A Human-Machine Collaborative Industrial Environmental Monitoring System Based on IoT and Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
To enhance the timeliness and precision of industrial environmental monitoring and achieve synergy between machine intelligence and human decision-making, this study develops a multi-terminal collaborative monitoring system integrating IoT and deep learning. The system architecture comprises: (1) a perception layer for real-time environmental data collection; (2) an intelligent analytics layer with a GRU model for 48-hour predictions and proactive warnings; and (3) a collaborative decision-making layer with a web platform and WeChat mini-program for visualization and emergency intervention. By continuously refining warnings based on human feedback and enabling minute-level human response, the system forms a closed-loop “monitoring-warning-decision” collaboration. It effectively resolves conflicts between passive machine alarms and delayed responses, offering a scalable intelligent monitoring model for Industry 4.0.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it