Cross-platform multi-source heterogeneous data scene digital identification method, system and device

Abstract

The application discloses a cross-platform multi-source heterogeneous data scene digital identification method, system and device, and relates to the technical field of data processing. The method comprises the following steps: constructing a structural standardized interface layer; defining a standardized contract comprising a core attribute set, an extended attribute slot and an inheritance constraint rule set; implementing non-destructive hierarchical management; constructing a dynamic expansion mechanism; adopting an abstract syntax tree (AST) to construct a cross-platform data topology model, supporting nested structure expression; calculating inter-level inheritance conflicts (based on a directed acyclic graph traversal algorithm) through an attribute dependency parser, and realizing the directed acyclic graph traversal algorithm; executing attribute operations in an isolated sandbox to ensure atomic layer data zero pollution; accessing each layer of the non-destructive hierarchical management through the constructed structural standardized interface layer, and then outputting a generated unified digital primitive through the standardized interface layer, so as to realize scene digital identification of cross-platform heterogeneous data.

Description

Technical Field

[0001] This invention belongs to the field of digital identity identification, specifically relating to a cross-platform, multi-source, heterogeneous data scenario-based digital identification method, system, and device. Background Technology

[0002] As the digital economy develops in depth, data has become a core production resource. Various industries are widely adopting digital identification technology to uniquely mark and manage data entities during their digital transformation. Current mainstream solutions are mainly based on W3C DID (Decentralized Identifier) ​​or traditional Handle System architectures. While these solutions perform adequately in a single, closed platform environment, they have significant shortcomings in supporting cross-industry, multi-scenario, and heterogeneous terminal collaboration.

[0003] In cross-domain data fusion scenarios, significant protocol barriers arise from differences in business attributes across government, healthcare, and industry sectors. Traditional identification systems rely on static interface definitions, which are incompatible with specialized data structures such as medical DICOM image parameters and industrial OPC UA device descriptions, necessitating the development of customized adaptation layers for cross-system collaboration. This rigid architecture not only increases integration costs by more than 30% but also causes "interface drift" due to frequent protocol iterations, forcing repeated system reconstructions.

[0004] At the data governance level, centralized storage architecture has fundamental limitations. When users add extended attributes such as copyright watermarks and time-space tags to digital works, the editing operation directly overwrites the original data storage area, putting core fingerprint information at risk of contamination. Although some solutions introduce blockchain notarization technology to ensure immutability, the performance bottleneck of on-chain storage prevents it from supporting high-frequency version editing, making it difficult to meet the needs of dynamic rights transfer of digital content.

[0005] Faced with the explosive growth of IoT scenarios, existing identifier rendering mechanisms have revealed their compatibility flaws. Heterogeneous hardware such as mobile terminals, AR devices, and industrial sensors have vastly different requirements for data representation. Traditional solutions use fixed templates to force the output of uniform identifiers, which causes severe lag in weak network environments or on terminals with low computing power.

[0006] While international standards organizations have promoted semantic interoperability frameworks such as JSON-LD, their lack of attribute inheritance constraints leads to frequent type conflicts when data is associated across multiple levels. Cross-platform rendering engines like Flutter, while optimizing front-end presentation, fail to decouple the data layer from the presentation layer logic, resulting in deep coupling between business rules and device capabilities. These patch-like improvements cannot overcome structural bottlenecks, hindering the efficiency of market-based circulation of data elements. Summary of the Invention

