The invention discloses a
monorail crane inspection
robot intelligent test method based on
data analysis, and relates to the technical field of intelligent detection, and the method comprises the following steps: synchronously collecting track images,
point cloud and attitude data, carrying out
time alignment and preprocessing, and outputting a standardized data packet; detecting an inspection target by using a YOLO detection network, and outputting a multi-scale
feature vector in combination with a
point cloud feature hierarchy extraction network and a
time sequence convolutional network; predicting a fault development trend in combination with an improved A-star
algorithm and a long short-
term memory network, optimizing an inspection path through
reinforcement learning, and outputting a maintenance
decision scheme; the maintenance
decision scheme is converted into a control instruction, the
robot is driven to execute an inspection task and feed back the operation state in real time,
incremental learning and
point cloud reconstruction are combined, and a visual diagnosis report is output. According to the method, the dynamic threshold
algorithm is adopted for self-adaptive analysis, and the key geometric indexes are calculated in combination with the cross-
modal attention mechanism, so that the recognition capability of structural anomalies is improved.