Digital Twin Workshop Modeling with Reusable Process Encapsulation
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Solution Overview
Problem
Existing workshop modeling and configuration design methods are inefficient, leading to repeated modeling operations, high error rates, and lengthy design cycles due to their static nature and lack of integration across multiple processes, resulting in increased costs and delays.
Innovation Solution
A generalization and encapsulation method based on a digital twin (DT) model that classifies devices, abstracts common processes, encapsulates continuous flows, and stores them in a database for fast retrieval, reducing repetition and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing static design methods are used for workshop modeling, then individualized design plans can be constructed, but repeated modeling and algorithmic operations are required for similar production lines, reducing work efficiency
Solution Approach 1:
The patent creates a master template model that can be copied and reused across multiple production lines. Instead of building models from scratch for each line, the system stores standardized process models in a database that can be rapidly instantiated and customized for similar production scenarios, eliminating repeated modeling operations
Solution Approach 2:
The patent develops universal process models that can serve multiple functions across different production lines. The standardized models are designed to be adaptable to various similar production scenarios, allowing one model to serve multiple purposes and reducing the need for individualized modeling for each line
2Reliability
If existing design methods are used, then comprehensive workshop models can be created, but the large amount of repeated operations increases the possibility of errors and wastes time
Solution Approach 1:
The patent performs preliminary modeling work by creating standardized master templates that have been pre-validated for accuracy. These pre-built models contain pre-configured algorithms and parameters that have been optimized beforehand, eliminating the need for time-consuming and error-prone manual configuration for each new production line
Solution Approach 2:
The system enables self-service modeling through automated model instantiation and parameter inheritance. When a new production line model is created, the system automatically retrieves and configures appropriate standardized models from the database, reducing manual intervention and the associated errors while maintaining high accuracy
3Adaptability or versatility
If serial design procedure is followed for workshop customization, then complete design coverage is achieved, but major changes in previous stages require re-design of subsequent stages, resulting in high change cost and long cycle
Solution Approach 1:
The patent segments the workshop design into independent, modular process models that can be individually modified without affecting other parts. Each standardized model represents a discrete functional unit that can be changed, added, or removed independently, allowing modifications at any stage without requiring complete re-design of subsequent stages
Solution Approach 2:
The system implements dynamic model configuration where standardized models can be flexibly adjusted and reconfigured based on changing requirements. The modular architecture allows the design to adapt dynamically to changes at any stage, with automatic updates propagated through the system without requiring sequential re-design
Data Source
AI summary
A generalization and encapsulation method based on a digital twin (DT) model of a workshop includes: classifying a device in a production line according to a basic operation and a functional characteristic of a process of the device; abstracting a commonality in terms of process action mode, process algorithm and action trigger mechanism; encapsulating according to a sequence characteristic of a process; comparing processes, and generalizing and encapsulating; encapsulating according to a time sequence, a space sequence and a logic characteristic of a specific process; storing a generalized and encapsulated process in a database; and calling the generalized and encapsulated process from the database to a device or a process. The generalization and encapsulation system includes an abstract process encapsulation module, a continuous process encapsulation module, a process action encapsulation module, a process algorithm encapsulation module, a database and a fast calling module.