Ticket business full-life-cycle anti-counterfeiting and verification system based on block chain trusted triple mapping

Abstract

The invention provides a ticket business full-life-cycle anti-counterfeiting and verification system based on block chain trusted triple mapping, and relates to the field of electric digital data processing. Comprising a trusted triple construction and cryptography binding module, a block chain hash anchoring and consensus evidence storage module, a trusted triple dynamic evolution and state transition module and a multi-level anti-counterfeiting verification and anomaly detection module, the block chain Hash anchoring and consensus evidence storage module is used for storing a trusted triple in a block chain in a tamper-proof manner, and the trusted triple dynamic evolution and state transition module is used for realizing state tracking and evolution management of ticket business in a full life cycle. The multi-level anti-counterfeiting verification and anomaly detection module is used for providing static identity verification, dynamic anti-counterfeiting vouchers and abnormal behavior detection capability; according to the system, the security problem in a ticketing system is solved by enhancing the security of the entity identifier and protecting state transition privacy.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing, specifically to a ticketing full lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping. Background Technology

[0002] Ticketing anti-counterfeiting and verification technologies are crucial for ensuring the normal operation of cultural entertainment, transportation, and exhibition sectors. With the continuous expansion of the ticketing market, problems such as counterfeit tickets, scalped tickets, and reuse have become increasingly prominent, causing significant losses to ticket issuers and consumers. Traditional ticketing anti-counterfeiting technologies primarily rely on physical anti-counterfeiting marks, including fluorescent ink, holographic labels, and watermarked paper. While these methods have increased the cost of counterfeiting to some extent, they still have limitations such as ease of imitation, low verification efficiency, and inability to trace the ticket circulation history. With the development of information technology, electronic ticketing systems have become increasingly widespread, using digital identifiers such as QR codes and barcodes for ticket management. However, centralized data storage makes the system vulnerable to single-point attacks, data can be tampered with, and cross-platform reliable verification is difficult to achieve.

[0003] In recent years, blockchain technology has demonstrated unique advantages in the field of anti-counterfeiting and traceability due to its decentralized, immutable, and traceable characteristics. Currently, there are three main blockchain-based anti-counterfeiting algorithms. The first is based on digital currency transfers. This algorithm assigns a unique blockchain address to each product and stores a small amount of digital currency. Upon initial verification, the currency is transferred to prevent mass counterfeiting. This method is simple and inexpensive, but it only provides one-time verification and cannot support multiple state changes and historical traceability during the ticket's circulation process. Furthermore, the volatility of digital currency may affect system stability. The second is a state machine anti-counterfeiting algorithm based on smart contracts. This method deploys smart contracts on the blockchain to define the lifecycle state transition rules for tickets. Each state change is automatically executed through the contract and recorded on the chain. This method enables automated state management and logical constraints, but the execution of smart contracts consumes high gas fees, and the contract code is difficult to upgrade once deployed, lacking flexibility. The complexity of smart contracts also increases the risk of potential security vulnerabilities. The third type is a hybrid on-chain and off-chain anti-counterfeiting algorithm based on hash anchoring. The complete ticket data is stored in an off-chain database, and only the data hash value is stored on the chain for evidence. Data integrity is verified by hash comparison. This method significantly reduces on-chain storage costs and improves query efficiency. However, the security of the off-chain database depends on centralized management and is subject to tampering. Furthermore, hash anchoring can only verify whether the data has been modified and cannot prove the legality of state transitions or the correctness of business logic.

[0004] US Patent 20210119771A1 discloses a system and method for providing traceability and anti-counterfeiting for components using blockchain technology. This patent records historical information about components from production to sales through a blockchain network, including lifecycle events such as ownership changes, installation / disassembly, and maintenance, and generates anti-counterfeiting labels for each component. The patent's shortcomings lie in its anti-counterfeiting mechanism, which primarily relies on identity verification during the initial authentication. Subsequent status changes lack dynamic anti-counterfeiting capabilities, failing to effectively prevent the reuse of screenshots or copied tickets in different time and space scenarios. Furthermore, the system lacks a timely credential generation mechanism for high-frequency, short-term ticket verification scenarios, making it difficult to meet the needs of rapid ticket verification at large events. Additionally, the patent does not provide an evolutionary chain branching solution for complex business scenarios such as ticket splitting and merging.

[0005] Chinese patent CN106096986A discloses a blockchain-based anti-counterfeiting system and method. This patent randomly generates a blockchain address for each product and allocates a small amount of digital currency. A query code displays product information and the number of scans. After the first scan, an anti-counterfeiting code transfers all the digital currency from the address to prevent mass counterfeiting. The main shortcomings of this patent are: its anti-counterfeiting mechanism is too simplistic, only enabling one-time verification and failing to support status tracking and historical tracing in multiple stages such as transfer, gifting, and verification. Furthermore, the system directly links complete product information to the blockchain address without privacy protection measures, posing a risk of sensitive information leakage. Additionally, the patent relies solely on the number of scans as the only indicator for anomaly detection, lacking the ability to identify complex fraud patterns such as state conflicts and temporal anomalies. Moreover, the system does not consider usability issues in offline verification scenarios and weak network environments, resulting in significant limitations in practical deployment. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings by proposing a ticketing full lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping.

[0007] The present invention adopts the following technical solution:

[0008] A ticketing full lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping includes a trusted triple construction and cryptographic binding module, a blockchain hash anchoring and consensus storage module, a trusted triple dynamic evolution and state transition module, and a multi-level anti-counterfeiting verification and anomaly detection module.

[0009] The trusted triplet construction and cryptographic binding module is responsible for constructing the unforgeable initial identity of the ticket; the blockchain hash anchoring and consensus storage module is used to store the trusted triplet in the blockchain in an immutable manner; the trusted triplet dynamic evolution and state transition module is used to realize the state tracking and evolution management of the entire life cycle of the ticket; and the multi-level anti-counterfeiting verification and anomaly detection module is used to provide static identity verification, dynamic anti-counterfeiting credentials, and abnormal behavior detection capabilities.

[0010] The trusted triple construction and cryptographic binding module includes an entity identifier generation unit, a state feature vector extraction and encoding unit, and an asymmetric cryptographic signature and ownership binding unit. The entity identifier generation unit integrates multi-dimensional information to generate a globally unique ticket entity identifier. The state feature vector extraction and encoding unit extracts and encodes the initial state attributes of the ticket into a structured feature vector. The asymmetric cryptographic signature and ownership binding unit uses the issuer's private key to sign the ticket information to form an undeniable proof of issuance.

[0011] The blockchain hash anchoring and consensus notarization module includes a triplet hash digest anchoring unit, a distributed consensus confirmation and timestamp proof unit, and a lightweight notarization and off-chain data association unit. The triplet hash digest anchoring unit calculates the hash digest of trusted triplets and only uploads the digest information to the chain to protect privacy. The distributed consensus confirmation and timestamp proof unit uses the distributed consensus mechanism of the blockchain to confirm the notarized data through multiple nodes. The lightweight notarization and off-chain data association unit only stores the hash pointer on the chain to reduce storage costs. The complete triplet data is stored in a trusted off-chain database.

[0012] The trusted triplet dynamic evolution and state transition module includes a lifecycle modeling unit, a triplet incremental update unit, and a multi-version triplet evolution chain construction unit. The lifecycle modeling unit is used to define the complete lifecycle state diagram of the ticketing service. The triplet incremental update unit records state changes through incremental updates rather than repeatedly storing complete information. The multi-version triplet evolution chain construction unit constructs all version triplets within the ticketing service lifecycle into a chain evolution structure.

[0013] The multi-layered anti-counterfeiting verification and anomaly detection module includes a static identity consistency verification unit, a dynamic validity certificate generation and verification unit, and an abnormal behavior detection unit. The static identity consistency verification unit performs triple consistency verification on the entity identifier, public key information, and digital signature of the ticket. The dynamic validity certificate generation and verification unit generates a one-time dynamic token and verifies it when the ticket is about to be used. The abnormal behavior detection unit is used to detect suspicious behavior in real time.

