The present invention relates to the field of software architecture technology, specifically to a bidirectional equivalence architecture between data description layer (NDData) and data manipulation layer (TCL/TQL), achieving complete equivalence and unification.
In traditional software architecture, data description (what data looks like) and data manipulation (how to operate data) are separated:
- Description and operation separation: Data described in JSON/XML, operations in API/SQL, two incompatible languages.
- Heavy cognitive burden: Developers must master both data description and manipulation languages.
- Consistency hard to guarantee: Data description and operations use different abstraction levels.
- AI understanding difficulty: AI must understand two different languages, increasing complexity and error rates.
- Limited extensibility: Adding operation types requires modifying operation language; adding data types requires modifying description language.
The present invention proposes a bidirectional equivalence architecture achieving complete equivalence between data description layer and data manipulation layer.
- Bidirectional equivalence: Data description (NDData) and data operation (TCL/TQL) are completely equivalent — every ND data point has a corresponding TCL path, and every TCL statement corresponds to specific ND data.
- Unified abstraction: Data description and data operation use the same abstraction (dimension + path), differing only in focus (description: "what it is", operation: "how to operate").
- Deterministic mapping: From TCL path to ND data, there is a deterministic mapping — same path + same operation = same result.
- Orthogonality: NDData manages "what data is", TCL manages "how to operate data"; responsibilities are naturally orthogonal and do not interfere with each other.
Bidirectional Equivalence Architecture:
NDData Description Layer:
┌─────────────────────────────┐
│ NDData: What data is │
│ ├── Dimensions: Dimension defs│
│ ├── Shape: Data shape │
│ └── Data: Actual values │
└─────────────────────────────┘
↕ Complete equivalence
TCL Operation Layer:
┌─────────────────────────────┐
│ TCL/TQL: How to operate data │
│ ├── get: Query │
│ ├── set: Modify │
│ ├── add: Add │
│ ├── del: Delete │
│ └── broadcast: Broadcast │
└─────────────────────────────┘
- Reduced cognitive burden: One abstraction (dimension + path) covers both description and operation.
- Guaranteed consistency: Architecture ensures consistency between data description and operation.
- AI-friendly: AI only needs to understand one abstraction to operate data.
- Predictability: Same path + same operation = same result.
- Composability: NDData and TCL can be independently extended.
- Cross-domain universal: Same bidirectional equivalence architecture applies to all domains.
Embodiment 1: Office Document
NDData: wenshu.string[0].content = "Hello World"
TCL: get wenshu.string[0].content
Same path structure for description and operation
Embodiment 2: Print Data
NDData: pfd.page[0].colorplate[0].element[0].fill = "#FF0000"
TCL: set pfd.page[0].colorplate[0].element[0].fill = "#000000"
Embodiment 3: Animation Data
NDData: objects[0].position.x = 100
TCL: set objects[0].position.x = 200
Copyright Ownership: All theories and product patents/invention patents/copyrights based on this theoretical system are owned by Chen Qin of Sichuan Longying Technology Co., Ltd.
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Inventor: 陈钦 (cq800229@qq.com)
Affiliation: 四川龙映科技有限公司
Filing Date: 2026-09-14