Batch Data Model Contextualization for Cross-Industry KPI Control
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Solution Overview
Problem
Current industrial data management systems require significant time and resources to integrate data from multiple vendors and technologies, leading to complex and costly custom data models for comprehensive analysis across multiple industry applications, limiting the ability to seamlessly share insights and manage industrial processes efficiently.
Innovation Solution
An extensible homogeneous data model that transforms and contextualizes data into a graph structure, allowing for the creation of a single data platform that can be extended across various industry applications, reducing the need for multiple custom models and enhancing data management and analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If custom data models are created for each industry application, then data analysis accuracy is improved, but system complexity and deployment time increase significantly
Solution Approach 1:
The patent implements a universal data model framework that can be applied across multiple industry applications (refining, chemicals, plastics, pharmaceuticals, etc.) without requiring custom models for each sector. The framework provides standardized data structures, contextualization rules, and analytics capabilities that work across diverse industrial domains, eliminating the need to build separate custom data models while maintaining high analysis accuracy through industry-specific configuration options within the universal framework.
Solution Approach 2:
The data model is segmented into modular components including standardized data structures, contextualization layers, and configurable analytics modules. This segmentation allows the system to maintain a core universal framework while enabling targeted adjustments for specific industry applications through modular additions rather than complete custom model building, thus reducing overall system complexity.
2Adaptability or versatility
If multiple custom data models are developed for different industry applications, then application-specific analytics are improved, but integration effort and costs increase
Solution Approach 1:
The framework provides a universal data model that serves multiple industry applications simultaneously, eliminating the need to develop and integrate separate custom models for each application. The standardized structure and contextualization capabilities enable the same core framework to deliver application-specific analytics across refining, chemicals, plastics, pharmaceuticals, and other industries without additional integration effort.
Solution Approach 2:
The patent establishes pre-defined data structures, contextualization rules, and analytics templates that are prepared in advance for various industry applications. This preliminary action eliminates the need for time-consuming custom model development and integration when deploying new applications, as the framework already contains ready-to-use configurations for multiple industries.
3Quantity of substance
If comprehensive custom data models are created, then data coverage is improved, but deployment time and resource requirements increase
Solution Approach 1:
The universal framework provides comprehensive data coverage across multiple industry applications through a single standardized model, eliminating the need to deploy separate comprehensive models for each application. The framework's modular architecture allows it to cover diverse data types and industry-specific requirements simultaneously, achieving broad data coverage while maintaining fast deployment speeds.
Solution Approach 2:
The patent merges the functionality of multiple industry-specific data models into a single universal framework that handles data from refining, chemicals, plastics, pharmaceuticals, and other sectors. This consolidation achieves comprehensive data coverage across all applications while reducing deployment time and resource requirements compared to deploying separate comprehensive models for each application.
4Adaptability or versatility
If industry-specific custom models are developed, then domain expertise utilization is improved, but system maintainability deteriorates
Solution Approach 1:
The framework provides a unified maintainable structure that incorporates domain expertise for multiple industries within standardized components. Rather than maintaining separate custom models for each industry, the universal framework centralizes maintenance activities while preserving the ability to utilize domain-specific expertise through configurable parameters and modular extensions, significantly improving system maintainability.
Data Source
AI summary
A system and method for monitoring and controlling production of batches of products in an industrial process, the method comprising: receiving, by a processing circuit, data describing a batch of products generated in an industrial process from one or more data sources; contextualizing, by the processing circuit, the data describing the batch of products generated in the industrial process; generating, by the processing circuit, a batch data model based on the contextualized data; executing, by the processing circuit, the batch data model to determine key performance indicators for the batch of products; comparing, by the processing circuit, the key performance indicators to pre-determined key performance indicators; and performing an automated action based on a result of the comparison.