Regarding the transmission line faults caused by bird behaviors

this study aims to address the shortcomings of existing monitoring technologies in their poor adaptability and delayed response in harsh environments

and proposes a real-time early warning method that integrates bird behavior characteristics with electric field sensing. Firstly

the multi-dimensional heterogeneity characteristics of bird behaviors are analyzed

and the association rules of species

environment

and time-space are established. Secondly

a deterministic prediction model is constructed based on behavioral patterns

and risk classification and localization of transmission towers are achieved relying on historical nest data

with an average prediction accuracy is above 93. 01%. This enables accurate prediction of prevention and control areas

providing theoretical guidance for the focused deployment of monitoring devices. Furthermore

aiming at the problem of poor adaptability of traditional monitoring devices under complex conditions

this paper innovatively proposes a monitoring device and algorithm for bird intrusion on transmission towers based on electric field sensing by utilizing the characteristic that electromagnetic fields are not affected by environmental interference. The mapping relationship between the voltage difference between two measurement points and the spatial electric field intensity is established

and on this basis

a bird intrusion monitoring algorithm based on spatial electric field distortion characteristics is developed to achieve accurate identification of bird flocks and bird nests. Finally

a differential dual-probe electric field sensing device is developed

and experimental verification is carried out on a scaled model built in the laboratory. The results show that the proposed method can achieve localization of bird nest positions and detection of bird activities

providing a high-precision

strong anti-interference active early warning solution for bird damage prevention in power grids.