Part generation method and system based on atomization disassembly and encapsulation data

Abstract

The invention relates to a part generation method based on atomization disassembly and encapsulation data. The part generation method comprises the following steps: step 1, analyzing a demand scene; step 2, carrying out atomization disassembly; step 3, performing standardization verification; step 4, packaging the function logic layer; step 5, packaging the element part layer; 6, packaging an interface layer; according to the method, the problems of function solidification and difficult splitting of a traditional data product are solved, and efficient circulation and value release of data elements are promoted; and reusability and circulation efficiency of data parts are improved through the element part layer.

Description

Technical Field

[0001] This invention relates to the field of data element circulation technology, specifically to a method and system for generating data components based on atomized disassembly and encapsulation, which solves problems such as fixed functions of data products, poor cross-platform compatibility, and insufficient trust in circulation. Background Technology

[0002] Traditional data products are typically delivered as a whole, such as customized reports or fixed-format datasets. Their functionality is integrated with the data, making it difficult to quickly split or adjust them according to business needs. For example, a reporting system containing user profiles cannot extract a separate geographic distribution statistics module for reuse in other business lines, resulting in the data's value not being fully realized. Existing technologies, such as software modular design, attempt to split functions, but they do not address the specific characteristics of data processing, such as data flow, privacy computing, and cross-domain collaboration, resulting in low reusability and high transaction risks for the split modules.

[0003] Insufficient data reusability; existing data solutions mostly rely on custom formats or proprietary protocols, resulting in poor cross-platform compatibility. For example, medical imaging components cannot be directly used in financial risk control systems, and the development threshold is high, requiring mastery of low-level languages ​​such as C / C++, making it difficult to form a large-scale circulation ecosystem.

[0004] The platform is highly dependent, and data processing units need to be repeatedly developed for different environments (browsers / servers / edge devices), which is costly. A lack of trust, coupled with the absence of standardized descriptions of functional boundaries and performance metrics, makes it difficult for buyers to verify reliability. Existing technologies, particularly modular software design, fail to address specific needs such as data sensitivity (e.g., privacy compliance), standardized input / output for fluid systems, and stateless cross-domain calls.

[0005] Therefore, there is an urgent need in the market for a new technology for disassembling and standardizing parts for scenario-based matching to solve the above problems and promote the efficient circulation and value release of data elements. Summary of the Invention

[0006] This invention proposes a method and system for generating data components based on atomized disassembly and encapsulation. Through demand scenario analysis, atomized disassembly, standardized verification, functional logic encapsulation, component layer encapsulation, and interface layer encapsulation, it achieves high reusability, high automation, and strong industry adaptability of data circulation, reduces data circulation costs, and promotes the market-oriented allocation of data elements.

[0007] To solve the aforementioned technical problems, the present invention adopts the following solution: A method for generating data components based on atomization and packaging: Step 1: Requirement scenario analysis; Input the original data scenario description and business requirements document, identify the core functional points in the scenario, draw the data flow diagram, and clarify the technical constraints that the disassembled parts must meet. Business requirements documents may require extracting user location, spending power, and credit score. Identify core functionalities in the scenario, such as location extraction, spending power calculation, and credit scoring model invocation; Draw a data flow diagram, such as user behavior log → cleaning → feature extraction → model inference → output profile, and label the input and output data of each step; Clearly define the technical constraints that the disassembled parts must meet, such as requiring desensitized parts to comply with GDPR and the Personal Information Protection Act, and requiring computing parts to support GPU acceleration.

[0008] Step 2: Atomization disassembly; Based on the three principles of functional independence, input-output integrity, and reuse potential, the scenario is decomposed into several atomic functional units.

