The Remote Monitoring System Based on the Internet of Things and Its Monitoring Method in the Design of Construction Machinery
Bibliographic record
Abstract
The construction machinery design based on the Internet of Things remote monitoring system is an important direction of the development of construction machinery design in China.Based on the principle of Internet of Things, this paper expands the global positioning GPS module module and GPRS wireless through the design and implementation of the construction machinery monitoring subsystem, and can send the construction machinery positioning data and bus status information to the monitoring center at any time.The software of the display and the remote monitor adopts the real-time system as the operating system, and various interfaces are extended with corresponding drivers.The hardware design of the construction machinery monitor adopts the popular embedded design in the market, and the relatively mature interface circuit is selected to ensure the stability of the hardware platform, reduce the difficulty of hardware development, and greatly shorten the development cycle of product hardware.Experimental data shows that combining the construction machinery design with the Internet of Things, the monitoring system adopts PHC monitorable programming system and PDRF system, which can realize the full cycle monitoring of the construction machinery design process.Experimental data shows that the Internet of Things system and the construction machinery engineering system can better complete the work, which improves its work efficiency by about 20%, and 80% of computer professional technicians apply the relevant Internet of Things technology in intelligent Has conducted in-depth exploration in the field of construction machinery monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".