A digital collectible rating and virtual-real interactive transaction system based on multi-technology fusion

Abstract

This invention discloses a digital collectible rating and virtual-real interactive trading system based on multi-technology integration, belonging to the field of digital collectible trading technology. Through deep integration of multiple technologies, this invention constructs a full lifecycle governance system for digital collectibles: The federated learning framework integrates cross-institutional risk characteristics, and the intellectual property valuation model incorporates legal constraints, enabling rating results to simultaneously reflect market fluctuations and IP value decay, thus improving pricing objectivity; the hardware fingerprint generator utilizes the physical characteristics of devices to generate tamper-proof identifiers, combined with AR challenge tasks to achieve human-machine-environment triad authentication, effectively suppressing fraudulent account operations; and the spectral clustering analysis of the transaction graph and cross-chain entropy detection form a dual spatial-temporal monitoring system, coupled with the dynamic verification condition insertion of smart contracts, to achieve precise interception of new distributed speculation methods.

Description

Technical Field

[0001] This invention relates to the field of digital collectibles trading technology, and in particular to a digital collectibles grading and virtual-real interactive trading system based on the integration of multiple technologies. Background Technology

[0002] Current digital collectible grading and trading systems deeply integrate blockchain, dynamic valuation models, and IoT technology. In the limited-edition card market, they use NFC chips to bind unique identifiers to physical cards and combine this with the SWA digital asset database to assess market popularity, IP derivative value, and historical transaction data, thereby achieving dynamic pricing of digital twins of the cards.

[0003] Limited-edition trading cards, due to their fixed issuance and high liquidity, have become a breeding ground for malicious speculation in the secondary market. Speculators exploit system vulnerabilities to manipulate prices through methods such as forging device identities, high-frequency self-trading, and cross-platform reselling, causing digital valuation models to fail and severely undermining market fairness. To address this speculative chaos, some solutions introduce anomaly detection algorithms based on transaction graphs, using graph neural networks (GNNs) to identify account connections and closed-loop fund flows, and to mark suspicious transactions on-chain. Alternatively, dynamic cooling mechanisms can be implemented, where smart contracts automatically trigger risk control strategies such as extending transaction intervals and tiered increases in transaction fees when the price volatility of the trading card exceeds a threshold.

[0004] While the above solutions improve defense capabilities to some extent, graph analysis relies on centralized transaction data and cannot cover cross-chain mixer wash-selling behavior; the cooling mechanism is easily bypassed by distributed account clusters, which gradually raise prices through alternating small transactions; therefore, there is an urgent need for a digital collectible grading and virtual-real interactive trading system based on the integration of multiple technologies to solve these problems. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] This invention provides a digital collectibles grading and virtual-real interactive trading system based on the integration of multiple technologies, which solves the structural defects of traditional systems such as subjective grading, opaque transactions, separation of virtual and real, and lack of cross-chain defense.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] This invention provides a digital collectible grading and virtual-real interactive trading system based on multi-technology integration, comprising:

[0009] The data acquisition module is configured to acquire physical feature data and environmental exposure parameters of physical cards through an NFC chip, and synchronize cross-platform transaction records of digital collectibles through a blockchain node;

[0010] The dynamic rating module communicates with the data acquisition module and is configured to integrate the market risk coefficient output by the federated learning framework and the derivative equity value calculated by the intellectual property valuation model to generate dynamically updated card rating scores.

[0011] The virtual-real interaction module integrates an AR rendering engine and a device fingerprint extraction unit, and is configured to activate differentiated virtual game benefits based on rating scores, and collect hardware fingerprint data of user terminals.

[0012] The transaction suppression module communicates bidirectionally with the dynamic rating module and the virtual-real interaction module. It is configured to intercept abnormal transaction behavior on-chain based on the spatiotemporal correlation between device fingerprint data and transaction records.

[0013] As a preferred embodiment of the digital collectible grading and virtual-real interactive trading system based on multi-technology integration described in this invention, the dynamic grading module includes:

[0014] The federated learning submodule is configured to aggregate historical hype case features from multiple card issuers to generate a risk weight vector.