[0009] Data components: refer to independent data processing units formed through atomization, possessing the following characteristics: Functional atomicity: It only undertakes a single or very few core functions, such as ID card number anonymization and user age calculation; Input and output standardization: Clearly define the input data format, such as {"id_card": "string"} and the output data format, such as {masked_id_card: "string"}; Tradeability: Supports independent pricing, ownership transfer, and cross-scenario reuse; Stateless: It does not depend on context such as user session and can be invoked in parallel; Measurability: The scene effectiveness coefficient is calculated based on the call frequency. The formula is: Scene effectiveness coefficient = Baseline value call count × Scene weight Step 3: Standardize verification to ensure parts tradability; Step 4: Encapsulate the functional logic layer; The functional logic layer is the core computing unit of the data components, and it achieves cross-platform execution through WebAssembly (WASM) or containerization (Docker) technologies. Step 5: Component layer encapsulation; The meta-component layer defines the full lifecycle attributes of data components through structured metadata, supporting rapid discovery and matching across systems; Step 6: Interface layer encapsulation; The interface layer provides standardized calling interfaces, supporting three protocols: REST, gRPC, and WASI, to solve interoperability issues between different systems.

[0010] Furthermore, step 2, the atomization decomposition, includes functional flow decomposition, data flow decomposition, and constraint flow decomposition. The functional flow decomposition introduces the Recap algorithm molecular bond fission mechanism to divide atomic units according to functional coupling degree. The data flow decomposition uses a dynamic partitioner to calculate the data dependency graph in real time and self-adaptively split the boundary. The constraint flow decomposition pre-embeds compliance tags for each part, supporting automatic activation and verification during reassembly.

[0011] Furthermore, the breakdown of step 2 is as follows: S21. Determine functional boundaries through techniques such as functional flow decomposition, data flow decomposition, and constraint flow decomposition: Define an input-processing-output closed loop for each candidate part; S22. Stateless verification: Verify whether candidate parts only depend on input data; for example, the spending power calculation part does not save the user's historical spending records. S23. Reuse potential assessment: evaluate its versatility by measuring historical scenario matching degree and usage frequency.

[0012] S24. Atomized candidate part list; such as region extraction parts, consumption capacity calculation parts, credit score parts.

[0013] Furthermore, step 3, the standardized verification, is used to verify the candidate parts list; Input / output schema validation: Use a JSON schema validator to check whether the input and output of candidate parts conform to a standardized format; for example, whether the input for "Extract parts by region" includes the required fields user_id and ip; Compliance verification: Verify whether the components meet the compliance requirements of the target scenario; for example, credit scoring components must comply with the minimum necessary principle of the Personal Information Protection Law. Performance benchmarking: Under standard conditions, such as 1000 QPS and 50ms latency, the processing capability of the component is tested, and a performance report is generated. (For example, if the average latency of the component extracted from a specific region is 45ms, the throughput is 12000 QPS). Output: Validated standardized data components.

[0014] Furthermore, if the standardized data component fails verification in step 3, the process returns to step 2 for re-disassembly.

[0015] Furthermore, step 4 is implemented by WASM compilation and containerization. WASM compilation implementation process: Technology selection: The core logic is written in Rust (leveraging its memory safety features), and the binding interface between WASM and the host environment is generated using wasm-bindgen; Compilation parameters: --release (enable LLVM optimization), --target wasm32-unknown-unknown (compatible with all WASM runtimes), wasm-opt -Oz (optimize bytecode size); Cross-platform verification: Verify the correctness of the function on browsers (Chrome 110+), servers (WasmEdge 0.12.0), edge devices (Raspberry Pi 4B + Wasmtime 10.0), and HarmonyOS.

[0016] The WASM encapsulation module uses the WebAssembly binary instruction format to encapsulate data functional logic for desensitization, feature extraction, and compliance checks. The WASM module supports cross-platform browser, server, and edge device execution, sandbox isolation, and high performance with low overhead. Containerization and encapsulation implementation process: Image building: Build a lightweight Docker image containing runtime, dependency libraries, core logic WASM modules or binaries; Cross-platform deployment: Adapt to different architectures using docker run --platform linux / amd64 (x86_64 server) or docker run --platform linux / arm64 (ARM64 edge device).

