A metaverse experience interaction system based on blockchain technology

Abstract

The application discloses a meta-universe experience interaction system based on a blockchain technology, relates to the fields of blockchain and information security technology, and constructs a real-time and automatic contribution degree evaluation and right distribution mechanism based on a blockchain, so that fine management of traceable processes, quantifiable contributions and dynamically adjustable rights can be realized; through high-frequency collection of user operation behaviors and fusion of multi-dimensional features for stream computing, subtle values of each creative action can be accurately captured, and the final right distribution result can truly reflect the actual contribution of each participant; not only the fairness and accuracy of copyright definition are greatly improved, but also potential disputes caused by unclear rights are effectively avoided; meanwhile, the system automatically converts complex contribution evaluation results into right share and income distribution rules on the chain through a smart contract, so that whole-process automation and trustlessness of right management are realized.

Description

Technical Field

[0001] This invention relates to the fields of blockchain and information security technology, and in particular to a metaverse experience interaction system based on blockchain technology. Background Technology

[0002] Metaverse digital artworks exhibit characteristics of dynamic generation and user-participatory co-creation in a virtual environment; these works are mostly generated through real-time interactive collaboration among multiple users, and the creation process includes continuous modification, overlay, and re-creation; every brushstroke and every addition of a three-dimensional element on the virtual canvas may come from different participants, forming a complex digital asset with a multi-layered contribution structure.

[0003] Most existing solutions employ an NFT-based copyright registration mechanism, using blockchain to record ownership information of the final product; some systems introduce smart contracts for static copyright division, pre-setting a fixed proportion for revenue distribution; and some solutions store the hash values ​​of key nodes in the creation process on the blockchain to provide traceability of some creative trajectories.

[0004] However, in practical applications, such solutions are difficult to adapt to the real-time copyright definition requirements in dynamic creation scenarios; when multiple users modify artworks at the same time, existing systems cannot accurately record the specific contribution of each participant; for continuously evolving interactive works, static copyright allocation mechanisms cannot reflect the changing contribution relationships during the creation process; at the same time, fragmented creation record data lacks an effective integration and analysis mechanism, resulting in limited accuracy in copyright definition. Summary of the Invention

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

[0006] This invention provides a metaverse experience interaction system based on blockchain technology to solve the problem that the copyright ownership of dynamically generated metaverse digital artworks is difficult to accurately define and trace due to real-time interaction and modification by multiple users.

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

[0008] In a first aspect, embodiments of the present invention provide a metaverse experience interaction system based on blockchain technology, comprising:

[0009] The behavior acquisition module is used to acquire user operation events and spatial trajectories on the target digital work in a unified coordinate system at a configurable sampling frequency.

[0010] The contribution evaluation module couples event sequence reconstruction with identity verification, performs streaming calculation of the marginal contributions of multiple users, and outputs the cumulative contribution.

[0011] The smart contract module generates and updates the on-chain equity share and revenue settlement rules corresponding to the work based on the accumulated contribution, and executes them in the blockchain network.

[0012] As a preferred embodiment of the metaverse experience interaction system based on blockchain technology described in this invention, the behavior acquisition module includes: an input interface adaptation subunit, a trajectory recording subunit, and an event integrity verification subunit; the trajectory recording subunit obtains three-dimensional coordinates and posture sequences through client rendering engine or input device callback, and performs interpolation or packet loss annotation for missing sampling points.

[0013] As a preferred embodiment of the metaverse experience interaction system based on blockchain technology described in this invention, the behavior acquisition module further includes an event timestamp and integrity subunit, which generates a signed timestamp for each operation based on the blockchain time reference and the local secure clock, and constructs a hash chain in the order of arrival to form an immutable operation commitment.

[0014] As a preferred embodiment of the metaverse experience interaction system based on blockchain technology described in this invention, the contribution evaluation module includes an operation type identification subunit, which uses a predefined operation mode library and threshold strategy to classify events into at least one of creation, modification, deletion, coloring, and scaling, and outputs the corresponding operation intensity and impact range labels.

[0015] As a preferred embodiment of the metaverse experience interaction system based on blockchain technology described in this invention, the contribution evaluation module includes a feature extraction and weighted estimation subunit, which extracts features such as path complexity, speed change rate, operation accuracy, affected voxel / mesh ratio, and change retention from trajectory and version differences, and calculates the weight of a single event based on operation type, time decay, and impact range; the cumulative contribution is aggregated according to the streaming approximation of the event marginal gain.

