Public data element asset right confirmation method and system based on multi-dimensional features and ownership chain

Abstract

The invention discloses a public data element asset right confirmation method based on multi-dimensional features and an ownership chain and a public data element asset right confirmation system based on the multi-dimensional features and the ownership chain, and the method comprises the steps: carrying out the data analysis processing of original data of a target data asset from a public credit information center database, and obtaining a data element object; performing multi-dimensional feature fingerprint generation processing based on the data element object to obtain a multi-dimensional feature fingerprint; on the basis of the multi-dimensional feature fingerprint, the data element object and the digital identity, carrying out ownership chain chaining processing to obtain a target ownership chain; when an ownership transfer event request corresponding to the target ownership chain is received, carrying out ownership change registration processing based on the ownership transfer event request to obtain a new target ownership chain; and when a query request is received, performing ownership information query processing based on the multi-dimensional feature fingerprint corresponding to the query request to obtain an ownership circulation history.

Description

Technical Field

[0001] This invention belongs to the field of data asset management technology, and more specifically, relates to a method for confirming the ownership of public data element assets based on multi-dimensional features and ownership chains, and a system for confirming the ownership of public data element assets based on multi-dimensional features and ownership chains. Background Technology

[0002] As the core hub for collecting, managing, and applying public credit information, the Public Credit Information Center processes data from a wide range of sources. After cleaning, integration, and processing, this data forms highly valuable data products that serve the construction of the social credit system.

[0003] In related technologies, after data is collected from multiple sources and enters the public credit information center, it undergoes multiple rounds of complex processing, blurring the ownership boundaries of the original data. When data products are used or disputes arise, it is difficult to accurately trace the original provider, processing path, and rights of each party involved in each data element, leading to unclear division of responsibilities and benefits regarding "who created, who contributed, and who benefited." Furthermore, existing methods for determining ownership often target the entire dataset or file, such as using hash calculations to ensure the file has not been tampered with. However, a data product is often composed of tens of thousands of fine-grained data elements. When it is necessary to determine ownership of individual data elements, coarse-grained methods based on the file level are ineffective.

[0004] Therefore, how to develop a precise, reliable, and traceable method for determining ownership of fine-grained data elements in order to solve the problems of unclear ownership, difficulty in tracing, and security risks in the current management of public data assets is a key issue of concern to those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for confirming the ownership of public data element assets based on multidimensional features and ownership chains, so as to solve the problems of unclear ownership, difficulty in tracing and security risks in the current management of public data assets.

[0006] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method for confirming ownership of public data element assets based on multi-dimensional features and ownership chains, comprising: The raw data of the target data assets is parsed and processed from the database of the public credit information center to obtain data element objects; A multidimensional feature fingerprint is generated based on the data element object. Based on the multidimensional feature fingerprint, the data element object, and the digital identity, the ownership chain is processed to obtain the target ownership chain. When a request for ownership transfer event corresponding to the target ownership chain is received, ownership change registration processing is performed based on the ownership transfer event request to obtain a new target ownership chain. When a query request is received, ownership information is queried based on the multi-dimensional feature fingerprint corresponding to the query request to obtain the ownership transfer history.

[0007] Optionally, the raw data of the target data asset is parsed and processed from the public credit information center database to obtain data element objects, including: Obtain the original data of the target data asset from the database of the public credit information center; The raw data is subjected to metadata recognition processing to obtain recognition information; Based on the identification information and the file parsing engine, the original data is processed to extract structured data. Data elements are extracted from the structured data to obtain data elements; The extracted data elements are formatted to obtain the data element object.

[0008] Optionally, a multidimensional feature fingerprint generation process is performed based on the data element object to obtain a multidimensional feature fingerprint, including: Content feature extraction is performed on the data element object to obtain content feature values; Structural features are extracted from the data element objects to obtain structural feature values; Context feature extraction is performed on the data element object to obtain context feature values; The content feature value, the structural feature value, and the context feature value are subjected to fingerprint synthesis processing to obtain the multidimensional feature fingerprint.