[0015] The intellectual property valuation submodule is configured to analyze the licensing scope, number of derivative works, and legal status of the card IP, and output the equity decay curve;

[0016] The real-time fusion unit is configured to align the risk weight vector with the equity decay curve in time and generate a rating score correction through a gated recurrent network.

[0017] The real-time fusion unit is configured as follows:

[0018] The risk weight vector is compensated for time decay, and the compensation coefficient decreases exponentially with the card circulation cycle.

[0019] The equity decay curve is discretized into time slices, and each slice is associated with an IP authorization status change event;

[0020] An attention mechanism is used to dynamically allocate the integration ratio of risk weight and equity value, with the attention weight of recent abnormal trading events increased by a preset value.

[0021] As a preferred embodiment of the digital collectible grading and virtual-real interactive trading system based on multi-technology fusion described in this invention, the real-time fusion unit constructs an aligned input and then generates a score correction value through GRU, the steps of which include:

[0022] At time t, take the historical risk weights and equity decay sequences with an input sliding window length of L, and concatenate them along the feature dimension:

[0023]

[0024] in, Let r represent the aligned input vector. t-L+1 e represents the risk weight at time t-L+1. t-L+1 Let L represent the equity decay value at time t-L+1, where L represents the sliding window length and t represents the current time.

[0025] The formula for updating the gate is:

[0026]

[0027] Among them, z t This represents updating the gate vector, σ represents the Sigmoid activation function, and a z This indicates updating the gate input weight matrix, b z This indicates updating the gate loop weight matrix, h. t-1 c represents the hidden state vector from the previous time step. z This indicates updating the gate bias vector;

[0028] The formula for resetting the door is:

[0029]

[0030] Among them, u t This represents resetting the gate vector, a r b represents the reset gate input weight matrix. r This represents resetting the gate loop weight matrix, c r This indicates the reset gate bias vector.

[0031] Generate candidate hidden states:

[0032]

[0033] in, Let denot be the candidate hidden state vector, tanh denote the hyperbolic tangent activation function, and a h Let b represent the candidate state input weight matrix. h The candidate state cyclic weight matrix is ​​represented by ⊙, which represents element-wise product. t-1 c represents the hidden state vector from the previous time step. h Represents the candidate state bias vector;

[0034] Hidden status update:

[0035]

[0036] Among them, h t This represents the hidden state vector at the current time step, where 1 indicates that all elements are 1 and are related to z. t Vectors of the same dimension, ⊙ represents element-wise product, h t-1z represents the hidden state in the previous moment. t This indicates updating the gate vector;

[0037] The rating score correction output is:

[0038] Δs t =d T h t +b o ,

[0039] Where, Δs t Let d represent the rating score correction at time t, d represent the output weight vector, T represent the transpose, and h represent the weight vector. t b represents the hidden state vector at the current time step. o This indicates the output bias scalar.

[0040] As a preferred embodiment of the digital collectible grading and virtual-real interactive trading system based on multi-technology integration described in this invention, the device fingerprint extraction unit in the virtual-real interaction module includes:

[0041] The hardware fingerprint generator is configured to combine GPU rendering features, baseband chip timing noise, and ambient light sensor sampling deviation to generate an irreversible device identification code.

[0042] The behavior fingerprint analyzer is configured to extract touch pressure waveforms, view switching frequencies, and gyroscope drift patterns during AR interaction to construct multi-dimensional behavior feature vectors.

[0043] The hardware fingerprint generator is configured as follows:

[0044] Microarchitectural timing differences in the GPU shader instruction set are extracted as the first feature factor;

[0045] The electromagnetic radiation ripple of the baseband chip in a preset frequency band is used as the second characteristic factor.

[0046] The background noise distribution of the ambient light sensor in the dark is used as the third characteristic factor;

[0047] The device identification code is generated by inputting three factors into the anti-collision hash function. If any factor changes by more than 5%, the identification code reconstruction is triggered.

[0048] As a preferred embodiment of the digital collectible grading and virtual-real interactive trading system based on multi-technology integration described in this invention, the trading inhibition module includes:

[0049] The transaction graph generation unit is configured to construct a weighted directed graph from the nodes of the buyer and seller, the transaction amount, and the timestamp.

[0050] The GCN detection unit is configured to perform spectral clustering analysis on the transaction graph to identify subgraph structures with abnormal node degree distribution.