[0017] Furthermore, the metadata definition fields in step 5 include basic information, input / output schema, applicable scenarios, compliance rules, performance metrics, dependencies, test cases, and measurable data. The basic information includes: Part ID (globally unique), Name, Creator, Creation Time, License (e.g., Apache-2.0), and Current Owner; The input / output schema includes: the type of the input field (e.g., string), description (e.g., "18-digit ID number"), example value (e.g., "110101199001011234"), and constraints (e.g., regular expressions). The applicable scenarios include: scenario tags, such as financial risk control, medical privacy computing, and priority high / medium / low; The compliance rules include: encryption level, such as AES-256, cross-border transfer restrictions (allowed / forbidden), and compliant regulations, such as GDPR; The performance metrics include: average latency (ms), maximum throughput (QPS), and memory usage (MB). The dependencies include: the ID and version constraints of the dependent parts, such as "dp-encrypt-v1.2>=1.2.0", and the relationship between the part and other parts; The test cases include: input data, expected output, test results (passed / failed), and test executor.

[0018] The measurable component includes: calculating the scene effectiveness coefficient based on the call frequency, using the formula: Scene effectiveness coefficient = Baseline value call count × Scene weight. Furthermore, in step 6, the REST protocol (HTTP / JSON): Interface definition: Conforms to the OpenAPI 3.0 specification, uses JWT authentication, and input and output conform to JSON Schema; Example call: The client sends a POST request to / v1 / desensitize, the request body contains the id_card field, the interface layer calls the WASM module of the functional logic layer to process it, and returns the de-identified result.

[0019] gRPC protocol (Protobuf): Service definition: Define strongly typed interfaces (such as DesensitizeService) through Protobuf files, supporting bidirectional streaming communication; Service discovery: Integrates with Consul 1.15+ to implement service registration and discovery, and the client automatically resolves the instance address by the service name.

[0020] WASI protocol (Wasm System Interface): Interface Specification: Conforms to the WASI 0.2 standard, defining system call interfaces for file reading and writing, network access, etc. Runtime security: When running WasmEdge 0.12.0, restrict file system access scope using --dir / tmp: / tmp:rwx.

[0021] The data component generation system based on atomization disassembly and packaging includes the following modules: Requirements Analysis Module: Extracts functional points and data flows from the scenario based on natural language processing technology; Atomization Decomposition Module: Integrates rule engines (such as Drools) and machine learning models (such as clustering algorithms) to automatically identify atomic functional units; the atomization decomposition module includes functional flow decomposition, data flow decomposition, and constraint flow decomposition. The functional flow decomposition introduces the Recap algorithm molecular bond fission mechanism to divide atomic units according to functional coupling degree; the data flow decomposition uses a dynamic partitioner to calculate the data dependency graph in real time and self-adaptively splits the boundaries; the constraint flow decomposition pre-embeds compliance tags for each part, supporting automatic activation and verification during reassembly.

[0022] The atomization disassembly module process is as follows: S21. Determine functional boundaries through techniques such as functional flow decomposition, data flow decomposition, and constraint flow decomposition: Define an "input-processing-output" closed loop for each candidate part; S22. Stateless verification: Verify whether candidate parts only depend on input data; for example, the spending power calculation part does not save the user's historical spending records. S23. Reuse potential assessment: evaluate its versatility by measuring historical scenario matching degree and usage frequency.

[0023] S24. Atomized candidate part list; such as region extraction parts, consumption capacity calculation parts, credit score parts.

[0024] Standardized verification module: includes a schema validator (JSON Schema), a compliance check engine (rule base), and a performance testing tool (JMeter); Functional logic encapsulation module: integrates Rust compiler, wasm-pack toolchain, and Docker engine, supporting WASM bytecode compilation and container image building; Metadata management module: Stores metadata and supports field retrieval, semantic search, and version management; Interface adaptation module: Includes REST server (Actix-web 4.0+), gRPC server (Tonic 0.16+), and WASI runtime (WasmEdge 0.12.0), supporting concurrent processing of multiple protocols; Elastic scheduling module: Based on the HPA controller of Kubernetes 1.29+, combined with Prometheus 2.50+ monitoring metrics, it automatically scales up and down; Compliance check engine: Built-in rule engine, supports dynamic loading of compliance rules and verification of component compatibility.

[0025] Parts Library Management Module: Stores verified parts and their metadata, and inputs / outputs schema, compliance rules, and performance metrics.

