Music work copyright blockchain authentication and transaction tracing platform and method thereof

Abstract

This application relates to the field of monitoring platform technology and discloses a blockchain-based authentication and transaction tracing platform for music copyright. The application provides a comprehensive technical solution for music copyright blockchain authentication and transaction tracing, encompassing everything from the source of rights confirmation to transaction execution, from off-chain storage to on-chain evidence preservation, and from a single platform to a cross-chain ecosystem. Compared to traditional technologies, this system improves upon traditional methods in ensuring the authenticity of rights confirmation, maintaining file availability, optimizing on-chain resource utilization, supporting complex ownership structures, automating accountability, enhancing cross-platform interoperability, and increasing the credibility of judicial evidence. It constructs a complete, logically rigorous, and practically deployable blockchain-based authentication and transaction tracing technology system for music copyright.

Description

Technical Field

[0001] This application relates to the field of monitoring platform technology, and in particular to a blockchain-based authentication and transaction tracing platform and method for music copyright. Background Technology

[0002] With the rapid development of internet technology and the digital content industry, digital copyright protection has gradually become an important research direction in the fields of information security and digital asset management. Among digital copyright protection technologies, blockchain technology, due to its decentralized, immutable, and traceable characteristics, is widely used in the copyright confirmation and transaction management of digital works such as music and film. Blockchain-based music copyright authentication and transaction traceability platforms typically digitize musical works, generating fingerprints or hash values, and write the corresponding copyright information, timestamps, and transaction records into the blockchain. This enables the confirmation of ownership, recording of transaction processes, and subsequent copyright traceability, providing technical support for the authorized use and infringement protection of musical works.

[0003] In the copyright management of musical works involving multiple rights holders, existing technologies have the following significant shortcomings: First, traditional copyright registration systems lack the ability to structurally express complex ownership relationships, failing to clearly record the rights derivative relationships between different types of works such as original works, adaptations, and samples, resulting in unclear ownership proportions and blurred authorization boundaries in multi-rights holder scenarios; second, existing blockchain copyright systems mostly adopt a uniform evidence storage strategy, failing to dynamically adjust the frequency and details of on-chain data based on the credibility of the work, resulting in both wasted on-chain resources and insufficient protection for low-credibility works; third, the collaborative confirmation process among multiple rights holders lacks automated revenue distribution rules. The recording and enforcement mechanisms are flawed, leading to frequent and difficult-to-trace disputes over the distribution of profits among rights holders. Furthermore, the rigid contract enforcement rules in the transaction process prevent flexible adjustments to transaction parameters based on the complex ownership status and real-time risk levels of works by multiple rights holders. When infringement or abnormal transactions occur, the determination of liability relies on manual investigation, which is inefficient and inaccurate. Finally, the lack of mutual recognition of ownership information between different blockchain platforms results in fragmented ownership information when works by multiple rights holders are transferred across platforms. Moreover, the inconsistent judicial acceptance standards for existing evidence materials lead to high costs of evidence conversion and difficulties in determining the validity of evidence when ownership disputes involving multiple rights holders enter judicial proceedings. Summary of the Invention

[0004] This application proposes a blockchain-based authentication and transaction tracing platform and method for music copyright, which improves upon the following aspects: ensuring the authenticity of rights confirmation, maintaining file availability, improving the efficiency of on-chain resource utilization, supporting complex ownership, automating the level of liability tracing, cross-platform interoperability, and the credibility of judicial evidence. It constructs a complete, logically rigorous, and practically deployable blockchain-based authentication and transaction tracing technology system for music copyright, thereby solving the problems mentioned in the background technology.

[0005] To achieve the above objectives, this application adopts the following technical solution: a blockchain-based authentication and transaction traceability platform for music copyright, comprising:

[0006] The work access module receives audio files and work information, performs format verification and standardization processing, verifies the authenticity of the uploading entity's identity, and records timestamps and operation trace data;

[0007] The fingerprint modeling module extracts audio fingerprints, melodic features, and structural features to generate multi-dimensional digital identifiers and establish a rights relationship graph to distinguish between original, adapted, and sampled works.

[0008] The off-chain storage module stores audio files in a distributed storage environment, records the storage location and access method, predicts availability risks based on node historical data, and triggers warnings or performs automatic migration when the risk threshold is exceeded.

[0009] The trusted verification module periodically performs integrity checks and availability tests to evaluate the trustworthiness of storage nodes and generate quantitative evaluation values ​​that include consistency, accessibility, and node trustworthiness.

[0010] The differentiated on-chain module dynamically adjusts the frequency and content of status summary on-chain based on quantitative evaluation values, and adds verification details and increases the on-chain frequency for works with low credibility.

[0011] The collaborative rights confirmation module performs collaborative rights confirmation for multiple rights holders based on the rights relationship graph, records the ownership ratio, authorization scope and benefit distribution rules of each rights holder, and links the rights confirmation results to the rights relationship graph on the blockchain;

[0012] The adaptive transaction module dynamically adjusts the execution rules of the transaction contract based on the copyright status, rights relationship graph and quantitative evaluation value. In the event of transaction abnormalities or infringement events, it automatically traces the responsible parties and liability paths based on on-chain records.

[0013] The cross-chain adaptation module provides a cross-chain mutual recognition interface to support mutual verification and conversion of ownership information on different chains. It also has a built-in judicial node access function, which brings the judicial verification rules to the evidence storage stage and generates evidence materials that meet the standards for judicial acceptance.

[0014] Furthermore, the work access module includes a receiving verification unit and a verification record unit;

[0015] The receiving and verification unit is used to obtain the audio file of the music work and the corresponding work information, perform format verification processing on the obtained audio file, and perform normalization processing on the audio file if the verification passes, and output the normalized audio file and the corresponding work information to the verification record unit.

[0016] The verification record unit is used to obtain the identity information of the uploading subject, perform authenticity verification on the obtained identity information, generate timestamp data and operation trace data of the uploading behavior when the verification is successful, associate and store the timestamp data, operation trace data and audio file, and output the verification result and associated stored data to the fingerprint modeling module.

