Exploration of Collaborative Optimization Strategy for Oxygen Production System and Interior Environment Control of Railway Locomotives
Bibliographic record
Abstract
With the increasing requirements of passenger comfort, safety and energy efficiency in railway transportation, the existing in-car environment control and oxygen production systems still have problems such as response lag, low energy efficiency and poor robustness in terms of multi-objective coordination, dynamic response capability and energy consumption control. To this end, this paper introduces the fusion technology of artificial intelligence and the Internet of Things to construct a collaborative optimization strategy based on the Model Predictive Control (MPC) framework to improve the comprehensive performance of the locomotive oxygen production system and the in-car temperature, humidity and air quality control. Specific methods include: building a multi-source sensor network to achieve real-time perception and data fusion of multiple parameters such as oxygen concentration, CO₂ level, temperature and humidity, using AI models to predict environmental conditions, and achieving coordinated control of oxygen concentration, temperature and humidity, and ventilation intensity based on MPC optimization objective functions. The experimental results in typical operation scenarios show that, in contrast, under the PID control strategy, the changes in oxygen concentration and temperature are large and exceed the limit. For example, at 180 seconds, the oxygen concentration dropped to 20.5% and the temperature is 22.9°C, both exceeding the set safety range. This paper provides theoretical support and technical paths for the low-carbon and intelligent operation of the intelligent locomotive environmental system.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".