[0007] This invention addresses the shortcomings of existing technologies by providing a cross-platform, multi-source, heterogeneous data scenario-based digital identification method, system, device, and storage medium.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A cross-platform, multi-source, heterogeneous data scenario-based digital identification method includes the following steps: Data is accessed through a structured and standardized interface layer, and the accessed data is processed based on a non-destructive layer. Then, a unified digital primitive is generated through the structured and standardized interface layer to realize the scenario-based digital identification of heterogeneous data across platforms. The non-destructive layer includes an atomic layer, a derived layer, and a scenario layer. The atomic layer stores immutable hash identifiers of access data and core attributes verified by consensus. The derived layer maps the atomic layer through versioned pointers and dynamically loads scenario-based extended attribute sets, which include user-defined metadata. The scenario layer combines multiple derived layer attributes based on the device context to generate a lightweight identifier that can be parsed by the terminal, thereby realizing a cross-device synchronization mechanism. A dynamic expansion mechanism is established, in which an abstract syntax tree is used to construct a cross-platform data topology model to realize nested structure expression, and an attribute dependency parser is used to calculate inheritance conflicts between levels in an isolated sandbox, so that the atomic layer data is free from pollution.

[0009] As one possible implementation, the structurally standardized interface layer includes a standardized contract of a core attribute set, extended attribute slots, and an inheritance constraint rule set; The core attribute set includes one or more attributes such as data type and constraint conditions, which are used to define the essential characteristics of the data entity and enforce type constraints using the JSON Schema specification. The essential characteristics of the data entity include the ISBN number and creation timestamp of the digital work. The extended attribute slot adopts a dynamic loading interface, which supports the loading of scenario-based attributes at runtime and data access based on the core attribute set. The inheritance constraint rule set sets up a multi-parent conflict detection mechanism, and uses a directed acyclic graph to model multi-level inheritance relationships to ensure that child entities automatically inherit the data verification rules of parent entities.

[0010] As one possible implementation, the inheritance constraint rule set sets up a multi-parent conflict detection mechanism, which models multi-level inheritance relationships through a directed acyclic graph to ensure that child entities automatically inherit the data validation rules of their parent entities, including the following steps: Force child entities to inherit the data type and value range constraints of the parent entity, wherein the value range constraints include integer range and string format; Conflicts are dynamically resolved based on the weight of the attribute source. The weight setting system presets a weight that is greater than the user-defined weight, which is greater than the weight injected by a third party. Merkle trees are used to store attribute overwrite history, supporting backtracking to any version by timestamp.

[0011] As one possible implementation, the non-destructive layer further includes: When a user initiates an edit request, a difference container is created in the derived layer to record the reference relationship between the modified content and the original attribute. The reference relationship is based on the B+ tree index to record the edit operation. The three-way merging algorithm automatically coordinates concurrent conflicts and preserves all user operation intentions. The three-way merging algorithm coordinates concurrent conflicts based on operation transformation (OT) technology. Based on a role-scenario dual-dimensional access control isolation hierarchy, it ensures that only system-level writes are allowed at the atomic layer.

[0012] As one possible implementation method, the dynamic expansion mechanism further includes: Configure the registry descriptor to support dynamic loading of domain plugins; The metadata format and real-time validation rules for declaring new attributes in typed extended slots; The serialization protocol is used to compress the size of the access data.

[0013] As one possible implementation, the cross-device synchronization mechanism also includes device performance profiling, an incremental synchronization engine, and a scene routing strategy; The device performance profile is used to analyze the terminal's CPU computing power, memory capacity, and network bandwidth, and dynamically degrade the rendering accuracy of the identifier. The incremental synchronization engine is used to identify changed data blocks through a difference bitmap and only transmit the difference portion. The scenario routing strategy is used to enable the QUIC protocol in high-speed networks and switch to a combined encrypted channel of CoAP+DTLS secure communication in weak network environments.

[0014] As one possible implementation, the step of connecting the structured normalized interface layer to the non-destructive layer for data processing and generating unified digital primitives through the structured normalized interface layer includes the following steps: The essential feature hash of the data entity is stored in the atomic layer, and the right description is stored in the derived layer. The processing of the essential feature hash of the data entity adopts the Blake3 algorithm, and the right description includes the authorizing party and the validity period. A hierarchical authorization strategy is constructed and bound to a scene layer identifier to form a traceable rights flow, wherein the hierarchical authorization strategy includes restrictions on sub-authorization; Generate unified digital primitives for third parties to verify the validity of rights without revealing details.