[0017] Furthermore, the fingerprint modeling module includes a feature extraction unit and a map construction unit;

[0018] The feature extraction unit is used to acquire the normalized audio file, perform audio fingerprint extraction, melody feature extraction and structural feature extraction on the audio file, generate a feature dataset corresponding to the audio file, and output the feature dataset to the graph construction unit;

[0019] The graph construction unit is used to receive feature datasets, generate multidimensional digital identifiers based on feature datasets, establish a rights relationship graph between different versions of works based on rights statement information, distinguish between original, adapted and sampled works, and output the multidimensional digital identifiers and rights relationship graphs to the off-chain storage module.

[0020] Furthermore, the off-chain storage module includes a storage record unit and a predictive response unit;

[0021] The storage recording unit is used to acquire audio files and their corresponding multi-dimensional digital identifiers, store the audio files in a distributed storage environment, record the storage location identifier and access method identifier of the audio files, form a storage information dataset, and output the storage information dataset to the prediction response unit.

[0022] The prediction response unit is used to acquire the storage information dataset and the historical operation data of the storage node, predict the availability risk level of the audio file based on the historical operation data, trigger an early warning message or perform an automatic migration operation when the risk level exceeds a preset threshold, and output the prediction results and operation records to the trusted verification module.

[0023] Furthermore, the trusted verification module includes a consistency detection unit and an evaluation generation unit;

[0024] The consistency detection unit acquires audio files and storage information datasets, verifies the integrity of audio files, detects the availability of access method identifiers contained in the storage information datasets, generates consistency and accessibility results, and outputs the detection results to the evaluation generation unit.

[0025] The evaluation generation unit obtains the detection results and historical operation data of the storage nodes, calculates the trust level of the storage nodes, and generates a quantitative evaluation value that includes consistency, accessibility and node trust, based on the detection results. The quantitative evaluation value is then output to the differentiated on-chain module.

[0026] Furthermore, the differentiated on-chain module includes a strategy determination unit and an on-chain execution unit;

[0027] The strategy determination unit obtains the quantitative evaluation value, analyzes the credibility level corresponding to the quantitative evaluation value, determines the on-chain frequency and on-chain content strategy of the state summary based on the credibility level, increases the on-chain frequency and adds verification details for low credibility levels, and outputs the determined on-chain strategy to the on-chain execution unit.

[0028] The on-chain execution unit obtains the on-chain strategy and state summary data, generates state summary information according to the on-chain strategy, writes the state summary information into the blockchain, and outputs the on-chain result to the collaborative rights confirmation module.

[0029] Furthermore, the collaborative rights confirmation module includes a rights confirmation execution unit and a result on-chain unit;

[0030] The rights confirmation execution unit is used to receive the rights relationship graph, perform collaborative rights confirmation processing on musical works involving multiple rights holders, record the ownership ratio information, authorization scope information and revenue distribution rules information of each rights holder, generate a rights confirmation result dataset, and output the rights confirmation result dataset to the result on-chain unit.

[0031] The result on-chain unit is used to receive the confirmation result dataset and the rights relationship graph, associate the confirmation result dataset and the rights relationship graph to form associated confirmation information, write the associated confirmation information into the blockchain, and output the on-chain result to the adaptive transaction module.

[0032] Furthermore, the rule adjustment unit performs the transaction contract execution parameter adjustment process based on copyright status data, rights relationship graph data, and quantitative evaluation value data, generates adjusted transaction rule parameter data, and outputs the adjusted transaction rule parameter data to the cross-chain adaptation module;

[0033] The responsibility tracing unit performs transaction anomaly or infringement event detection and processing. When an anomaly or event is detected, it performs responsibility tracing processing based on on-chain recorded data, generates responsibility tracing result data, and outputs the responsibility tracing result data to the cross-chain adaptation module.

[0034] Furthermore, the cross-chain adaptation module includes a mutual recognition interface unit and a judicial adaptation unit;

[0035] The mutual recognition interface unit provides a cross-chain mutual recognition interface to support the mutual verification and conversion of rights confirmation information on different chains, and outputs the mutual recognition results to the judicial adaptation unit.

[0036] The judicial adaptation unit has a built-in judicial node access function, which brings the judicial verification rules to the evidence storage stage and generates evidence materials that meet the judicial acceptance standards.

[0037] This application also provides a method for blockchain authentication and transaction traceability of music copyrights, with the following specific steps:

[0038] S1. Receive audio files and work information, perform format verification and standardization processing, verify the identity of the uploading entity, and record timestamps and operation traces;

[0039] S2. Extract audio fingerprints, melodic features, and structural features from the standardized audio files, generate multi-dimensional digital identifiers, establish a rights relationship graph based on rights statement information, and distinguish between original, adapted, and sampled works.

[0040] S3. Store audio files in a distributed storage environment, record the storage location and access method, predict the availability risk level based on the node's historical operation data, and trigger an early warning or perform automatic migration when the risk level exceeds a preset threshold.

[0041] S4. Periodically perform integrity checks on audio files, perform availability checks on storage locations and access methods, calculate node trustworthiness levels by combining historical node operation data, and generate quantitative evaluation values ​​that include consistency, accessibility, and node trustworthiness.

[0042] S5. Obtain quantitative evaluation values ​​and analyze the credibility level. Based on the credibility level, dynamically adjust the on-chain frequency and content of the status summary. For low credibility levels, increase the on-chain frequency and add verification details. Write the status summary information into the blockchain.

[0043] S6. Based on the rights relationship graph, perform collaborative rights confirmation for works by multiple rights holders, record the ownership ratio, authorization scope and revenue distribution rules of each rights holder, and write the rights confirmation results into the blockchain after associating them with the rights relationship graph.

[0044] S7. Obtain copyright status, rights relationship graph and quantitative evaluation value data, dynamically adjust the execution parameters of the transaction contract, monitor the transaction execution process, and trace the responsible party and responsibility path based on on-chain records when an abnormal transaction or infringement event is detected.

[0045] S8 provides a cross-chain mutual recognition interface to support mutual verification and conversion of ownership information on different chains, and has a built-in judicial node access function to bring the judicial verification rules to the evidence storage stage and generate evidence materials that meet the judicial acceptance standards.

