Method for predicting and decision-making of spirochetosis based on behavior data of carassius auratus in chuzhou
By real-time monitoring of the dorsal fin oscillation frequency and gill cover opening and closing cycle of crucian carp in Chuzhou, a behavioral feature tensor was constructed and a graph attention network was used to solve the problems of lag and high false alarm rate in the existing technology for monitoring sporozoan diseases, and to achieve early accurate prediction and reliable early warning.
Patent Information
- Application Number
- CN202610837912.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-11
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for monitoring sporozoanosis in crucian carp in Chuzhou suffer from low efficiency, strong lag, difficulty in covering all fish populations, and susceptibility to environmental noise interference based on single behavioral indicators, resulting in high false alarm or false negative rates. Furthermore, they fail to uncover the synergistic changes in dorsal fin wagging frequency and respiratory rhythm over time.
By real-time monitoring of the dorsal fin oscillation frequency and gill cover opening and closing cycle of crucian carp in Chuzhou, the power spectral density distribution and respiratory rhythm array are extracted using sliding window transformation. Abnormal peaks are located and behavioral feature tensors are constructed. Combined with graph attention network, the temporal correlation between abnormal events is captured, and the probability of spore infection risk is output.
It enables accurate prediction of early diagnosis of sporozoan diseases, reduces false alarm rate, improves prediction timeliness and accuracy, and provides reliable early warning for aquaculture management.
Smart Images
Figure CN122414838A_ABST