[0016] The aggregation steps include:

[0017] On the global sequence generated by the operation sequence reconstruction and conflict resolution subunit, each event and its user identifier, timestamp, scope of impact and type label are read in the order of event arrival;

[0018] Based on the spatial trajectory and version differences of the event, the set of voxels / mesh that were touched and the coverage ratio are labeled, and the extracted features, including the intensity, accuracy and velocity changes of the event, are associated.

[0019] For a single event, based on the local version difference of its influence domain and the extracted features, the marginal gain of the event is approximated, and interval pruning and normalization are performed. If a subsequent overlay or rollback event occurs, an entry to be deducted is registered for the historical event.

[0020] When reading in the k-th event, update the cumulative contribution of the user u to which the event belongs as follows:

[0021]

[0022] Among them, C u (t k ) indicates at time t k The cumulative contribution of user u after processing the k-th event, δ∈(0,1] represents the time decay base, δ is the decay base per unit time, Δt k Measured in the same units, C represents the time interval since the last update of the cumulative contribution for user u. u (t k-1 ) represents the cumulative contribution at the time of the last update, s k This represents the approximate marginal gain of the k-th event;

[0023] In the formula:

[0024] in, s represents the approximate original marginal gain of the event. max >0 indicates the upper bound of the clipping, and clip(x,a,b) means restricting x to the interval [a,b].

[0025] Online single-channel update, late and overwrite events are corrected by negative deduction, and when δ<1 and |s k |When bounded, the cumulative value has an upper bound;

[0026] When subsequent events overlap or prune the historical influence domain, negative values ​​are generated according to the overlap ratio. k The entry is entered into the update as a new event;

[0027] Within the sliding window, the cumulative contribution of each user is scaled or normalized in batches. The normalization only applies to the display and settlement input and does not write back the original cumulative amount.

[0028] Once the relative change in cumulative contribution exceeds the threshold and passes through the freeze window, a snapshot of the current cumulative contribution is submitted to the smart contract module to drive share updates and revenue settlement.

[0029] As a preferred embodiment of the metaverse experience interaction system based on blockchain technology described in this invention, the contribution evaluation module includes an operation sequence reconstruction and conflict resolution subunit. It adopts a collaborative editing protocol running on the client and server to generate globally verifiable event sequence identifiers, and selects the version to be retained based on priority and timestamp strategy and records the reasons for the decision when a conflict is detected.

[0030] As a preferred embodiment of the metaverse experience interaction system based on blockchain technology described in this invention, the smart contract module includes an equity update subunit and a settlement subunit; when the relative change in the cumulative contribution exceeds a preset threshold, the on-chain equity share is updated after the freeze window expires; the settlement subunit automatically distributes transaction revenue according to the current equity share and retains a versioned allocation snapshot to support backtracking.

[0031] As a preferred embodiment of the metaverse experience interaction system based on blockchain technology described in this invention, the system further includes a dispute resolution module, which is used to retrieve on-chain operation commitments, contribution evaluation logs and contract version snapshots, replay the contribution calculation process within a specified time window and generate an audit report with a verifiable summary for dispute resolution.

[0032] As a preferred embodiment of the metaverse experience interaction system based on blockchain technology described in this invention, the system includes a privacy protection module, which is used to separate the original data containing biometric features from the operational metadata used for contribution evaluation locally, upload only the desensitized or digested metadata, and generate a zero-knowledge proof to prove that the metadata satisfies the validity rules and corresponds to the signature subject.

[0033] The steps for generating zero-knowledge proofs include:

[0034] At the terminal, the original data containing sensitive information is extracted into metadata m for evaluation, commitment M is calculated, and the signing entity generates a signature σ on m. The original sensitive data is not transmitted outside.

[0035] The validity rules are organized into a set of decisionable constraints and compiled offline into decision logic that can be executed by the circuit.

[0036] Let the common input be (M, pk, Θ), where the rule version is identified by the commitment Θ, and the witness is (m, σ, r);

[0037] When constructing the circuit, the judgment corresponding to the signature body and the validity rule are merged into the same relation, and a zero-knowledge proof is given on the proof side for the following relation:

[0038]

[0039] in, The circuit represents the constraint relationship, M represents the metadata commitment, pk represents the public key of the signing subject, Θ represents the commitment or version identifier of the validity rule, m represents local metadata, σ represents the digital signature of m, Com(·;·) represents the commitment function, r represents the locally generated random factor, Verify(·) represents the Boolean value output by the signature verification algorithm, and Φ represents the constraint relationship within the circuit. Θ (·) indicates the decision result of the rule set in the circuit;

[0040] The proof is generated locally, and the on-chain verification key is fixed in the contract. Once the verification is successful, it is considered that the metadata was issued by the subject corresponding to the public key and meets the current rules.

