Production Line Semantic Mapping for Scalable KPI Data Acquisition
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Solution Overview
Problem
Existing methods for calculating Key Performance Indicators (KPIs) in production lines require complex configurations for each production line, making it inefficient and inflexible, especially when dealing with a large number of production lines.
Innovation Solution
Establishing a semantic model that defines semantic relationships between semantic units and data source identifiers, allowing for a unified configuration that simplifies the process of data acquisition and is adaptable to different production lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a virtual model is established for each production line with variable-data source binding in model configuration files, then data can be obtained for KPI calculations, but the configuration workload increases significantly when the number of production lines is large
Solution Approach 1:
The patent creates a unified semantic model that serves multiple production lines simultaneously. Instead of establishing separate virtual models for each production line, a single semantic model with standardized semantic units and data source identifiers can be reused across numerous production lines, eliminating redundant configuration work while maintaining data acquisition capabilities.
Solution Approach 2:
The patent transforms the configuration approach from detailed variable-data source binding to a higher-level semantic abstraction. By changing the parameter representation from specific variables to semantic units with standardized identifiers, the system achieves the same data acquisition function with significantly reduced configuration complexity.
2Reliability
If detailed model configurations are created for each production line, then data sources can be properly bound, but the configurations cannot be flexibly suitable for different applications
Solution Approach 1:
The semantic model uses standardized semantic units and data source identifiers that can be universally applied across different production line applications. The same semantic model structure can serve diverse applications by simply changing the semantic relationships and bindings, providing both accuracy and flexibility.
Solution Approach 2:
The patent introduces dynamic semantic relationships that can be flexibly configured and adjusted based on different application requirements. The semantic model allows for adaptable binding between semantic units and data sources, enabling the same framework to suit different applications without requiring rigid, application-specific configurations.
3Manufacturing precision
If variables in KPI calculation formulas are associated with data sources in configuration files, then data can be read and substituted, but the process becomes inefficient when dealing with large numbers of production lines
Solution Approach 1:
The patent uses semantic units as reusable templates that can be copied and applied across multiple production lines. Instead of manually configuring each production line from scratch, the standardized semantic model acts as a template that can be instantiated repeatedly, maintaining calculation accuracy while dramatically improving configuration efficiency.
Solution Approach 2:
The patent performs preliminary work by establishing a standardized semantic model with pre-defined semantic units and data source identifier mappings. This preliminary configuration creates a reusable framework that eliminates the need for repetitive variable-data source binding for each production line, improving efficiency while maintaining precision through the standardized structure.
Data Source
AI summary
Provided in an embodiment of the present disclosure is a method for acquiring data of a data source associated with a production line, including: acquiring a semantic model, the semantic model including semantic relationships between respective semantic units and data source identifiers corresponding to one or more production lines; receiving production line identifiers and acquiring one or more semantic units; converting, based upon the semantic model, the one or more semantic units to data source identifiers corresponding to production lines indicated by the production line identifiers; and acquiring data of data sources indicated by the data source identifiers. Implementing the embodiments disclosed in the present disclosure simplifies a configuration file required for a virtual model of a production line, thereby greatly reducing configuration workload, and enhancing convenience of acquiring data of a data source.