[0015] A cross-platform, multi-source, heterogeneous data scenario-based digital identification system includes a primitive generation module, a hierarchical governance module, and an isolation and parsing module; The primitive generation module accesses data through a structured normalization interface layer, processes the accessed data based on a non-destructive layer, and then generates unified digital primitives through the structured normalization interface layer to realize scenario-based digital identification of cross-platform heterogeneous data. The layered governance module includes a non-destructive layer comprising an atomic layer, a derived layer, and a scenario layer. The atomic layer stores immutable hash identifiers and consensus-verified core attributes of the access data. The derived layer maps the atomic layer through versioned pointers and dynamically loads scenario-based extended attribute sets, which include user-defined metadata. The scenario layer combines multiple derived layer attributes based on the device context to generate a lightweight identifier that can be parsed by the terminal, thereby achieving a cross-device synchronization mechanism. The isolated parsing module establishes a dynamic expansion mechanism. It uses an abstract syntax tree to construct a cross-platform data topology model to realize nested structure expression, and calculates inheritance conflicts between levels through an attribute dependency parser in an isolated sandbox, so that the atomic layer data is free from pollution.

[0016] A cross-platform, multi-source, heterogeneous data contextual digital identification device includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the following method: Data is accessed through a structured and standardized interface layer, and the accessed data is processed based on a non-destructive layer. Then, a unified digital primitive is generated through the structured and standardized interface layer to realize the scenario-based digital identification of heterogeneous data across platforms. The non-destructive layer includes an atomic layer, a derived layer, and a scenario layer. The atomic layer stores immutable hash identifiers of access data and core attributes verified by consensus. The derived layer maps the atomic layer through versioned pointers and dynamically loads scenario-based extended attribute sets, which include user-defined metadata. The scenario layer combines multiple derived layer attributes based on the device context to generate a lightweight identifier that can be parsed by the terminal, thereby realizing a cross-device synchronization mechanism. A dynamic expansion mechanism is established, in which an abstract syntax tree is used to construct a cross-platform data topology model to realize nested structure expression, and an attribute dependency parser is used to calculate inheritance conflicts between levels in an isolated sandbox, so that the atomic layer data is free from pollution.

[0017] A computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method: Data is accessed through a structured and standardized interface layer, and the accessed data is processed based on a non-destructive layer. Then, a unified digital primitive is generated through the structured and standardized interface layer to realize the scenario-based digital identification of heterogeneous data across platforms. The non-destructive layer includes an atomic layer, a derived layer, and a scenario layer. The atomic layer stores immutable hash identifiers of access data and core attributes verified by consensus. The derived layer maps the atomic layer through versioned pointers and dynamically loads scenario-based extended attribute sets, which include user-defined metadata. The scenario layer combines multiple derived layer attributes based on the device context to generate a lightweight identifier that can be parsed by the terminal, thereby realizing a cross-device synchronization mechanism. A dynamic expansion mechanism is established, in which an abstract syntax tree is used to construct a cross-platform data topology model to realize nested structure expression, and an attribute dependency parser is used to calculate inheritance conflicts between levels in an isolated sandbox, so that the atomic layer data is free from pollution.

