Design of Electric Power Maintenance Evaluation System Based on immersive VR
Bibliographic record
Abstract
In order to solve the problems of low efficiency and danger in the existing power maintenance training methods, a power maintenance evaluation system based on immersive virtual reality technology is developed in this paper. Based on the equipment 3D accurate model and virtual reality simulation technology, the system imports the power plant scene and equipment accurate model through MakeRea13D platform for content development. Using key technologies such as model lightweight, 3D UI display and VR multi means interaction, an immersive virtual maintenance and virtual scene operation simulation platform is established. At the same time, five led-cave, mobile virtual platform and helmet immersive simulation environment are built, and three environments can be used for rendering display. According to the field environment of the actual operation and the real structure of the equipment, the system establishes three-dimensional virtual scenes such as maintenance and operation required for the evaluation, and supplements the accurate model data required for the evaluation. The actual working environment of the power station is reconstructed according to the requirements of the evaluation content, which helps to improve the professional technicians’ sense of substitution and realism of the operation scene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".