[0009] Optionally, based on the multi-dimensional feature fingerprint, the data element object, and the digital identity, on-chain processing of ownership is performed to obtain the target ownership chain, including: Based on the multidimensional feature fingerprint, the data element object, and the digital identity, a credential construction process is performed to obtain an initial ownership credential. The initial ownership certificate is digitally signed based on the data provider's private key to obtain an initial ownership certificate with a digital signature. An initial ownership chain is created based on the multidimensional feature fingerprint, and the initial ownership certificate with digital signature is used as the genesis block of the initial ownership chain to obtain the target ownership chain.

[0010] Optionally, upon receiving a request for a transfer of ownership corresponding to the target ownership chain, ownership change registration is performed based on the request to obtain a new target ownership chain, including: When a request for ownership transfer event corresponding to the target ownership chain is received, the ownership transfer event request is verified based on a digital signature. Once the verification is successful, a new block is requested based on the ownership transfer event; The new block is added to the end of the target ownership chain to obtain the new target ownership chain, and then broadcast to all nodes.

[0011] Optionally, upon receiving a query request, ownership information query processing is performed based on the multi-dimensional feature fingerprint corresponding to the query request to obtain the ownership transfer history, including: Fingerprint features are generated based on the original data corresponding to the query request to obtain the multidimensional feature fingerprint to be queried. Based on the multidimensional feature fingerprint to be queried, the corresponding ownership chain is retrieved in the distributed storage; When the corresponding ownership chain is found, all blocks on the chain are traversed sequentially starting from the genesis block. The ownership events recorded in each block are parsed out to obtain the ownership transfer history.

[0012] This application also provides a public data element asset ownership confirmation system based on multi-dimensional features and ownership chains, including: The data acquisition module is used to perform data parsing and processing on the raw data of the target data assets from the public credit information center database to obtain data element objects; The feature fingerprint generation module is used to perform multi-dimensional feature fingerprint generation processing based on the data element object to obtain a multi-dimensional feature fingerprint. The data on-chain processing module is used to perform ownership chain on-chain processing based on the multi-dimensional feature fingerprint, the data element object, and the digital identity to obtain the target ownership chain. The ownership change processing module is used to perform ownership change registration processing based on the ownership transfer event request when it receives the ownership transfer event request corresponding to the target ownership chain, so as to obtain a new target ownership chain. The ownership query module is used to perform ownership information query processing based on the multi-dimensional feature fingerprint corresponding to the query request when a query request is received, so as to obtain the ownership transfer history.

[0013] Optionally, the data on-chain processing module is specifically used to construct a certificate based on the multi-dimensional feature fingerprint, the data element object, and the digital identity to obtain an initial ownership certificate; to digitally sign the initial ownership certificate based on the private key of the data provider to obtain an initial ownership certificate with a digital signature; to create an initial ownership chain based on the multi-dimensional feature fingerprint, and to use the initial ownership certificate with the digital signature as the genesis block of the initial ownership chain to obtain the target ownership chain.

[0014] This application provides a method for confirming the ownership of public data element assets based on multi-dimensional features and ownership chains, comprising: performing data parsing processing on the original data of the target data asset from the public credit information center database to obtain data element objects; performing multi-dimensional feature fingerprint generation processing on the data element objects to obtain multi-dimensional feature fingerprints; performing ownership chain on-chain processing based on the multi-dimensional feature fingerprints, the data element objects, and digital identities to obtain a target ownership chain; when a ownership transfer event request corresponding to the target ownership chain is received, performing ownership change registration processing based on the ownership transfer event request to obtain a new target ownership chain; when a query request is received, performing ownership information query processing based on the multi-dimensional feature fingerprint corresponding to the query request to obtain the ownership transfer history.