[0046] The beneficial effects of this invention are as follows:

[0047] This application provides a blockchain-based authentication and transaction tracing platform and method for music copyright, achieving a complete technical solution from the source of rights confirmation to transaction execution, from off-chain storage to on-chain evidence preservation, and from a single platform to a cross-chain ecosystem. Compared with traditional technical methods, this system improves in terms of ensuring the authenticity of rights confirmation, maintaining file availability, on-chain resource utilization efficiency, support for complex ownership structures, automation of liability tracing, cross-platform interoperability, and the credibility of judicial evidence. It constructs a complete, logically rigorous, and practically deployable blockchain-based authentication and transaction tracing technology system for music copyright. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort:

[0049] Figure 1 This is a flowchart of the system steps.

[0050] Figure 2 This is a flowchart of the method. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1, as Figure 1 A blockchain-based platform for music copyright authentication and transaction traceability, including:

[0053] The work access module receives audio files and work information, performs format verification and standardization processing, verifies the authenticity of the uploading entity's identity, and records timestamps and operation trace data;

[0054] The fingerprint modeling module extracts audio fingerprints, melodic features, and structural features to generate multi-dimensional digital identifiers and establish a rights relationship graph to distinguish between original, adapted, and sampled works.

[0055] The off-chain storage module stores audio files in a distributed storage environment, records the storage location and access method, predicts availability risks based on node historical data, and triggers warnings or performs automatic migration when the risk threshold is exceeded.

[0056] The trusted verification module periodically performs integrity checks and availability tests to evaluate the trustworthiness of storage nodes and generate quantitative evaluation values ​​that include consistency, accessibility, and node trustworthiness.

[0057] The differentiated on-chain module dynamically adjusts the frequency and content of status summary on-chain based on quantitative evaluation values, and adds verification details and increases the on-chain frequency for works with low credibility.

[0058] The collaborative rights confirmation module performs collaborative rights confirmation for multiple rights holders based on the rights relationship graph, records the ownership ratio, authorization scope and benefit distribution rules of each rights holder, and links the rights confirmation results to the rights relationship graph on the blockchain;

[0059] The adaptive transaction module dynamically adjusts the execution rules of the transaction contract based on the copyright status, rights relationship graph and quantitative evaluation value. In the event of transaction abnormalities or infringement events, it automatically traces the responsible parties and liability paths based on on-chain records.

[0060] The cross-chain adaptation module provides a cross-chain mutual recognition interface to support mutual verification and conversion of ownership information on different chains. It also has a built-in judicial node access function, which brings the judicial verification rules to the evidence storage stage and generates evidence materials that meet the standards for judicial acceptance.

[0061] In this embodiment, the work access module achieves the beneficial effect of ensuring the credibility of music work data sources and the uniformity of formats by using technical means such as verifying the authenticity of the uploader's identity and recording timestamp operation traces. This optimizes the problems of false uploads, identity theft, and non-standard initial data in the blockchain copyright system.

[0062] The fingerprint modeling module achieves the beneficial effects of unique identification of musical works and structured expression of complex ownership relationships through multi-dimensional extraction of audio fingerprints, melodic features and structural features and construction of rights relationship graphs. It optimizes the technical problem that traditional fingerprint technology cannot distinguish between original works, legal adaptations and sampled works, and supports refined copyright management.

[0063] The off-chain storage module achieves the beneficial effect of long-term stable availability of off-chain files through the construction of a distributed storage environment, availability risk prediction, and automatic migration early warning. It also optimizes the technical defects of off-chain storage file loss, inaccessibility, and passive response, and realizes proactive storage maintenance.

[0064] The trusted verification module achieves the beneficial effect of dynamically reflecting the true state of off-chain files through technical means such as periodic integrity verification, availability detection, and quantitative evaluation of storage node trust. It optimizes the technical problems of inconsistency between on-chain evidence storage and actual off-chain state, and the existence of risk windows in the verification cycle.

[0065] The differentiated on-chain module achieves the beneficial effect of efficient allocation of on-chain resources through the technical means of credibility grading assessment and dynamic adjustment of on-chain strategy. It optimizes the high cost and low efficiency problem caused by using a uniform on-chain frequency for all works, and achieves a balance between evidence strength and cost.

[0066] The collaborative rights confirmation module, through the technical means of multi-rights holder collaborative rights confirmation and income distribution rule recording driven by rights relationship graph, has achieved the beneficial effect of clearly defining complex ownership relationships, and optimized the technical problems of unclear ownership ratio, unclear authorization scope and frequent income distribution disputes in multi-rights holder scenarios.

[0067] The adaptive transaction module achieves the beneficial effects of flexible and efficient transaction execution and automated liability determination through the technical means of dynamically adjusting transaction rules driven by copyright status and credibility assessment and automatic liability tracing. It optimizes the technical limitations of rigid execution of traditional transaction contracts and reliance on manual judgment for liability attribution.

[0068] The cross-chain adaptation module provides technical means to advance the rules of judicial nodes through the cross-chain mutual recognition interface, which has achieved the beneficial effects of cross-platform mutual recognition of rights confirmation information and evidence materials meeting judicial acceptance standards, thus optimizing the technical obstacles of blockchain copyright system platform silos and low judicial acceptance.

[0069] This system, through the collaborative work of eight modules, forms a complete technical solution encompassing the entire chain from the source of rights confirmation to transaction execution, from off-chain storage to on-chain evidence preservation, and from a single platform to a cross-chain ecosystem. Compared to traditional technical methods, this system improves upon traditional methods in ensuring the authenticity of rights confirmation, maintaining document availability, optimizing on-chain resource utilization, supporting complex ownership structures, automating accountability, enhancing cross-platform interoperability, and increasing the credibility of judicial evidence. It constructs a technically complete, logically rigorous, and practically deployable blockchain-based authentication and transaction traceability system for music copyright.

[0070] Example 2, as Figure 1 The work access module includes a receiving verification unit and a verification record unit;

[0071] The receiving and verification unit is used to obtain the audio file of the music work and the corresponding work information, perform format verification processing on the obtained audio file, and perform normalization processing on the audio file if the verification passes, and output the normalized audio file and the corresponding work information to the verification record unit.

[0072] The verification record unit is used to obtain the identity information of the uploading subject, perform authenticity verification on the obtained identity information, generate timestamp data and operation trace data of the uploading behavior when the verification is successful, associate and store the timestamp data, operation trace data and audio file, and output the verification result and associated stored data to the fingerprint modeling module.

