Early warning technology and optimization research on real-time perception of environmental protection risk during the construction period of power transmission and transformation projects based on image processing algorithm
Bibliographic record
Abstract
The level of informationization infrastructure of the power system is constantly improving, and it is of great practical significance to carry out real-time perception and early warning of environmental risks during the construction period of the project based on image processing algorithms.This paper proposes a multi-scale parallel real-time detection algorithm based on SSD, which optimizes the network structure of SSD algorithm, combines and splices different sizes of inverted residual blocks and different types of activation functions with each other, and designs a kind of lightweight feature extraction network EPNets.Then, it proposes a lightweight parallel fusion structure, which is applied to the multi-scale prediction of the lightweight feature extraction network, and optimizes the environmental risk real-time detection speed of the algorithm.The algorithm is optimized for realtime environmental risk detection speed.A Bayesian network-based environmental risk behavior warning model is constructed to provide real-time warning for the detected risk behaviors.By comparing with the original algorithm and existing target detection algorithms, the multi-scale parallel fusion detection algorithm based on SSD proposed in this paper can maintain good detection speed with low loss degree, and its environmental protection risk identification time is only 9ms.Meanwhile, the early warning algorithm in this paper realizes the accurate early warning of the soil erosion risk in the study area through the soil erosion environmental protection risk during the construction period of transmission and transformation projects detected.It provides an objective guideline for the control of environmental protection risks and work priorities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".