[0051] The challenge verification unit is configured to initiate a dynamic gesture verification request in the AR environment to the user terminal within the associated subgraph, and determine the authenticity of the operation based on the gyroscope trajectory data.

[0052] As a preferred embodiment of the digital collectible grading and virtual-real interactive trading system based on multi-technology integration described in this invention, the GCN detection unit is configured as follows:

[0053] The transaction amount is logarithmically transformed and used as the edge weight;

[0054] The cosine similarity is used to measure the node feature vector, which includes: the reciprocal of the transaction time interval, the geolocation code of the associated IP address, and the Hamming distance of the device fingerprint.

[0055] As a preferred embodiment of the digital collectible grading and virtual-real interactive trading system based on multi-technology integration described in this invention, the on-chain interception operation performed by the transaction suppression module includes:

[0056] For high-risk trading pairs detected, smart contract verification conditions are dynamically inserted, requiring the buyer's terminal to complete a 3D jigsaw puzzle in an AR scene within a preset time.

[0057] The distribution pattern of the graphic fragments in the 3D puzzle is bound to the hash value of the current transaction timestamp, and the puzzle reset action triggers a secondary verification of the device fingerprint code;

[0058] The distribution pattern of the graphic fragments in the 3D puzzle includes:

[0059] The direction of the fragment cutting line is controlled by the last hexadecimal digit of the transaction timestamp;

[0060] The fragment hue offset is linearly related to the first four bits of the hash value of the current block height.

[0061] As a preferred embodiment of the digital collectible grading and virtual-real interactive trading system based on multi-technology integration described in this invention, it further includes:

[0062] The cross-chain analysis module is configured as a light node that connects to multiple heterogeneous blockchain networks to extract the overlapping time windows and fragmentation patterns of cross-chain coin mixing transactions.

[0063] The transaction suppression module is further configured to trigger a full-chain transaction cooling-off period when it is detected that cross-chain transactions associated with the same device fingerprint meet a preset fragmentation threshold.

[0064] As a preferred embodiment of the digital collectible grading and virtual-real interactive trading system based on multi-technology integration described in this invention, the cross-chain analysis module includes:

[0065] A cross-chain gateway adapter is configured to convert transaction data formats from different blockchain networks and extract the entropy characteristics and timestamp density of transaction addresses.

[0066] The federated learning framework is configured to collaboratively train a cross-chain monetization behavior recognition model across multiple trading platforms without sharing the original data.

[0067] The transaction suppression module is further configured to automatically freeze the transaction permissions of the associated device fingerprint code when the cross-chain transaction entropy value is lower than the dynamic threshold and the timestamp density exceeds the baseline value.

[0068] The timestamp density is calculated as follows:

[0069] The number of cross-chain transactions initiated by the target device within a preset time window is counted.

[0070] An alarm is triggered when the number of occurrences exceeds the average of three standard deviations for devices of the same type.

[0071] As a preferred embodiment of the digital collectible grading and virtual-real interactive trading system based on multi-technology integration described in this invention, in the step of extracting the entropy value characteristics and timestamp density of the transaction address in the cross-chain analysis module, two types of core features are extracted from the cross-chain transaction data, including the entropy value characteristics of the transaction address and the density characteristics of the transaction timestamp. The extraction steps include:

[0072] Calculate the address entropy value to measure the uniformity of address distribution in cross-chain transactions, using the Shannon entropy definition:

[0073]

[0074] Where H(A) represents address entropy, A represents the discrete random variable of address, and p i Let N represent the probability of a transaction from an address of type i, N represent the total number of addresses, and log represent the natural logarithm.

[0075] The formula for calculating the probability of a transaction is:

[0076] Where, n i This represents the number of transactions for the i-th type of address. This represents the total number of transactions across all addresses.

[0077] Let the current time be t, select a cross-chain transaction time window of length Δ [t-Δ, t], and count the total number of transactions within this window:

[0078]

[0079] Where, k t M represents the number of transactions within the window [t-Δ, t], where t is the number of transactions within the window. j Let represent the timestamp of the j-th transaction, and 1(·) represent the indicator function;

[0080] Transaction density is defined based on the counting results:

[0081]

[0082] Where, ρ t k represents the transaction density within a unit time window. t This represents the total number of transactions within the window, and Δ represents the window duration.