[0073] In this embodiment: the receiving and verification unit is used to acquire the audio file of the musical work and the corresponding work information, perform format verification processing on the acquired audio file, and perform normalization processing on the audio file if the verification passes, and output the normalized audio file and the corresponding work information to the verification record unit. This unit achieves standardized access to musical work data by performing format verification and normalization processing on the audio file, optimizing data processing anomalies caused by inconsistent formats, ensuring that the audio files entering the system have unified encoding parameters and data structures, and providing a stable data input foundation for subsequent modules.

[0074] The verification and recording unit is used to obtain the identity information of the uploading entity, perform authenticity verification on the obtained identity information, and generate timestamp data and operation trace data of the uploading behavior if the verification is successful. The timestamp data, operation trace data, and audio file are then associated and stored, and the verification result and associated stored data are output to the fingerprint modeling module. This unit, by verifying the authenticity of the uploading entity's identity and recording timestamps and operation traces, achieves traceable management of the source of musical works, optimizes the problems of identity forgery and unauditable behavior in blockchain copyright systems, ensures that the access behavior of each musical work can be effectively traced and verified, and enhances the transparency and auditability of the copyright confirmation process.

[0075] Through the collaborative work of the receiving verification unit and the verification recording unit, the work access module realizes a complete access process for music works, from data format standardization to traceable source. Compared with the traditional approach of simply receiving files without in-depth verification, this module adds identity verification and operation behavior recording mechanisms to the access stage. This optimizes the technical problems of insufficient data source credibility and untraceable behavior, achieving the technical effects of unified data format, verifiable subject identity, and auditable operation behavior, thus building the first line of defense for music work copyright management.

[0076] Example 3, as Figure 1 The fingerprint modeling module includes a feature extraction unit and a graph construction unit;

[0077] The feature extraction unit is used to acquire the normalized audio file, perform audio fingerprint extraction, melody feature extraction and structural feature extraction on the audio file, generate a feature dataset corresponding to the audio file, and output the feature dataset to the graph construction unit;

[0078] The graph construction unit is used to receive feature datasets, generate multidimensional digital identifiers based on feature datasets, establish a rights relationship graph between different versions of works based on rights statement information, distinguish between original, adapted and sampled works, and output the multidimensional digital identifiers and rights relationship graphs to the off-chain storage module.

[0079] In this embodiment, the feature extraction unit acquires the normalized audio file, performs audio fingerprint extraction, melody feature extraction, and structural feature extraction on the audio file, generates a feature dataset corresponding to the audio file, and outputs the feature dataset to the graph construction unit. This unit, by performing multi-dimensional feature extraction on the audio file, achieves a comprehensive characterization of the musical work's content features, optimizes the technical limitations of single feature extraction in fully reflecting the characteristics of the work, and generates a feature dataset containing audio fingerprints, melody features, and structural features, providing a rich data foundation for subsequently constructing a unique identifier for the work.

[0080] The rights graph construction unit receives a feature dataset, generates multidimensional digital identifiers based on the dataset, establishes a rights relationship graph between different versions of the work according to the rights statement information, distinguishes between original, adapted, and sampled works, and outputs the multidimensional digital identifiers and rights relationship graphs to the off-chain storage module. This unit, by generating multidimensional digital identifiers and establishing rights relationship graphs based on the feature dataset, achieves a structured expression of the unique identification of musical works and complex ownership relationships. It optimizes the technical problems of single work identification and chaotic ownership relationships in traditional copyright management, supporting the differentiation and definition of rights for original, adapted, and sampled works.

[0081] Through the collaborative work of the feature extraction unit and the graph construction unit, the fingerprint modeling module realizes a complete processing flow from multi-dimensional feature extraction of audio files to the generation of unique identifiers for works and the construction of rights relationship graphs. Compared with traditional technical methods that rely solely on simple hash values ​​or single features for work identification, this module optimizes the technical shortcomings of high repetition rates of work identifiers and the inability to distinguish between legal adaptations and infringing copies by using multi-dimensional feature extraction and rights relationship modeling. It achieves the technical effects of enhanced uniqueness of work identifiers, clear ownership relationships, and distinguishable work types, providing technical support for accurate ownership and refined management of music copyrights.

[0082] Example 4, as Figure 1 The off-chain storage module includes a storage record unit and a predictive response unit;

[0083] The storage recording unit is used to acquire audio files and their corresponding multi-dimensional digital identifiers, store the audio files in a distributed storage environment, record the storage location identifier and access method identifier of the audio files, form a storage information dataset, and output the storage information dataset to the prediction response unit.

[0084] The prediction response unit is used to acquire the storage information dataset and the historical operation data of the storage node, predict the availability risk level of the audio file based on the historical operation data, trigger an early warning message or perform an automatic migration operation when the risk level exceeds a preset threshold, and output the prediction results and operation records to the trusted verification module.

[0085] In this embodiment, the storage recording unit acquires the audio file and its corresponding multi-dimensional digital identifier, stores the audio file in a distributed storage environment, records the storage location identifier and access method identifier of the audio file, forms a storage information dataset, and outputs the storage information dataset to the prediction response unit. This unit, by storing audio files in a distributed environment and fully recording the storage location and access method, achieves comprehensive archiving of the music work's offline storage information. It optimizes the technical problems of high single-point failure risk and incomplete storage information recording in traditional centralized storage, ensuring that the storage location of each audio file is locatable and the access method is callable, providing a data foundation for subsequent availability prediction.

[0086] The predictive response unit acquires the storage information dataset and historical operational data of storage nodes. Based on this historical data, it predicts the availability risk level of audio files. When the risk level exceeds a preset threshold, it triggers an early warning or performs an automatic migration operation, outputting the prediction results and operation records to the trusted verification module. This unit, by predicting availability risks based on historical node operational data and triggering proactive responses, transforms off-chain storage from passive maintenance to proactive management. It overcomes the technical shortcomings of traditional off-chain storage, such as the lack of risk warnings and the inability to recover lost files. By providing early warnings or automatically migrating files before they become unavailable, it ensures the continuous accessibility of audio files.

[0087] Through the collaborative work of the storage recording unit and the predictive response unit, the off-chain storage module achieves a complete process of secure storage, complete information recording, availability risk prediction, and proactive maintenance of audio files for musical works. Compared with the limitations of traditional centralized storage, such as concentrated risks, lack of predictive capabilities, and delayed maintenance response, this module optimizes the technical problems of insufficient storage reliability and weak availability assurance capabilities through a distributed storage architecture and predictive maintenance mechanism. It achieves technical effects such as transparent storage location, controllable access methods, proactive risk warning, and proactive maintenance response, thereby improving the overall reliability and availability assurance capabilities of the off-chain storage link.