[0014] Furthermore, the entity identifier generation unit includes a multi-source data acquisition processor, a hash fusion calculation processor, and a collision detection and retry processor. The multi-source data acquisition processor is responsible for extracting multi-dimensional raw data from the ticketing system. The hash fusion calculation processor combines the acquired multi-source data according to predefined splicing rules and generates a ticketing entity identifier. The collision detection and retry processor performs collision detection between the newly generated entity identifier and existing identifiers in the system.

[0015] The hash fusion calculation processor generates the entity identifier Entity_ID according to the following formula:

[0016] ;

[0017] Hcas() is a concatenated hash function. D is a nonlinear coupling fusion function. i Identify the i-th input factor. Here is the time-sensitivity weight matrix, t issue is the ticket issuance timestamp, and n is the number of input factors.

[0018] Furthermore, the triplet incremental update unit includes a behavior event capture processor, a state incremental calculation processor, and an incremental triplet generation and hash linking processor. The behavior event capture processor monitors various behavior events in the ticketing system in real time and extracts key information. The state incremental calculation processor converts the captured behavior events into changes in the state feature vector, generating lightweight state incremental data. The incremental triplet generation and hash linking processor constructs new triples based on the state incremental data and the original entity identifier, embeds the hash value of the previous triplet as an inheritance pointer in the new triplet, and links the evolution process through a hash chain structure to form an unbreakable and reliable evolution chain.

[0019] The incremental triplet generator and hash linker constructs triplets according to the following formula:

[0020] ;

[0021] in, Let m represent the m-th version of the triplet. Indicates from state S m-1 to state S m Incremental data, For proof of the legality of state transition, For bidirectional hash anchor value, t m For state change timestamps, Digital signature for the operator.

[0022] Furthermore, the dynamic validity certificate generation and verification unit includes a spatiotemporal parameter acquisition processor, a one-time token generation processor, and a token validity verification processor. The spatiotemporal parameter acquisition processor collects the current spatiotemporal parameters when the ticket is about to be used and obtains the latest triplet state of the current ticket as the input element for token generation. The one-time token generation processor generates a dynamic token with short-term validity based on the spatiotemporal parameters, triplet hash value, and key information. After receiving the dynamic token at the ticket checking terminal, the token validity verification processor verifies the token's validity, uniqueness, and binding, ensuring the authenticity and legality of the token through multiple verifications.

[0023] Furthermore, the one-time token generator generates a one-time dynamic token (Token) according to the following formula:

[0024] ;

[0025] Where Cha() is the chaotic token generation function, H(T) cur ) represents the hash value of the latest triple. () represents the dynamic drift time window, H user Indicates user's historical behavior characteristics, Inject a function to the geographic location entropy, where L is the current geographic location coordinate. D represents the time-dependent geographic fuzzy radius. fp For device fingerprint.

[0026] The beneficial effects achieved by this invention are:

[0027] This system achieves deep coupling among multiple factors in the entity identifier generation process, ensuring that even minute changes in any input factor are amplified and propagated to the entire fusion result under nonlinear mapping. This significantly enhances the entity identifier's resistance to collision attacks. During the construction of the triplet evolution chain, the bidirectional hash anchoring mechanism deeply binds the current version with the context digest and cumulative hash chain verification value of the previous state through a backward hash function, forming an unbreakable trust transfer chain. Any tampering with historical states will cause the hash verification of all subsequent versions to fail. The system introduces high sensitivity to the initial seed through a chaotic mapping function, and the multiple iterations of chaotic parameters make the token generation process unpredictable and irreversible. Through the design of fuzzy geohashing and a geoentropy pool, the token is deeply bound to a specific geographical area and real-time pedestrian density. The geoentropy pool, constructed using interest point sets and pedestrian density estimation, provides a difficult-to-forge environmental fingerprint, effectively preventing the reuse of screenshot tickets in different locations. The system achieves technological innovation in multiple aspects such as entity identifier generation, state evolution management, privacy protection, and dynamic anti-counterfeiting, constructing a highly secure, scalable, and adaptable ticketing lifecycle anti-counterfeiting and verification system.

[0028] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall structural framework of the present invention;

[0030] Figure 2 This is a schematic diagram illustrating the construction of the trusted triplet and cryptographic binding module of the present invention;

[0031] Figure 3 This is a schematic diagram of the blockchain hash anchoring and consensus notarization module of the present invention;

[0032] Figure 4 This is a schematic diagram of the trusted triplet dynamic evolution and state transition module of the present invention;

[0033] Figure 5 This is a schematic diagram of the multi-layered anti-counterfeiting verification and anomaly detection module of the present invention;

[0034] Figure 6 This is a schematic diagram comparing the anti-counterfeiting attack resistance capabilities of the present invention with those of other methods;

[0035] Figure 7 This is a schematic diagram comparing the response times of different operations of the present invention with those of other methods;

[0036] Figure 8 This is a schematic diagram of the interactive interface (UI) of the present invention. Detailed Implementation

[0037] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated beforehand. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0038] Example 1.

[0039] A ticketing lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping, combined with Figure 1It includes a trusted triple construction and cryptographic binding module, a blockchain hash anchoring and consensus storage module, a trusted triple dynamic evolution and state transition module, and a multi-level anti-counterfeiting verification and anomaly detection module.

[0040] The trusted triplet construction and cryptographic binding module is responsible for constructing the unforgeable initial identity of the ticket. It ensures the uniqueness and security of the ticket entity identifier through multi-factor fusion and cryptographic technology. The blockchain hash anchoring and consensus notarization module focuses on storing the trusted triplet in the blockchain in an immutable manner and uses a distributed consensus mechanism to achieve trusted notarization. The trusted triplet dynamic evolution and state transition module realizes the state tracking and evolution management of the entire life cycle of the ticket. It records every state transition through a state machine model and chain structure. The multi-level anti-counterfeiting verification and anomaly detection module provides static identity verification, dynamic anti-counterfeiting credentials, and abnormal behavior detection capabilities to ensure the authenticity and security of the ticket during the use stage.

[0041] Combination Figure 2 The trusted triplet construction and cryptographic binding module includes an entity identifier generation unit, a state feature vector extraction and encoding unit, and an asymmetric cryptographic signature and ownership binding unit. The entity identifier generation unit integrates multi-dimensional information such as ticket type, issuer public key, timestamp, and hardware fingerprint, and uses a collision-resistant hash algorithm to generate a globally unique ticket entity identifier, ensuring the identifier's non-copyability and non-collision. The state feature vector extraction and encoding unit extracts and encodes the initial state attributes of the ticket into a structured feature vector, and uses an extensible encoding scheme to support subsequent state evolution. The asymmetric cryptographic signature and ownership binding unit uses the issuer's private key to sign the ticket information to form an undeniable proof of issuance, and cryptographically binds the ticket owner's public key to the entity identifier, ensuring the verifiability and non-transferability of ownership.

[0042] Combination Figure 3 The blockchain hash anchoring and consensus notarization module includes a triple hash digest anchoring unit, a distributed consensus confirmation and timestamp proof unit, and a lightweight notarization and off-chain data association unit. The triple hash digest anchoring unit calculates hash digests for trusted triples and only uploads the digest information to the blockchain to protect privacy. Multiple triples construct a Merkle tree structure, and the Merkle root hash is anchored to the blockchain to achieve batch notarization. The distributed consensus confirmation and timestamp proof unit uses the distributed consensus mechanism of the blockchain to confirm the notarized data through multiple nodes. The immutability of the blockchain and the block timestamp together constitute a trusted time proof. The lightweight notarization and off-chain data association unit only stores hash pointers on the chain to reduce storage costs. The complete triple data is stored in a trusted off-chain database, and strong association and consistency verification of on-chain and off-chain data are achieved through hash pointers.

[0043] Combination Figure 4 The trusted triplet dynamic evolution and state transition module includes a lifecycle modeling unit, a triplet incremental update unit, and a multi-version triplet evolution chain construction unit. The lifecycle modeling unit defines a complete lifecycle state diagram of the ticket from issuance, circulation, use to expiration, and clarifies the transition conditions and triggering rules between each state, providing a formal theoretical basis for state evolution. The triplet incremental update unit generates incremental triplets each time the ticket state changes. The incremental triplets inherit the hash pointer of the previous state to form a chain association, recording state changes through incremental updates rather than repeatedly storing complete information. The multi-version triplet evolution chain construction unit constructs all version triplets in the ticket lifecycle into a chain evolution structure, supporting state backtracking and historical tracking, and can handle branch evolution requirements in complex scenarios such as ticket splitting and merging.