[0041] The dispute resolution module uses the same rule commitment Θ and commitment M for replay verification, and outputs a verifiable summary for use in the audit report.

[0042] Secondly, this invention provides a metaverse experience interaction method based on blockchain technology, comprising the following steps:

[0043] Step S1: Collect and verify user operation events and trajectories;

[0044] Step S2: Perform type identification and sequence reconstruction of the events;

[0045] Step S3: Based on streaming marginal contribution estimation, aggregate the cumulative contribution of each user;

[0046] Step S4: When the cumulative contribution changes meet the update conditions, update the on-chain equity share through the smart contract and automatically settle the revenue.

[0047] Step S5: Upon receiving a dispute request, replay the calculation within the specified window and output a verifiable audit report.

[0048] The beneficial effects of this invention are as follows: By constructing a real-time, automated contribution assessment and rights allocation mechanism based on blockchain, this invention achieves refined management with traceable processes, quantifiable contributions, and dynamically adjustable rights. Through high-frequency collection of user actions and the integration of multi-dimensional features for streaming computation, it can accurately capture the subtle value of each creative action, ensuring that the final rights allocation truly reflects the actual contribution of each participant. This not only greatly improves the fairness and accuracy of copyright definition but also effectively avoids potential disputes arising from unclear ownership. Simultaneously, the system automatically transforms complex contribution assessment results into on-chain rights shares and revenue distribution rules through smart contracts, achieving full automation and trustlessness in rights management. Every rights change is confirmed and notarized through the blockchain network, forming an immutable and transparent rights ledger, laying a solid foundation of trust for the circulation, trading, and value realization of digital artworks. Furthermore, the embedded privacy protection mechanism ensures that while users enjoy accurate rights allocation services, their sensitive original data, such as biometrics, do not need to leave their local devices, effectively balancing the contradiction between data utilization and privacy security.

[0049] The entire system of this invention forms a complete closed loop from the collection of creative behavior, value assessment, rights distribution and dispute resolution. It solves the core pain point of copyright ownership in dynamic creation, and activates the vitality of co-creation of metaverse digital artworks through technical means, providing core infrastructure support for building a prosperous, fair and credible metaverse digital creative economy ecosystem. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation on the scope of this application.

[0051] Figure 1 This is a schematic diagram of the framework of the metaverse experience interaction system based on blockchain technology in the embodiment.

[0052] Figure 2 This is a flowchart illustrating the metaverse experience interaction method based on blockchain technology in this embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] All terms used in this application (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0055] For example, the terms “first” and “second” used in this application are only used to distinguish and describe similar objects, to differentiate the first object from another object, and are not used to describe a specific order or sequence, nor should they be interpreted as indicating or implying relative importance.

[0056] This application proposes a metaverse experience interaction system based on blockchain technology, combined with... Figure 1 As shown, the system includes:

[0057] The behavior acquisition module is used to acquire user operation events and spatial trajectories on the target digital work in a unified coordinate system at a configurable sampling frequency.

[0058] The contribution evaluation module couples event sequence reconstruction with identity verification, performs streaming calculation of the marginal contributions of multiple users, and outputs the cumulative contribution.

[0059] The smart contract module generates and updates the on-chain equity share and revenue settlement rules corresponding to the work based on the cumulative contribution, and executes them in the blockchain network;

[0060] In one embodiment, the behavior acquisition module includes: an input interface adaptation subunit, a trajectory recording subunit, and an event integrity verification subunit; the trajectory recording subunit obtains the three-dimensional coordinates and attitude sequence through the client rendering engine or input device callback, and performs interpolation or packet loss annotation for missing sampling points;

[0061] In this embodiment, the unified coordinate system refers to a three-dimensional coordinate expression based on the scene's world coordinates and with a fixed scale unit. The pose is represented using quaternions or equivalent Euler angles while maintaining right-handed consistency. The sampling frequency is 120Hz by default, with an adjustable range of 60Hz to 240Hz, determined based on device callback stability and jitter spectral density. Linear interpolation is performed when the interval between consecutive missing samples is less than 50ms; otherwise, only packet loss is marked and the system enters an abnormal path. Impact range statistics are performed using voxels or meshes as the smallest granularity, with voxel side lengths defaulting to 1-5cm and adaptively adjusting to the scene size. Optionally, to reduce the impact of high-frequency jitter on trajectory complexity, a first-order low-pass filter can be applied locally to the input sequence, with a default cutoff frequency of 10Hz and no change to the timestamp. In the event of an input device disconnection or pose calculation failure, the minimum executable approach is to retain the last valid pose and mark it as frozen. Recording stops and a missing measurement event is reported if the frozen state lasts for more than 500ms.