[0088] Example 5, as Figure 1 The trusted verification module includes a consistency detection unit and an evaluation generation unit;

[0089] The consistency detection unit acquires audio files and storage information datasets, verifies the integrity of audio files, detects the availability of access method identifiers contained in the storage information datasets, generates consistency and accessibility results, and outputs the detection results to the evaluation generation unit.

[0090] The evaluation generation unit obtains the detection results and historical operation data of the storage nodes, calculates the trust level of the storage nodes, and generates a quantitative evaluation value that includes consistency, accessibility and node trust, based on the detection results. The quantitative evaluation value is then output to the differentiated on-chain module.

[0091] In this embodiment, the consistency detection unit acquires the audio file and storage information dataset, verifies the integrity of the audio file, detects the availability of the access method identifiers contained in the storage information dataset, generates consistency and accessibility results, and outputs the detection results to the evaluation generation unit. This unit achieves dual verification of the off-chain file content and access channels by performing integrity verification on the audio file and availability detection on the access method identifiers contained in the storage information dataset. This optimizes the limitations of traditional technologies that only verify the file itself while ignoring the availability of the storage environment, generating detection results that reflect file consistency and access feasibility, providing basic data support for subsequent quantitative evaluation.

[0092] The evaluation and generation unit acquires the detection results and historical operational data of storage nodes, calculates the trustworthiness level of the storage nodes, and generates a quantitative evaluation value that includes consistency, accessibility, and node trustworthiness based on the detection results. This quantitative evaluation value is then output to the differentiated on-chain module. By introducing historical operational data of storage nodes and calculating node trustworthiness levels, this unit achieves a dynamic quantitative evaluation of the trustworthiness of the storage environment. It overcomes the technical shortcomings of traditional technologies, such as the lack of node-level trustworthiness evaluation and the static nature of evaluation results. Furthermore, by combining consistency detection results with the generated quantitative evaluation value containing multi-dimensional indicators, it provides a precise decision-making basis for differentiated on-chain strategies.

[0093] Through the collaborative work of the consistency detection unit and the evaluation generation unit, the trusted verification module achieves a technological upgrade from single file integrity verification to a comprehensive evaluation across three dimensions: file, access channel, and storage node. Compared to the limitations of traditional techniques that only perform periodic file hash verification, lack dynamic evaluation of the storage environment, and whose evaluation results cannot directly guide on-chain decisions, this module optimizes the technical problems of single verification dimensions, static evaluation results, and insufficient decision-making basis through a dual detection mechanism and a dynamic quantitative evaluation mechanism. It achieves the technical effects of synchronous monitoring of file status and storage environment status, dynamic quantification of node trustworthiness, and evaluation results that can directly drive on-chain strategies, thereby improving the accuracy of trustworthiness evaluation and decision support capabilities of the off-chain storage link.

[0094] Example 6, as Figure 1 The differentiated on-chain module includes a strategy determination unit and an on-chain execution unit;

[0095] The strategy determination unit obtains the quantitative evaluation value, analyzes the credibility level corresponding to the quantitative evaluation value, determines the on-chain frequency and on-chain content strategy of the state summary based on the credibility level, increases the on-chain frequency and adds verification details for low credibility levels, and outputs the determined on-chain strategy to the on-chain execution unit.

[0096] The on-chain execution unit obtains the on-chain strategy and state summary data, generates state summary information according to the on-chain strategy, writes the state summary information into the blockchain, and outputs the on-chain result to the collaborative rights confirmation module.

[0097] In this embodiment: The strategy determination unit acquires a quantitative evaluation value, analyzes the credibility level corresponding to the quantitative evaluation value, determines the on-chain frequency and on-chain content strategy of the state summary based on the credibility level, increases the on-chain frequency and adds verification details for low credibility levels, and outputs the determined on-chain strategy to the on-chain execution unit. This unit achieves differentiated management of on-chain resource allocation by dynamically adjusting the on-chain strategy based on the quantitative evaluation value. This optimizes the resource waste and insufficient protection of low credibility works caused by using a uniform on-chain frequency for all works in traditional technologies. It automatically matches the corresponding on-chain frequency and content details according to the credibility level of the work, improving the efficiency of on-chain resource utilization and the rationality of evidence strength.

[0098] The on-chain execution unit acquires on-chain strategy and state summary data, generates state summary information based on the on-chain strategy, writes the state summary information into the blockchain, and outputs the on-chain result to the collaborative rights confirmation module. This unit, by executing differentiated on-chain strategies and writing state summary information into the blockchain, effectively implements the on-chain strategy and solidifies the evidence storage results on the blockchain. It optimizes the technical shortcomings of the disconnect between the on-chain strategy and the execution phase, and the inability to provide timely feedback on the on-chain results. This ensures that the differentiated strategies formulated by the strategy determination unit are accurately executed, guaranteeing that low-credibility works receive high-frequency, high-detail evidence storage services, while high-credibility works receive appropriate evidence storage services.

[0099] Through the collaborative work of the strategy determination unit and the on-chain execution unit, the differentiated on-chain module achieves a technological shift from a uniform on-chain frequency to differentiated dynamic adjustments. Compared to the limitations of traditional technologies such as fixed-frequency on-chaining, averaged resource allocation, and inability to flexibly adjust according to the risk level of the work, this module optimizes the technical problems of low on-chain resource utilization efficiency, insufficient evidence protection for low-credibility works, and excessive evidence storage for high-credibility works through credibility grading assessment and automatic strategy execution mechanisms. It achieves the technical effects of on-demand resource allocation, matching evidence strength with the risk level of the work, and overall optimization of on-chain costs, thereby improving the flexibility and economy of the blockchain copyright system in the evidence storage process.

[0100] Example 7, as Figure 1 The collaborative rights confirmation module includes a rights confirmation execution unit and a result on-chain unit;

[0101] The rights confirmation execution unit is used to receive the rights relationship graph, perform collaborative rights confirmation processing on musical works involving multiple rights holders, record the ownership ratio information, authorization scope information and revenue distribution rules information of each rights holder, generate a rights confirmation result dataset, and output the rights confirmation result dataset to the result on-chain unit.