[0083] The intensity is normalized to a standard score to eliminate baseline differences in activity levels across different devices. The normalization formula is as follows:

[0084]

[0085] Among them, D t μ represents the normalized density. ρ σ represents the average transaction density across all devices within the same window. ρ This represents the standard deviation of the transaction density across all devices.

[0086] The beneficial effects of this invention are as follows: This invention constructs a full lifecycle governance system for digital collectibles through the deep integration of multiple technologies: the federated learning framework integrates cross-institutional risk characteristics, and the intellectual property valuation model is infused with legal constraints, enabling rating results to reflect market fluctuations and IP value decay simultaneously, thus improving pricing objectivity; the hardware fingerprint generator uses the physical characteristics of the device to generate tamper-proof identifiers, and combines AR challenge tasks to achieve human-machine-environment triad authentication, effectively suppressing fraudulent account operations; the spectral clustering analysis of the transaction graph and cross-chain entropy detection form a dual spatial-temporal monitoring system, which, together with the dynamic verification condition insertion of smart contracts, achieves precise interception of new distributed speculation methods; from physical data collection and dynamic value assessment to the exchange of virtual and real rights, a scalable technical architecture is formed, providing infrastructure support for the deep integration of digital collectibles and the real economy. Attached Figure Description

[0087] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0088] Figure 1 This is a schematic diagram of the framework of a digital collectibles grading and virtual-real interactive trading system based on the integration of multiple technologies in Example 1. Detailed Implementation

[0089] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0090] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0091] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0092] Example 1, referring to Figure 1 This embodiment provides a digital collectible grading and virtual-real interactive trading system based on the integration of multiple technologies, including:

[0093] The data acquisition module is configured to acquire physical feature data and environmental exposure parameters of physical cards through an NFC chip, and synchronize cross-platform transaction records of digital collectibles through a blockchain node;

[0094] The dynamic rating module communicates with the data acquisition module and is configured to integrate the market risk coefficient output by the federated learning framework and the derivative equity value calculated by the intellectual property valuation model to generate dynamically updated card rating scores.

[0095] The dynamic rating module includes:

[0096] The federated learning submodule is configured to aggregate historical hype case features from multiple card issuers to generate a risk weight vector.

[0097] The intellectual property valuation submodule is configured to analyze the licensing scope, number of derivative works, and legal status of the card IP, and output the equity decay curve;

[0098] The real-time fusion unit is configured to align the risk weight vector with the equity decay curve in time and generate a rating score correction through a gated recurrent network.

[0099] The real-time fusion unit is configured as follows:

[0100] The risk weight vector is compensated for time decay, and the compensation coefficient decreases exponentially with the card circulation cycle.

[0101] The equity decay curve is discretized into time slices, and each slice is associated with an IP authorization status change event;

[0102] An attention mechanism is used to dynamically allocate the integration ratio of risk weight and equity value, with the attention weight of recent abnormal trading events increased by a preset value;

[0103] In the real-time fusion unit, an aligned input is constructed, and then a fractional correction is generated through a GRU. The steps include:

[0104] At time t, take the historical risk weights and equity decay sequences with an input sliding window length of L, and concatenate them along the feature dimension:

[0105]

[0106] in, Let r represent the aligned input vector. t-L+1 e represents the risk weight at time t-L+1. t-L+1 Let L represent the equity decay value at time t-L+1, where L represents the sliding window length and t represents the current time.

[0107] The formula for updating the gate is:

[0108]

[0109] Among them, z t This represents updating the gate vector, σ represents the Sigmoid activation function, and a z This indicates updating the gate input weight matrix, b z This indicates updating the gate loop weight matrix, h. t-1 c represents the hidden state vector from the previous time step. z This indicates updating the gate bias vector;

[0110] The formula for resetting the door is:

[0111]

[0112] Among them, u t This represents the reset gate vector, a r Let b represent the reset gate input weight matrix. r This represents resetting the gate loop weight matrix, c r This indicates the reset gate bias vector.