[0026] This invention, based on an atomic disassembly and encapsulation method and system for generating data components, has the following beneficial effects: (1) This invention solves the problem of fixed functions and difficult decomposition of traditional data products by breaking down complex data scenarios into the smallest data units (data parts) that can be traded independently and reused across scenarios through three major steps: demand scenario analysis, atomized decomposition and standardized verification. This promotes the efficient circulation and value release of data elements. (2) This invention solves the problems of low reusability, poor cross-platform compatibility and insufficient transaction trust of traditional data components by using a three-layer structure design: WASM / containerized cross-platform implementation of the functional logic layer, extended metadata description of the component layer, and multi-protocol adaptation of the interface layer. (3) This invention defines standardized metadata, including basic information, input / output schema, compliance rules, and other extended fields, through a component layer; the interface layer supports three protocols: REST, gRPC, and WASI, ensuring cross-system interoperability and reliable data flow. This invention improves the reusability and flow efficiency of data components; (4) This invention supports multi-language development, lowers the threshold for developers, promotes the prosperity of the data circulation ecosystem, and covers applications in various industries. Attached Figure Description

[0027] Figure 1 : Flowchart of the method for generating data components based on atomization disassembly and packaging in this invention. Detailed Implementation

[0028] This invention proposes a method and system for generating data components based on atomized disassembly and encapsulation. Through demand scenario analysis, atomized disassembly, standardized verification, functional logic encapsulation, component layer encapsulation, and interface layer encapsulation, it achieves high reusability, high automation, and strong industry adaptability of data circulation, reduces data circulation costs, and promotes the market-oriented allocation of data elements.

[0029] To solve the aforementioned technical problems, the present invention adopts the following solution: A method for generating data components based on atomization and packaging: Step 1: Requirement scenario analysis; Input the original data scenario description and business requirements document, identify the core functional points in the scenario, draw the data flow diagram, and clarify the technical constraints that the disassembled parts must meet. Business requirements documents may require extracting user location, spending power, and credit score. Identify core functionalities in the scenario, such as location extraction, spending power calculation, and credit scoring model invocation; Draw a data flow diagram, such as user behavior log → cleaning → feature extraction → model inference → output profile, and label the input and output data of each step; Clearly define the technical constraints that the disassembled parts must meet, such as requiring desensitized parts to comply with GDPR and the Personal Information Protection Act, and requiring computing parts to support GPU acceleration.

[0030] Step 2: Atomization disassembly; Based on the three principles of functional independence, input-output integrity, and reuse potential, the scenario is decomposed into several atomic functional units.

[0031] Data components: refer to independent data processing units formed through atomization, possessing the following characteristics: Functional atomicity: It only undertakes a single or very few core functions, such as ID card number anonymization and user age calculation; Input and output standardization: Clearly define the input data format, such as {"id_card": "string"} and the output data format, such as {masked_id_card: "string"}; Tradeability: Supports independent pricing, ownership transfer, and cross-scenario reuse; Stateless: It does not depend on context such as user session and can be invoked in parallel; Measurability: The scene effectiveness coefficient is calculated based on the call frequency. The formula is: Scene effectiveness coefficient = Baseline value call count × Scene weight Step 3: Standardize verification to ensure parts tradability; Step 4: Encapsulate the functional logic layer; The functional logic layer is the core computing unit of the data components, and it achieves cross-platform execution through WebAssembly (WASM) or containerization (Docker) technologies. Step 5: Component layer encapsulation; The meta-component layer defines the full lifecycle attributes of data components through structured metadata, supporting rapid discovery and matching across systems; Step 6: Interface layer encapsulation; The interface layer provides standardized calling interfaces, supporting three protocols: REST, gRPC, and WASI, to solve interoperability issues between different systems.

[0032] Furthermore, step 2, the atomization decomposition, includes functional flow decomposition, data flow decomposition, and constraint flow decomposition. The functional flow decomposition introduces the Recap algorithm molecular bond fission mechanism to divide atomic units according to functional coupling degree. The data flow decomposition uses a dynamic partitioner to calculate the data dependency graph in real time and self-adaptively split the boundary. The constraint flow decomposition pre-embeds compliance tags for each part, supporting automatic activation and verification during reassembly.

