Industrial Data Modeling Platform for Automated Expert Allocation
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Solution Overview
Problem
Industrial data modeling and application development in complex industrial settings, such as chemical plants, face inefficiencies due to manual data identification and workflow control, which are inaccurate and labor-intensive, especially when dealing with diverse and complex data sets requiring collaboration among personnel with varying expertise.
Innovation Solution
An automated platform that utilizes an industrial graph knowledgebase and operational database to analyze data modeling requests, identify responsible personnel, provide data modeling and analysis components, and automate the data modeling process, bridging various databases and individuals to streamline data model and application development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data identification and workflow control are used, then personnel can handle complex industrial data tasks, but the process becomes inefficient and inaccurate
Solution Approach 1:
The system enables automated self-service through the workflow management module that automatically identifies personnel based on task requirements and expertise profiles, eliminating manual intervention while maintaining high accuracy in data identification and workflow control
Solution Approach 2:
Manual mechanical processes of personnel identification and workflow control are replaced by an automated digital system using the knowledge graph and workflow management module, which queries personnel databases and automatically assigns tasks based on predefined criteria and expertise matching
2Productivity
If automated platform is implemented, then workflow efficiency improves, but system complexity increases
Solution Approach 1:
The platform integrates multiple functions including knowledge graph construction, personnel database management, workflow automation, and data modeling capabilities into a single unified system, reducing overall complexity by eliminating the need for separate systems for each function
Solution Approach 2:
The workflow management module acts as an intermediary layer between the knowledge graph/personnel database and the data modeling processes, abstracting complex automated operations into manageable workflows that improve efficiency without exposing full system complexity to end users
3Reliability
If manual task assignment is used, then personnel expertise can be matched to tasks, but time consumption increases
Solution Approach 1:
The system performs preliminary action by pre-building comprehensive personnel profiles and expertise databases in advance, storing detailed information about personnel skills, qualifications, and areas of expertise that enable rapid automated matching when tasks are assigned, eliminating time-consuming manual assessment
Solution Approach 2:
The workflow management module implements feedback mechanisms that continuously learn from task completion outcomes and personnel performance data, refining the expertise matching algorithm over time to improve reliability of personnel-task matching while reducing assignment time through more accurate initial selections
Data Source
AI summary
This disclosure relates to industrial data services, data modeling and applications for controlling an industrial operation. In one implementation, a platform is disclosed for allocating a data modeling request to a collaborative group of experts based on a two-dimensional data modeling flow data structure and a multilayer resource allocation graph to obtain a data model for controlling the industrial operation. The two-dimensional data modeling flow data structure and the multilayer resource allocation graph are established from an industrial graph knowledgebase using various data analytics and machine learning techniques.