Cross-platform-based digital copyright transaction method, device and system and medium

Abstract

The invention relates to a cross-platform-based digital copyright transaction method, device and system and a medium, and belongs to the technical field of distributed digital asset management, cross-chain communication protocols, smart contracts and distributed computing. The method comprises the following steps: generating a digital fingerprint through a multi-mode hash algorithm, and storing digital copyright information in an IPFS network in a distributed manner; accessing a CoinGecko TM API (Application Program Interface) by adopting an oracle machine to obtain a market index in real time, and dynamically adjusting the pricing of copyright assets according to the market index and the copyright information; cross-chain asset transfer is realized on the basis of a Polkadot XCMP protocol; graph Protocol is integrated to construct a real-time risk control network, and real-time monitoring and risk assessment of digital copyright transactions are realized; and transaction settlement is automatically executed through the smart contract. According to the invention, the problems of high transaction barrier, rigid pricing mechanism, prominent security risk, difficulty in cross-chain collaboration and the like in the existing digital copyright transaction platform are solved, and efficient circulation and secure transaction of digital copyright assets are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed digital asset management, cross-chain communication protocols, smart contracts and distributed computing, and specifically relates to a cross-platform digital copyright transaction method, device, system and medium. Background Art

[0002] With the booming development of the digital content industry, the digital copyright trading market has gradually become an important economic field, and the creation and distribution of digital content has become faster and more convenient, which has led to the rapid growth of digital copyright assets. With the development of Internet technology, the demand for digital copyright trading is also increasing. Digital copyright owners hope to distribute and sell their works through various channels and platforms, while digital copyright users hope to have more choices and more convenient trading methods.

[0003] However, the existing digital copyright trading platforms still have some technical bottlenecks, which restrict the development of the digital copyright trading market. These technical bottlenecks include: (1) High transaction barriers: There is a serious phenomenon of data islands between different platforms, copyright information is difficult to communicate, the digital copyright trading market is fragmented, and there is a lack of unified trading platforms and standards. This makes it difficult for copyright owners and users to find suitable trading partners, which invisibly increases the cost and time of transactions; (2) Rigid pricing mechanism: Existing digital copyright trading platforms usually rely on manually set fixed rates and cannot dynamically respond to changes in market supply and demand. This pricing mechanism is rigid and cannot reflect the true value of the work. Copyright owners and users cannot flexibly price and negotiate according to market demand and work quality, which limits the fairness and efficiency of transactions. (3) Outstanding security risks: Existing digital copyright trading platforms usually use centralized servers to store and manage digital copyright information. This centralized data architecture has the risk of single-point tampering, is vulnerable to hacker attacks and data leakage, and has the risk of price manipulation in centralized databases, which leads to the inability to effectively protect the rights and interests of copyright owners and users. The above-mentioned technical bottlenecks restrict the development of the digital copyright trading market and hinder the asset circulation and value realization of digital copyrights.

[0004] In a Chinese patent with announcement number CN109727134B and titled “A method and device for copyright trading of images”, a method for copyright trading of images is provided, which uses digital fingerprints and a points system to achieve copyright trading, but it lacks support for multi-party collaboration and cross-chain transactions, and has limited scalability.

[0005] In the Chinese patent with the publication number CN111125778B and the title "A Method and Device for Processing Copyright Transaction Information", it emphasizes the processing of copyright transaction information using smart contracts and blockchain consensus, but it does not solve the problems of cross-platform data synchronization and user privacy protection. Summary of the Invention

[0006] The technical problem that the present invention attempts to solve is how to achieve the efficient circulation and secure transaction of digital copyright assets by constructing a distributed trading network, a dynamic pricing mechanism, and a smart contract system.

[0007] In order to overcome the deficiencies in the prior art and solve the above-mentioned technical problems, the present invention provides a cross-platform-based digital copyright trading method, device, system, and medium, and adopts the following technical solutions.

[0008] In the first aspect, the present invention provides a cross-platform-based digital copyright trading method, and the method includes:

[0009] Step S100: Generate a digital fingerprint through a multimodal hashing algorithm, and distribute and store digital copyright information in the IPFS network;

[0010] Step S200: Use an oracle to access the CoinGecko TM API to obtain the market index in real time, and dynamically adjust the pricing of copyright assets according to the market index and copyright information;

[0011] Step S300: Implement cross-chain asset transfer based on the Polkadot XCMP protocol;

[0012] Step S400: Integrate the Graph Protocol to build a real-time risk control network to achieve real-time monitoring and risk assessment of digital copyright transactions;

[0013] Step S500: Automatically execute transaction settlement through a smart contract.

[0014] Preferably, in step S100, the multimodal hashing algorithm includes: when extracting image features, converting the image from the spatial domain to the frequency domain, separating high-frequency details and low-frequency contour information, and selecting the first 16 alternating current coefficients, while discarding the direct current component.

