Smart Farm Error Diagnosing Apparatus for Remote Equipment Monitoring
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Solution Overview
Problem
Smart farms face challenges in accurately and quickly detecting errors in ICT-related equipment due to harsh environmental conditions and remote operation, hindering their efficiency and widespread adoption.
Innovation Solution
An error diagnosing method and apparatus that receive control messages, analyze data using preset diagnostic rules, and provide error determination results to users through a user interface, utilizing semantic web-based data conversion and ontology-based reasoning to identify issues in sensors and controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If ICT-related equipment is deployed in smart farms for remote monitoring and control, then labor saving and convenience are improved, but equipment malfunction rate increases due to harsh environmental conditions
Solution Approach 1:
The system performs preliminary error diagnosis by analyzing control data and sensing data before equipment failure occurs. The error diagnosing apparatus continuously monitors equipment status and detects potential issues in advance, allowing preventive maintenance before actual malfunction happens in the harsh greenhouse environment.
Solution Approach 2:
The system implements a feedback mechanism where sensing data from sensors and control data from controllers are continuously collected and analyzed. The error diagnosis results are fed back to users through user terminals, enabling real-time monitoring and response to equipment status changes, thus improving reliability through continuous verification.
2Ease of operation
If equipment is operated under remote control in smart farms, then labor saving is improved, but ability to detect errors worsens due to lack of visual confirmation
Solution Approach 1:
The error diagnosing apparatus acts as an intermediary between the remotely controlled equipment and the user. It collects control data from controllers and sensing data from sensors, analyzes this data using error diagnostic rules, and provides error diagnosis results to users through user terminals, enabling error detection without direct visual confirmation.
Solution Approach 2:
The system replaces mechanical visual inspection with automated data analysis. Instead of requiring users to physically visit and visually check equipment, the system uses error diagnosing apparatus to automatically analyze control and sensing data, substituting mechanical inspection with intelligent data processing and error detection algorithms.
3Measurement precision
If comprehensive monitoring is implemented in smart farms, then error detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the error diagnosis function into a separate error diagnosing apparatus that operates independently. This modular approach allows comprehensive monitoring of multiple equipment parameters without significantly increasing the complexity of individual equipment components. The diagnosis function is divided into data collection, analysis, and result provision stages.
Solution Approach 2:
The error diagnosing apparatus serves multiple functions: collecting control data from various controllers, collecting sensing data from multiple sensors, analyzing data using error diagnostic rules, and providing results to different user terminals. This multi-functional design achieves comprehensive monitoring while avoiding the need for separate systems for each function.
Data Source
AI summary
An error diagnosing method performed by an apparatus for diagnosing an error of operating equipment in a smart farm includes: receiving a control message which triggers an error diagnosis; analyzing data collected from a smart farm on the basis of a preset error diagnostic rule when the control message is received; outputting a result of determining whether an error of the operating equipment installed in the smart farm occurs according to an analysis result; and providing the error determination result to a user through a user interface. It is possible to stably operate and efficiently manage a smart farm.