Building Adaptive Model of Transmission Inspection Data under the Background of Artificial Intelligence

With the development and application of artificial intelligence (AI) technology, the analysis and application of transmission inspection data has also been greatly improved. Based on AI technology, this paper constructs an adaptive model of transmission inspection data through machine learning decision tree method, aiming at providing more intuitive, efficient and comprehensive data analysis and decision support. Firstly, feature extraction technology is used to preprocess the transmission inspection data, and the key features are obtained. Then, the Iterative Dichotomiser 3 (ID3) algorithm is used to classify and sort these features, and a decision tree model is obtained. This paper uses data processing technology to process transmission inspection data, so that users can intuitively understand and analyze the data. The experimental results show that the monitoring data can better understand the monitoring situation in a specific area, so that the abnormal situation can be found in time, and the real-time monitoring of the monitoring data can be realized. The accuracy rate, recall rate and F1 value of ID3 algorithm in monitoring transmission inspection anomalies are 0.98, 0.91 and 0.94 respectively. Through experimental evaluation, the AI method can effectively process transmission inspection data, and provides a strong support for transmission inspection work.