectrum fingerprint technology is used to authenticate the ticket to ensure the uniqueness of the ticket, thereby forming a radio spectrum fingerprint ticket authentication system; The ticket authentication system based on the radio spectrum fingerprint uses the Internet of Things technology to track and record the location and status information of the ticket in real time, forming an Internet of Things ticket tracking system; Based on the IoT ticket tracking system, a physical random number generator is used to add a unique identifier to the ticket to generate a ticket with a random number identifier; Based on the ticket with the random number identifier, an optical signal is embedded in the ticket using visible light communication technology, and reading and verification are performed to form a ticket verification system based on optical communication technology; The ticket verification system based on optical communication technology uses cloud computing and big data analysis to evaluate the risks in the process of ticket use in real time, forming a ticket risk assessment system; The blockchain-based ticket recording and authentication system uses a deep learning algorithm to identify and analyze ticket image data to ensure the uniqueness of the ticket. The steps to form a ticket image recognition model are as follows: A convolutional neural network is used to extract features from ticket images to obtain a ticket image feature set; Based on the ticket image feature set, a support vector machine is used to perform classification training to generate a ticket image classification model; Based on the ticket image classification model, a new ticket image is identified using the ticket image classification model to generate a ticket image classification result; Performing deep learning analysis on the ticket image classification results to determine the uniqueness of the ticket and form a ticket image recognition model; Based on the ticket image recognition model, the radio spectrum fingerprint technology is used to authenticate the ticket to ensure the uniqueness of the ticket. The steps of forming the radio spectrum fingerprint ticket authentication system are as follows: Collect the amplitude, phase and frequency information of the radio signal to obtain the original spectrum data of the ticket; Based on the original spectrum data of the ticket, a spectrum analysis is performed using a fast Fourier transform to generate a spectrum fingerprint of the ticket; Performing pattern matching and verification on the ticket spectrum fingerprint to generate a ticket spectrum verification result; Based on the ticket spectrum verification result, the uniqueness of the ticket is ensured, and a ticket authentication system of radio spectrum fingerprint is formed.

2. The ticket anti-counterfeiting method according to claim 1, characterized in that: Use blockchain technology to record ticket transaction information and verification data, encrypt and store ticket information through encryption algorithms, and use smart contracts to verify and control ticket transactions. The specific steps for generating a blockchain-based ticket record and authentication system are as follows: The transaction data of the ticket is sorted using the Merkle tree structure to generate the ticket transaction data structure; Based on the ticket transaction data structure, a secure hash algorithm is used to perform hash processing on the data to generate a ticket transaction hash value; Using public key encryption technology to encrypt the ticket transaction hash value to generate encrypted ticket transaction information; Based on the encrypted ticket transaction information, smart contracts are used to verify and control transactions, and a blockchain-based ticket record and authentication system is generated.

3. The ticket anti-counterfeiting method according to claim 1, characterized in that: The ticket authentication system based on the radio spectrum fingerprint uses the Internet of Things technology to track and record the location and status information of the ticket in real time. The steps of forming the Internet of Things ticket tracking system are as follows: Installing GPS modules and sensors on the tickets to collect real-time location and status information of the tickets and generate real-time location and status information data; Based on the real-time location and status information data, the data is transmitted to the central server using ad hoc wireless communication technology to form real-time data received by the central server; The central server processes and analyzes the real-time data, optimizes the accuracy of the data using an optimal filtering method based on state estimation, and generates optimized real-time position and state information; The optimized real-time location and status information is updated to the ticket tracking system in real time via the Internet to form an Internet of Things ticket tracking system.

4. The ticket anti-counterfeiting method according to claim 1, characterized in that: Based on the IoT ticket tracking system, a physical random number generator is used to add a unique identifier to the ticket. The steps of generating a ticket with a random number identifier are as follows: Generate a random number using a physical random number generator; Based on the random number, a unique identifier is generated using a hash function; Binding the unique identifier with the real-time location and status information of the IoT ticket tracking system to generate bound ticket information; The bound ticket information is stored in the blockchain to ensure that the data cannot be tampered with, and a ticket with a random number identifier is generated.