[0044] Combination Figure 5 The multi-layered anti-counterfeiting verification and anomaly detection module includes a static identity consistency verification unit, a dynamic time-limited certificate generation and verification unit, and an abnormal behavior detection unit. The static identity consistency verification unit performs triple consistency verification on the entity identifier, public key information, and digital signature of the ticket, and compares it with the hash digest stored in the blockchain to ensure the authenticity and integrity of the ticket identity. The dynamic time-limited certificate generation and verification unit generates a one-time dynamic token that is strongly bound to the current triplet state, time, and geographical location when the ticket is about to be used. This token has time-limited validity and scenario specificity, which can effectively prevent fraudulent behaviors such as screenshot tickets and duplicate tickets. The abnormal behavior detection unit analyzes the evolution chain history of the ticket to detect suspicious behaviors such as repeated use, state conflict, and time sequence anomalies in real time. It uses the cross-verification mechanism of the evolution chain and intelligent analysis model to identify potential fraud risks and mark abnormal ticket states in a timely manner.

[0045] The entity identifier generation unit includes a multi-source data acquisition processor, a hash fusion calculation processor, and a collision detection and retry processor. The multi-source data acquisition processor is responsible for extracting multi-dimensional raw data such as ticket type, issuing entity information, issuer public key, timestamp, and hardware fingerprint from the ticketing system, and performing format standardization processing to ensure the consistency of subsequent calculations. The hash fusion calculation processor combines the collected multi-source data according to predefined splicing rules and uses a collision-resistant hash algorithm to calculate and generate ticket entity identifiers, ensuring the uniqueness and irreversibility of the identifiers. The collision detection and retry processor performs collision detection between the newly generated entity identifier and existing identifiers in the system. When a potential collision is detected, it automatically adjusts the random entropy value or adds an additional factor and regenerates the identifier until a globally unique entity identifier is obtained.

[0046] The hash fusion calculation processor generates the entity identifier Entity_ID according to the following formula:

[0047] ;

[0048] Hcas() is a concatenated hash function. D is a nonlinear coupling fusion function. i Identify the i-th input factor. Here is the time-sensitivity weight matrix, t issue Here, n is the ticket issuance timestamp, and n is the number of input factors.

[0049] The expression for the nonlinear coupling fusion function is:

[0050] ;

[0051] Where Hi() is the pre-hash function for the i-th factor. Let N be the coupling strength coefficient between the i-th factor and the j-th factor, and let NFL() be the nonlinear coupling function.

[0052] The expression for the new flight coupling function is:

[0053] ;

[0054] in, This represents the absolute value of the difference between the hash values ​​of the two factors. For the chaotic perturbation parameters of the factor pair;

[0055] The time-sensitivity weight matrix is ​​calculated according to the following formula:

[0056] ;

[0057] in, To construct a diagonal matrix, w i (t) represents the time series weight of the i-th factor, where t is the time variable;

[0058] The time-series weights are calculated according to the following formula:

[0059] ;

[0060] in, Let be the basic weight constant of the i-th factor. Let be the attenuation magnitude coefficient of the i-th factor. For system initialization time, Let i be the time decay constant of the i-th factor. Let be the periodic fluctuation coefficient of the i-th factor. Let be the periodic parameter of the i-th factor. The modulo operation parameter for the i-th factor;

[0061] The cascaded hash function uses three different hash algorithms in a cascaded manner, which is a combination of existing technologies and will not be described in detail here.

[0062] The state feature vector extraction and encoding unit includes a state attribute parsing processor, a vector encoding conversion processor, and a scalability verification processor. The state attribute parsing processor extracts various attribute information of the initial state from the ticketing business rules, including circulation status, validity period, transferability rules, usage restrictions, etc., and performs structured classification and semantic parsing on these attributes. The vector encoding conversion processor converts the parsed state attributes into numerical feature vectors, and uses fixed-length encoding or variable-length encoding schemes to map different types of state information into a unified vector representation form, which is convenient for subsequent calculation and storage. The scalability verification processor detects whether the encoding scheme supports the state attribute types that may be added in the future, verifies the scalability and backward compatibility of the vector structure, and ensures that the encoding system remains effective when the business rules evolve.

[0063] The asymmetric cryptographic signature and ownership binding unit includes a key pair management processor, a digital signature generation processor, and an initial triplet encapsulation processor. The key pair management processor is responsible for the generation, storage, and lifecycle management of the public and private key pairs of the issuer and the ticket owner. It adopts a secure key generation algorithm and provides a key rotation and revocation mechanism. The digital signature generation processor uses the issuer's private key to digitally sign the ticket data containing entity identifiers and state features, generating an undeniable proof of issuance. It also records the signature timestamp to ensure the verifiability of the time sequence. The initial triplet encapsulation processor encapsulates the entity identifier, state feature vector, digital signature, owner's public key, and other elements according to a predefined triplet structure. It then uses a cryptographic hash function to strongly bind the owner's public key to the entity identifier, forming a complete initial trusted triplet.

[0064] The triple hash digest anchoring unit includes a hash digest calculation processor, a Merkle tree construction processor, and a root hash anchoring processor. The hash digest calculation processor performs hash operations on the complete trusted triple data to generate a fixed-length digest information, which protects the privacy of the original data and ensures the data integrity and verifiability. The Merkle tree construction processor uses the hash digests of multiple triples as leaf nodes and calculates the hash value of the parent node layer by layer according to the Merkle tree algorithm until a unique root hash value is generated, realizing efficient storage and selective verification capabilities for batch triples. The root hash anchoring processor submits the calculated Merkle root hash value to the blockchain network, and permanently records the root hash in the block through blockchain transactions, establishing an immutable anchoring relationship between the triple data and the blockchain.

[0065] The distributed consensus confirmation and timestamp proof unit includes a consensus request distribution processor, a multi-node response aggregation processor, and a timestamp encapsulation processor. The consensus request distribution processor encapsulates the hash data to be notified into a blockchain transaction format and broadcasts the notification request to multiple consensus nodes in the blockchain network, initiating a distributed consensus process to ensure the data is recognized across the entire network. The multi-node response aggregation processor collects the verification responses of each consensus node to the notified transaction, determines whether the consensus threshold has been reached based on the consensus algorithm used by the blockchain, and aggregates the consensus results to form a unified confirmation proof. The timestamp encapsulation processor obtains the exact timestamp of the transaction being packaged into a block from the blockchain network, binds the timestamp with the notified data to form an immutable time proof, and provides a reliable basis for subsequent time sequence verification.

[0066] The lightweight evidence storage and off-chain data association unit includes a data separation decision processor, a hash pointer generation processor, and an on-chain / off-chain mapping management processor. The data separation decision processor intelligently determines which data is suitable for on-chain storage and which data is suitable for off-chain storage based on factors such as data sensitivity, access frequency, and storage cost, and formulates a data separation strategy to optimize system performance and cost. The hash pointer generation processor calculates a hash pointer for complete triplet data that needs to be stored off-chain. This pointer is both a unique identifier for the data and an integrity check code. Only the hash pointer, not the complete data, is recorded on the blockchain. The on-chain / off-chain mapping management processor maintains the mapping relationship between the on-chain hash pointer and the actual off-chain data storage location, provides efficient data retrieval services, and periodically verifies the consistency between off-chain data and on-chain hash pointers to prevent data tampering.

[0067] The lifecycle modeling unit includes a state diagram definition processor, a transition condition judgment processor, and a rule conflict detection processor. The state diagram definition processor defines a complete set of lifecycle states and legal transition paths between states according to the ticketing business requirements, and constructs a formalized finite state machine model. The transition condition judgment processor defines triggering conditions and pre-constraints for each state transition, such as requiring digital signatures from both parties for transfer operations and time and location verification for cancellation operations. It judges in real time whether the conditions are met when a state transition occurs. The rule conflict detection processor detects whether there are logical conflicts or deadlock states in the state machine definition, verifies whether all states are reachable, and whether there are unexpected state transition paths, to ensure the completeness and correctness of the state machine model.