[0033] Furthermore, the breakdown of step 2 is as follows: S21. Determine functional boundaries through techniques such as functional flow decomposition, data flow decomposition, and constraint flow decomposition: Define an input-processing-output closed loop for each candidate part; S22. Stateless verification: Verify whether candidate parts only depend on input data; for example, the spending power calculation part does not save the user's historical spending records. S23. Reuse potential assessment: evaluate its versatility by measuring historical scenario matching degree and usage frequency.

[0034] S24. Atomized candidate part list; such as region extraction parts, consumption capacity calculation parts, credit score parts.

[0035] Furthermore, step 3, the standardized verification, is used to verify the candidate parts list; Input / output schema validation: Use a JSON schema validator to check whether the input and output of candidate parts conform to a standardized format; for example, whether the input for "Extract parts by region" includes the required fields user_id and ip; Compliance verification: Verify whether the components meet the compliance requirements of the target scenario; for example, credit scoring components must comply with the minimum necessary principle of the Personal Information Protection Law. Performance benchmarking: Under standard conditions, such as 1000 QPS and 50ms latency, the processing capability of the component is tested, and a performance report is generated. (For example, if the average latency of the component extracted from a specific region is 45ms, the throughput is 12000 QPS). Output: Validated standardized data components.

[0036] Furthermore, if the standardized data component fails verification in step 3, the process returns to step 2 for re-disassembly.

[0037] Furthermore, step 4 is implemented by WASM compilation and containerization. WASM compilation implementation process: Technology selection: The core logic is written in Rust (leveraging its memory safety features), and the binding interface between WASM and the host environment is generated using wasm-bindgen; Compilation parameters: --release (enable LLVM optimization), --target wasm32-unknown-unknown (compatible with all WASM runtimes), wasm-opt -Oz (optimize bytecode size); Cross-platform verification: Verify the correctness of the function on browsers (Chrome 110+), servers (WasmEdge 0.12.0), edge devices (Raspberry Pi 4B + Wasmtime 10.0), and HarmonyOS.

[0038] The WASM encapsulation module uses the WebAssembly binary instruction format to encapsulate data functional logic for desensitization, feature extraction, and compliance checks. The WASM module supports cross-platform browser, server, and edge device execution, sandbox isolation, and high performance with low overhead. Containerization and encapsulation implementation process: Image building: Build a lightweight Docker image containing runtime, dependency libraries, core logic WASM modules or binaries; Cross-platform deployment: Adapt to different architectures using docker run --platform linux / amd64 (x86_64 server) or docker run --platform linux / arm64 (ARM64 edge device).

[0039] Furthermore, the metadata definition fields in step 5 include basic information, input / output schema, applicable scenarios, compliance rules, performance metrics, dependencies, test cases, and measurable data. The basic information includes: Part ID (globally unique), Name, Creator, Creation Time, License (e.g., Apache-2.0), and Current Owner; The input / output schema includes: the type of the input field (e.g., string), description (e.g., "18-digit ID number"), example value (e.g., "110101199001011234"), and constraints (e.g., regular expressions). The applicable scenarios include: scenario tags, such as financial risk control, medical privacy computing, and priority high / medium / low; The compliance rules include: encryption level, such as AES-256, cross-border transfer restrictions (allowed / forbidden), and compliant regulations, such as GDPR; The performance metrics include: average latency (ms), maximum throughput (QPS), and memory usage (MB). The dependencies include: the ID and version constraints of the dependent parts, such as "dp-encrypt-v1.2>=1.2.0", and the relationship between the part and other parts; The test cases include: input data, expected output, test results (passed / failed), and test executor.

[0040] The measurable component includes: calculating the scene effectiveness coefficient based on the call frequency, using the formula: Scene effectiveness coefficient = Baseline value call count × Scene weight. Furthermore, in step 6, the REST protocol (HTTP / JSON): Interface definition: Conforms to the OpenAPI 3.0 specification, uses JWT authentication, and input and output conform to JSON Schema; Example call: The client sends a POST request to / v1 / desensitize, the request body contains the id_card field, the interface layer calls the WASM module of the functional logic layer to process it, and returns the de-identified result.