[0113] Generate candidate hidden states:

[0114]

[0115] in, Let denot be the candidate hidden state vector, tanh denote the hyperbolic tangent activation function, and a h Let b represent the candidate state input weight matrix. h The candidate state cyclic weight matrix is ​​represented by ⊙, which represents element-wise product. t-1 c represents the hidden state vector from the previous time step. h Represents the candidate state bias vector;

[0116] Hidden status update:

[0117]

[0118] Among them, h t This represents the hidden state vector at the current time step, where 1 indicates that all elements are 1 and are related to z. t Vectors of the same dimension, ⊙ represents element-wise product, h t-1 z represents the hidden state in the previous moment. t This indicates updating the gate vector;

[0119] The rating score correction output is:

[0120] Δs t =d T h t +b o ,

[0121] Where, Δs t Let d represent the rating score correction at time t, d represent the output weight vector, T represent the transpose, and h represent the weight vector. t b represents the hidden state vector at the current time step. o Indicates the output bias scalar;

[0122] Specifically, this process uses a unified sliding window to input risk and equity decay sequences in parallel, achieving cross-feature temporal alignment and deep fusion. Within the GRU, the update gate dynamically determines the degree to which historical memory is retained, and the reset gate controls the degree of information forgetting, ensuring that the model can flexibly respond to sudden changes in market risk and the decay of IP value. Candidate hidden states are generated under dual gating, which not only retains key old information but also absorbs new input. Finally, the hidden state is combined with the linear mapping to output an interpretable score correction. This innovatively integrates cross-domain feature alignment, time decay compensation, and the GRU gating mechanism, providing fine-grained temporal decision-making capabilities for dynamic rating, which helps to improve the real-time accuracy and stability of rating scores.

[0123] The virtual-real interaction module integrates an AR rendering engine and a device fingerprint extraction unit, and is configured to activate differentiated virtual game benefits based on rating scores, and collect hardware fingerprint data of user terminals.

[0124] The device fingerprint extraction unit in the virtual-real interaction module includes:

[0125] The hardware fingerprint generator is configured to combine GPU rendering features, baseband chip timing noise, and ambient light sensor sampling deviation to generate an irreversible device identification code.

[0126] The behavior fingerprint analyzer is configured to extract touch pressure waveforms, view switching frequencies, and gyroscope drift patterns during AR interaction to construct multi-dimensional behavior feature vectors.

[0127] The hardware fingerprint generator is configured as follows:

[0128] Microarchitectural timing differences in the GPU shader instruction set are extracted as the first feature factor;

[0129] The electromagnetic radiation ripple of the baseband chip in a preset frequency band is used as the second characteristic factor.

[0130] The background noise distribution of the ambient light sensor in the dark is used as the third characteristic factor;

[0131] The three factors are input into the anti-collision hash function to generate the device identification code. If any factor changes by more than 5%, the identification code reconstruction is triggered.

[0132] The transaction suppression module communicates bidirectionally with the dynamic rating module and the virtual-real interaction module. It is configured to intercept abnormal transaction behavior on-chain based on the spatiotemporal correlation between device fingerprint data and transaction records.

[0133] The trading suppression module includes:

[0134] The transaction graph generation unit is configured to construct a weighted directed graph from the nodes of the buyer and seller, the transaction amount, and the timestamp.

[0135] The GCN detection unit is configured to perform spectral clustering analysis on the transaction graph to identify subgraph structures with abnormal node degree distribution.

[0136] The challenge verification unit is configured to initiate a dynamic gesture verification request in the AR environment to the user terminal within the associated subgraph, and determine the authenticity of the operation based on the gyroscope trajectory data.

[0137] The GCN detection unit is configured as follows:

[0138] The transaction amount is logarithmically transformed and used as the edge weight;

[0139] The cosine similarity is used to measure the node feature vector, which includes: the reciprocal of the transaction time interval, the geolocation code of the associated IP address, and the Hamming distance of the device fingerprint.

[0140] The on-chain interception operations performed by the transaction suppression module include:

[0141] For high-risk trading pairs detected, smart contract verification conditions are dynamically inserted, requiring the buyer's terminal to complete a 3D jigsaw puzzle in an AR scene within a preset time.

