Video Messaging for Real-Time Equipment Event Recognition
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Solution Overview
Problem
Existing systems lack an efficient method to recognize events involving equipment objects in real-time using camera systems and control them accordingly, especially in dynamic environments like airports, where precise monitoring and operation of equipment such as aircraft, ground support equipment, and pre-conditioned air units are critical.
Innovation Solution
An event recognition system comprising cameras and controllers that capture image data, process it to recognize events by comparing parameters with reference data sets, and execute control procedures such as positioning, operating, or communicating with equipment objects, transmitting this data to network-based clients for real-time monitoring and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time event recognition and control of equipment objects is implemented using camera systems, then safety and operational efficiency are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of event recognition into distinct modules: image capture by camera systems, parameter extraction through image processing, event determination logic, and control execution. This modular segmentation allows each component to specialize in specific functions, improving overall system reliability while making the complexity manageable through organized decomposition of the recognition and control pipeline.
Solution Approach 2:
The system performs preliminary actions by pre-establishing reference data sets containing expected parameter ranges and equipment states before real-time operation. During runtime, captured image parameters are compared against these pre-prepared references to rapidly determine events. This preliminary preparation reduces real-time computational burden, enabling safe and efficient operation without excessive complexity during critical monitoring phases.
2Measurement precision
If comprehensive image data processing and parameter comparison is performed to accurately recognize events, then measurement precision is improved, but processing time and computational load increase
Solution Approach 1:
The system extracts only the essential parameters needed for event recognition from the captured image data, such as position, orientation, and key dimensional measurements of equipment objects. By selectively extracting only the critical parameters rather than processing all image data comprehensively, the system achieves sufficient measurement precision for safety-critical event detection while significantly reducing processing time and computational load compared to full-image analysis.
Solution Approach 2:
The system applies partial action by focusing computational resources on comparing extracted parameters against specific reference values that are most critical for event determination. Rather than performing exhaustive analysis of all possible image features, the system concentrates processing on the essential parameter comparisons needed for accurate and timely event recognition, achieving the necessary precision with reduced computational overhead.
Data Source
AI summary
Video messaging systems includes a plurality of camera systems, a plurality of network-based clients, a messaging hub communicatively connectable to the plurality of network-based clients, and a video frame transmission service communicatively connected to the messaging hub. The messaging hub is configured to transmit image data as encoded data to each of the plurality of network-based clients. The video frame transmission service is configured to selectively connect with at least one of the plurality of camera systems for a time period that is based upon a request received from at least one of the plurality of network-based clients.