[0102] The result on-chain unit is used to receive the confirmation result dataset and the rights relationship graph, associate the confirmation result dataset and the rights relationship graph to form associated confirmation information, write the associated confirmation information into the blockchain, and output the on-chain result to the adaptive transaction module.

[0103] In this embodiment, the rights confirmation execution unit receives the rights relationship graph, performs collaborative rights confirmation processing on musical works involving multiple rights holders, records the ownership ratio, authorization scope, and revenue distribution rules of each rights holder, generates a rights confirmation result dataset, and outputs the dataset to the results on-chain unit. This unit, by performing collaborative rights confirmation on multi-rights holder scenarios and recording ownership ratios, authorization scope, and revenue distribution rules, achieves structured processing of complex ownership relationships. It optimizes the technical problems of ambiguous ownership definitions and unclear revenue distribution rules in traditional copyright management, ensuring that each rights holder's share of rights and authorization boundaries are clearly recorded, providing accurate ownership basis for copyright transactions and revenue distribution.

[0104] The on-chain result unit receives the rights confirmation result dataset and the rights relationship graph, associates the dataset with the graph to form associated rights confirmation information, writes this information to the blockchain, and outputs the on-chain result to the adaptive transaction module. This unit, by associating the rights confirmation result dataset with the rights relationship graph and writing it to the blockchain, achieves on-chain synchronization and solidification of the rights confirmation result and its ownership background information. This optimizes the technical shortcomings of traditional technologies, such as the separation of rights confirmation results from rights source information and the inability to trace ownership background, ensuring the integrity and immutability of the rights confirmation information and providing reliable ownership background support for subsequent transaction processes.

[0105] Through the collaborative work of the rights confirmation execution unit and the results on-chain unit, the collaborative rights confirmation module achieves a complete process from the collaborative definition of complex ownership relationships to the complete solidification of rights confirmation information on the blockchain. Compared with the technical limitations of traditional methods, such as low efficiency in handling multiple rights holders' rights confirmation, incomplete ownership information recording, and the separation of rights confirmation results from rights background information, this module optimizes the technical problems of insufficient confirmation accuracy, fragmented ownership information, and low credibility of rights confirmation results in complex ownership scenarios through collaborative rights confirmation mechanisms and associated on-chain mechanisms. It achieves the technical effects of clarifying the ownership relationships of multiple rights holders, ensuring complete on-chaining of rights confirmation information, and making the ownership background traceable, thereby improving the copyright system's ability to handle complex ownership relationships and ensuring the credibility of rights confirmation results.

[0106] Example 8, as Figure 1 The adaptive trading module includes a rule adjustment unit and a responsibility tracking unit;

[0107] The rule adjustment unit performs the adjustment of the transaction contract execution parameters based on copyright status data, rights relationship graph data and quantitative evaluation value data, generates the adjusted transaction rule parameter data, and outputs the adjusted transaction rule parameter data to the cross-chain adaptation module;

[0108] The responsibility tracing unit performs transaction anomaly or infringement event detection and processing. When an anomaly or event is detected, it performs responsibility tracing processing based on on-chain recorded data, generates responsibility tracing result data, and outputs the responsibility tracing result data to the cross-chain adaptation module.

[0109] In this embodiment, the rule adjustment unit performs parameter adjustment processing on the transaction contract execution regulations based on copyright status data, rights relationship graph data, and quantitative assessment value data, generating adjusted transaction rule parameter data, and outputting the adjusted transaction rule parameter data to the cross-chain adaptation module. This unit dynamically adjusts the transaction contract execution regulations parameters by comprehensively considering multi-dimensional data such as copyright status, rights relationship graph, and quantitative assessment value, achieving flexible adaptation of transaction rules. This optimizes the technical shortcomings of traditional rigid execution of transaction contracts, which cannot flexibly adjust according to the actual risk level and ownership status of the work. It enables transaction rules to automatically optimize as the work's status changes, improving the rationality and efficiency of transaction execution.

[0110] The liability tracing unit performs transaction anomaly or infringement event detection and processing. Upon detecting anomalies or events, it traces the responsible party and liability path based on on-chain record data, generates liability tracing result data, and outputs the liability tracing result data to the cross-chain adaptation module. This unit proactively monitors the transaction process and automatically triggers liability tracing when anomalies or infringement events occur, achieving real-time perception and rapid response to transaction risks. It optimizes the technical shortcomings of traditional copyright transactions, such as delayed anomaly detection and reliance on manual investigation for liability determination, ensuring that the responsible party and liability path can be quickly located based on immutable on-chain records, thus improving the efficiency and accuracy of liability determination.

[0111] Through the collaborative work of the rule adjustment unit and the responsibility tracing unit, the adaptive transaction module achieves closed-loop management of dynamic adaptation of transaction rules and automatic responsibility tracing. Compared with the technical limitations of traditional methods, such as fixed transaction contracts, passive and lagging anomaly handling, and low efficiency in responsibility determination, this module optimizes the technical problems of insufficient transaction flexibility, weak risk response capabilities, and high responsibility determination costs by introducing a multi-dimensional data-driven rule adjustment mechanism and a responsibility tracing mechanism based on on-chain records. It achieves the technical effects of intelligent adjustment of transaction rules according to the status of the work, real-time monitoring of transaction risks, and automatic location of the responsible party, thereby improving the intelligence level and risk prevention and control capabilities of the copyright transaction process.

[0112] Example 9, as Figure 1 The cross-chain adaptation module includes a mutual recognition interface unit and a judicial adaptation unit;

[0113] The mutual recognition interface unit provides a cross-chain mutual recognition interface to support the mutual verification and conversion of rights confirmation information on different chains, and outputs the mutual recognition results to the judicial adaptation unit.

[0114] The judicial adaptation unit has a built-in judicial node access function, which brings the judicial verification rules to the evidence storage stage and generates evidence materials that meet the judicial acceptance standards.