[0041] gRPC protocol (Protobuf): Service definition: Define strongly typed interfaces (such as DesensitizeService) through Protobuf files, supporting bidirectional streaming communication; Service discovery: Integrates with Consul 1.15+ to implement service registration and discovery, and the client automatically resolves the instance address by the service name.

[0042] WASI protocol (Wasm System Interface): Interface Specification: Conforms to the WASI 0.2 standard, defining system call interfaces for file reading and writing, network access, etc. Runtime security: When running WasmEdge 0.12.0, restrict file system access scope using --dir / tmp: / tmp:rwx.

[0043] The data component generation system based on atomization disassembly and packaging includes the following modules: Requirements Analysis Module: Extracts functional points and data flows from the scenario based on natural language processing technology; Atomization Decomposition Module: Integrates rule engines (such as Drools) and machine learning models (such as clustering algorithms) to automatically identify atomic functional units; the atomization decomposition module includes functional flow decomposition, data flow decomposition, and constraint flow decomposition. The functional flow decomposition introduces the Recap algorithm molecular bond fission mechanism to divide atomic units according to functional coupling degree; the data flow decomposition uses a dynamic partitioner to calculate the data dependency graph in real time and self-adaptively splits the boundaries; the constraint flow decomposition pre-embeds compliance tags for each part, supporting automatic activation and verification during reassembly.

[0044] The atomization disassembly module process is as follows: S21. Determine functional boundaries through techniques such as functional flow decomposition, data flow decomposition, and constraint flow decomposition: Define an "input-processing-output" closed loop for each candidate part; S22. Stateless verification: Verify whether candidate parts only depend on input data; for example, the spending power calculation part does not save the user's historical spending records. S23. Reuse potential assessment: evaluate its versatility by measuring historical scenario matching degree and usage frequency.

[0045] S24. Atomized candidate part list; such as region extraction parts, consumption capacity calculation parts, credit score parts.

[0046] Standardized verification module: includes a schema validator (JSON Schema), a compliance check engine (rule base), and a performance testing tool (JMeter); Functional logic encapsulation module: integrates Rust compiler, wasm-pack toolchain, and Docker engine, supporting WASM bytecode compilation and container image building; Metadata management module: Stores metadata and supports field retrieval, semantic search, and version management; Interface adaptation module: Includes REST server (Actix-web 4.0+), gRPC server (Tonic 0.16+), and WASI runtime (WasmEdge 0.12.0), supporting concurrent processing of multiple protocols; Elastic scheduling module: Based on the HPA controller of Kubernetes 1.29+, combined with Prometheus 2.50+ monitoring metrics, it automatically scales up and down; Compliance check engine: Built-in rule engine, supports dynamic loading of compliance rules and verification of component compatibility.

[0047] Parts Library Management Module: Stores verified parts and their metadata, and inputs / outputs schema, compliance rules, and performance metrics.

[0048] This invention, based on an atomic disassembly and encapsulation method and system for generating data components, has the following beneficial effects: (1) This invention solves the problem of fixed functions and difficult decomposition of traditional data products by breaking down complex data scenarios into the smallest data units (data parts) that can be traded independently and reused across scenarios through three major steps: demand scenario analysis, atomized decomposition and standardized verification. This promotes the efficient circulation and value release of data elements. (2) This invention solves the problems of low reusability, poor cross-platform compatibility and insufficient transaction trust of traditional data components by using a three-layer structure design: WASM / containerized cross-platform implementation of the functional logic layer, extended metadata description of the component layer, and multi-protocol adaptation of the interface layer. (3) This invention defines standardized metadata, including basic information, input / output schema, compliance rules, and other extended fields, through a component layer; the interface layer supports three protocols: REST, gRPC, and WASI, ensuring cross-system interoperability and reliable data flow. This invention improves the reusability and flow efficiency of data components; (4) This invention supports multi-language development, lowers the threshold for developers, promotes the prosperity of the data circulation ecosystem, and covers applications in various industries.