[0068] The triplet incremental update unit includes a behavior event capture processor, a state incremental calculation processor, and an incremental triplet generation and hash linking processor. The behavior event capture processor monitors various behavior events in the ticketing system in real time, including operations such as transfer, gifting, locking, unlocking, and verification, and extracts key information such as event type, timestamp, operation subject, and operation parameters. The state incremental calculation processor converts the captured behavior events into changes in state feature vectors, calculates the state fields that need to be updated and their changes by comparing the old and new states, and generates lightweight state incremental data. The incremental triplet generation and hash linking processor constructs new triplets based on the state incremental data and the original entity identifier, embeds the hash value of the previous triplet as an inheritance pointer in the new triplet, and links the evolution process through a hash chain structure to form an unbreakable and reliable evolution chain.

[0069] The incremental triplet generator and hash linker constructs triplets according to the following formula:

[0070] ;

[0071] in, Let m represent the m-th version of the triplet. Indicates from state S m-1 to state S m Incremental data, For proof of the legality of state transition, For bidirectional hash anchor value, t m For state change timestamps, Digital signature for the operator;

[0072] The proof of the legality of the state transition is constructed using zero-knowledge proof technology, which proves the legality of the state transition without revealing the specific state content;

[0073] The proof semantics are:

[0074] ;

[0075] in, The proof states that there exists a prestate S. m-1 and behavior A m This allows the state transition function F to pass through. state Get the current state S m Furthermore, the transfer complies with business rules;

[0076] The data structure is as follows:

[0077] ;

[0078] Among them, c statec represents a pre-state commitment. action Indicates commitment to action, This represents the core data for zero-knowledge proofs. To verify the challenge value;

[0079] The expression for the bidirectional hash anchor function is:

[0080] ;

[0081] Among them, H bkw () is the backward hash function, H fow () represents the forward hash reservation function, and m is the current version number;

[0082] The expression for the backward hash function is:

[0083] ;

[0084] Ser() is the triple serialization function, CTX m-1 This is a context summary of the previous state. This is the cumulative hash chain verification value;

[0085] The expression for the forward hash reservation function is:

[0086] ;

[0087] in, Seed for forward validation;

[0088] The multi-version triplet evolution chain construction unit includes a version sequence management processor, an evolution chain index construction processor, and a branch merging processor. The version sequence management processor assigns a globally unique version number to each triplet version generated during the ticketing lifecycle, maintains the version sequence in chronological order, and supports fast retrieval and historical backtracking based on version numbers. The evolution chain index construction processor concatenates all version triples using hash pointers to form an evolution chain, constructing a multi-level index structure to accelerate traversal and querying of the evolution chain, and providing efficient historical state access capabilities. The branch merging processor handles complex scenarios such as ticketing splitting and merging, supports branch creation and multi-branch merging operations in the evolution chain, and maintains the logical relationships and version consistency between branches.

[0089] The static identity consistency verification unit includes an identity information extraction processor, a multi-dimensional consistency verification processor, and an on-chain comparison verification processor. The identity information extraction processor extracts key identity elements such as entity identifier, owner's public key, issuer's signature, and state characteristics from the ticketing data to be verified, and performs format standardization and integrity checks on these elements. The multi-dimensional consistency verification processor performs multi-dimensional cross-verification on the extracted identity elements, including the binding consistency between entity identifier and public key, the cryptographic consistency between signature and issuer's public key, and the logical consistency between state characteristics and business rules, to ensure the internal self-consistency of ticketing identity information. The on-chain comparison verification processor compares the hash digest calculated from the local ticketing data with the hash value stored on the blockchain to verify whether the data has been tampered with, and simultaneously searches the evolution chain to confirm whether the current ticketing status is the latest legal status.

[0090] The dynamic validity certificate generation and verification unit includes a spatiotemporal parameter acquisition processor, a one-time token generation processor, and a token validity verification processor. The spatiotemporal parameter acquisition processor collects spatiotemporal parameters such as current time information, geographical location coordinates, and usage scenario identifier when the ticket is about to be used, and obtains the latest triplet state of the current ticket as the input element for token generation. The one-time token generation processor generates a dynamic token with short-term validity based on spatiotemporal parameters, triplet hash value, and key information, using a time synchronization algorithm or challenge response mechanism. This token is strongly bound to a specific spatiotemporal environment and is unpredictable. After receiving the dynamic token at the ticket checking terminal, the token validity verification processor verifies the token's validity, uniqueness, and binding, and ensures the authenticity and legality of the token through multiple verifications.

[0091] The one-time token generation processor generates a one-time dynamic token Token according to the following formula:

[0092] ;

[0093] Where Cha() is the chaotic token generation function, H(T) cur ) represents the hash value of the latest triple. () represents the dynamic drift time window, H user Indicates user's historical behavior characteristics, Inject a function to the geographic location entropy, where L is the current geographic location coordinate. D represents the time-dependent geographic fuzzy radius. fp For device fingerprint;

[0094] The expression for the chaotic token generation function is:

[0095] ;

[0096] Among them, Llog () is the chaotic mapping function, K cha For the chaotic key, h, , d represents four function parameters;

[0097] The expression for the chaotic mapping function is:

[0098] ;

[0099] Where r is the chaos parameter, N is the number of iterations, and x is the normalized input seed. i This is the seed value after i iterations;

[0100] The expression for the dynamic drift time window is:

[0101] ;

[0102] Among them, W base (t) is the base time window value. This is an adaptive adjustment value based on user behavior;

[0103] The adaptive adjustment value is calculated according to the following formula:

[0104] ;

[0105] Among them, w i h represents the weight of the i-th feature. i Let M be the number of historical behaviors, where i is the i-th historical behavior feature. As a credibility threshold, This represents the window drift amount;

[0106] The expression for the geographic location entropy injection function is:

[0107] ;

[0108] Where FuzzyHash() is a fuzzy geohash, EntropyPool() is a geoentropy pool, and R is the effective radius of the venue;

[0109] The expression for the geographic entropy pool is:

[0110] ;

[0111] Where N(L, R) is the set of interest points in the domain with L as the center and R as the radius, P is the interest point, and Crowd(P, t) is the crowd density estimate of interest point P at time t;

[0112] The abnormal behavior detection unit includes an evolution chain traversal analysis processor, an abnormal pattern recognition processor, and a risk scoring and marking processor. The evolution chain traversal analysis processor retrieves the complete evolution chain of the target ticket from the blockchain, traverses all historical triplet versions in chronological order, and extracts behavioral features such as state transition sequences, time intervals, and operating entities to construct a behavioral trajectory map. The abnormal pattern recognition processor performs pattern matching and intelligent analysis on the behavioral trajectory based on a predefined abnormal rule library or machine learning model to identify suspicious behavioral patterns such as duplicate cancellation, time sequence conflicts, state rollback, and abnormal high-frequency flow. The risk scoring and marking processor calculates the risk score of the ticket based on the identified abnormal patterns. When the risk score exceeds a preset threshold, the ticket is automatically marked as high-risk or invalid, and an abnormal alarm message is generated to notify relevant parties for manual review or handling.

[0113] The i, j, and m mentioned above are ordinal numbers used to represent sequence numbers and have no actual meaning.

[0114] To verify the effectiveness, an experiment was conducted using a blockchain environment built on the Ethereum test network. A complete system was deployed, including a trusted triplet construction module, a hash anchoring module, a dynamic evolution module, and a multi-level verification module. A traditional QR code anti-counterfeiting system, a simple blockchain hash storage system, and a smart contract state machine system were also built as control groups. 10,000 simulated ticket data sets were generated, including concert tickets, movie tickets, exhibition tickets, and other types. Each type of ticket was configured with different issuers, timestamps, owner information, and state attributes, forming a test sample set covering the complete lifecycle. Five attack scenarios were designed: 1000 ticket duplication attacks, 800 screenshot ticket reuse attacks, 500 entity identifier collision attacks, 600 state tampering attacks, and 700 time-series forgery attacks. Attack tests were conducted on the four anti-counterfeiting systems, and the success rate was recorded. Stress testing tools were used to simulate 1000 concurrent users performing ticket verification operations, monitoring system throughput, average response time, and failure rate to evaluate the system's stability and scalability under high load. The data was then processed... Figure 6 and Figure 7 .

[0115] Example 2.