[0015] It has the following beneficial effects: This invention directly establishes ownership rights for the smallest business unit—the data element—with extremely fine granularity, solving the problem that traditional methods can only establish ownership rights for the entire file or dataset. This makes the management and value assessment of data assets more precise. By establishing an independent and tamper-proof ownership chain for each data element, the entire lifecycle history from creation to each processing and use is fully recorded. This provides solid and credible technical evidence for resolving data ownership disputes and reasonably allocating data revenue. The distributed and chain-based encrypted structure of the ownership chain effectively prevents malicious tampering of data ownership and transfer history. Combined with digital signature technology, it ensures that every ownership change can be traced back to the specific responsible party, greatly improving the security of public data assets. A multi-dimensional feature fingerprint is used instead of a single content hash, integrating content, structure, and contextual information. This fingerprint has better tolerance for formatting changes that do not substantially affect the data, while accurately identifying the essential identity of the data, making the ownership confirmation mechanism more stable and reliable. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a method for confirming ownership of public data element assets based on multidimensional features and ownership chains, provided as an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a public data element asset ownership confirmation system based on multi-dimensional features and ownership chains, provided as an embodiment of this application. Detailed Implementation

[0018] The purpose of this application is to provide a method and system for confirming the ownership of public data element assets based on multidimensional features and ownership chains, so as to solve the problems of unclear ownership, difficulty in tracing and security risks in the current management of public data assets.

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0020] The following example illustrates a method for confirming the ownership of public data element assets based on multi-dimensional features and ownership chains provided in this application.

[0021] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for confirming the ownership of public data element assets based on multidimensional features and ownership chains, provided as an embodiment of this application.

[0022] In this embodiment, the method may include: S101, perform data parsing and processing on the raw data of the target data asset from the public credit information center database to obtain data element objects; This step aims to parse and process the raw data of the target data asset from the public credit information center database to obtain data element objects. This step transforms the massive, heterogeneous, and unstructured raw data in the public credit information center database into minimal, atomic information units with clear business meaning that can be identified and manipulated in subsequent rights confirmation processes—that is, data element objects—through a standardized processing flow. In the context of public data assetization, the smallest granularity of an asset is not the original file or the entire database table, but rather a single information item within it that carries specific value.

[0023] Furthermore, this step may include: First, the system retrieves the raw data of the target data asset from the public credit information center database. This data comes from diverse sources and formats, including annual report PDFs from market regulators, tax record XML messages from tax authorities, and judgment documents TXT files from the court system. Next, the system performs metadata recognition processing on the retrieved raw data, reading or parsing its metadata information to obtain recognition information. This step is crucial because it provides the basis for selecting the correct processing tools. For example, if the recognition information indicates the file is in PDF format, the system will invoke an Optical Character Recognition (OCR) engine; if it is recognized as XML, it will invoke an XML parser.

[0024] Subsequently, based on the identification information obtained in the previous step and a pre-built file parsing engine library, the system performs structured extraction processing on the raw data, transforming unstructured or semi-structured content into machine-readable structured data. For example, it converts table content in a PDF into a data table object in memory. After obtaining the structured data, the system extracts data elements according to a predefined "public data element standard library," identifying and extracting the smallest business units that conform to the standards from the structured data to obtain data elements. For example, from a table containing basic enterprise information, it extracts independent fields such as "Unified Social Credit Code," "Enterprise Name," and "Registered Capital," along with their corresponding values.

[0025] Finally, to ensure the universality of the process and the convenience of subsequent processing, the system standardizes the format of each extracted data element, encapsulating it into a standard-format data element object. Thus, a raw data file is successfully decomposed into a series of standardized, independently addressable data element objects.

[0026] As can be seen, this step can transform massive amounts of raw public data with different sources, formats, and structures into a series of well-organized, unified, and atomic data element objects.

