The invention belongs to the technical field of equipment early warning, and particularly relates to a multi-source
information fusion real-time monitoring and
data analysis method and
system, and the method comprises the steps: achieving the automatic recognition and data collection,
remote assistance and support, and intelligent path planning and navigation functions in the inspection process of a chemical device, so as to standardize the inspection process; the inspection efficiency and the result accuracy are improved; establishing a chemical device image recognition model, collecting and preprocessing chemical device image data, performing model training by using a
convolutional neural network deep learning model, and deploying the model to actual equipment; an abnormal
feature extraction algorithm is developed, the operation state of the equipment is monitored in real time in combination with an intelligent early warning and
processing system, early warning is quickly given out, and corresponding
processing measures are taken; by adopting the
anomaly detection and classification technology, an
anomaly detection algorithm is realized, an efficient alarm
information transmission mechanism and an intelligent alarm
information processing system are developed, and an alarm
information feedback and closed-loop management mechanism is established, so that the accuracy and timeliness of
anomaly detection are improved.