[0062] In one embodiment, the behavior acquisition module further includes an event timestamp and integrity subunit, which generates a signed timestamp for each operation based on a blockchain time reference and a local secure clock, and constructs a hash chain in the order of arrival to form an immutable operation commitment.

[0063] Specifically, the blockchain time reference is calibrated by reading the latest block header time and combining it with the local secure clock. The allowed deviation threshold is ±100ms by default, adjustable from ±50ms to ±250ms. When the deviation exceeds the limit, the submission is delayed and a one-time recalibration is triggered. Each operation commitment loads an operation digest, the digest of the previous commitment, and a signature digest. The signature is generated using a trusted key pair within the device, and the key rotation cycle is 24 hours by default. Optionally, to suppress order jitter caused by batch submissions, the batch size can be set to a maximum of 128 entries. If this is exceeded, the submissions are split into batches while maintaining the continuity of the hash chain. If a verification failure or a commitment is broken, this embodiment terminates the addition of new data to the chain and enters the replay verification process until the most recent complete commitment is confirmed.

[0064] In one embodiment, the contribution evaluation module includes an operation type identification subunit, which uses a predefined operation mode library and threshold strategy to classify events into at least one of creation, modification, deletion, coloring, and scaling, and outputs the corresponding operation intensity and impact range labels.

[0065] For example, operation intensity is measured by the effective amount of change to the target voxel or mesh per unit time, normalized to [0,1] by combining path length and stroke pressure (if any); the influence range is labeled by the proportion of voxels or mesh faces in the changed mask, with a default local neighborhood radius of 5-15cm to account for boundary effects. The pattern library threshold is set jointly by offline sample statistics and expert rules, and allows loading difference thresholds by session type; when cross-class mixed operations occur, the class with the dominant sequence accounting for more than 60% is taken as the final category, and if it cannot be determined, it degenerates into modification. In abnormal cases where the intensity or influence range field is missing, classification can still be performed but labeled as unknown, and given the lowest weight in subsequent weighted estimation.

[0066] In one embodiment, the contribution evaluation module includes a feature extraction and weighted estimation subunit, which extracts features such as path complexity, velocity change rate, operation accuracy, affected voxel / mesh ratio, and change retention from trajectory and version differences, and calculates the weight of a single event based on operation type, time decay, and impact range; the cumulative contribution is aggregated according to the streaming approximation of the event marginal gain.

[0067] Similarly, path complexity is characterized by the normalized tortuosity and curvature change of the polyline path. The rate of change of velocity is taken as the sliding statistic of the difference between adjacent sampled velocities. Operational accuracy is measured inversely by the proportion of changes outside the error band within the target region. Retention rate is estimated by the proportion of changes that are still retained in subsequent versions. The default sliding window length is 30s, adjustable from 10s to 120s. Time decay uses exponential decay of the decay base per unit time, with a default base of 0.997, adjustable from 0.995 to 0.999, determined based on the expected proportion of recent activity in the session. The upper bound of the single event weight is s. max By default, the upper quantile (e.g., P95) of the original marginal gain approximation within the window is used to suppress extreme values. To ensure consistency, all statistics are aligned within the same window, and incremental updates are used when the window scrolls to avoid full recalculation.

[0068] The steps for aggregating cumulative contributions using a streaming approximation of event marginal gains include:

[0069] On the global sequence generated by the operation sequence reconstruction and conflict resolution subunit, each event and its user identifier, timestamp, scope of impact and type label are read in the order of event arrival;

[0070] Based on the spatial trajectory and version differences of the event, the set of voxels / mesh that were touched and the coverage ratio are labeled, and the extracted features, including the intensity, accuracy and velocity changes of the event, are associated.

[0071] For a single event, based on the local version difference of its influence domain and the extracted features, the marginal gain of the event is approximated, and interval pruning and normalization are performed. If a subsequent overlay or rollback event occurs, an entry to be deducted is registered for the historical event.

