Collaborative Research on Panoramic Display of Grid One Map Production Information and Big Data Analysis Algorithm Based on Electricity 3D Engine
Bibliographic record
Abstract
The power grid, as a unified whole composed of various links of generation, transmission, transformation, distribution and use, needs mutual coordination and unified scheduling in terms of operation characteristics.This paper establishes a power 3D engine based on lightweight 3D engine technology, and builds a panoramic display platform for grid one map generation information on this basis.In order to explore the feasibility of this platform on grid optimization and scheduling, this paper establishes a two-layer optimization and scheduling model of the grid by taking the minimization of the operation cost as the objective function of the upper layer, and combining the supply-side and demand-side balancing objectives of the lower microgrid.The PSO algorithm is improved by introducing the immune mechanism, linear adjustment, and linear combination, and the HPSO algorithm is used to solve the grid two-layer optimal dispatch model.The simulation shows that the economic cost and environmental cost are reduced by 51.78% and 23.07%, respectively, and the total cost is reduced by 8.66*106 yuan after considering the uncontrollable residential electricity load.Relying on the One Grid Map platform can realize the accurate analysis of the peaking capacity and climbing residual capacity of the grid at typical time periods, providing reliable data support to meet the peak shaving and valley filling of the grid.Combined with the lightweight 3D engine technology, the panoramic display platform of production information of one map of power grid helps to obtain real-time grid operation and environmental information, realize monitoring and comprehensive analysis of the system, and make real-time decisions and interactions for optimal dispatching of power grid.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".