[0015] Preferably, in step S100, the multimodal hashing algorithm includes: when extracting audio features, extracting 13 mel-frequency cepstral coefficients, and compressing the spectral envelope through discrete cosine transform to retain the acoustic features of the audio.

[0016] Preferably, in step S100, the multimodal hashing algorithm includes: when converting the color space, converting the RGB model to the HSV model.

[0017] In a second aspect, the present invention provides a cross-platform digital copyright trading device, which includes:

[0018] Module M100, configured to: generate digital fingerprints through a multimodal hashing algorithm and distribute and store digital copyright information in the IPFS network;

[0019] Module M200, configured to: access CoinGecko TM using an oracle API to obtain market indices in real time and dynamically adjust the pricing of copyright assets according to the market indices and copyright information;

[0020] Module M300, configured to: implement cross-chain asset transfer based on the Polkadot XCMP protocol;

[0021] Module M400, configured to: integrate the Graph Protocol to build a real-time risk control network to achieve real-time monitoring and risk assessment of digital copyright transactions;

[0022] Module M500, configured to: automatically execute transaction settlement through smart contracts.

[0023] In a third aspect, the present invention provides a computer system, including a processor, a memory, and a computer program stored on the memory and executable by the processor. When the processor runs the computer program, it implements the cross-platform digital copyright trading method as described in the first aspect of the present invention.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements the cross-platform digital copyright trading method as described in the first aspect of the present invention.

[0025] According to the above-provided technical solution, since the present invention uses technical means such as multimodal hashing algorithms, dynamic pricing mechanisms, Polkadot XCMP protocols, and Graph Protocol protocols, it solves technical problems such as high transaction barriers, rigid pricing mechanisms, prominent security risks, and difficult cross-chain collaboration in existing digital copyright trading platforms, and realizes the efficient circulation and secure trading of digital copyright assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 : Block diagram of the steps of the method of the present invention;

[0027] Figure 2 : Block diagram of the modules of the device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To more clearly illustrate the features of the technical solution of the present invention, the present invention will be further elaborated in detail below through specific embodiments and in conjunction with the accompanying drawings.

[0029] As a first embodiment, the present invention provides a cross-platform digital copyright trading method, and the method includes:

[0030] Step S100: Generate digital fingerprints through a multi-modal hashing algorithm and distribute and store digital copyright information in the IPFS network;

[0031] Step S200: Use an oracle to access the CoinGecko TM API to obtain market indices in real time and dynamically adjust the pricing of copyright assets according to market indices and copyright information;

[0032] Step S300: Implement cross-chain asset transfer based on the Polkadot XCMP protocol;

[0033] Step S400: Integrate the Graph Protocol protocol to build a real-time risk control network to achieve real-time monitoring and risk assessment of digital copyright transactions;

[0034] Step S500: Automatically execute transaction settlement through smart contracts.

[0035] Figure 1 The block diagrams of the steps in the above method embodiments are given.

[0036] Regarding the cross-platform digital copyright registration in step S100, the description is as follows:

[0037] Multiple types of data (such as images, audio, video, etc.) are mapped into hash values of a fixed length through a multi-modal hashing algorithm. Digital fingerprints are unique identifiers generated by this algorithm and are used to verify the integrity and uniqueness of digital copyright information. IPFS (InterPlanetary File System) is a distributed file storage system that allows users to distribute and store files on multiple nodes, achieving decentralized storage and sharing of data. Distributing and storing digital copyright information in the IPFS network can achieve decentralized storage and sharing of data to ensure the uniqueness and integrity verification of digital copyright information, improve the reliability and security of digital copyright information, which helps to solve the problem of high transaction barriers in existing digital copyright trading platforms and promotes the interconnection and sharing of digital copyright information.

[0038] Step S100 is the core link of digital copyright registration, and its technical meaning can be analyzed from the following three levels:

[0039] First, as a hash calculation method that can process multiple data types (such as text, images, audio, video, etc.) simultaneously, the multi-modal hash algorithm generates a globally unique identifier by fusing the features of different modal data. The fusion method usually integrates the multi-modal feature matrix into a unified hash value through a weighted fusion algorithm to ensure that unique identifiers can be generated for copyright content in different carrier forms;

[0040] Second, the generated digital fingerprints have the functions of uniqueness verification and legal evidentiary effect: in terms of uniqueness verification, it has the characteristics of anti-collision (theoretically impossible to find two different contents generating the same hash value) and sensitivity (any minor change in the content will cause a complete change in the hash value); in terms of legal evidentiary effect, through blockchain deposit, it can be used as electronic evidence for judicial proof;

[0041] Third, in the distributed storage mechanism of the IPFS network, copyright files (such as PDF, MP4, etc.) generate a unique CID (Content ID) through content addressing, and at the same time, the file is split and stored in multiple IPFS nodes globally, and each node only stores part of the data. This storage mechanism not only avoids the risk of server downtime in traditional cloud storage, but also because the file hash value is bound to the CID, if the content is tampered with, the CID will become invalid, thus preventing the file from being tampered with, and can also achieve fast content transmission through the P2P network (such as the BitTorrent protocol), so as to achieve efficient distribution.

