Edge Device IoT Model Graph Classification
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Solution Overview
Problem
Industrial Internet of Things (IoT) data acquisition is hindered by the need for massive manual configuration, slow data acquisition, and inadequate communication between field systems and cloud platforms, particularly when dealing with complex systems and diverse communication protocols.
Innovation Solution
An information acquiring method that uses an edge device to transmit data and industrial IoT models to a cloud platform, where the model is converted into a graph, undergoes similarity analysis using a random walk algorithm, and is classified to generate a reusable industrial IoT model, reducing manual configuration and enabling automatic data mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration is used for industrial IoT model and data sources, then configuration flexibility is maintained, but configuration time and labor cost increase significantly
Solution Approach 1:
The system enables self-service configuration by automatically extracting device information and generating industrial IoT models without manual intervention. The edge device autonomously collects device data, converts it to standardized models, and transmits to the cloud platform, eliminating the need for engineer manual configuration while maintaining configuration flexibility through automated adaptation to different device types
Solution Approach 2:
The system changes the configuration parameter from manual definition to automated extraction. By transforming device information into standardized industrial IoT models through automated conversion processes, the system maintains configuration flexibility while dramatically reducing configuration time and labor requirements
2Measurement precision
If manual configuration of data sources and data points is performed, then configuration accuracy can be controlled, but data acquisition speed becomes extremely slow
Solution Approach 1:
The system performs self-service data acquisition by automatically extracting data points and their meanings from device information. The edge device autonomously identifies data sources, converts them to standardized formats, and transmits to the cloud platform, achieving both high configuration accuracy through automated parsing and rapid data acquisition without manual engineer intervention
Solution Approach 2:
The system performs preliminary action by pre-converting device information to standardized industrial IoT models before data acquisition. The automated conversion process prepares all necessary data mappings and configurations in advance, enabling rapid subsequent data acquisition without manual configuration delays
3Device complexity
If cloud platforms support only one communication language, then platform simplicity is maintained, but adaptability to diverse industrial devices is limited
Solution Approach 1:
The system introduces an intermediary layer in the form of standardized industrial IoT models that act as translators between diverse device communication protocols and the cloud platform. The edge device converts various protocol formats into unified standardized models, enabling the cloud platform to maintain simplicity while achieving broad protocol compatibility through this intermediate conversion layer
Solution Approach 2:
The system achieves universality by creating a universal standardized industrial IoT model that can represent devices from multiple communication protocols. The standardized model serves as a multi-functional interface that accommodates diverse device types while maintaining platform simplicity, allowing one cloud platform to support numerous protocol types through this universal representation
Data Source
AI summary
Various embodiments include a method for deploying field device into an Internet of Things (IoT). The method may include: acquiring information from a field device using an edge device; transmitting the acquired information to a cloud platform; wherein the information comprises data and an industrial IoT model; converting the industrial IoT model into a graph; performing similarity analysis based on the graph; classifying the industrial IoT model based on the similarity analysis; generating a first industrial IoT model comprising a type or an example; performing data mapping on the first industrial IoT model; and operating the field device as part of the IoT.