[0072] Optionally, local version differentiation is obtained by comparing the modified mask with the target version mask, using voxel or mesh-level differences and similarities statistics; normalization uses in-session quantile scaling as the default caliber, mapping to the [-1,1] interval to accommodate negative deduction. Registered deduction entries record the original event identifier, the coverage ratio, and the corresponding negative gain percentage, with a minimum retention period of 15 minutes to ensure disputes can be reviewed. If local differentiation data is missing, the statistics of the most recent successful differentiation are used as a safety net estimate and marked as low confidence.

[0073] When reading in the k-th event, update the cumulative contribution of the user u to which the event belongs as follows:

[0074]

[0075] Among them, C u (t k ) indicates at time t k The cumulative contribution of user u after processing the k-th event, δ∈(0,1] represents the time decay base, δ is the decay base per unit time, Δt k Measured in the same units, C represents the time interval since the last update of the cumulative contribution for user u. u (t k-1 ) represents the cumulative contribution at the time of the last update, s k This represents the approximate marginal gain of the k-th event (it can be positive, and negative when covering / rollback is offset);

[0076] In this embodiment, Δt k Calculated from the effective update time difference between two consecutive updates to the same user, if a large number of late events occur, Δt k Use the smaller of the arrival time and the logical time to reduce jitter. k The value is modulated by the operation type coefficient and the scope of influence. The negative items generated by deletion and overwrite are allowed to account for no more than 70% of the positive items in the user's most recent window, in order to prevent a single anomaly from completely erasing historical contributions.

[0077] In the formula:

[0078] in, s represents the approximate original marginal gain of the event. max >0 indicates the upper bound of the clipping, and clip(x,a,b) means restricting x to the interval [a,b].

[0079] Furthermore, s max By default, the window is in view. The P95 setting is adjustable from P90 to P99, and the setting is based on controlling the instantaneous impact of extreme operations on equity shares; when the number of participants in the session is less than 3, to improve stability, s max Degenerates to a fixed constant and is broadcast at the start of the session. Original marginal gain. The results are obtained by taking the local version difference and feature vector of the influence domain as input, and using a linear or piecewise linear combination. The combination coefficients are derived from offline labeled samples and expert scoring calibration. If the combination coefficients are missing or insufficiently sampled, the estimation is reduced to relying solely on the proportion of the influence range and the degree of retention.

[0080] Online single-channel update, late and overwrite events are corrected by negative deduction, and when δ<1 and |s k |When bounded, the cumulative value has an upper bound;

[0081] When subsequent events overlap or prune the historical influence domain, negative values ​​are generated according to the overlap ratio. k Entries are entered into the update as new events, diluting historical gains without requiring a full replay;

[0082] Optionally, the overlap ratio is calculated based on the voxel cross-union ratio (CUNR) of the changed mask; when the CUNR is below 5%, deductions are ignored to reduce noise; when the CUNR is above 80% and initiated by the same user, the upper limit of the deduction ratio is relaxed to 100% to support self-rollback. To avoid deduction storms, the number of deductions for the same historical event is limited to no more than 3 by default; any excess will be merged into one equivalent deduction.

[0083] To avoid offset caused by high-frequency operations by a single user, the cumulative contribution of each user can be scaled or normalized in batches within the sliding window. The normalization only applies to the display and settlement input and does not write back the original cumulative amount.

[0084] In this embodiment, quantile scaling defaults to median-quartile normalization, and the display and settlement inputs are updated every 30 seconds. The group granularity of batch normalization is taken from the set of online users in the session, with a minimum group size of 3. If the group size is lower than 3, batch normalization is disabled to avoid overfitting. In the event of extreme outliers, the display weight of that user in the current window can be temporarily reduced to 50% to maintain page stability, and will be automatically restored after the window closes.

[0085] Once the relative change in cumulative contribution exceeds the threshold and passes through the freeze window, a snapshot of the current cumulative contribution is submitted to the smart contract module to drive share updates and revenue settlement. Similarly, the default relative change threshold is 2%-5%, and the default freeze window is 10-30 minutes, dynamically adjusted based on transaction activity and on-chain transaction fees. If a significant dispute flag appears within the freeze window, the share update is postponed once and merged at the end of the window to reduce the probability of on-chain rollback.

[0086] Specifically, the event-level gain evaluation is coupled with the single-step recursive time decay to form a lightweight online aggregation mechanism; the marginal gain does not rely on full replay or global optimal distribution, but uses the influence domain and version difference for local approximation, combined with the intensity and range information obtained by feature extraction, to generate event benefit terms that can be directly superimposed; the recursive update means that each time only requires constant memory and constant calculation, which is suitable for high-frequency interaction scenarios, and the time decay parameter is used to reduce the impact of long-term contributions on the current share, making it easy to introduce recent activity factors;

[0087] Coverage deduction avoids backtracking and recalculation by encoding conflict effects as negative events into the same recursion. In actual deployment, the deduction threshold can be linked with the freeze window to balance responsiveness and stability. Cross-user standardization is used to suppress the offset caused by differences in operation frequency, making the cumulative amount comparable among different users and providing a stable input for subsequent contract allocation.

