Intelligent Water Monitoring System Based on the Internet of Things
Bibliographic record
Abstract
This paper introduces an intelligent water monitoring system based on the Internet of Things, which includes water use information acquisition board, mobile phone APP and Internet of Things cloud platform. The system uses STM32G030F6P6 as the main control chip, and combines with water quality detection module, water pressure detection module, water flow module, solenoid valve control module, ESP8266-01S WIFI module and OneNet cloud platform big data analysis technology to realize real-time supervision, monitoring water data recording and water leakage warning. The system can detect information such as water quality, temperature, total water use and water leakage, and transmit the data to the cloud platform in real time for the convenience of subsequent big data analysis and resource recycling. At the same time, users can realize remote monitoring of data and remote control of valve switch through mobile phone APP, so as to avoid the occurrence of leakage accidents in the home. The system is not only practical, but also has certain promotion value. It can be extended to various fields, such as household, industry and agriculture, so as to promote sustainable water resource utilization and management. The intelligent water supervision system proposed in this paper has certain innovation and practical application value, which is of great significance for improving the utilization efficiency of water resources and managing water resources.[1]
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".