Integrating Technical System Models for Interchangeable Cloud Simulation
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Solution Overview
Problem
Current modeling technologies for technical systems, such as machines and process engineering plants, are complex, require significant programming effort, and struggle with integrating high sampling rate measurement signals, making it difficult to simulate and test plant controllers effectively, especially in cloud-based environments.
Innovation Solution
An integrating model that allows interchangeable individual models with machine-machine interfaces, data pre-processing, and a cloud-based framework, enabling the integration of models from different manufacturers and programming languages, and facilitating real-time data processing and analysis without extensive programming knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual models from different manufacturers and programming languages are integrated, then model versatility and interchangeability are improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent introduces a standardized interface layer and common data exchange format that acts as an intermediary between individual models from different manufacturers. This interface layer handles the translation and adaptation between different programming languages and model formats, enabling seamless integration without directly connecting incompatible systems. The standardized interface serves as a mediator that resolves the complexity of integrating diverse models while maintaining their unique characteristics.
Solution Approach 2:
The patent creates a universal model integration framework that can accommodate multiple programming languages and model formats through a common standardized interface. This universal interface design allows the same integration architecture to work with various manufacturers' models without requiring custom integration logic for each case, thereby improving versatility while controlling complexity through reuse of the same integration mechanisms.
2Measurement precision
If high sampling rate measurement signals are processed, then measurement precision and real-time analysis capability are improved, but computational load and data processing complexity increase
Solution Approach 1:
The patent segments the high sampling rate measurement signals into manageable data blocks or frames for processing. By dividing the continuous high-rate signal stream into discrete segments, the system can process each segment independently using standardized algorithms, reducing the computational complexity compared to processing the entire continuous signal at once while maintaining the precision benefits of high sampling rates.
Solution Approach 2:
The patent employs parameter changes by adjusting the sampling rate dynamically based on the specific analysis requirements. For certain analyses, the system may use the full high sampling rate to maintain precision, while for other analyses with lower requirements, the sampling rate is reduced to decrease computational load. This adaptive parameter adjustment allows the system to optimize between measurement precision and processing complexity for different operational contexts.
3Ease of operation
If cloud-based model operation is implemented, then model accessibility and collaboration are improved, but data transmission requirements and network dependency increase
Solution Approach 1:
The patent extracts and separates the essential model parameters and configuration data from the complete model datasets. Instead of transmitting entire high-fidelity models and all their associated data through the network, the system extracts only the critical parameters needed for cloud-based operation. This extraction approach maintains model accessibility in the cloud while significantly reducing the data transmission volume required.
Solution Approach 2:
The patent performs preliminary data processing and model preparation locally before cloud transmission. Essential preprocessing steps, data filtering, and model simplification are executed on-site before uploading to the cloud environment. This preliminary action reduces the volume of data that needs to be transmitted over the network while ensuring that the cloud-based model operation receives pre-processed, ready-to-use data that maintains analytical precision.
Data Source
AI summary
An integrating model for a technical system composed of at least one of a machine, a component of a machine, and a technical process includes a machine-machine interface, and a plurality of individual models having each assigned a raw model and a data pre-processing, wherein different individual models model different parts of the technical system and have different raw models and a different data pre-processing, and wherein at least one of the individual models is interchangeable for a part of the technical system.