[0142] The distribution pattern of the graphic fragments in the 3D puzzle is bound to the hash value of the current transaction timestamp, and the puzzle reset action triggers a secondary verification of the device fingerprint code;

[0143] The distribution patterns of graphic fragments in a 3D jigsaw puzzle include:

[0144] The direction of the fragment cutting line is controlled by the last hexadecimal digit of the transaction timestamp;

[0145] The fragment hue offset is linearly related to the first 4 bits of the hash value of the current block height;

[0146] The trading system also includes:

[0147] The cross-chain analysis module is configured as a light node that connects to multiple heterogeneous blockchain networks to extract the overlapping time windows and fragmentation patterns of cross-chain coin mixing transactions.

[0148] The transaction suppression module is further configured to trigger a full-chain transaction cooling-off period when it is detected that cross-chain transactions associated with the same device fingerprint meet a preset fragmentation threshold.

[0149] The cross-chain analysis module includes:

[0150] A cross-chain gateway adapter is configured to convert transaction data formats from different blockchain networks and extract the entropy characteristics and timestamp density of transaction addresses.

[0151] The federated learning framework is configured to collaboratively train a cross-chain monetization behavior recognition model across multiple trading platforms without sharing the original data.

[0152] The transaction suppression module is further configured to automatically freeze the transaction permissions of the associated device fingerprint code when the cross-chain transaction entropy value is lower than the dynamic threshold and the timestamp density exceeds the baseline value.

[0153] The timestamp density is calculated as follows:

[0154] The number of cross-chain transactions initiated by the target device within a preset time window is counted.

[0155] An alarm is triggered when the number of occurrences exceeds the average value of similar devices by 3 standard deviations.

[0156] In the cross-chain analysis module, the steps for extracting the entropy features of transaction addresses and the density of timestamps involve extracting two core features from cross-chain transaction data: the entropy features of transaction addresses and the density features of transaction timestamps. The extraction steps include:

[0157] Calculate the address entropy value to measure the uniformity of address distribution in cross-chain transactions, using the Shannon entropy definition:

[0158]

[0159] Where H(A) represents address entropy, A represents the discrete random variable of address, and p i Let N represent the probability of a transaction from an address of type i, N represent the total number of addresses, and log represent the natural logarithm.

[0160] The formula for calculating the probability of a transaction is:

[0161] Where, n i This represents the number of transactions for the i-th type of address. This represents the total number of transactions across all addresses.

[0162] Let the current time be t, select a cross-chain transaction time window of length Δ [t-Δ, t], and count the total number of transactions within this window:

[0163]

[0164] Where, k t M represents the number of transactions within the window [t-Δ, t], where t is the number of transactions within the window. j Let represent the timestamp of the j-th transaction, and 1(·) represent the indicator function;

[0165] Transaction density is defined based on the counting results:

[0166]

[0167] Where, ρ t k represents the transaction density within a unit time window. t This represents the total number of transactions within the window, and Δ represents the window duration.

[0168] The intensity is normalized to a standard score to eliminate baseline differences in activity levels across different devices. The normalization formula is as follows:

[0169]

[0170] Among them, D t μ represents the normalized density. ρ σ represents the average transaction density across all devices within the same window. ρ The standard deviation of transaction density across all devices;

[0171] Specifically, by quantifying address distribution characteristics using Shannon entropy, it can reveal whether there is a phenomenon of concentrated transactions by a few addresses in cross-chain transactions. Low entropy values ​​suggest potential wash trading or manipulation, while high entropy values ​​indicate that the transaction sources are more dispersed and the risks are dispersed. Secondly, the counting and density calculation within the time window enable the capture of short-term high-frequency transaction behavior, while the standardization steps unify the activity differences of different devices or links, making it convenient to set a unified threshold or use it as a comparable input for federated learning models. The overall method provides quantitative characteristics in two dimensions: spatial-address distribution and time-transaction frequency.

