Environment View Construction Using Selective Image Capture
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Solution Overview
Problem
Existing maintenance technologies for complex industrial environments, such as factories, face inefficiencies due to indiscriminate video streaming that consumes network bandwidth, drains battery resources, and requires operator guidance, leading to low-quality and inefficient remote assessments.
Innovation Solution
A system that selectively determines environment images based on criteria like frequency, blur, and content difference, reducing unnecessary data transmission and enabling efficient construction of spatial and temporal environment views for remote experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If indiscriminate video streaming is used to enable remote assessment, then complete environment coverage is achieved, but network bandwidth consumption increases and battery resources are drained
Solution Approach 1:
The system extracts only the necessary environment images from the continuous video stream based on predetermined selection criteria (frequency, blur, content difference) rather than transmitting the entire stream. This extraction process removes redundant information while preserving essential environmental data for remote assessment, thereby reducing energy consumption without compromising assessment completeness.
Solution Approach 2:
The system applies different selection criteria to different aspects of image quality: frequency control for temporal sampling, blur threshold for sharpness requirements, and content difference for informational value. This local quality approach ensures that each transmitted image meets specific quality standards appropriate for its purpose, optimizing the balance between information completeness and resource consumption.
2Loss of information
If indiscriminate video streaming is used to enable remote assessment, then complete environment coverage is achieved, but network bandwidth consumption increases
Solution Approach 1:
The system extracts only the necessary environment images from the continuous video stream based on predetermined selection criteria (frequency, blur, content difference) rather than transmitting the entire stream. This extraction process removes redundant information while preserving essential environmental data for remote assessment, thereby reducing network bandwidth consumption without compromising assessment completeness.
Solution Approach 2:
The system transmits a partial set of images that are sufficient for effective environment assessment rather than the complete video stream. By selecting images that meet specific criteria (frequency thresholds, blur limits, content differences), the system achieves adequate assessment coverage with a fraction of the total data, reducing network bandwidth consumption while maintaining assessment effectiveness.
3Speed
If continuous video streaming is used, then real-time monitoring is achieved, but computational resources are consumed and network latency increases
Solution Approach 1:
The system performs preliminary selection and filtering of images on the client device before transmission, applying frequency, blur, and content difference criteria to pre-process the video stream. This preliminary action reduces the volume of data requiring transmission and processing, thereby reducing network latency and improving response time for remote assessment without sacrificing monitoring effectiveness.
4Measurement precision
If operator guidance is required for video streaming, then targeted assessment is achieved, but operational complexity increases
Solution Approach 1:
The system automatically selects and transmits environment images based on predetermined selection criteria without requiring real-time operator guidance. The client device autonomously evaluates images against frequency, blur, and content difference thresholds, and the server independently navigates the environment views. This self-service approach maintains assessment accuracy through systematic criteria-based selection while significantly simplifying operation by eliminating the need for operator intervention.
Data Source
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AI summary
A computing system may include a client device and a server. The client device may be configured to access a stream of image frames that depict an environment, determine, from the stream of image frames, environment images that satisfy selection criteria, and transmit the environment images to the server. The server may be configured to receive the environment images from the client device, construct a spatial view of the environment based on position data included with the environment images, and navigate the spatial view, including by receiving a movement direction and progressing from a current environment image depicted for the spatial view to a next environment image based on the movement direction.