Research on intelligent control of an agricultural greenhouse based on fuzzy PID control
Bibliographic record
Abstract
With the development of agricultural science and technology in China, the number of greenhouses is increasing rapidly, but the control of greenhouse temperature, the most critical factor in the greenhouse environment, can no longer meet the requirements of the management and operation of agricultural greenhouse by manpower. Therefore, the perfect combination of greenhouse control and intelligent technology has become an urgent demand in the field of agricultural greenhouses. Research on the combination of greenhouse control and intelligent technology will help save human and financial resources and achieve the objectives of more efficient and stable control. Finally, it can guarantee an increase in crop yield and the profit of greenhouse operators. In this study, the temperature control of an agricultural greenhouse based on fuzzy proportion, integration and differentiation (PID) was taken as the research subject and a greenhouse temperature model was constructed by mathematical expression. Finally, simulation effect charts under simple fuzzy control and PID control were obtained, and the results were compared. Finally, it is concluded that fuzzy PID temperature control has the advantages of short response time and stable temperature-control effect, which is the optimal intelligent control mode of an agricultural greenhouse.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".