5. The ticket anti-counterfeiting method according to claim 1, characterized in that: Based on the ticket with the random number identifier, the steps of embedding an optical signal on the ticket using visible light communication technology and performing reading verification to form a ticket verification system based on optical communication technology are as follows: Converting the information of the ticket with the random number identifier into an optical signal to generate optical signal information; Based on the optical signal information, the optical signal is embedded in the ticket using visible light communication technology to generate a ticket embedded with the optical signal; Using an optical signal reader to read and verify the ticket embedded with the optical signal to obtain a verification result of the ticket; According to the verification result of the ticket, the validity of the ticket is determined, forming a ticket verification system based on optical communication technology.

6. The ticket anti-counterfeiting method according to claim 1, characterized in that: The ticket verification system based on optical communication technology uses cloud computing and big data analysis to evaluate the risks in the process of using tickets in real time. The steps of forming the ticket risk assessment system are as follows: The ticket verification system based on optical communication technology collects data during the ticket usage process and generates a ticket usage data set; Based on the ticket usage data set, big data analysis technology is used to process and analyze the data to obtain a ticket usage risk analysis result; Uploading the ticket usage risk analysis result to the cloud server to generate a cloud-based risk analysis result; Based on the risk analysis results in the cloud, the ticket risk assessment system is updated in real time to form a ticket risk assessment system.

7. A ticket anti-counterfeiting system, characterized in that: The ticket anti-counterfeiting system is used to execute the ticket anti-counterfeiting method according to any one of claims 1 to 6, and the ticket anti-counterfeiting system is composed of a blockchain recording module, an image recognition module, a spectrum fingerprint authentication module, an Internet of Things tracking module, a random number identification module, and an optical communication verification module; The blockchain recording module uses a Merkle tree structure to organize the transaction data of the ticket, and uses a secure hash algorithm to perform hash processing, and after obtaining the hash value, performs public key encryption, uses smart contracts for verification and control, and generates a blockchain-based ticket record and authentication system; The image recognition module is based on the ticket record and authentication system of the blockchain, uses a convolutional neural network to extract features from the ticket image, uses a support vector machine for classification training based on the obtained feature set, recognizes new ticket images, and generates a ticket image recognition model; The spectrum fingerprint authentication module collects the amplitude, phase and frequency information of the radio signal based on the ticket image recognition model, uses fast Fourier transform to perform spectrum analysis, and performs pattern matching and verification on the obtained ticket spectrum fingerprint to generate a ticket authentication system of the radio spectrum fingerprint; The IoT tracking module is based on a spectrum fingerprint authentication system, with GPS modules and sensors installed on the ticket, and collects and transmits real-time location and status information to the central server through ad hoc wireless communication technology, which is then updated to the ticket tracking system in real time after optimization to generate an IoT ticket tracking system; The random number identification module is based on the Internet of Things ticket tracking system, uses a physical random number generator to generate a random number, and uses a hash function to generate a unique identifier, binds the unique identifier with the real-time location and status information of the Internet of Things ticket tracking system, stores it in the blockchain, and generates a ticket with a random number identifier; The optical communication verification module converts the ticket information into an optical signal based on the ticket with a random number identifier and embeds it into the ticket, uses an optical signal reader to read and verify, determines the validity of the ticket based on the verification result, and generates a ticket verification system based on optical communication technology.

8. The ticket anti-counterfeiting system according to claim 7, characterized in that: The blockchain recording module includes a transaction data sorting submodule, a hash processing submodule, and a public key encryption submodule; The image recognition module includes a feature extraction submodule, a classification training submodule and an image recognition submodule; The spectrum fingerprint authentication module includes a signal acquisition submodule, a spectrum analysis submodule and a pattern matching submodule; The IoT tracking module includes a GPS positioning submodule, a wireless transmission submodule and a state optimization submodule; The random number identification module includes a random number generation submodule, an identification generation submodule and a state binding submodule; The optical communication verification module includes an optical signal conversion submodule, an optical signal embedding submodule and an optical signal reading submodule.

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