[0042] In short, step S100 here constructs a unique digital copyright identifier through multi-modal feature fusion, combines the IPFS decentralized storage to achieve the immutability and efficient distribution of copyright information, and provides a reliable data basis for subsequent cross-chain transactions, dynamic pricing, and risk monitoring. This step can solve the problems of "high transaction barriers" (realize cross-platform interoperability through standardized digital fingerprints) and "prominent security risks" (resist attacks through decentralized storage) in the existing technology.

[0043] Regarding the dynamic pricing mechanism in step S200, the explanation is as follows:

[0044] An oracle is a mechanism used to connect a smart contract to a data source in the external world, and the CoinGecko TM API is a set of APIs (Application Programming Interface) provided by the CoinGecko TM platform for accessing the cryptocurrency market data it includes, including real-time price, market value, trading volume and other information. By adopting an oracle to access CoinGecko TMThe API can obtain market indices in real time, enabling a dynamic pricing mechanism to achieve real-time pricing and transaction matching of digital copyright assets. This means that the trading price of digital copyrights can be flexibly adjusted according to market demand and work quality, reflecting the true value of the work. This helps to solve the problem of rigid pricing mechanisms in existing digital copyright trading platforms and improve the fairness and efficiency of transactions.

[0045] Regarding the execution of cross-chain transactions in step S300, the following is an explanation:

[0046] As a blockchain protocol, Polkadot aims to achieve interoperability between different blockchain networks. And XCMP (Cross-Chain Message Passing) is a protocol in Polkadot that allows the transfer of messages and assets between different blockchain networks. By implementing cross-chain asset transfer based on the XCMP protocol, seamless transfer of digital copyright assets between different blockchain networks can be achieved, improving the efficiency and flexibility of digital copyright transactions. This helps to solve the problem of difficult cross-chain collaboration in existing digital copyright trading platforms, expand the scale and scope of digital copyright transactions, break down the barriers between different blockchain networks, and achieve seamless transfer of copyright assets between different blockchain networks.

[0047] Regarding the real-time risk monitoring in step S400, the following is an explanation:

[0048] As a data indexing protocol for building decentralized applications, the Graph Protocol can enable real-time query and monitoring of blockchain data and is used to build a real-time risk control network. By integrating the Graph Protocol to build a real-time risk control network, real-time monitoring and risk assessment of digital copyright transactions can be achieved, fraud in transactions can be detected and processed in a timely manner, and the security of transactions can be improved. This helps to detect and process fraud in transactions in a timely manner and improve the security of transactions. This helps to solve the prominent problem of security risks in existing digital copyright trading platforms and protect the rights and interests of digital copyright owners and users.

[0049] Regarding the transaction settlement in step S500, the following is an explanation:

[0050] Use smart contracts to automatically execute the settlement process of digital copyright transactions. Smart contracts run on the blockchain, ensuring the transparency, immutability, efficiency, and fairness of transactions. By reducing manual intervention and intermediate links, smart contracts improve transaction efficiency, reduce costs, and safeguard the interests of all parties to the transaction. This technical feature provides a more secure, efficient, and reliable solution for digital copyright transactions.

[0051] As a preferred embodiment, in step S100, the multi-modal hashing algorithm includes: when extracting image features, converting the image from the spatial domain to the frequency domain, separating high-frequency details and low-frequency contour information, and selecting the first 16 alternating current coefficients while discarding the direct current component.

[0052] In this preferred embodiment, when performing DCT (Discrete Cosine Transform) on the image content, the image is converted from the spatial domain, i.e., the brightness distribution of pixels, to the frequency domain, i.e., the distribution of different frequency components in the image. During this process, only the first 16 AC coefficients (AC Coefficients) are extracted. This is because the AC coefficients reflect the changes in the image texture, and the first 16 coefficients concentrate the intermediate frequency information that is sensitive to the human eye. Different from traditional hashing algorithms (such as SHA-256) that only focus on the overall hash of the file, this embodiment realizes fine-grained copyright authentication (such as distinguishing local differences between similar images) by selectively extracting local features.

[0053] As a preferred embodiment, in step S100, the multi-modal hashing algorithm includes: when extracting audio features, extracting 13 Mel-frequency cepstral coefficients and compressing the spectral envelope through discrete cosine transform to retain the acoustic features of the audio.

