An Efficient Server Lid Detection System Based on Sound Recognition and Deep Learning

This paper proposes an innovative server lid detection method based on sound recognition and AI algorithms. This method utilizes piezoelectric sensors for sound collection, emitting specific frequency sounds through piezoelectric speakers and reflecting them back to the collection module. Unlike traditional mechanical switch detection methods, this method features simplified structure, low cost, and minimal environmental impact. Detailed mathematical models and flowcharts are provided, along with a comparison of relevant research progress domestically and internationally. In specific implementation, improved KNN algorithm and ResNet-autoencoder-based image classification detection technology are employed for sound feature extraction and classification. Additionally, an immune genetic algorithm is introduced to optimize parameter selection to enhance algorithm robustness and generalization performance. Experimental results demonstrate high accuracy, low false positive rate, and low false negative rate in open and closed state detection across various server scenarios.