[0172] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A digital collectible grading and virtual-real interactive trading system based on multi-technology integration, characterized in that, include, The data acquisition module is configured to acquire physical feature data and environmental exposure parameters of physical cards through an NFC chip, and synchronize cross-platform transaction records of digital collectibles through a blockchain node; The dynamic rating module communicates with the data acquisition module and is configured to integrate the market risk coefficient output by the federated learning framework and the derivative equity value calculated by the intellectual property valuation model to generate dynamically updated card rating scores. The virtual-real interaction module integrates an AR rendering engine and a device fingerprint extraction unit, and is configured to activate differentiated virtual game benefits based on rating scores, and collect hardware fingerprint data of user terminals. The transaction suppression module communicates bidirectionally with the dynamic rating module and the virtual-real interaction module. It is configured to intercept abnormal transaction behavior on-chain based on the spatiotemporal correlation between device fingerprint data and transaction records. The dynamic rating module includes: The federated learning submodule is configured to aggregate historical hype case features from multiple card issuers to generate a risk weight vector. The intellectual property valuation submodule is configured to analyze the licensing scope, number of derivative works, and legal status of the card IP, and output the equity decay curve; The real-time fusion unit is configured to align the risk weight vector with the equity decay curve in time and generate a rating score correction through a gated recurrent network. The real-time fusion unit is configured as follows: The risk weight vector is compensated for time decay, and the compensation coefficient decreases exponentially with the card circulation cycle. The equity decay curve is discretized into time slices, and each slice is associated with an IP authorization status change event; An attention mechanism is used to dynamically allocate the integration ratio of risk weight and equity value, with the attention weight of recent abnormal trading events increased by a preset value; In the real-time fusion unit, an aligned input is constructed, and then a fraction correction value is generated through GRU. The steps include: At time t, take the historical risk weights and equity decay sequences with an input sliding window length of L, and concatenate them along the feature dimension: in, Let r represent the aligned input vector. t-L+1 e represents the risk weight at time t-L+1. t-L+1 Let L represent the equity decay value at time t-L+1, where L represents the sliding window length and t represents the current time. The formula for updating the gate is: Among them, z t This represents updating the gate vector, σ represents the Sigmoid activation function, and a z This indicates updating the gate input weight matrix, b z This indicates updating the gate loop weight matrix, h. t-1 c represents the hidden state vector from the previous time step. z This indicates updating the gate bias vector; The formula for resetting the door is: Among them, u t This represents resetting the gate vector, a r b represents the reset gate input weight matrix. r This represents resetting the gate loop weight matrix, c r This indicates the reset gate bias vector. Generate candidate hidden states: in, Let denot be the candidate hidden state vector, tanh denote the hyperbolic tangent activation function, and a h Let b represent the candidate state input weight matrix. h The candidate state cyclic weight matrix is ​​represented by ⊙, which represents element-wise product. t-1 c represents the hidden state vector from the previous time step. h Represents the candidate state bias vector; Hidden status update: Among them, h t This represents the hidden state vector at the current time step, where 1 indicates that all elements are 1 and are related to z. t Vectors of the same dimension, ⊙ represents element-wise product, h t-1 z represents the hidden state in the previous moment. t This indicates updating the gate vector; The rating score correction output is: Where, Δs t Let d represent the rating score correction at time t, and d represent the output weight vector. Indicates transpose, h t b represents the hidden state vector at the current time step. o Indicates the output bias scalar; The device fingerprint extraction unit in the virtual-real interaction module includes: The hardware fingerprint generator is configured to combine GPU rendering features, baseband chip timing noise, and ambient light sensor sampling deviation to generate an irreversible device identification code. The behavior fingerprint analyzer is configured to extract touch pressure waveforms, view switching frequencies, and gyroscope drift patterns during AR interaction to construct multi-dimensional behavior feature vectors. The hardware fingerprint generator is configured as follows: Microarchitectural timing differences in the GPU shader instruction set are extracted as the first feature factor; The electromagnetic radiation ripple of the baseband chip in a preset frequency band is used as the second characteristic factor. The background noise distribution of the ambient light sensor in the dark is used as the third characteristic factor; The three factors are input into the anti-collision hash function to generate the device identification code. If any factor changes by more than 5%, the identification code reconstruction is triggered. The transaction suppression module includes: The transaction graph generation unit is configured to construct a weighted directed graph from the nodes of the buyer and seller, the transaction amount, and the timestamp. The GCN detection unit is configured to perform spectral clustering analysis on the transaction graph to identify subgraph structures with abnormal node degree distribution. The challenge verification unit is configured to initiate a dynamic gesture verification request in the AR environment to the user terminal within the associated subgraph, and determine the authenticity of the operation based on the gyroscope trajectory data.