[0115] In this embodiment, the mutual recognition interface unit provides a cross-chain mutual recognition interface to support the mutual verification and conversion of rights confirmation information on different chains, and outputs the mutual recognition results to the judicial adaptation unit. This unit, by constructing a standardized cross-chain mutual recognition interface, achieves the interoperability and mutual recognition of rights confirmation information between different blockchain platforms, optimizing the technical problem of rights confirmation information being unable to flow across platforms due to platform silos in blockchain copyright systems. This enables the mutual verification and conversion of music works' rights confirmation information on different chains, improving the system's interoperability and openness.

[0116] The judicial adaptation unit, with built-in judicial node access functionality, brings judicial verification rules to the evidence storage stage, generating evidence materials that meet judicial acceptance standards. By bringing judicial verification rules to the forefront and incorporating judicial node access functionality, this unit aligns the evidence storage process with judicial standards. It overcomes the technical shortcomings of traditional blockchain-based evidence storage, such as low judicial acceptance and the need for post-conversion, ensuring that evidence materials generated during the storage stage directly meet judicial acceptance requirements and enhancing the legal validity of blockchain-based evidence.

[0117] Through the collaborative work of the mutual recognition interface unit and the judicial adaptation unit, the cross-chain adaptation module achieves the dual functions of cross-platform mutual recognition of rights confirmation information and pre-implementation of judicial standards. Compared with the technical limitations of traditional blockchain technologies, such as closed and isolated blockchain platforms and poor judicial adaptability of stored evidence, this module optimizes the technical problems of high data barriers between platforms and low judicial conversion rates of evidence through standardized mutual recognition interfaces and pre-implementation judicial rules. It achieves the technical effect of unimpeded flow of rights confirmation information across platforms and direct judicial acceptance of stored evidence, thereby enhancing the ecological openness and judicial practicality of the blockchain copyright system.

[0118] This application also provides methods for blockchain authentication and transaction traceability of music copyrights, such as... Figure 2 The specific steps are as follows:

[0119] S1. Receive audio files and work information, perform format verification and standardization processing, verify the identity of the uploading entity, and record timestamps and operation traces;

[0120] S2. Extract audio fingerprints, melodic features, and structural features from the standardized audio files, generate multi-dimensional digital identifiers, establish a rights relationship graph based on rights statement information, and distinguish between original, adapted, and sampled works.

[0121] S3. Store audio files in a distributed storage environment, record the storage location and access method, predict the availability risk level based on the node's historical operation data, and trigger an early warning or perform automatic migration when the risk level exceeds a preset threshold.

[0122] S4. Periodically perform integrity checks on audio files, perform availability checks on storage locations and access methods, calculate node trustworthiness levels by combining historical node operation data, and generate quantitative evaluation values ​​that include consistency, accessibility, and node trustworthiness.

[0123] S5. Obtain quantitative evaluation values ​​and analyze the credibility level. Based on the credibility level, dynamically adjust the on-chain frequency and content of the status summary. For low credibility levels, increase the on-chain frequency and add verification details. Write the status summary information into the blockchain.

[0124] S6. Based on the rights relationship graph, perform collaborative rights confirmation for works by multiple rights holders, record the ownership ratio, authorization scope and revenue distribution rules of each rights holder, and write the rights confirmation results into the blockchain after associating them with the rights relationship graph.

[0125] S7. Obtain copyright status, rights relationship graph and quantitative evaluation value data, dynamically adjust the execution parameters of the transaction contract, monitor the transaction execution process, and trace the responsible party and responsibility path based on on-chain records when an abnormal transaction or infringement event is detected.

[0126] S8 provides a cross-chain mutual recognition interface to support mutual verification and conversion of ownership information on different chains, and has a built-in judicial node access function to bring the judicial verification rules to the evidence storage stage and generate evidence materials that meet the judicial acceptance standards.

[0127] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A blockchain-based platform for copyright authentication and transaction traceability of musical works, characterized in that: include: The work access module receives audio files and work information, performs format verification and standardization processing, verifies the authenticity of the uploading entity's identity, and records timestamps and operation trace data; The fingerprint modeling module extracts audio fingerprints, melodic features, and structural features to generate multi-dimensional digital identifiers and establish a rights relationship graph to distinguish between original, adapted, and sampled works. The off-chain storage module stores audio files in a distributed storage environment, records the storage location and access method, predicts availability risks based on node historical data, and triggers warnings or performs automatic migration when the risk threshold is exceeded. The trusted verification module periodically performs integrity checks and availability tests to evaluate the trustworthiness of storage nodes and generate quantitative evaluation values ​​that include consistency, accessibility, and node trustworthiness. The differentiated on-chain module dynamically adjusts the frequency and content of status summary on-chain based on quantitative evaluation values, and adds verification details and increases the on-chain frequency for works with low credibility. The collaborative rights confirmation module performs collaborative rights confirmation for multiple rights holders based on the rights relationship graph, records the ownership ratio, authorization scope and benefit distribution rules of each rights holder, and links the rights confirmation results to the rights relationship graph on the blockchain; The adaptive transaction module dynamically adjusts the execution rules of the transaction contract based on the copyright status, rights relationship graph and quantitative evaluation value. In the event of transaction abnormalities or infringement events, it automatically traces the responsible parties and liability paths based on on-chain records. The cross-chain adaptation module provides a cross-chain mutual recognition interface to support mutual verification and conversion of ownership information on different chains. It also has a built-in judicial node access function, which brings the judicial verification rules to the evidence storage stage and generates evidence materials that meet the standards for judicial acceptance.

2. The music copyright blockchain authentication and transaction traceability platform according to claim 1, characterized in that, The work access module includes a receiving verification unit and a verification record unit; The receiving and verification unit is used to obtain the audio file of the music work and the corresponding work information, perform format verification processing on the obtained audio file, and perform normalization processing on the audio file if the verification passes, and output the normalized audio file and the corresponding work information to the verification record unit. The verification record unit is used to obtain the identity information of the uploading subject, perform authenticity verification on the obtained identity information, generate timestamp data and operation trace data of the uploading behavior when the verification is successful, associate and store the timestamp data, operation trace data and audio file, and output the verification result and associated stored data to the fingerprint modeling module.

3. The music copyright blockchain authentication and transaction traceability platform according to claim 1, characterized in that, The fingerprint modeling module includes a feature extraction unit and a graph construction unit; The feature extraction unit is used to acquire the normalized audio file, perform audio fingerprint extraction, melody feature extraction and structural feature extraction on the audio file, generate a feature dataset corresponding to the audio file, and output the feature dataset to the graph construction unit; The graph construction unit is used to receive feature datasets, generate multidimensional digital identifiers based on feature datasets, establish a rights relationship graph between different versions of works based on rights statement information, distinguish between original, adapted and sampled works, and output the multidimensional digital identifiers and rights relationship graphs to the off-chain storage module.