[0116] An alternative implementation scheme for a ticketing full lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping is presented. While maintaining the core technical architecture, this scheme provides multiple technical implementation paths and optimization strategies for different application scenarios. By introducing diverse combinations of cryptographic algorithms, flexible storage architecture, and adaptive verification mechanisms, the system performance and security are further improved. This embodiment is particularly suitable for application scenarios such as large performance venues, transportation hubs, and exhibition centers that need to process massive amounts of tickets and have high real-time requirements.

[0117] In the implementation of the trusted triplet construction and cryptographic binding module, the entity identifier generation unit adopts a hybrid concatenated scheme based on the SM3 national cryptographic hash algorithm and the BLAKE3 high-speed hash algorithm. Specifically, the SHA-3 algorithm is first used to perform an initial hash operation on the ticket type, issuer public key, and timestamp to generate a 256-bit digest value. Then, this digest value and the hardware fingerprint are hashed a second time using the SM3 algorithm to obtain the second-level digest. Finally, the BLAKE3 algorithm is used to fuse the two digests to generate the final 512-bit entity identifier. This three-level concatenated structure reduces the collision probability to 2^-384 compared to a single hash algorithm. No identifier collisions were observed in a large-scale test involving 5 million tickets. The multi-source data acquisition processor integrates an NFC chip reading module (model MF-RC522) for acquiring the hardware fingerprint. The timestamp server uses the NTP protocol for synchronization to ensure millisecond-level time accuracy. Non-linear coupling is also implemented. During the fusion process, a Logistic chaotic mapping function was introduced to replace the original chaotic perturbation parameter. Specifically, the chaotic parameter r was set to 3.9 to ensure that the system was in a completely chaotic state. The number of iterations N was set to 50 to fully diffuse the small differences in the input factors. The experimental data showed that when any input factor changed by 1 bit, the average Hamming distance of the output identifier reached 256 bits, close to the ideal 50% ratio. The time-sensitivity weight matrix adopted a piecewise linear decay function instead of exponential decay. For tickets issued within 30 days, the basic weight remained above 0.9. Between 30 and 90 days, it linearly decayed to 0.6, and after 90 days, it decayed to 0.3. This piecewise strategy gave recent tickets a higher time-sensitivity weight, while long-term valid tickets such as annual passes maintained a relatively stable weight distribution. In a test set containing 10,000 tickets with a time span from 1 day to 365 days, this weight strategy improved the time sensitivity of the identifier by 37 percentage points.

[0118] The state feature vector extraction and encoding unit implements an efficient encoding scheme based on the Protobuf protocol buffer, reducing the vector size by an average of 62% compared to JSON format encoding. The specific encoding structure is defined as follows: `Message TicketState` contains a `circulation_status` field of type `uint32`, a `validity_period` field of type `int64`, an `transfer_rules` array of type `repeated`, and a map.<string,string> The `usage_constraints` type uses a constraint mapping table, which supports nested definitions and dynamic expansion. When new state attributes are added for business requirements, backward compatibility can be achieved simply by adding the new field to the Protobuf definition file and recompiling. The state attribute parsing processor uses a Finite State Automaton (FSA) model for semantic parsing, constructing a state transition table based on 120 predefined business rules. Each rule contains three elements: preconditions, triggering events, and poststates. The parsing process uses a table-driven method with an average time of only 8 milliseconds. The vector encoding conversion processor implements an automatic switching mechanism between fixed-length and variable-length encoding for states with fewer than 10 attributes. Simple ticketing uses 128-byte fixed-length encoding for fast access. For ticketing with complex constraints, variable-length encoding is used, supporting up to 2048 bytes. In practical applications, 85% of ticketing uses fixed-length encoding, and 15% uses variable-length encoding, with an average encoding length of 142 bytes. The scalability verification processor implements a vector schema version management mechanism. Each vector header contains an 8-bit version number field. The system maintains a version compatibility matrix that supports parsing up to 32 historical versions. When an old version vector is detected, the corresponding version parser is automatically called for conversion. In a test scenario with 5 version iterations, the version conversion success rate reached 100%, and the average conversion time was 12 milliseconds.

[0119] The asymmetric cryptographic signature and ownership binding unit adopts ECC elliptic curve cryptography instead of the traditional RSA algorithm, specifically using the secp256k1 curve, which is the same standard used by a mainstream cryptocurrency. The public key length is 256 bits, equivalent to the security strength of RSA-3072, but the key length is only one-eighth of the latter. Private key generation uses the CSPRNG cryptographically secure pseudo-random number generator. The entropy source comes from the / dev / urandom device provided by the operating system, combined with the hardware random number generator HRNG model TrueRNG3. This dual entropy source ensures the true randomness and unpredictability of the private key. The key pair management processor implements a key escrow scheme based on the HSM hardware security module, model nCipher nShield Connect 6000. All private key operations are completed internally within the HSM and cannot be exported. It supports FIPS 140-2 Level. 3. Authentication standards: Key lifecycle management employs an automatic rotation strategy. The issuer's master key is automatically rotated every 90 days, while the owner's key supports user-initiated updates. Historical keys are retained for 18 months after secure archiving for historical data verification. The digital signature generation processor uses the ECDSA algorithm. The signature process includes three steps: message hashing, random number generation, and elliptic curve point arithmetic. RFCs are used to prevent random number attacks. The 6979 standard uses a deterministic random number generation method with a fixed signature length of 64 bytes, including 32 bytes each for the r and s values. Signature verification employs parallel processing technology to support batch verification, capable of verifying 15,000 signatures per second on a server equipped with an 8-core processor. The initial triplet encapsulation processor serializes and encapsulates the entity identifier (Entity_ID), state vector (State_Vector), digital signature (Signature), and owner public key (Owner_PubKey) in Type-Length-Value format, keeping the total length within 512 bytes. The encapsulated triplet is encoded using CBOR (Brief Binary Object Representation), which reduces the size by 45% and improves parsing speed by 3 times compared to JSON. A 32-byte integrity checksum is generated for the triplet using the HMAC-SHA256 algorithm and appended to the end of the data. The receiver can quickly verify whether the data has been tampered with during transmission using the checksum. In 100,000 transmission tests, the data integrity verification success rate was 100%.

[0120] The blockchain hash anchoring and consensus notarization module adopts a consortium blockchain architecture instead of a public blockchain solution to improve performance and reduce costs. Specifically, it uses the Hyperledger Fabric framework version 2.4.6, which supports pluggable consensus mechanisms and smart contract execution environments. The network deployment uses five organizational nodes representing the ticket issuer, sales platform, verification terminal, regulatory agency, and auditing agency, respectively. Each organization runs two peer nodes and one orderer node. The node server configuration includes an Intel Xeon Gold 6248R processor with 24 cores and 48 threads, 128GB of DDR4 memory, and 2TB of NVMe RAM. SSD storage with 10 Gigabit Ethernet network bandwidth. The triple hash digest anchoring unit adopts a batch processing strategy, constructing a Merkle tree for every 100 triples. The tree construction uses the SHA-256 algorithm. When the number of leaf nodes is less than a power of 2, the last node is copied to make up the difference. The tree height is 7 levels, which can accommodate 128 leaf nodes. The root hash value is 32 bytes. The complete Merkle tree is stored off-chain in a database, with only the root hash value on-chain. This batch strategy reduces the on-chain storage cost per ticket to 0.32 bytes. In a test of 1 million tickets, the total on-chain storage space required is only 320MB. The Merkle tree construction processor adopts a parallel algorithm, using a multi-core processor to simultaneously calculate the hash of the parent node of different branches. In actual testing, it takes only 135 milliseconds to build a Merkle tree with 10,000 leaf nodes on a 24-core processor. The root hash anchoring processor encapsulates the root hash value into a Fabric transaction and submits it to the channel. The transaction includes metadata such as timestamp, batch number, and ticket quantity.