2. The digital collectible grading and virtual-real interactive trading system based on multi-technology integration as described in claim 1, characterized in that, The GCN detection unit is configured as follows: The transaction amount is logarithmically transformed and used as the edge weight; The cosine similarity is used to measure the node feature vector, which includes: the reciprocal of the transaction time interval, the geolocation code of the associated IP address, and the Hamming distance of the device fingerprint.

3. The digital collectible grading and virtual-real interactive trading system based on multi-technology integration as described in claim 2, characterized in that, The on-chain interception operations performed by the transaction suppression module include: For high-risk trading pairs detected, smart contract verification conditions are dynamically inserted, requiring the buyer's terminal to complete a 3D jigsaw puzzle in an AR scene within a preset time. The distribution pattern of the graphic fragments in the 3D puzzle is bound to the hash value of the current transaction timestamp, and the puzzle reset action triggers a secondary verification of the device fingerprint code; The distribution pattern of the graphic fragments in the 3D puzzle includes: The direction of the fragment cutting line is controlled by the last hexadecimal digit of the transaction timestamp; The fragment hue offset is linearly related to the first four bits of the hash value of the current block height.

4. The digital collectible grading and virtual-real interactive trading system based on multi-technology integration as described in claim 1, characterized in that, Also includes: The cross-chain analysis module is configured as a light node to access multiple heterogeneous blockchain networks, extracting the overlapping time windows and fragmentation patterns of cross-chain coin mixing transactions. The transaction suppression module is further configured to trigger a full-chain transaction cooling-off period when it is detected that cross-chain transactions associated with the same device fingerprint meet a preset fragmentation threshold.

5. The digital collectible grading and virtual-real interactive trading system based on multi-technology integration as described in claim 4, characterized in that, The cross-chain analysis module includes: A cross-chain gateway adapter is configured to convert transaction data formats from different blockchain networks and extract the entropy characteristics and timestamp density of transaction addresses. The federated learning framework is configured to collaboratively train a cross-chain monetization behavior recognition model across multiple trading platforms without sharing the original data. The transaction suppression module is further configured to automatically freeze the transaction permissions of the associated device fingerprint code when the cross-chain transaction entropy value is lower than the dynamic threshold and the timestamp density exceeds the baseline value. The timestamp density is calculated as follows: The number of cross-chain transactions initiated by the target device within a preset time window is counted. An alarm is triggered when the number of occurrences exceeds the average of three standard deviations for devices of the same type.

6. The digital collectible grading and virtual-real interactive trading system based on multi-technology integration as described in claim 5, characterized in that, In the steps of extracting the entropy value features and timestamp density of transaction addresses in the cross-chain analysis module, two types of core features are extracted from the cross-chain transaction data, including the entropy value features of transaction addresses and the density features of transaction timestamps. The extraction steps include: Calculate the address entropy value to measure the uniformity of address distribution in cross-chain transactions, using the Shannon entropy definition: Where H(A) represents address entropy, A represents the discrete random variable of address, and p i Let N represent the probability of a transaction from an address of type i, N represent the total number of addresses, and log represent the natural logarithm. The formula for calculating the probability of a transaction is: Where, n i This represents the number of transactions for the i-th type of address. This represents the total number of transactions across all addresses. Let the current time be t, select a cross-chain transaction time window of length Δ [t-Δ, t], and count the total number of transactions within this window: Where, k t M represents the number of transactions within the window [t-Δ, t], where t is the number of transactions within the window. j Let represent the timestamp of the j-th transaction, and 1(·) represent the indicator function; Transaction density is defined based on the counting results: Where, ρ t k represents the transaction density within a unit time window. t This represents the total number of transactions within the window, and Δ represents the window duration. The intensity is normalized to a standard score to eliminate baseline differences in activity levels across different devices. The normalization formula is as follows: Among them, D t μ represents the normalized density. ρ σ represents the average transaction density across all devices within the same window. ρ This represents the standard deviation of the transaction density across all devices.

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