[0088] In one embodiment, the contribution evaluation module includes an operation sequence reconstruction and conflict resolution subunit, which uses a collaborative editing protocol running on the client and server to generate globally verifiable event sequence identifiers, and selects the version to be retained based on priority and timestamp strategy and records the reasons for the decision when a conflict is detected.

[0089] In one embodiment, the smart contract module includes an equity update subunit and a settlement subunit; when the relative change in cumulative contribution exceeds a preset threshold, the on-chain equity share is updated after the freeze window expires; the settlement subunit automatically distributes transaction revenue based on the current equity share and retains a versioned allocation snapshot to support backtracking;

[0090] In this embodiment, the equity share update call is set to no more than 6 times per hour to control costs, and the minimum granularity of a single update is 0.1 percentage points; the allocation snapshot includes a share vector summary and a trigger event window identifier, and the retention period is no less than 180 days. Optionally, if on-chain congestion or cost exceeding the threshold is detected in the current period, the update is delayed and submitted in batches during the next idle period, with a batch submission limit of 3 updates to limit state growth.

[0091] In one embodiment, the system further includes a dispute resolution module, which can retrieve on-chain operation commitments, contribution evaluation logs and contract version snapshots, replay the contribution calculation process within a specified time window and generate an audit report with a verifiable summary for dispute resolution;

[0092] Optionally, the replay window is 15-60 minutes by default. The audit report includes a summary of the event sequence, a summary of the version number of key parameters, and a numerical summary of the share difference after replay. The numerical summary is presented as a percentage and includes a window hash for alignment. If some logs are missing, the minimum executable approach is to replay using on-chain commitments and existing local cache, and the missing segments and estimated ranges are marked in the report.

[0093] In one embodiment, the system includes a privacy protection module for separating raw data containing biometrics from operational metadata used for contribution assessment locally, uploading only de-identified or digested metadata, and generating a zero-knowledge proof to prove that the metadata satisfies validity rules and corresponds to the signing subject.

[0094] Furthermore, biometric information, including but not limited to electromyography of gestures, eye movement trajectories, and facial region features, is all stored locally by default, with only a summary of operation metadata related to contribution assessment being output. The maximum local cache duration is 72 hours by default to allow for dispute replay, and it is automatically cleared after the expiration period. When the terminal's computing power is insufficient to generate proof within the target latency, it can temporarily store the proof in the submission queue and process it in batches during idle periods, with a maximum batch size of 64 entries.

[0095] The steps to generate zero-knowledge proofs include:

[0096] At the terminal, the original data containing sensitive information is extracted into metadata m for evaluation, commitment M is calculated, and the signing entity generates a signature σ on m. The original sensitive data is not transmitted outside.

[0097] The validity rules are organized into a set of decidable constraints (type and range, structural consistency, reference relationship, time window, etc.), and compiled offline into decision logic that can be executed by the circuit.

[0098] Let the common input be (M, pk, Θ), where the rule version is identified by the commitment Θ, and the witness is (m, σ, r);

[0099] When constructing the circuit, the judgment corresponding to the signature body and the validity rule are merged into the same relation, and a zero-knowledge proof is given on the proof side for the following relation:

[0100]

[0101] in, The circuit represents the constraint relationship, M represents the metadata commitment, pk represents the public key of the signing subject, Θ represents the commitment or version identifier of the validity rule, m represents local metadata, σ represents the digital signature of m, Com(·;·) represents the commitment function, r represents the locally generated random factor, Verify(·) represents the Boolean value output by the signature verification algorithm, and Φ represents the constraint relationship within the circuit. Θ (·) indicates the judgment result of the rule set in the circuit; a single relationship binds identity and rules simultaneously, public inputs do not reveal witness details, and rules can be updated in a versioned manner without changing the commitments already on the chain;

[0102] The proof is generated locally, and the on-chain verification key is fixed in the contract. Once the verification is successful, it is considered that the metadata was issued by the subject corresponding to the public key and meets the current rules.