[0027] S102, perform multi-dimensional feature fingerprint generation processing based on data element objects to obtain multi-dimensional feature fingerprints; Building upon S101, this step aims to generate multidimensional feature fingerprints based on data element objects. The principle behind this step is to create a stable, unique, and highly robust digital identity for each data element object generated in the previous step—a multidimensional feature fingerprint. During data flow and processing, the content of data elements may change, as may their context. Traditional single-content hashing methods are too fragile; any minor, meaningless change (such as spaces or case variations) will result in completely different hash values, failing to effectively identify the "essential identity" of the data element. In contrast, fingerprints generated using multidimensional feature fusion can comprehensively represent the essential attributes of a data element across its "content," "semantics," and "origin." This fingerprint can accurately distinguish different data elements and resist interference from non-essential changes to a certain extent, thus providing a stable and unchanging identifier for the data element throughout its lifecycle, serving as a unique anchor point for indexing and associating it on the ownership chain.

[0028] Furthermore, this step may include: First, content feature extraction is performed. The system extracts the value portion of the data element object, normalizes it, and then uses a deterministic cryptographic hash algorithm to calculate its hash value, thus obtaining the content feature value. This step ensures that the core information content of the data element is uniquely represented.

[0029] Secondly, structural feature extraction is performed. The system extracts the key portion of the data element object, or the unique code corresponding to that key in the public data element standard library. This key or code represents the semantic category of the data element. Similarly, a hash calculation is performed to obtain the structural feature value. This step ensures that data elements with the same value but different business meanings can be clearly distinguished.

[0030] Next, contextual feature extraction is performed. The system extracts metadata associated with the data element object, such as the original data provider ID, data collection batch number, and data entry timestamp. This contextual information is concatenated into a string in a predetermined order, and then hashed to obtain the contextual feature value. This step ensures that even data elements with identical content and structure can be uniquely distinguished as long as their source or generation time differs.

[0031] Finally, the system performs fingerprint synthesis processing, concatenating the previously generated content feature values, structural feature values, and context feature values ​​in a fixed, predefined order. Then, it performs an overall hash calculation on the concatenated long string to generate a fixed-length, unique multidimensional feature fingerprint.

[0032] As can be seen, this step generates a unique and highly condensed identity for each fine-grained data element object. This multi-dimensional feature fingerprint not only solidifies the content, semantics, and source information of the data element at the time of its generation, but its multi-dimensional composition makes it more robust and secure than a single hash value, greatly reducing the probability of "identity collisions" and effectively preventing malicious attackers from forging identities by constructing specific data.

[0033] S103, based on multi-dimensional feature fingerprints, data element objects, and digital identities, performs on-chain processing of ownership chain to obtain the target ownership chain; Building upon S102, this step aims to process ownership on the blockchain based on multi-dimensional feature fingerprints, data element objects, and digital identities, resulting in the target ownership chain. The principle behind this step is to leverage the immutability, traceability, and decentralized trust characteristics of distributed ledger technology to declare and solidify the initial ownership of each newly identified data element object with a generated fingerprint. Simply generating a fingerprint only creates an identity; this step authoritatively binds this identity to its original owner and records it in a way that cannot be unilaterally denied or tampered with, forming the genesis record of this data element asset. By creating an ownership chain specifically for this data element and using the initial ownership information as its first block (genesis block), this method constructs a trusted, independent ownership history ledger for this data element, spanning its entire lifecycle—the target ownership chain.

[0034] Furthermore, this step may include: First, the system performs credential construction processing, which involves creating a structured initial ownership credential. This credential is a data packet whose content should include at least: a multidimensional feature fingerprint, the complete content of the data element object, the digital identity of the data provider, a timestamp of credential creation, and an initial rights statement.

[0035] Subsequently, to ensure the authenticity and non-repudiation of the credential, the system uses the data provider's private key to digitally sign the entire initial ownership credential, generating a digitally signed initial ownership credential. Anyone can use the data provider's public key to verify this signature, thus confirming that the credential was indeed issued by the provider and that its content has not been tampered with. Finally, the system performs the creation and on-chaining of the ownership chain. The system uses the multi-dimensional feature fingerprint of this data element as a unique identifier to create an initial ownership chain.

[0036] Then, the initial ownership certificate with a digital signature is written into the distributed ledger as the core data content of the chain's genesis block. This genesis block does not point to any preceding block and is the starting point of the chain. After this step is completed, an immutable target ownership chain that records the initial ownership of specific data elements is officially generated.