[0121] The distributed consensus confirmation and timestamp proof unit uses the Raft consensus algorithm instead of the traditional PBFT algorithm. The Raft algorithm has simpler implementation logic and higher performance, achieving a throughput of 3000 transactions per second in a 5-node consortium blockchain, with an average consensus latency of 200 milliseconds. The consensus request distribution processor broadcasts the root hash value to be notified to all orderer nodes. The broadcast uses the gRPC protocol for communication, supporting TLS encryption and two-way authentication. Each orderer node performs preliminary verification upon receiving the request, including format checking, signature verification, and replay attack protection. The multi-node response aggregation processor uses a voting mechanism, requiring at least a majority (3 or more) of the orderer nodes to reach a consensus on the transaction. Only then can transactions be packaged into blocks. Each block can contain a maximum of 500 transactions, and the block generation interval is 2 seconds. Under normal network conditions, the time from transaction submission to final confirmation does not exceed 6 seconds. The timestamp encapsulation processor extracts the Unix timestamp from the block header with millisecond precision. This timestamp is jointly recognized by all consensus nodes and is non-repudiable. To enhance the credibility of the timestamp, the system also integrates the Trusted Timestamp Service (TSA) provided by the National Time Service Center. After each block is generated, it automatically applies for a timestamp token from the TSA and writes the token hash value into the next block to form a time chain. In the timestamp accuracy verification experiment, the maximum deviation between the system timestamp and the TSA timestamp does not exceed 50 milliseconds, which meets the time accuracy requirements of the ticketing anti-counterfeiting scenario.

[0122] The lightweight evidence storage and off-chain data association unit uses MongoDB document database to store complete triplet data. The database server is configured with 32GB of memory and a 4TB SSD storage array in RAID10 configuration. The MongoDB version is 5.0.14, supporting sharded clusters and replica sets. The database deployment adopts a 3-node replica set architecture, with one master node responsible for read and write operations and two slave nodes responsible for data backup and read load balancing. The replica set uses an asynchronous replication mechanism with an average replication latency of 100 milliseconds. The data separation decision processor dynamically decides the storage strategy based on the access frequency and sensitivity of the triplet. For frequently accessed active tickets, such as tickets created or updated within the last 7 days, the core fields of the triplet are cached in a Redis in-memory database. The Redis server is configured with 64GB of memory, supporting persistence and master-slave replication, with a cache hit rate of 92%. For infrequently accessed historical tickets, such as tickets not updated for more than 30 days, the data is migrated to object storage service. The object storage uses the MinIO open-source solution, reducing storage costs by 70%. The hash pointer generation processor calculates a SHA-256 hash value for each triplet as a unique identifier. The identifier, which is 32 bytes long and 64 characters in hexadecimal encoding, is compressed to 44 characters using Base58 encoding when stored on the blockchain. The hash pointer also serves as the primary key index for the off-chain database. Query performance is optimized using a B-tree index structure, with an average query latency of 5 milliseconds. The on-chain and off-chain mapping management processor maintains an index table that records the mapping relationship between hash pointers and off-chain storage locations. The index table uses a LevelDB key-value database for storage, supporting fast point queries and range scans. The index table is periodically garbage collected to clean up expired ticket mapping records and release storage space. To ensure the consistency of on-chain and off-chain data, the system implements a timed verification mechanism. Every 24 hours, 1000 hash pointers are randomly selected from the blockchain to verify whether the corresponding off-chain data is complete and the hash values ​​match. In three months of continuous operation, the consistency verification pass rate was 100%. When data inconsistency is detected, the system immediately triggers an alarm and restores the data from the replica set.

[0123] The Trusted Triple Dynamic Evolution and State Transition Module adopts an Event Sourcing architecture, where all state changes are recorded as events. The ticketing state at any given moment can be reconstructed by replaying the event sequence. The lifecycle modeling unit defines eight core states: Issued, Pending Payment, Paid, Transferred, In Use, Used, Refunded, and Expired. There are 26 state transition paths, each with strict preconditions and triggering events. The state diagram uses a Directed Acyclic Graph (DAG) structure, supporting concurrent states and conditional branches. The state diagram definition file is stored in YAML format for easy manual reading and maintenance. The state diagram definition processor loads the YAML file at system startup and compiles it into a finite state machine object in memory. The state machine object supports dynamic updates at runtime without requiring a service restart. The transition condition judgment processor implements rule-based... The rule engine uses the Drools framework, version 7.59.0, to evaluate conditions. It supports complex logical expressions and rule chains. The rule for the transfer operation is defined as allowing a transfer to the transferred state when the ticket status is equal to paid, the transferor has signed, the transferee has confirmed, and the number of transfers has not exceeded the limit. The rule evaluation uses the RETE algorithm to optimize performance. In a scenario with 500 rules, the single evaluation time does not exceed 10 milliseconds. The rule conflict detection processor implements a static analysis tool to automatically detect logical conflicts, circular dependencies, or unreachable states before deploying new rules. The detection algorithm uses reachability analysis and loop detection in graph theory. The detection time in a state graph with 26 transfer paths is 50 milliseconds. The detection results show that all states are reachable and there are no deadlock states.

[0124] The triplet incremental update unit employs a Write-Ahead Log (WAP) mechanism to ensure the atomicity and durability of state updates. The behavior event capture processor monitors key operation events in the ticketing system. Event collection utilizes the Kafka message queue middleware version 3.1.0. Each event is serialized in JSON format and sent to a Kafka topic configured with 12 partitions and a 3-replication factor. Message retention is 7 days. The event consumer group uses a parallel consumption mode with 12 consumer threads processing events from different partitions simultaneously, achieving a consumption rate of 8000 events per second. The event type definition includes fields such as event ID, event type, timestamp, ticketing ID, operator ID, operation parameters, previous state, and subsequent state. The event ID is generated using the Snowflake algorithm to ensure global uniqueness and order. The state incremental calculation processor extracts changed fields by comparing the previous and current states to generate an incremental data structure. The incremental data is represented in JSON Patch format, conforming to RFC. The 6902 standard typically reduces incremental data size to 80 bytes, an 84% reduction compared to the 512 bytes of complete state data. In a test of 1 million state updates, the total incremental data size was 76MB, while the complete data required 488MB, saving 85% of storage space. The incremental triple generation and hash chaining processor first calculates the SHA-256 hash value of the previous version triple as a backward anchor when constructing a new version triple. Then, it assembles the incremental data, state transition proof, backward hash value, timestamp, and operator signature into a new version triple. The forward hash reserved field of the new triple is initialized to zero and will be filled when the next version is generated. This chain structure allows the integrity verification of any version to be traced forward along the hash chain. In a ticketing sample containing 20 state transitions, the integrity verification took an average of 45 milliseconds.

[0125] The state transition validity proof adopts a zk-SNARKs zero-knowledge concise non-interactive knowledge argument to replace the original scheme. Specifically, it uses the Groth16 proof system, which has a fixed proof size of 128 bytes and a constant verification time of 2 milliseconds, unaffected by circuit complexity. The proof generation uses the libsnark cryptography library version 0.3.0. The circuit design includes an arithmetic representation of the state transition function with a total of 15,000 constraints. The proof and verification keys are generated through a trusted setup ceremony. The ceremony uses a multi-party computation (MPC) protocol where five independent parties jointly generate the keys. As long as one party is honest... In practice, key security is guaranteed. The proof generation phase takes an average of 800 milliseconds on a server equipped with 32GB of memory. The proof data structure includes four fields: state commitment, behavior commitment, core proof data, and verification challenge value. The commitment adopts the Pedersen commitment scheme, which has perfect concealment and computational binding. The verifier only needs to verify the key, public input, and proof data to verify the legality of the state transition without knowing the specific state content. In 10,000 proof verification tests, the verification success rate is 100% with no false positives. Compared with traditional plaintext verification schemes, the privacy protection capability is improved by 100%.

[0126] The bidirectional hash anchoring function implements hash links in both forward and backward directions. The backward hash function uses the HMAC-SHA256 algorithm, and the key is a version-specific key derived from the system master key to ensure that different versions use different HMAC keys. The backward hash value is bound to the complete serialized data of the previous version's triple, the context digest, and the cumulative hash chain check value. The context digest contains immutable attributes such as ticket creation time, initial owner, and ticket type. The cumulative hash chain check value is calculated through chained hashing, accumulating from version 0 to the current version to form an unbreakable trust chain. The forward hash reservation function uses a placeholder pattern, and the forward verification seed is initialized to random when version m is generated. When version m+1 is generated, its hash value is backfilled into the forward verification seed field of version m, and the hash value of version m is recalculated. This bidirectional link allows the evolution chain to traverse forward or backward from any version. In an evolution chain containing 50 versions, the time complexity of forward and backward traversal is O(n). In actual tests, traversing 50 versions takes an average of 120 milliseconds. The bidirectional hash anchor also supports branch verification. When ticketing is split, both the main chain and the branch chain maintain a backward hash link with the parent version. The branch chain additionally records the version number and branch type of the branch point. In the ticketing merging scenario, the backward hash value of the new version is bound to the hash values ​​of multiple parent versions to form a multi-parent node structure.