[0103] In this embodiment, the version consistency of the rule commitment is checked before verification. If the commitment does not match, verification is rejected and an event identifier is recorded. The upper limit of the public input length and the proof size is pre-configured according to the on-chain transaction limit, and by default does not exceed 50% of the gas limit of a single transaction. In the event of proof verification failure or timeout, the relevant metadata does not participate in the current share update, but local evidence is retained for subsequent replay.

[0104] The dispute resolution module uses the same rule commitment Θ and commitment M for replay verification, and outputs a verifiable summary for audit reports;

[0105] Specifically, this model integrates identity binding and rule compliance within the same circuit. The public input consists only of the commitment, the subject's public key, and the rule commitment, avoiding the exposure of any sensitive fields on the chain. The rule set is abstracted into a decidable function, and its version is identified by the commitment, allowing rule replacement to be accomplished by updating the commitment without affecting the verifiability of existing data. Signature verification and commitment consistency jointly ensure the consistency between metadata and the signing subject, thus providing a joint assertion on source and compliance without revealing the plaintext. This scheme is naturally integrated with the dispute resolution process: in case of disagreement, verification can be replayed by referencing the same commitment and public key, without the auditor needing to access the plaintext. Due to the unified circuit relationship, the proof logic has composable and upgradeable implementation space, allowing finer-grained rule items to be gradually compiled into the same decision function, thereby maintaining the closed-loop consistency and evolution capability of the system.

[0106] This embodiment also provides a metaverse experience interaction method based on blockchain technology, combined with Figure 2 As shown, it includes:

[0107] Step S1: Collect and verify user operation events and trajectories;

[0108] Step S2: Perform type identification and sequence reconstruction of the events;

[0109] Step S3: Based on streaming marginal contribution estimation, aggregate the cumulative contribution of each user;

[0110] Step S4: When the cumulative contribution changes meet the update conditions, update the on-chain equity share through the smart contract and automatically settle the revenue.

[0111] Step S5: Upon receiving a dispute request, replay the calculation within the specified window and output a verifiable audit report;

[0112] For example, the default maximum number of concurrent dispute requests is 5; requests exceeding this limit will be queued. The default maximum timeout for a single replay is 120 seconds; exceeding this time will result in a partial replay report with the incomplete portion marked. To prevent abuse, the maximum number of disputes per user within 24 hours can be set to 3; exceeding this limit requires waiting for a cooldown period before another dispute can be initiated.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0114] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of this application and form different embodiments. For example, all the embodiments above can be used in any combination. The information disclosed in this background section is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

Claims

1. A metaverse experience interaction system based on blockchain technology, characterized in that, include, The behavior acquisition module is used to acquire user operation events and spatial trajectories on the target digital work in a unified coordinate system at a configurable sampling frequency. The contribution evaluation module couples event sequence reconstruction with identity verification, performs streaming calculation of the marginal contributions of multiple users, and outputs the cumulative contribution. The smart contract module generates and updates the on-chain equity share and revenue settlement rules corresponding to the work based on the accumulated contribution, and executes them in the blockchain network; The contribution evaluation module includes feature extraction and weighted estimation sub-units. It extracts features such as path complexity, velocity change rate, operation accuracy, affected voxel / mesh ratio, and change retention from trajectory and version differences, and calculates the weight of a single event based on operation type, time decay, and impact range. The cumulative contribution is aggregated using a streaming approximation of the event's marginal gain; The aggregation steps include: On the global sequence generated by the operation sequence reconstruction and conflict resolution subunit, each event and its user identifier, timestamp, scope of impact and type label are read in the order of event arrival; Based on the spatial trajectory and version differences of the event, the set of voxels / mesh that were touched and the coverage ratio are labeled, and the extracted features, including the intensity, accuracy and velocity changes of the event, are associated. For a single event, based on the local version difference of its influence domain and the extracted features, the marginal gain approximation of the event is obtained, and interval pruning and normalization are performed. If a subsequent overlay or rollback event occurs, a deduction entry is registered for the historical event corresponding to the single event. Reading the first When an event occurs, update the user to which the event belongs as follows: Cumulative contribution: ， in, Indicates time After processing the first After the event, the user The cumulative contribution Indicates the time decay base. The decay base per unit time, Measured in the same unit, indicating since the last time for the user The time interval since the cumulative contribution was updated. This indicates the cumulative contribution at the time of the last update. Indicates the first Approximate marginal gain of an event; In the formula: , in, This represents an approximation of the original marginal gain of the event. Indicates the upper limit of the clipping. Indicates will Limited to the range Inside; Online single-channel updates, late and overwrite events corrected by negative deduction, and in and When bounded, the cumulative value has an upper bound; When subsequent events overlap or prune the historical impact domain, negative values ​​are generated according to the overlap ratio. The entry is entered into the update as a new event; Within the sliding window, the cumulative contribution of each user is scaled or normalized in batches. The normalization only applies to the display and settlement input and does not write back the original cumulative amount. Once the relative change in cumulative contribution exceeds the threshold and passes through the freeze window, a snapshot of the current cumulative contribution is submitted to the smart contract module to drive share updates and revenue settlement. The contribution assessment module includes operation sequence reconstruction and conflict resolution sub-units. It adopts a collaborative editing protocol running on the client and server to generate globally verifiable event sequence identifiers, and selects the version to be retained based on priority and timestamp strategy when a conflict is detected, and records the reasons for the decision.