[0037] As can be seen, this step successfully transforms an abstract data element into a digital asset with clearly defined ownership and verifiable origin. It utilizes cryptography and distributed ledger technology to provide legally valid technical proof of the data asset's creation, solving the problems of ambiguous initial ownership and reliance on centralized institutional endorsement in traditional data management.

[0038] S104: When a request for ownership transfer event corresponding to the target ownership chain is received, ownership change registration is performed based on the ownership transfer event request to obtain a new target ownership chain. Building upon S103, this step aims to register ownership changes upon receiving a request for an ownership transfer event corresponding to the target ownership chain, thereby obtaining a new target ownership chain. The principle behind this step is to continuously and securely record all ownership-related changes occurring after the confirmation of ownership for data elements, constructing a complete, coherent, and tamper-proof audit trail. The value of data assets lies in their flow and use, which involves various ownership transfer events such as processing, authorization, trading, and sharing. This step aims to capture each such event and append it as a new "block" to the target ownership chain of the corresponding data element. A hash-based chaining mechanism between blocks ensures that historical records are not tampered with; digital signature verification of each operation ensures that all ownership changes originate from the genuine intentions of the legitimate rights holder.

[0039] Furthermore, this step may include: when the system receives a request for a transfer of ownership corresponding to the target ownership chain, the request is a structured data packet, which must contain: the multi-dimensional feature fingerprint of the target data element, the event type, the event participant information, the event details, and most importantly, the digital signature of the request made by the current rights holder using their private key.

[0040] Upon receiving a request, the system first verifies the ownership transfer event request based on the digital signature. Specifically, the system locates the corresponding target ownership chain based on the multi-dimensional feature fingerprint in the request and reads the last block on the chain to determine the current legitimate rights holder and their digital identity (public key). Then, the system uses the public key to decrypt and verify the digital signature in the request. Only when the signature verification is successful can it be proven that the request was indeed initiated by the legitimate rights holder and that the intention to operate is genuine and valid.

[0041] Once verification is successful, the system requests the creation of a new block based on the ownership transfer event. This new block will contain all the information of this ownership transfer event, a precise timestamp, and the hash value of the previous block. Including the hash of the previous block in the new block is the key to building the "chain," ensuring that any tampering with historical blocks will break the chain.

[0042] Finally, the system adds this new block to the end of the target ownership chain, forming a new target ownership chain, and selectively broadcasts this update to all nodes according to the system architecture to achieve consensus in the distributed network and ensure the redundancy and consistency of records.

[0043] S105, when a query request is received, the ownership information query is performed based on the multi-dimensional feature fingerprint corresponding to the query request to obtain the ownership transfer history.

[0044] Building upon S103, this step aims to process ownership information based on the multi-dimensional feature fingerprint corresponding to the query request upon receipt, thereby obtaining the ownership transfer history. The principle behind this step is to provide a reliable and efficient channel, enabling any authorized user to query and obtain the complete and tamper-proof ownership transfer history of a given data element since its inception. By calculating the multi-dimensional feature fingerprint of the data to be queried in real time and using this fingerprint as a key to retrieve the corresponding ownership chain, this embodiment ensures the accuracy of the query. By traversing the entire ownership chain and parsing each block, the entire historical trajectory of the data element asset can be reconstructed in a reliable manner, thus meeting the application needs of compliance auditing, dispute arbitration, value assessment, and other aspects.

[0045] Furthermore, this step may include: When the system receives a query request, the request typically contains the raw data that the user wants to retrieve about their history.

[0046] The system's first step is to generate fingerprint features based on the original data corresponding to the query request. This involves completely replicating the process in S102, calculating the multi-dimensional feature fingerprint of the user-provided data. This is done to obtain a unique "ID" for the data element within the ownership chain system, ensuring the accuracy of the query. After obtaining the multi-dimensional feature fingerprint, the system retrieves the corresponding ownership chain from the distributed storage based on this fingerprint. The distributed storage system is designed to efficiently index and locate ownership chain data using the fingerprint (as the primary key).

