Research and Development of ZJ15/750Y Intelligent Hydraulic Drilling Rig
Bibliographic record
Abstract
Automatic and intelligent operation of drilling rig is the pursue of the drilling equipment industry， and there are less efforts on research and development of intelligent hydraulic drilling rig. This paper presents the research and development of ZJ15/750Y intelligent hydraulic drilling rig. This drilling rig with the hydraulic cylinder lifting system as the main part is associated with automatic string processing tool to form a drilling rig system that is capable of automatic makeup， breakout and striping of drill pipes， as well as rotary drilling. It realizes the whole process “one-click linkage” function that combines the automation of surface string movement and the automation of drilling operation， and incorporates the “digital twin” technology that enables real-time and accurate simulation of equipment action through remote intelligent monitoring. For conventional stripping operations， it achieves unmanned remote control on drilling to the predetermined well depth and release of drill string at the predetermined well depth just by one-key action. With no human intervention in the whole process in the field application， it allows for stable intelligent operations. The proposed device functions normally in remote monitoring， allowing for real-time transmission of the operation status of the drilling rig.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".