Digital Twin Model Retrieval With Binary Feature Tags
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Solution Overview
Problem
Existing digital twin systems face challenges in quickly retrieving digital twin models that match specific conditions due to the large amount of data involved, hindering their performance.
Innovation Solution
A method and device that utilize binary tags to represent features of digital twin models, enabling quick retrieval through logical operations like AND or XNOR between retrieval binary tags and model binary tags, with a database block storing these models and tags, and a processing block managing their generation and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple digital twin models are generated to manage large amounts of data, then the system can represent more complex real-world scenarios, but the time required to retrieve matching models increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and encoding model features into binary tags before retrieval operations. The feature extraction and binary encoding are performed in advance during model creation, so that when retrieval is needed, the system only needs to perform fast binary tag comparison rather than analyzing full model data, thus reducing retrieval time while maintaining the ability to handle complex scenarios
Solution Approach 2:
The patent extracts essential features from complex digital twin models and represents them as simplified binary tags. By separating the core identifying features from the full model data and storing them as discrete binary attributes, the system enables rapid filtering and retrieval without needing to process the entire model dataset, resolving the contradiction between complexity representation and retrieval speed
2Ease of operation
If traditional data search methods are used to retrieve digital twin models, then comprehensive searching is possible, but the retrieval speed decreases due to large data volume
Solution Approach 1:
The patent replaces traditional mechanical search methods (sequential scanning and comparison of model data) with a binary tag-based retrieval system. The binary encoding allows the system to use bitwise logical operations for rapid filtering, substituting slow data-by-data comparison with fast binary arithmetic operations that can process multiple features simultaneously, thus maintaining comprehensive searching capability while dramatically improving retrieval speed
3Measurement precision
If binary tags with multiple bits are used to represent features, then feature representation precision increases, but the complexity of logical operations increases
Solution Approach 1:
The patent changes the parameter representation from continuous or categorical model attributes to discrete binary values. By encoding features as binary tags where each bit represents a specific feature state, the system achieves precise feature representation while enabling the use of efficient bitwise logical operations (AND, OR, XOR, NOT) that are simpler and faster to compute than traditional comparison operations on complex data structures
Data Source
AI summary
Disclosed is a method of retrieving a digital twin model of a digital twin device. The method includes generating a retrieval binary tag that matches a retrieval condition, performing a logical operation between the retrieval binary tag and the binary tag representing features of the digital twin model, and adding model IDs of each of digital twin models corresponding to the binary tag in a retrieval list when it is determined that the retrieval binary tag is the same as the binary tag, based on a result of the logical operation, and each of binary tags includes a plurality of bits to which a plurality of tags are respectively assigned, and each of the plurality of bits has a first logical value or a second logical value based on whether the digital twin models have features assigned to the corresponding tags.