Design of Intelligent Control Platform for Electric Power Informatization Based on Intelligent Data Analysis
Bibliographic record
Abstract
In order to improve the intelligent processing capability of the server of the electric power information platform, the intelligent control platform of electric power informatization based on intelligent data analysis is designed.Taking the regional electric power headquarters as the base point, deploying the electric power informatization intelligent management and control workbench, connecting the necessary systems for electric power operation through the telecommunication management network (TMN), and completing the platform hardware structure design.Divide the platform monitoring function into four parts: query instruction issuance, feedback data reception, data parsing, and data storage, and monitor power data in real time.Deploy data collection algorithms on the data collection server to collect power data such as power harmonics, effective voltage and current, active and reactive power, and harmonic distortion.And Deep Belief Network (DBN) is used to train the anomaly detection model, which realizes the detection of abnormal behavior of the system.Determine the experimental methods and steps, and test the results: the server of the intelligent control platform for electric power informatization designed in this project passed the pressure test of the number of clicks per second and throughput of 100 and 500 simulated users, and has superior traffic processing capability.Application test of the platform, through the test to achieve the design requirements of the system's various functional modules, in the distribution network line and equipment operation status monitoring, fault precision judgment, fault time statistical analysis and daily repair and other work has achieved certain results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".