[0018] This invention, by adopting the above technical solutions, has significant technical effects: This invention completely solves the data compatibility problem between multi-source heterogeneous systems by defining a standardized interface layer containing a core attribute set, extended attribute slots, and inheritance constraint rule set. This design enforces semantic alignment of cross-platform data, enabling lossless mapping of specialized data structures in fields such as healthcare and finance to a unified paradigm. The dynamic loading interface mechanism provides the system with unprecedented elastic expansion capabilities; adding new business attributes does not require reconstructing the underlying architecture, fundamentally eliminating the "data silo" phenomenon caused by protocol iterations in traditional solutions, and providing atomic-level support for cross-industry data fusion. Adopting a three-level separation architecture of atomic layer, derived layer, and scenario layer, it pioneers a new governance path that combines data integrity and scenario flexibility. The atomic layer uses cryptographic hashing to solidify the essence of the original data, ensuring that core attributes are immutable; the derived layer achieves zero-pollution editing through versioned reference chains, allowing users to freely add scenario tags without disturbing the underlying data; the scenario layer dynamically synthesizes lightweight identifiers based on device context, enabling the same data entity to automatically adapt to the optimal expression form on heterogeneous terminals such as mobile phones and AR glasses. This mechanism ends the traditional dilemma of "maintaining data integrity at the expense of business agility." A platform-independent description model built on Abstract Syntax Trees (AST), combined with an attribute dependency parser and an isolation sandbox, enables machine-readable, parsable, and executable cross-domain data. The AST topology transforms complex business relationships into standard nested expressions, eliminating semantic distortion in traditional cross-system transmission; the sandboxed execution environment ensures strict restrictions on attribute operations, blocking the risk of unauthorized access; and the dependency parsing engine automatically resolves multi-level inheritance conflicts, fundamentally improving the efficiency of data association computation in ultra-large-scale systems such as government affairs and the Internet of Things, laying the foundation for building true global data intelligence and demonstrating significant technical benefits. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the overall system of the present invention. Detailed Implementation

[0020] To clearly illustrate the present invention and make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, so that those skilled in the art can implement the invention based on the description. The technology of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1: A cross-platform, multi-source, heterogeneous data scenario-based digital identification method, such as Figure 1 As shown, it includes the following steps: S100. Data is accessed through a structured and standardized interface layer, and the accessed data is processed based on a non-destructive layer. Then, a unified digital primitive is generated through the structured and standardized interface layer to realize the scenario-based digital identification of cross-platform heterogeneous data. S200. The non-destructive layer includes an atomic layer, a derived layer, and a scenario layer. The atomic layer stores immutable hash identifiers of the access data and core attributes verified by consensus. The derived layer maps the atomic layer through versioned pointers and dynamically loads a scenario-based extended attribute set, which includes user-defined metadata. The scenario layer combines multiple derived layer attributes based on the device context to generate a lightweight identifier that can be parsed by the terminal, thereby realizing a cross-device synchronization mechanism. S300. Establish a dynamic expansion mechanism, in which an abstract syntax tree is used to construct a cross-platform data topology model to realize nested structure expression, and an attribute dependency parser is used in an isolated sandbox to calculate inheritance conflicts between levels, so that the atomic layer data is free from pollution.

[0022] In this embodiment, the structurally standardized interface layer is first constructed, including the following steps: Step 1-1: Define the essential characteristics of the data entity using the core attribute set (such as the ISBN number of the digital work and the creation timestamp), and enforce type constraints using the JSON Schema specification; the essential characteristics of the data entity include the ISBN number of the digital work and the creation timestamp; Steps 1-2: The extended attribute slot adopts the Dynamic Loading Interface (DLI) to support the loading of scene-specific attributes at runtime (such as adding 3D model accuracy parameters for VR devices); the scene-specific attributes include the 3D model accuracy parameters that VR devices need to add. Steps 1-3: Inheritance constraint rule set: Multi-level inheritance relationship is modeled through a directed acyclic graph (DAG) to ensure that child entities automatically inherit the data validation rules of the parent entity.

[0023] In this embodiment, the implementation of the inheritance constraint rule set further includes the following steps: Interface contract template: Force child entities to inherit the data type and value range constraints (such as integer range, string format) of the parent entity; the value range constraints include integer range and string format; Priority matrix algorithm: Dynamically resolve conflicts based on attribute source weights (system preset > user-defined > third-party injection); the weights are set such that the system preset weights are greater than the user-defined weights, which are greater than the third-party injection weights. Incremental snapshot chain: Uses a Merkle tree to store attribute overwriting history, supporting backtracking to any version by timestamp.

