Design of Intelligent Management System for Underground Cable Tunnels and Integration of Robotic Inspection and Defect Recognition Technology
Bibliographic record
Abstract
Underground cable tunnels are important infrastructures to maintain the normal operation of cities, and problems such as cable insulation aging and discharge can easily cause fires or even explosions, so the requirements for maintenance are high.In this study, the DGPS positioning method is used to optimise the positioning system of the intelligent inspection robot for underground cable tunnels, and the LQR controller is used to realise the deviation correction of angle and position in the motion path of the intelligent inspection robot.Then the inspection robot and UHF sensor are used to detect and accurately locate the defects in the cable tunnel, and finally the deviation correction and defect detection methods are integrated to design an intelligent management system for underground cable tunnels.The results of simulation experiments and field surveys show that the proposed method can correct the deviation of the robot in the inspection process in a timely manner, avoiding the problems of hitting the obstacles and the path around the long distance, and the average time consumed in the simulation map scenario is only 6.89 s.The communication scheme of the intelligent management system is practicable, and it can effectively detect and identify the defects and the specific location of the defects in the underground cable tunnels.The system proposed in this paper is able to detect defects and faults in time in practical applications, providing a new solution for the inspection of underground cable tunnels.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".