[0047] Once the corresponding ownership chain is found, the query process enters the final parsing phase. Starting from the genesis block of that chain, the system traverses all blocks sequentially. It reads the genesis block and parses out the initial ownership information. Then, using the "previous block hash" recorded in the current block, it finds and verifies the previous block, then reads the next block, repeating this process until the end of the chain. During the traversal, the system parses out the ownership events recorded in each block, including event type, participants, time, and authorization details.

[0048] Finally, the system organizes and formats all the parsed event information in chronological order to form a clear and complete ownership transfer history report and returns it to the queryer.

[0049] In summary, this embodiment of the invention directly establishes ownership rights for the smallest business unit—the data element—with extremely fine granularity. This solves the problem that traditional methods can only establish ownership rights for the entire file or dataset, making the management and value assessment of data assets more precise. By establishing an independent and tamper-proof ownership chain for each data element, the entire lifecycle history from creation to each processing and use is fully recorded. This provides solid and credible technical evidence for resolving data ownership disputes and reasonably allocating data revenue. The distributed and chain-based encrypted structure of the ownership chain effectively prevents malicious tampering of data ownership and transfer history. Combined with digital signature technology, it ensures that every ownership change operation can be traced back to the specific responsible party, greatly improving the security of public data assets. Using multi-dimensional feature fingerprints instead of single content hashes integrates content, structure, and contextual information. This fingerprint has better tolerance for formatting changes that do not substantially affect the data, while accurately identifying the essential identity of the data, making the ownership confirmation mechanism more stable and reliable.

[0050] The following describes a public data element asset ownership confirmation system based on multi-dimensional features and ownership chains provided in the embodiments of this application. The public data element asset ownership confirmation system based on multi-dimensional features and ownership chains and the public data element asset ownership confirmation method based on multi-dimensional features and ownership chains described below can be referred to in correspondence with each other.

[0051] Please refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of a public data element asset ownership confirmation system based on multi-dimensional features and ownership chains, provided as an embodiment of this application.

[0052] In this embodiment, the device may include: The data acquisition module 100 is used to perform data parsing and processing on the raw data of the target data assets from the public credit information center database to obtain data element objects; The feature fingerprint generation module 200 is used to perform multi-dimensional feature fingerprint generation processing based on data element objects to obtain multi-dimensional feature fingerprints. The data on-chain processing module 300 is used to perform ownership chain on-chain processing based on multi-dimensional feature fingerprints, data element objects, and digital identities to obtain the target ownership chain. The ownership change processing module 400 is used to perform ownership change registration processing based on the ownership transfer event request when it receives the ownership transfer event request corresponding to the target ownership chain, so as to obtain a new target ownership chain. The ownership query module 500 is used to query ownership information based on the multi-dimensional feature fingerprint corresponding to the query request when a query request is received, and to obtain the ownership transfer history.

[0053] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0054] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0055] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0056] The foregoing has provided a detailed description of a method and system for confirming the ownership of public data element assets based on multi-dimensional features and ownership chains, as well as a method for confirming the ownership of public data element assets based on multi-dimensional features and ownership chains. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for confirming ownership of public data element assets based on multi-dimensional features and ownership chains, characterized in that, include: The raw data of the target data assets is parsed and processed from the database of the public credit information center to obtain data element objects; A multidimensional feature fingerprint is generated based on the data element object. Based on the multidimensional feature fingerprint, the data element object, and the digital identity, the ownership chain is processed to obtain the target ownership chain. When a request for ownership transfer event corresponding to the target ownership chain is received, ownership change registration processing is performed based on the ownership transfer event request to obtain a new target ownership chain. When a query request is received, ownership information is queried based on the multi-dimensional feature fingerprint corresponding to the query request to obtain the ownership transfer history.