[0054] In this preferred embodiment, when extracting MFCC (Mel-Frequency Cepstral Coefficients) parameters from the audio signal, the auditory characteristics of the human ear are simulated, and 13 MFCC parameters, which are the characteristic parameters closely related to human perception in the audio signal, are extracted. Then, the spectral envelope is compressed through DCT transform to retain the core acoustic features of the audio (such as speech, music, etc.), so as to solve the problem that traditional hashing algorithms cannot process audio content and achieve accurate recognition of sound copyright through auditory perception features (such as distinguishing audio segments with similar tones and rhythms).

[0055] As a preferred embodiment, in step S100, the multi-modal hashing algorithm includes: when converting the color space, converting the RGB model to the HSV model.

[0056] In this preferred embodiment, the color model is converted from the red-green-blue (RGB) space to the hue, saturation, value (HSV) space. When calculating the chromaticity moments, statistical features such as the mean, variance, and skewness of the hue (H) and saturation (S) in the HSV space are statistically calculated. Here, the deficiency of pure text hashing is compensated by the color distribution features, which is applicable to copyright authentication of mixed text and image content (such as visually dominant works like comics and posters).

[0057] As a second embodiment, the present invention provides a cross-platform digital rights trading device, which includes:

[0058] Module M100, configured to: generate digital fingerprints through a multimodal hashing algorithm and distribute and store digital rights information in the IPFS network;

[0059] Module M200, configured to: access CoinGecko TM API to obtain market indices in real time and dynamically adjust the pricing of copyright assets according to the market indices and copyright information;

[0060] Module M300, configured to: implement cross-chain asset transfer based on the Polkadot XCMP protocol;

[0061] Module M400, configured to: integrate the Graph Protocol protocol to build a real-time risk control network and realize real-time monitoring and risk assessment of digital rights trading;

[0062] Module M500, configured to: automatically execute transaction settlement through smart contracts.

[0063] Figure 2 Block diagrams of each module in the above system embodiment are given.

[0064] As a third embodiment, the present invention provides a computer system, including a processor, a memory, and a computer program stored on the memory and executable by the processor. When the processor runs the computer program, it implements the cross-platform digital rights trading method as described in the first embodiment of the present invention.

[0065] As a fourth embodiment, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements the cross-platform digital rights trading method as described in the first embodiment of the present invention.

[0066] Finally, it should be noted that although the present invention has been exemplarily described through specific embodiments, it does not constitute a limitation on the scope of patent protection of the present invention. Those skilled in the art should understand that various equivalent substitutions and optimization improvements can still be made to the specific embodiments of the present invention, and any substitution and improvement without departing from the spirit of the present invention should be covered within the scope of patent protection of the present invention.

Claims

1. A cross-platform digital rights trading method, characterized in that, The method includes: Step S100: Generate digital fingerprints through a multimodal hashing algorithm and distributively store digital copyright information in the IPFS network; Step S200: Use an oracle to access CoinGecko TM API to obtain the market index in real time, and dynamically adjust the pricing of copyright assets based on the market index and copyright information; Step S300: Implement cross-chain asset transfer based on the Polkadot XCMP protocol; Step S400: Integrate the Graph Protocol to build a real-time risk control network to achieve real-time monitoring and risk assessment of digital copyright transactions; Step S500: Automatically execute transaction settlement through smart contracts.

2. The method according to claim 1, wherein In step S100, the multimodal hashing algorithm includes: when extracting image features, converting the image from the spatial domain to the frequency domain, separating high-frequency details and low-frequency contour information, and selecting the first 16 alternating current coefficients while discarding the direct current component.

3. The method according to claim 1, wherein In step S100, the multimodal hashing algorithm includes: when extracting audio features, extracting 13 Mel-frequency cepstral coefficients and compressing the spectral envelope through discrete cosine transform to retain the acoustic features of the audio.

4. The method according to claim 1, wherein In step S100, the multimodal hashing algorithm includes: when converting color spaces, converting the RGB model to the HSV model.

5. A cross-platform digital rights trading device, characterized in that, The device includes: Module M100, which is used to: generate digital fingerprints through a multimodal hashing algorithm and distributively store digital copyright information in the IPFS network; Module M200, for: accessing CoinGecko using an oracle TM obtaining market indices in real time through an API, and dynamically adjusting the pricing of copyright assets based on the market indices and copyright information; Module M300, which is used to: implement cross-chain asset transfer based on the Polkadot XCMP protocol; Module M400, which is used to: integrate the Graph Protocol to build a real-time risk control network to achieve real-time monitoring and risk assessment of digital copyright transactions; Module M500, which is used to: automatically execute transaction settlement through smart contracts.

6. A computer system, comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, characterized in that, When the processor runs the computer program, it implements the cross-platform digital copyright trading method as described in claim 1 or 2.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it implements the cross-platform digital copyright trading method as described in claim 1 or 2.

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