Abstract: The present invention relates to the technical field of network security, and in particular to an abnormal data detection method, system and device for industrial Internet. This detection method compares data distribution of an initial node with a normal feature expression performance in first normal data distribution subject to extraction processing to obtain a first anomaly score, compares the data distribution of the initial node with the normal feature expression performance in second normal data distribution subject to enhancement processing to obtain a second anomaly score, obtains a risk level of the node based on the first anomaly score and the second anomaly score, and immediately provides corresponding limits on a node communication permission; and the method provides dual detection, is high in accuracy and stable in detection results, and facilitates the maintenance of industrial Internet security.

Abstract: A lifting desk support having rapid mounting structures and a lifting desk are provided. The lifting desk support includes a longitudinal beam and a cross beam assembly. The lifting desk support further includes a set of rapid mounting structures, which comprises a boss having a slope arranged on the longitudinal beam and a notch provided on the cross beam assembly. A locking block of the boss is embedded into the cross beam assembly through a first groove section of the notch, and a force along an axial direction of a cross beam of the cross beam assembly is applied to the longitudinal beam, such that the locking block enters a second groove section of the notch, and the locking and the assembly between the longitudinal beam and the cross beam assembly are achieved in a rapid and efficient manner with cost saved.

Abstract: The present invention relates to the technical field of network security, and in particular to an abnormal data detection method, system and device for industrial Internet. This detection method compares data distribution of an initial node with a normal feature expression performance in first normal data distribution subject to extraction processing to obtain a first anomaly score, compares the data distribution of the initial node with the normal feature expression performance in second normal data distribution subject to enhancement processing to obtain a second anomaly score, obtains a risk level of the node based on the first anomaly score and the second anomaly score, and immediately provides corresponding limits on a node communication permission; and the method provides dual detection, is high in accuracy and stable in detection results, and facilitates the maintenance of industrial Internet security.

Abstract: The present invention discloses a human-robot collaboration method based on a multi-scale graph convolutional neural network. The method includes the following steps: S1, data acquisition: acquiring a dataset of a human skeleton in human-robot collaboration scenes, and performing pre-processing to obtain pre-processed data; S2, model training: loading the pre-processed data, and obtaining a human behavior recognition network model by training a multi-scale graph convolutional neural network; S3, human behavior recognition: predicting human behaviors through a trained deep learning network model; and S4, human-robot interaction: sending predicted information to a robot system through a communication algorithm, and enabling a robot to make action plans based on the human behaviors. By the human-robot collaboration method based on a multi-scale graph convolutional neural network disclosed by the present invention, a robot can predict human behaviors and intents in real scenes and make correct interaction.