[0024] In this embodiment, the process of editing the non-destructive layer includes the following steps: When a user initiates an edit request, a Delta Container is created in the derived layer to record the reference relationship between the modified content and the original attribute (based on a B+ tree index). The reference relationship records the edit operation (add / delete / modify) based on the B+ tree index, and each operation is associated with the content hash reference of the atomic layer attribute. The three-way merging algorithm (based on Operational Transformation) automatically coordinates concurrent conflicts while preserving all user operation intentions; the three-way merging algorithm coordinates concurrent conflicts based on Operational Transformation (OT) technology. Based on role-scenario dual-dimensional access control (e.g., role-based permission RBAC + scenario-based attribute ABAC), hierarchical permissions are isolated to ensure that only system-level writes are allowed at the atomic level.

[0025] In this embodiment, the dynamic expansion mechanism further includes: Configure the registry descriptor to support dynamic loading of domain plugins; Typed extension slots declare the metadata format (JSON Schema) of new attributes and real-time validation rules; Sparse binary encoding: The serialization protocol Protocol Buffers is used to compress the data volume, and the measured cross-device transmission load is reduced.

[0026] In this embodiment, the cross-device synchronization mechanism further includes: Device performance profiling: Analyze terminal CPU computing power, memory capacity and network bandwidth, and dynamically degrade the rendering accuracy of the identifier; Incremental synchronization engine: Identifies changed data blocks through a difference bitmap and only transmits the differences; Scenario Routing Strategy: Enable the QUIC protocol in high-speed networks, and switch to a combined encrypted channel of CoAP+DTLS secure communication in weak network environments. The QUIC protocol is a next-generation high-efficiency transport layer protocol based on UDP, developed by Google. It aims to optimize network performance through low-latency connections, multiplexing, and secure transmission, and is gradually becoming the foundational protocol for HTTP / 3. The CoAP+DTLS encrypted channel is a combination scheme where the CoAP (Constrained Application Protocol) protocol achieves secure communication through DTLS (Datagram TLS). CoAP+DTLS provides encrypted communication capabilities for IoT devices by overlaying the TLS protocol on top of the User Datagram Protocol (UDP). DTLS is optimized for the unreliable transmission characteristics of UDP, ensuring the integrity and confidentiality of data during transmission.

[0027] In this embodiment, the generation of the unified digital primitives for the access data includes: Content fingerprint separation storage: The essential feature hash of the data entity of the accessed data (Blake3 algorithm) is stored in the atomic layer, and the rights description (licensor, validity period) is stored in the derived layer; the processing of the hash of the work content adopts the Blake3 algorithm; the rights description includes the licensor and the validity period; Blake3 is a new hash algorithm designed for file integrity verification, cryptographic signature and message authentication, with high parallel computing capability and security.

[0028] Rights chain structure: The hierarchical authorization strategy (such as sub-licensing restrictions) is bound to the scene layer identifier to form a traceable rights flow; the hierarchical authorization strategy includes sub-licensing restrictions; Verifiable Credentials (VC): Generates unified digital primitives based on zk-SNARKs, allowing third parties to verify the validity of rights without revealing details.

[0029] Example 2: A cross-platform, multi-source, heterogeneous data scenario-based digital identification system, such as Figure 2 As shown, it includes a primitive generation module 100, a hierarchical governance module 200, and an isolation parsing module 300; The primitive generation module 100 accesses data through a structured normalization interface layer, processes the accessed data based on a non-destructive layer, and then generates unified digital primitives through the structured normalization interface layer to realize scenario-based digital identification of cross-platform heterogeneous data. The layered governance module 200 includes a non-destructive layer comprising an atomic layer, a derived layer, and a scenario layer. The atomic layer stores immutable hash identifiers and consensus-verified core attributes of the access data. The derived layer maps the atomic layer through versioned pointers and dynamically loads a scenario-based extended attribute set, which includes user-defined metadata. The scenario layer combines multiple derived layer attributes based on the device context to generate a lightweight identifier that can be parsed by the terminal, thereby achieving a cross-device synchronization mechanism. The isolation parsing module 300 establishes a dynamic expansion mechanism, in which an abstract syntax tree is used to construct a cross-platform data topology model to realize nested structure expression, and an attribute dependency parser is used in the isolation sandbox to calculate inheritance conflicts between levels, so that the atomic layer data is free from pollution.

