Problems of Quantitative Estimation of the TPP’ ACS TP Intelligence Level
Bibliographic record
Abstract
Actual problems of increasing the intelligence of TPP' (thermal power plant) automated control systems (ACS) built on the basis of modern PTC (program and technical complexes) are considered.It is shown that from the standpoint of the modern approach, complex technical control systems satisfy the definition of intelligent control systems as systems that act rationally and optimally.It is from these positions that the report considers the problems of increasing the intelligence of the TPP' automated control system based on the creation of a unified system for improving the quality of control and solving optimization problems at all hierarchical levels of technological and production processes control.As an estimation of the level of intelligence, it is proposed to use a conditional "intelligence coefficient", the essence of which is to determine the share of intelligent technologies in the total volume of performed functions of the automated control system on the considered task or control function.A method for determining the intelligence coefficient at hierarchical levels of control and the automated control system as a whole is proposed.An illustrative example of calculating this coefficient at all hierarchical levels of control in relation to TPPs with CCGT (combine circle gas turbine)) PGU-450 is provided.It is shown that for a significant increase in the level of intelligence of the ACS based on PTC, special attention should be paid to the intellectualization of optimization problems at the block and station levels of control.
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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 | 0.005 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".