2. The metaverse experience interaction system based on blockchain technology as described in claim 1, characterized in that, The behavior acquisition module includes: an input interface adaptation subunit, a trajectory recording subunit, and an event integrity verification subunit; the trajectory recording subunit obtains the three-dimensional coordinates and attitude sequence through the client rendering engine or input device callback, and performs interpolation or packet loss annotation for missing sampling points.

3. The metaverse experience interaction system based on blockchain technology as described in claim 1, characterized in that, The behavior collection module also includes an event timestamp and integrity subunit, which generates a signed timestamp for each operation based on a blockchain time reference and a local secure clock, and builds a hash chain in the order of arrival to form an immutable operation commitment.

4. The metaverse experience interaction system based on blockchain technology as described in claim 1, characterized in that, The contribution evaluation module includes an operation type identification subunit, which uses a predefined operation mode library and threshold strategy to classify events into at least one of creation, modification, deletion, coloring, and scaling, and outputs the corresponding operation intensity and impact range labels.

5. The metaverse experience interaction system based on blockchain technology as described in claim 1, characterized in that, The smart contract module includes an equity update subunit and a settlement subunit. When the relative change in cumulative contribution exceeds a preset threshold, the on-chain equity share is updated after the freeze window expires. The settlement subunit automatically distributes transaction revenue based on the current equity share and retains a versioned allocation snapshot to support backtracking.

6. The metaverse experience interaction system based on blockchain technology as described in claim 1, characterized in that, The system also includes a dispute resolution module, which retrieves on-chain operation commitments, contribution evaluation logs, and contract version snapshots, replays the contribution calculation process within a specified time window, and generates an audit report with a verifiable summary for dispute resolution.

7. The metaverse experience interaction system based on blockchain technology as described in claim 1, characterized in that, The system includes a privacy protection module, which separates the raw data containing biometrics from the operational metadata used for contribution assessment locally, uploads only the de-identified or digested metadata, and generates zero-knowledge proofs to prove that the metadata satisfies the validity rules and corresponds to the signing subject; The steps for generating zero-knowledge proofs include: At the terminal, the raw data containing sensitive information is extracted into metadata for evaluation. Calculate commitment And the signatory shall verify Generate signature Original sensitive data will not be disclosed. The validity rules are organized into a set of decisionable constraints and compiled offline into decision logic that can be executed by the circuit. Let the common input be The rule version is based on a commitment Identification, witnessing ; When constructing the circuit, the judgment corresponding to the signature body and the validity rule are merged into the same relation, and a zero-knowledge proof is given on the proof side for the following relation: ， in, This indicates the constraint relationships within the circuit. Indicates metadata commitment, The public key representing the signing entity. This indicates a commitment or version identifier to the validity of the rule. Represents local metadata. Indicates to Digital signature, Represents the commitment function, Represents a locally generated random factor. This represents the Boolean value output by the signature verification algorithm. This indicates the determination result of the rule set in the circuit; The proof is generated locally, and the on-chain verification key is fixed in the contract. Once the verification is successful, it is considered that the metadata was issued by the subject corresponding to the public key and meets the current rules. The dispute resolution module references the same rule commitment. With promise Perform replay verification and output a verifiable summary for use in the audit report.

8. A metaverse experience interaction method based on blockchain technology, based on the metaverse experience interaction system based on blockchain technology as described in any one of claims 1 to 7, characterized in that, include: Step S1: Collect and verify user operation events and trajectories; Step S2: Perform type identification and sequence reconstruction of the events; Step S3: Based on streaming marginal contribution estimation, aggregate the cumulative contribution of each user; Step S4: When the cumulative contribution changes meet the update conditions, update the on-chain equity share through the smart contract and automatically settle the revenue. Step S5: Upon receiving a dispute request, replay the calculation within the specified window and output a verifiable audit report.

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