[0030] Example 3: A cross-platform, multi-source heterogeneous data contextual digital identification device is disclosed. This digital identification device can be a server or a mobile terminal. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores all data of the computer device. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a cross-platform, heterogeneous data contextual digital identification method. Data is accessed through a structured and standardized interface layer, and the accessed data is processed based on a non-destructive layer. Then, a unified digital primitive is generated through the structured and standardized interface layer to realize the scenario-based digital identification of heterogeneous data across platforms. The non-destructive layer includes an atomic layer, a derived layer, and a scenario layer. The atomic layer stores immutable hash identifiers of access data and core attributes verified by consensus. The derived layer maps the atomic layer through versioned pointers and dynamically loads scenario-based extended attribute sets, which include user-defined metadata. The scenario layer combines multiple derived layer attributes based on the device context to generate a lightweight identifier that can be parsed by the terminal, thereby realizing a cross-device synchronization mechanism. A dynamic expansion mechanism is established, in which an abstract syntax tree is used to construct a cross-platform data topology model to realize nested structure expression, and an attribute dependency parser is used to calculate inheritance conflicts between levels in an isolated sandbox, so that the atomic layer data is free from pollution.

[0031] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0032] The above description of the embodiments is provided to enable those skilled in the art to understand and apply the present invention. It will be apparent to those skilled in the art that various modifications can be made to the above embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made to the present invention by those skilled in the art based on the disclosure thereof should be within the scope of protection of the present invention.

Claims

1. A cross-platform, multi-source, heterogeneous data scenario-based digital identification method, characterized in that, Includes the following steps: Data is accessed through a structured and standardized interface layer, and the accessed data is processed based on a non-destructive layer. Then, a unified digital primitive is generated through the structured and standardized interface layer to realize the scenario-based digital identification of heterogeneous data across platforms. The non-destructive layer includes an atomic layer, a derived layer, and a scenario layer. The atomic layer stores immutable hash identifiers of the access data and core attributes verified by consensus. The derived layer maps the atomic layer through versioned pointers and dynamically loads scenario-based extended attribute sets, which include user-defined metadata. The scenario layer combines multiple derived layer attributes based on the device context to generate a lightweight identifier that can be parsed by the terminal, thereby realizing a cross-device synchronization mechanism. A dynamic expansion mechanism is established, in which an abstract syntax tree is used to construct a cross-platform data topology model to realize nested structure expression, and an attribute dependency parser is used to calculate inheritance conflicts between levels in an isolated sandbox, so that the atomic layer data is free from pollution.

2. The cross-platform, multi-source, heterogeneous data scenario-based digital identification method according to claim 1, characterized in that, The structured normalized interface layer includes a standardized contract of core attribute set, extended attribute slots, and inherited constraint rule set; The core attribute set includes one or more attributes such as data type and constraint conditions, which are used to define the essential characteristics of the data entity and enforce type constraints using the JSON Schema specification. The essential characteristics of the data entity include the ISBN number and creation timestamp of the digital work. The extended attribute slot adopts a dynamic loading interface, which supports the loading of scenario-based attributes at runtime and data access based on the core attribute set. The inheritance constraint rule set sets up a multi-parent conflict detection mechanism, and uses a directed acyclic graph to model multi-level inheritance relationships to ensure that child entities automatically inherit the data verification rules of parent entities.

