Industrial Process Control Templates for Unstructured Data Mapping
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Solution Overview
Problem
Conventional industrial process control systems struggle with unstructured data, leading to inconsistent attribute mapping, lengthy deployment times, and increased error rates due to ad hoc attribute definitions, necessitating custom models for each system instance.
Innovation Solution
A system and method that utilize predefined industrial system templates with normalized attributes and machine learning features, enabling consistent mapping and automated control instruction generation, reducing deployment time and error rates by using pre-defined attribute-to-model feature mappings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional systems use ad hoc attribute definitions for unstructured industrial data, then users can easily assign attributes in a free-form manner, but this creates inconsistent attribute mapping across different users and system instances
Solution Approach 1:
The patent transforms the parameter structure by converting free-form, unstructured attribute definitions into a standardized, structured format with predefined schemas. This parameter change enables consistent interpretation across different users and systems while maintaining ease of use through template-based assignment.
Solution Approach 2:
The patent segments the attribute definition process into distinct, standardized components with specific schemas and validation rules. By dividing the monolithic free-form approach into structured segments, the system achieves both consistency in mapping and ease of operation through guided assignment.
2Adaptability or versatility
If conventional systems develop custom models end-to-end for each new industrial system instance, then the models can be tailored to specific system requirements, but this significantly increases deployment time
Solution Approach 1:
The patent performs preliminary actions by pre-defining attribute schemas, data structures, and model templates before deployment. This advance preparation allows rapid instantiation of customized models for different industrial systems without requiring end-to-end custom development, thus reducing deployment time while maintaining adaptability.
Solution Approach 2:
The patent uses template copying and instantiation to create customized models for different industrial system instances. By copying standardized templates and configuring them with system-specific parameters, the system achieves model customization without the time cost of developing each model from scratch.
3Adaptability or versatility
If conventional systems allow ad hoc attribute mapping without strict definitions, then the system is flexible in data assignment, but this increases error rates due to bad or incorrect mapping
Solution Approach 1:
The patent implements feedback mechanisms through schema validation and data quality checks that provide immediate verification of attribute mapping accuracy. This feedback loop maintains data assignment flexibility while preventing errors by validating mappings against predefined schemas before they are committed.
Solution Approach 2:
The patent provides beforehand cushioning by establishing predefined attribute schemas, validation rules, and error-checking mechanisms before data assignment occurs. This preparatory structure prevents incorrect mappings while preserving flexibility within the defined framework.
Data Source
AI summary
In variants, a method for industrial process control can include: determining an industrial system representation using a set of industrial system templates, wherein each template is associated with a control model and a set of attributes corresponding to control model features; determining associations between the attributes of the industrial system representation and data streams from the industrial system; and generating a set of control instructions for the industrial system based on the data streams associated with the attributes.