[0127] The multi-version triplet evolution chain construction unit uses a linked list data structure to store the evolution chain. Each node contains fields such as version number, triplet hash pointer, predecessor node pointer, successor node pointer, parent node pointer array, and child node pointer array. The head node of the linked list stores the initial version, i.e., version 0. The version sequence management processor assigns an incrementing version number to each version. The version number is represented by a 64-bit unsigned integer, theoretically supporting 1.8 x 10^19 versions. The version number generation uses a distributed ID generation algorithm to ensure that the version number is globally unique and incrementing in multi-instance deployment scenarios. The version index uses a B+ tree structure to establish a mapping from the version number to the triplet hash pointer. The B+ tree has an order of 128, and when the tree height is 3 levels, it can index 2 million versions. A single query requires an average of 2 disk I / O operations and takes about 3 milliseconds. In addition to the version number index, the evolution chain index construction processor also establishes a timestamp index, an operator index, and a state type index to support multi-dimensional queries. The timestamp index uses a skip list structure. The system supports efficient range queries, with an average query time of 10 milliseconds for all versions within a certain time period. The operator index records the ticket versions operated by each user and uses an inverted index structure to support fast retrieval of a user's operation history. The branch merging processor implements the complete logic of ticket splitting and merging. When splitting a ticket, multiple branches are created from the current version of the original ticket. Each branch inherits some attributes of the original ticket, such as splitting the seat number from A1 into A1a and A1b. The entity identifier of the branch version is regenerated but retains the reference to the parent version. When merging a ticket, a new merged version is created. This version contains multiple parent version pointers, and the state attribute of the merged version is the union of all parent version attributes. In the complex evolution chain containing branches and merges, the relationship between versions forms a directed acyclic graph (DAG) structure. The topological sorting of the DAG ensures the logical order of the versions. In 1000 ticket samples containing branch merging, the success rate of evolution chain construction is 100%, and the pass rate of DAG structure correctness verification is 100%.

[0128] The multi-layered anti-counterfeiting verification and anomaly detection module adopts a pipelined processing architecture. Static verification, dynamic verification, and anomaly detection are executed in parallel without blocking each other. The static identity consistency verification unit first performs a fast verification process, including format validation and signature verification. The identity information extraction processor uses a combination of regular expression matching and binary parsing to extract key fields. The regular expression library uses the RE2 engine to ensure linear time complexity and prevent regular expression denial-of-service attacks. Binary parsing uses zero-copy technology to operate directly on the receive buffer, avoiding memory copying overhead. The extraction process takes an average of 2 milliseconds. The multi-dimensional consistency verification processor implements a policy-based multi-level verification mechanism. The first level verifies the binding relationship between the entity identifier and the public key by recalculating the entity identifier to verify if they match. The second level verifies the cryptographic consistency between the digital signature and the issuer's public key using ECDSA. The verification algorithm performs a third-level check to verify the logical consistency between the status characteristics and business rules. This third-level check employs short-circuit logic, returning immediately if any level fails to reduce unnecessary computation. In normal ticketing scenarios, all three levels of verification pass in an average of 15 milliseconds. In counterfeit ticketing scenarios, the average time is 3 milliseconds because the first level fails. The on-chain comparison verification processor uses RPC to call the blockchain node to query the on-chain hash value of the ticket. The RPC call uses connection pooling technology to reuse TCP connections and reduce handshake overhead. The connection pool is configured with a minimum of 10 connections, a maximum of 50 connections, and an idle timeout of 30 seconds. The median query response time is 25 milliseconds. The locally calculated hash value is compared with the on-chain hash value using a constant-time comparison algorithm to prevent timing attacks. Simultaneously, the evolution chain is searched to confirm if the current version is the latest version. If a newer version exists, a version conflict error is returned, prompting the user to refresh the ticketing information.

[0129] The dynamic validity credential generation and verification unit adopts the TOTP time-based one-time cryptography algorithm, which is widely used in two-factor authentication scenarios. The spatiotemporal parameter acquisition processor first obtains the current system time and rounds it down to a 30-second time window. The geographic location information is obtained through the GPS module with latitude and longitude coordinates with an accuracy of 10 meters. The GPS module model is u-bloxNEO-M8N, which supports multi-constellation positioning including GPS, GLONASS, Galileo, and BeiDou, with a positioning time of less than 1 second. The geographic location also includes the base station ID, which is obtained through the mobile network API as an auxiliary positioning method. Scene identifiers are used to extract such as the performance venue ID or flight number from ticketing metadata. The one-time token generation processor concatenates the time window, geographic location hash, scene identifier, triple hash value, and user device fingerprint, and then uses the HMAC-SHA256 algorithm to calculate and generate a 256-bit token raw value. Subsequently, a chaotic function was applied to the original token value to perturb it. The iteration count was 20 to control computational overhead while maintaining chaos. The first 64 bits of the chaotic token value were truncated and converted into a hexadecimal string as the final token, with a total length of 16 characters. User historical behavior features included the number of successful verifications, the number of failed verifications, and the average verification time interval in the last 30 days. Behavioral features were statistically analyzed using a sliding window. The credibility score was calculated based on success rate and consistency. Users with a success rate greater than 95% and a time interval variance of less than 10 minutes were judged as high-credibility users. The dynamic drift time window was extended to 60 seconds for high-credibility users and shortened to 15 seconds for low-credibility users. The adaptive adjustment value was calculated based on the weighted sum of user behavior features. The weights were obtained through training a machine learning model. The model used a random forest algorithm containing 100 decision trees. The training dataset contained 100,000 historical verification records, and the model accuracy reached 92%.

[0130] The geographic entropy injection function implements a geofence-based entropy pool construction mechanism. Fuzzy geohashing uses the Geohash algorithm to encode latitude and longitude coordinates into 12-bit strings. The first 8 characters represent an area of ​​approximately 38 meters by 19 meters as the fuzzy region. This fuzzification process protects the user's precise location privacy while preserving geographic proximity information. The geographic entropy pool retrieves Points of Interest (POI) data within the fuzzy region by querying a GIS database. The GIS database uses PostgreSQL with PostGIS extensions to support spatial indexing and geographic queries. The spatial index uses an R-tree structure to query POIs within a 500-meter radius, with an average query time of 8 milliseconds. POI types include 20 categories such as commercial facilities, transportation hubs, and public services. Pedestrian density estimation uses a real-time traffic data API provided by a large map service platform. The returned data includes pedestrian density levels for a specified area at the current time, categorized into 5 levels from sparse to crowded. The pedestrian density level is combined with a timestamp to generate a time-varying entropy value. This entropy value is unpredictable and tamper-proof. The entropy value of the geographic entropy pool is calculated... The calculation formula is to sum the products of the hash values ​​of all POIs and the crowd density, and then take the modulo. The entropy pool size is 256 bits. The average Hamming distance of entropy values ​​generated at different times but in the same geographical location is 128 bits, achieving an ideal difference of 50%. The average Hamming distance of entropy values ​​generated at two different locations 100 meters apart is 145 bits. This geographic binding mechanism gives the token strong spatial constraints. The token validity verification processor recalculates the expected token value during ticket checking and compares it with the token value submitted by the user. During the comparison, the time window is allowed to fluctuate by one window period before and after, i.e., plus or minus 30 seconds, to tolerate clock deviation. Geographic location verification requires that the user's current location is within the venue fence. The fence is defined as a polygon with vertex coordinates stored in the database. The determination of a point within the polygon uses the ray casting algorithm with a complexity of O(n), where n is the number of polygon vertices. A typical venue fence contains 20 vertices, and the determination time is less than 1 millisecond. In 100,000 token verification tests, the verification success rate is 99.7%. The 0.3% failure cases are mainly due to user device time errors exceeding 90 seconds or GPS positioning deviations exceeding the venue fence boundary.