3. The cross-platform, multi-source, heterogeneous data scenario-based digital identification method according to claim 2, characterized in that, The inheritance constraint rule set sets up a multi-parent conflict detection mechanism, and models multi-level inheritance relationships through a directed acyclic graph to ensure that child entities automatically inherit the data validation rules of parent entities, including the following steps: Force child entities to inherit the data type and value range constraints of the parent entity, wherein the value range constraints include integer range and string format; Conflicts are dynamically resolved based on the weight of the attribute source, where the system-preset weight is greater than the user-defined weight, which is greater than the weight injected by the third party. Merkle trees are used to store attribute overwrite history, supporting backtracking to any version by timestamp.

4. The cross-platform, multi-source, heterogeneous data scenario-based digital identification method according to claim 1, characterized in that, The non-destructive layer further includes: When a user initiates an edit request, a difference container is created in the derived layer to record the reference relationship between the modified content and the original attribute. The reference relationship is based on the B+ tree index to record the edit operation. The three-way merging algorithm automatically coordinates concurrent conflicts and preserves all user operation intentions. The three-way merging algorithm coordinates concurrent conflicts based on operation transformation (OT) technology. Based on a role-scenario dual-dimensional access control isolation hierarchy, it ensures that only system-level writes are allowed at the atomic layer.

5. The cross-platform, multi-source, heterogeneous data scenario-based digital identification method according to claim 1, characterized in that, The dynamic expansion mechanism also includes: Configure the registry descriptor to support dynamic loading of domain plugins; The metadata format and real-time validation rules for declaring new attributes in typed extended slots; The serialization protocol is used to compress the size of the access data.

6. The cross-platform, multi-source, heterogeneous data scenario-based digital identification method according to claim 1, characterized in that, The cross-device synchronization mechanism also includes device performance profiling, incremental synchronization engine, and scenario routing strategy; The device performance profile is used to analyze the terminal's CPU computing power, memory capacity, and network bandwidth, and dynamically degrade the rendering accuracy of the identifier. The incremental synchronization engine is used to identify changed data blocks through a difference bitmap and only transmit the difference portion. The scenario routing strategy is used to enable the QUIC protocol in high-speed networks and switch to a combined encrypted channel of CoAP+DTLS secure communication in weak network environments.

7. The cross-platform multi-source heterogeneous data scenario-based digital identification method according to claim 1 or 2, characterized in that, The process of accessing data through a structured normalized interface layer, processing the accessed data based on a non-destructive layer, and then generating unified digital primitives through the structured normalized interface layer includes the following steps: The essential feature hash of the data entity is stored in the atomic layer, and the right description is stored in the derived layer. The essential feature hash of the data entity is obtained by the Blake3 algorithm, and the right description includes the authorizing party and the validity period. A hierarchical authorization strategy is constructed and bound to a scene layer identifier to form a traceable rights flow, wherein the hierarchical authorization strategy includes restrictions on sub-authorization; Generate unified digital primitives for third parties to verify the validity of rights without revealing details.

8. A cross-platform, multi-source, heterogeneous data scenario-based digital identification system, characterized in that, It includes a primitive generation module, a hierarchical governance module, and an isolation parsing module; The primitive generation module accesses data through a structured normalization interface layer, processes the accessed data based on a non-destructive layer, and then generates unified digital primitives through the structured normalization interface layer to realize scenario-based digital identification of cross-platform heterogeneous data. The layered governance module includes a non-destructive layer comprising an atomic layer, a derived layer, and a scenario layer. The atomic layer stores immutable hash identifiers and consensus-verified core attributes of the access data. The derived layer maps the atomic layer through versioned pointers and dynamically loads scenario-based extended attribute sets, which include user-defined metadata. The scenario layer combines multiple derived layer attributes based on the device context to generate a lightweight identifier that can be parsed by the terminal, thereby realizing a cross-device synchronization mechanism. The isolated parsing module establishes a dynamic expansion mechanism. It uses an abstract syntax tree to construct a cross-platform data topology model to realize nested structure expression, and calculates inheritance conflicts between levels through an attribute dependency parser in an isolated sandbox, so that the atomic layer data is free from pollution.

9. A cross-platform, multi-source, heterogeneous data scenario-based digital identification device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that the processor, when executing the computer program, implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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