4. The music copyright blockchain authentication and transaction traceability platform according to claim 1, characterized in that, The off-chain storage module includes a storage record unit and a predictive response unit; The storage recording unit is used to acquire audio files and their corresponding multi-dimensional digital identifiers, store the audio files in a distributed storage environment, record the storage location identifier and access method identifier of the audio files, form a storage information dataset, and output the storage information dataset to the prediction response unit. The prediction response unit is used to acquire the storage information dataset and the historical operation data of the storage node, predict the availability risk level of the audio file based on the historical operation data, trigger an early warning message or perform an automatic migration operation when the risk level exceeds a preset threshold, and output the prediction results and operation records to the trusted verification module.

5. The music copyright blockchain authentication and transaction traceability platform according to claim 1, characterized in that, The trusted verification module includes a consistency detection unit and an evaluation generation unit; The consistency detection unit acquires audio files and storage information datasets, verifies the integrity of audio files, detects the availability of access method identifiers contained in the storage information datasets, generates consistency and accessibility results, and outputs the detection results to the evaluation generation unit. The evaluation generation unit obtains the detection results and historical operation data of the storage nodes, calculates the trust level of the storage nodes, and generates a quantitative evaluation value that includes consistency, accessibility and node trust, based on the detection results. The quantitative evaluation value is then output to the differentiated on-chain module.

6. The music copyright blockchain authentication and transaction traceability platform according to claim 1, characterized in that, The differentiated on-chain module includes a strategy determination unit and an on-chain execution unit; The strategy determination unit obtains the quantitative evaluation value, analyzes the credibility level corresponding to the quantitative evaluation value, determines the on-chain frequency and on-chain content strategy of the state summary based on the credibility level, increases the on-chain frequency and adds verification details for low credibility levels, and outputs the determined on-chain strategy to the on-chain execution unit. The on-chain execution unit obtains the on-chain strategy and state summary data, generates state summary information according to the on-chain strategy, writes the state summary information into the blockchain, and outputs the on-chain result to the collaborative rights confirmation module.

7. The music copyright blockchain authentication and transaction traceability platform according to claim 1, characterized in that, The collaborative rights confirmation module includes a rights confirmation execution unit and a result on-chain unit; The rights confirmation execution unit is used to receive the rights relationship graph, perform collaborative rights confirmation processing on musical works involving multiple rights holders, record the ownership ratio information, authorization scope information and revenue distribution rules information of each rights holder, generate a rights confirmation result dataset, and output the rights confirmation result dataset to the result on-chain unit. The result on-chain unit is used to receive the confirmation result dataset and the rights relationship graph, associate the confirmation result dataset and the rights relationship graph to form associated confirmation information, write the associated confirmation information into the blockchain, and output the on-chain result to the adaptive transaction module.

8. The music work copyright blockchain authentication and transaction traceability platform according to claim 1, characterized in that, The adaptive trading module includes a rule adjustment unit and a responsibility tracking unit; The rule adjustment unit performs the adjustment of the transaction contract execution parameters based on copyright status data, rights relationship graph data and quantitative evaluation value data, generates the adjusted transaction rule parameter data, and outputs the adjusted transaction rule parameter data to the cross-chain adaptation module; The responsibility tracing unit performs transaction anomaly or infringement event detection and processing. When an anomaly or event is detected, it performs responsibility tracing processing based on on-chain recorded data, generates responsibility tracing result data, and outputs the responsibility tracing result data to the cross-chain adaptation module.

9. The music copyright blockchain authentication and transaction traceability platform according to claim 1, characterized in that, The cross-chain adaptation module includes a mutual recognition interface unit and a judicial adaptation unit; The mutual recognition interface unit provides a cross-chain mutual recognition interface to support the mutual verification and conversion of rights confirmation information on different chains, and outputs the mutual recognition results to the judicial adaptation unit. The judicial adaptation unit has a built-in judicial node access function, which brings the judicial verification rules to the evidence storage stage and generates evidence materials that meet the judicial acceptance standards.

10. A method for blockchain authentication and transaction traceability of music copyright, characterized in that, The method for blockchain authentication and transaction tracing of music copyright is executed through the music copyright blockchain authentication and transaction tracing platform described in any of claims 1 to 9 above, and the specific steps are as follows: S1. Receive audio files and work information, perform format verification and standardization processing, verify the identity of the uploading entity, and record timestamps and operation traces; S2. Extract audio fingerprints, melodic features, and structural features from the standardized audio files, generate multi-dimensional digital identifiers, establish a rights relationship graph based on rights statement information, and distinguish between original, adapted, and sampled works. S3. Store audio files in a distributed storage environment, record the storage location and access method, predict the availability risk level based on the node's historical operation data, and trigger an early warning or perform automatic migration when the risk level exceeds a preset threshold. S4. Periodically perform integrity checks on audio files, perform availability checks on storage locations and access methods, calculate node trustworthiness levels by combining historical node operation data, and generate quantitative evaluation values ​​that include consistency, accessibility, and node trustworthiness. S5. Obtain quantitative evaluation values ​​and analyze the credibility level. Based on the credibility level, dynamically adjust the on-chain frequency and content of the status summary. For low credibility levels, increase the on-chain frequency and add verification details. Write the status summary information into the blockchain. S6. Based on the rights relationship graph, perform collaborative rights confirmation for works by multiple rights holders, record the ownership ratio, authorization scope and revenue distribution rules of each rights holder, and write the rights confirmation results into the blockchain after associating them with the rights relationship graph. S7. Obtain copyright status, rights relationship graph and quantitative evaluation value data, dynamically adjust the execution parameters of the transaction contract, monitor the transaction execution process, and trace the responsible party and responsibility path based on on-chain records when an abnormal transaction or infringement event is detected. S8 provides a cross-chain mutual recognition interface to support mutual verification and conversion of ownership information on different chains, and has a built-in judicial node access function to bring the judicial verification rules to the evidence storage stage and generate evidence materials that meet the judicial acceptance standards.

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