[0131] The abnormal behavior detection unit employs a hybrid detection model combining rule-based and machine learning approaches. The evolutionary chain traversal analysis processor loads the complete evolutionary chain of the target ticket from the blockchain and off-chain databases. The loading uses a lazy loading strategy, retrieving version data from the storage layer on demand to avoid loading large amounts of data at once. During traversal, features such as the state type, timestamp, operator, and geographical location of each version are extracted to construct a behavior sequence. This behavior sequence is stored using a time-series data structure, supporting time window queries and sliding window statistics. The average time for traversal and feature extraction in an evolutionary chain containing 30 versions is 80 milliseconds. The abnormal pattern recognition processor first applies a rule base for rapid screening. The rule base contains 50 expert knowledge rules, such as those for a single ticket. Three consecutive reconciliations within 5 minutes are considered duplicate reconciliation anomalies; strictly decreasing timestamps are considered time-series forgery anomalies; and a status change from "used" to "paid" is considered a status rollback anomaly. Rule matching uses a decision tree algorithm with an average matching time of 5 milliseconds. For complex anomaly patterns not covered by the rule base, a machine learning model is used for identification. The model employs an LSTM (Long Short-Term Memory) network, with a network structure including a 128-dimensional input layer, two LSTM layers with 256 hidden units each, a 128-dimensional fully connected layer, and a 2-dimensional output layer representing normal and abnormal categories. The model training dataset contains 1 million behavioral sequences, with 5% labeled as anomalies. Training uses the Adam optimizer with a learning rate of 0.001, and batch sizes are used. With a size of 64, after 50 epochs of training, the system achieved an accuracy of 94.5%, a recall of 89.2%, a precision of 91.8%, and an F1 score of 90.5% on the test set. Model inference was accelerated using ONNX Runtime, with a single inference time of 15 milliseconds. The risk scoring and labeling processor calculated risk scores based on the severity of abnormal patterns, ranging from 0 to 100 points. Duplicate cancellations received 60 points, state tampering received 80 points, and time-series forgery received 90 points. When multiple anomalies were combined, the highest score was taken, and 50% of the scores of other anomalies were added. The risk threshold was set to 70 points; tickets exceeding the threshold were automatically marked as high-risk and triggered alarms. Alarm information was pushed to the monitoring center in real time via WebSocket, and SMS and email notifications were sent to relevant responsible persons. The labeling information was written to the blockchain as immutable evidence. During 30 days of continuous operation, the system detected 385 abnormal behaviors, including 371 true positives and 14 false positives, achieving an accuracy of 96.4%. The average detection latency was 2.3 seconds, meeting real-time monitoring requirements.

[0132] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A ticketing full lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping, characterized in that, It includes a trusted triple construction and cryptographic binding module, a blockchain hash anchoring and consensus storage module, a trusted triple dynamic evolution and state transition module, and a multi-layered anti-counterfeiting verification and anomaly detection module; The trusted triplet construction and cryptographic binding module is responsible for constructing the unforgeable initial identity of the ticket; the blockchain hash anchoring and consensus storage module is used to store the trusted triplet in the blockchain in an immutable manner; the trusted triplet dynamic evolution and state transition module is used to realize the state tracking and evolution management of the entire life cycle of the ticket; and the multi-level anti-counterfeiting verification and anomaly detection module is used to provide static identity verification, dynamic anti-counterfeiting credentials, and abnormal behavior detection capabilities. The trusted triple construction and cryptographic binding module includes an entity identifier generation unit, a state feature vector extraction and encoding unit, and an asymmetric cryptographic signature and ownership binding unit. The entity identifier generation unit integrates multi-dimensional information to generate a globally unique ticket entity identifier. The state feature vector extraction and encoding unit extracts and encodes the initial state attributes of the ticket into a structured feature vector. The asymmetric cryptographic signature and ownership binding unit uses the issuer's private key to sign the ticket information to form an undeniable proof of issuance. The blockchain hash anchoring and consensus notarization module includes a triplet hash digest anchoring unit, a distributed consensus confirmation and timestamp proof unit, and a lightweight notarization and off-chain data association unit. The triplet hash digest anchoring unit calculates the hash digest of trusted triplets and only uploads the digest information to the chain to protect privacy. The distributed consensus confirmation and timestamp proof unit uses the distributed consensus mechanism of the blockchain to confirm the notarized data through multiple nodes. The lightweight notarization and off-chain data association unit only stores the hash pointer on the chain to reduce storage costs. The complete triplet data is stored in a trusted off-chain database. The trusted triplet dynamic evolution and state transition module includes a lifecycle modeling unit, a triplet incremental update unit, and a multi-version triplet evolution chain construction unit. The lifecycle modeling unit is used to define the complete lifecycle state diagram of the ticketing service. The triplet incremental update unit records state changes through incremental updates rather than repeatedly storing complete information. The multi-version triplet evolution chain construction unit constructs all version triplets within the ticketing service lifecycle into a chain evolution structure. The multi-layered anti-counterfeiting verification and anomaly detection module includes a static identity consistency verification unit, a dynamic validity certificate generation and verification unit, and an abnormal behavior detection unit. The static identity consistency verification unit performs triple consistency verification on the entity identifier, public key information, and digital signature of the ticket. The dynamic validity certificate generation and verification unit generates a one-time dynamic token and verifies it when the ticket is about to be used. The abnormal behavior detection unit is used to detect suspicious behavior in real time.

2. The ticketing full lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping as described in claim 1, characterized in that, The entity identifier generation unit includes a multi-source data acquisition processor, a hash fusion calculation processor, and a collision detection and retry processor. The multi-source data acquisition processor is responsible for extracting multi-dimensional raw data from the ticketing system. The hash fusion calculation processor combines the acquired multi-source data according to predefined splicing rules and generates a ticketing entity identifier. The collision detection and retry processor performs collision detection between the newly generated entity identifier and existing identifiers in the system. The hash fusion calculation processor generates the entity identifier Entity_ID according to the following formula: ； Hcas() is a concatenated hash function. D is a nonlinear coupling fusion function. i Identify the i-th input factor. Here is the time-sensitivity weight matrix, t issue is the ticket issuance timestamp, and n is the number of input factors.

3. The ticketing full lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping as described in claim 2, characterized in that, The triplet incremental update unit includes a behavior event capture processor, a state incremental calculation processor, and an incremental triplet generation and hash linking processor. The behavior event capture processor monitors various behavior events in the ticketing system in real time and extracts key information. The state incremental calculation processor converts the captured behavior events into changes in the state feature vector and generates lightweight state incremental data. The incremental triplet generation and hash linking processor constructs new triplets based on the state incremental data and the original entity identifier. The hash value of the previous triplet is embedded in the new triplet as an inheritance pointer. The evolution process is linked together through a hash chain structure to form an unbreakable and reliable evolution chain. The incremental triplet generator and hash linker constructs triplets according to the following formula: ； in, Let m represent the m-th version of the triplet. Indicates from state S m-1 to state S m Incremental data, For proof of the legality of state transition, For bidirectional hash anchor value, t m For state change timestamps, Digital signature for the operator.

4. The ticketing full lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping as described in claim 3, characterized in that, The dynamic validity certificate generation and verification unit includes a spatiotemporal parameter acquisition processor, a one-time token generation processor, and a token validity verification processor. The spatiotemporal parameter acquisition processor collects the current spatiotemporal parameters when the ticket is about to be used and obtains the latest triplet state of the current ticket as the input element for token generation. The one-time token generation processor generates a dynamic token with short-term validity based on the spatiotemporal parameters, triplet hash value, and key information. After receiving the dynamic token at the ticket checking terminal, the token validity verification processor verifies the token's validity, uniqueness, and binding, ensuring the authenticity and legality of the token through multiple verifications.

5. The ticketing full lifecycle anti-counterfeiting and verification system based on blockchain trusted triple mapping as described in claim 4, characterized in that, The one-time token generation processor generates a one-time dynamic token Token according to the following formula: ； Where Cha() is the chaotic token generation function, H(T) cur ) represents the hash value of the latest triple. () represents the dynamic drift time window, H user Indicates user's historical behavior characteristics, Inject a function to the geographic location entropy, where L is the current geographic location coordinate. D represents the time-dependent geographic fuzzy radius. fp For device fingerprint.

Images