Cloud-Edge Forecasting for Aluminum Oxide Production
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Solution Overview
Problem
Existing aluminum oxide production index forecasting processes are inefficient and unable to meet real-time requirements, making it difficult to provide timely feedback and adjustments in the production process.
Innovation Solution
A cloud-edge collaboration forecasting system and method that involves a data acquisition device, a hardware platform with a cloud model training server and an edge-end forecasting computer, and a software system for selective management of forecasting algorithms, model training, big data analysis, and parameter correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate processing of industrial process data and laboratory assay analysis data is performed with artificial re-processing, then data consistency can be ensured, but real-time forecasting requirements cannot be met
Solution Approach 1:
The system divides data processing into two independent modules: an industrial process data processing module that processes sensor data in real-time, and a laboratory assay analysis data processing module that processes laboratory data. These modules operate independently and feed into separate forecasting models, eliminating the need for artificial re-processing while maintaining data consistency through the dual-module architecture.
2Measurement precision
If manual data processing and model training is performed, then data consistency can be maintained, but real-time feedback cannot be provided to staff
Solution Approach 1:
The system replaces manual mechanical data processing with automated computer-based processing. The industrial process data processing module and laboratory assay analysis data processing module automatically process data and generate forecasting results, which are then transmitted to user terminals in real-time, eliminating delays associated with manual operations.
3Measurement precision
If staff manually acquire and verify production data, then data accuracy can be ensured, but product quality improvement and energy consumption reduction become difficult
Solution Approach 1:
The system implements self-service through automated data acquisition and verification. The industrial process data processing module automatically acquires sensor data, and the laboratory assay analysis data processing module automatically processes laboratory data. The system performs self-verification through the forecasting models, eliminating the need for manual data acquisition and verification by staff while maintaining data accuracy.
4Ease of manufacture
If existing separate data processing methods are used, then data can be processed, but real-time forecasting and adjustment capabilities are lost
Solution Approach 1:
The system achieves multi-functionality by integrating two independent processing modules that can handle different types of data (industrial process data and laboratory assay data) through a unified architecture. Both modules feed into forecasting models that provide real-time predictions, enabling the system to maintain data processing capabilities while gaining real-time forecasting and adjustment capabilities through the coordinated operation of multiple functional components.
Data Source
AI summary
Provided is a cloud-edge collaboration forecasting system and method for aluminum oxide production indexes. The forecasting system performs forecasting algorithm selection, parameter configuration and model training on indexes and variables of the aluminum oxide production process at a cloud model training server, performs evaluation and parameter correction on the trained model to obtain an optimal training model, and pre-processes the data in the aluminum oxide production process at an aluminum oxide production index forecasting computer at an edge end. The trained model parameters are imported from the cloud, and further the trained forecasting model is used for forecasting aluminum oxide production indexes for different production processes. The forecasting system and method can provide powerful calculating resources by training an aluminum oxide production index forecasting model through the cloud model training server, and real-time convenient aluminum oxide production index forecasting through the computer at the edge end.