[0049] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating data components based on atomization disassembly and packaging, comprising the following steps: Step 1: Requirement scenario analysis; Input the original data scenario description and business requirements document, identify the core functional points in the scenario, draw the data flow diagram, and clarify the technical constraints that the disassembled parts must meet. Step 2: Atomization disassembly; Based on the three principles of functional independence, input-output integrity, and reusability potential, the scenario is decomposed into several atomic functional units; Step 3: Standardize verification to ensure parts tradability; Step 4: Encapsulate the functional logic layer; The functional logic layer is the core computing unit of the data components, and it enables cross-platform execution through WebAssembly or containerization technology; Step 5: Component layer encapsulation; The meta-component layer defines the full lifecycle attributes of data components through structured metadata, supporting rapid discovery and matching across systems; Step 6: Interface layer encapsulation; The interface layer provides standardized calling interfaces, supporting three protocols: REST, gRPC, and WASI, to solve interoperability issues between different systems.

2. The method for generating data components based on atomized disassembly and packaging according to claim 1, characterized in that, Step 2, atomization decomposition, includes functional flow decomposition, data flow decomposition, and constraint flow decomposition. The functional flow decomposition introduces the Recap algorithm molecular bond fission mechanism to divide atomic units according to functional coupling degree. The data flow decomposition uses a dynamic partitioner to calculate the data dependency graph in real time and self-adaptively split the boundary. The constraint flow decomposition pre-embeds compliance tags for each part, supporting automatic activation and verification during reassembly.

3. The method for generating data components based on atomized disassembly and packaging according to claim 1, characterized in that, The standardization verification in step 3 includes: Input / output schema validation: Use a JSON schema validator to check whether the input and output of candidate parts conform to a standardized format; for example, whether the input for "Extract parts by region" includes the required fields user_id and ip. Compliance verification verifies whether the components meet the compliance requirements of the target scenario; for example, credit scoring components must comply with the minimum necessary principle of the Personal Information Protection Law. Performance benchmarking tests the processing capabilities of parts under standard conditions and generates performance reports.

4. The method for generating data components based on atomized disassembly and packaging according to claim 1, characterized in that, The metadata definition fields in step 5 include basic information, input / output schema, applicable scenarios, compliance rules, performance metrics, dependencies, test cases, and measurable data.

5. A data component generation system based on atomization disassembly and packaging, comprising the following modules: Requirements Analysis Module: Extracts functional points and data flows from the scenario based on natural language processing technology; Atomization Decomposition Module: Integrates a rule engine and machine learning model to automatically identify atomic functional units; the atomization decomposition module includes function flow decomposition, data flow decomposition, and constraint flow decomposition. Standardized verification module: includes a schema validator, a compliance check engine rule base, and performance testing tools; Functional logic encapsulation module: integrates Rust compiler, wasm-pack toolchain, and Docker engine, supporting WASM bytecode compilation and container image building; Metadata management module: Stores metadata and supports field retrieval, semantic search, and version management; Interface adaptation module: Includes REST server, gRPC server, and WASI runtime, supporting concurrent processing of multiple protocols; Elastic scheduling module: Based on the HPA controller of Kubernetes 1.29+, combined with Prometheus 2.50+ monitoring metrics, it automatically scales up and down; Compliance check engine: Built-in rule engine, supports dynamic loading of compliance rules and verification of component compatibility; Parts Library Management Module: Stores verified parts and their metadata, and inputs / outputs schema, compliance rules, and performance metrics.

6. The data component generation system based on atomized disassembly and packaging according to claim 5, characterized in that: The functional flow decomposition introduces the Recap algorithm molecular bond fission mechanism to divide atomic units according to functional coupling degree; the data flow decomposition uses a dynamic partitioner to calculate the data dependency graph in real time and self-adaptively split the boundary; the constraint flow decomposition pre-embeds compliance tags for each part and supports automatic activation verification during reassembly. The atomization disassembly module process is as follows: S21. Determine functional boundaries using techniques such as functional flow decomposition, data flow decomposition, and constraint flow decomposition: Define an "input-processing-output" closed loop for each candidate part. S22. Stateless verification: Verify whether candidate parts only depend on input data; for example, the spending power calculation part does not save the user's historical spending records. S23. Reuse potential assessment: evaluate its versatility by measuring historical scenario matching degree and usage frequency; S24. Atomized candidate part list; such as region extraction parts, consumption capacity calculation parts, credit score parts.

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