2. The method for confirming ownership of public data element assets according to claim 1, characterized in that, The raw data of the target data assets is parsed and processed from the public credit information center database to obtain data element objects, including: Obtain the original data of the target data asset from the database of the public credit information center; The raw data is subjected to metadata recognition processing to obtain recognition information; Based on the identification information and the file parsing engine, the original data is processed to extract structured data. Data elements are extracted from the structured data to obtain data elements; The extracted data elements are formatted to obtain the data element object.

3. The method for confirming ownership of public data element assets according to claim 2, characterized in that, Based on the data element object, a multidimensional feature fingerprint generation process is performed to obtain a multidimensional feature fingerprint, including: Content feature extraction is performed on the data element object to obtain content feature values; Structural features are extracted from the data element objects to obtain structural feature values; Context feature extraction is performed on the data element object to obtain context feature values; The content feature value, the structural feature value, and the context feature value are subjected to fingerprint synthesis processing to obtain the multidimensional feature fingerprint.

4. The method for confirming ownership of public data element assets according to claim 3, characterized in that, Based on the multidimensional feature fingerprint, the data element object, and the digital identity, ownership chain on-chain processing is performed to obtain the target ownership chain, including: Based on the multidimensional feature fingerprint, the data element object, and the digital identity, a credential construction process is performed to obtain an initial ownership credential. The initial ownership certificate is digitally signed based on the data provider's private key to obtain an initial ownership certificate with a digital signature. An initial ownership chain is created based on the multidimensional feature fingerprint, and the initial ownership certificate with digital signature is used as the genesis block of the initial ownership chain to obtain the target ownership chain.

5. The method for confirming ownership of public data element assets according to claim 4, characterized in that, When a request for a transfer of ownership corresponding to the target ownership chain is received, ownership change registration processing is performed based on the request to obtain a new target ownership chain, including: When a request for ownership transfer event corresponding to the target ownership chain is received, the ownership transfer event request is verified based on a digital signature. Once the verification is successful, a new block is requested based on the ownership transfer event; The new block is added to the end of the target ownership chain to obtain the new target ownership chain, and then broadcast to all nodes.

6. The method for confirming ownership of public data element assets according to claim 5, characterized in that, When a query request is received, ownership information is queried based on the multi-dimensional feature fingerprint corresponding to the query request to obtain the ownership transfer history, including: Fingerprint features are generated based on the original data corresponding to the query request to obtain the multidimensional feature fingerprint to be queried. Based on the multidimensional feature fingerprint to be queried, the corresponding ownership chain is retrieved in the distributed storage; When the corresponding ownership chain is found, all blocks on the chain are traversed sequentially starting from the genesis block. The ownership events recorded in each block are parsed out to obtain the ownership transfer history.

7. A public data element asset ownership confirmation system based on multi-dimensional features and ownership chains, characterized in that, include: The data acquisition module is used to perform data parsing and processing on the raw data of the target data assets from the public credit information center database to obtain data element objects; The feature fingerprint generation module is used to perform multi-dimensional feature fingerprint generation processing based on the data element object to obtain a multi-dimensional feature fingerprint. The data on-chain processing module is used to perform ownership chain on-chain processing based on the multi-dimensional feature fingerprint, the data element object, and the digital identity to obtain the target ownership chain. The ownership change processing module is used to perform ownership change registration processing based on the ownership transfer event request when it receives the ownership transfer event request corresponding to the target ownership chain, so as to obtain a new target ownership chain. The ownership query module is used to perform ownership information query processing based on the multi-dimensional feature fingerprint corresponding to the query request when a query request is received, so as to obtain the ownership transfer history.

8. The public data element asset ownership confirmation system according to claim 7, characterized in that, The data on-chain processing module is specifically used to construct a certificate based on the multi-dimensional feature fingerprint, the data element object, and the digital identity to obtain an initial ownership certificate; and to perform digital signature processing on the initial ownership certificate based on the private key of the data provider to obtain an initial ownership certificate with a digital signature. An initial ownership chain is created based on the multidimensional feature fingerprint, and the initial ownership certificate with digital signature is used as the genesis block of the initial ownership chain to obtain the target ownership chain.

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