Construction of Intelligent Power Preservation System under the Integration of Internet of Things and Artificial Intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Distribution network line project acceptance is a key link in the quality control of distribution network line project, an important factor affecting the safe and stable operation of the distribution network, which directly determines the level of safe operation of the distribution network.In this paper, for the distribution network line manual acceptance time-consuming and laborious, rare quality defects found rate identification rate is low and other issues, to carry out visual positioning and image recognition based on the distribution network drone automated acceptance technology research.In order to optimize the spatial positioning, attitude sensing and target tracking of the UAV, five coordinate systems, including the world coordinate system, body coordinate system, and photocentric coordinate system, are selected for spatial transformation.Based on the visual localization of the UAV, the path planning algorithm for UAV distribution line inspection combined with the path acquisition scheme is proposed.Gaussian denoising and histogram equalization are performed on the UAV inspection collected images, and Sarsa reinforcement learning algorithm is applied to train the samples to improve the automatic identification capability of safety hazards and other security hazards in the distribution network inspection.Visualization and analysis of UAV distribution line inspection path.Combine the distribution network defects dataset for optimal training strategy selection for distribution networks.The automatic identification algorithm for distribution network defects proposed in this paper achieves a mAP value of 79.60% in the target detection experiment.And in multiple dynamic path planning, the UAV nodes are able to accomplish the path planning tasks in different environments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it