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 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.

Abstract: Disclosed is a Bayesian classification recognition system based on an industrial PaaS platform, comprising: an IaaS infrastructure service layer, a G-PaaS graph neural network processing layer, an O-PaaS docking service layer and an SaaS system application layer. The G-PaaS graph neural network processing layer is configured for point cloud feature generation, point cloud feature learning, point cloud structure estimation and point cloud model classification; and the recognition accuracy of a workpiece point cloud model is improved through the Bayesian classification recognition system based on the industrial PaaS platform.

Abstract: The invention relates to the field of blockchain technologies, and is particularly a method, system and device for detecting an abnormal node in a blockchain and a storage medium. In the detection method, node data are extracted and analyzed in a defeatured manner through various information sources, and finally, a high-quality graph structure with high accuracy, strong applicability and small error is obtained, so that high-order neighbor features with rich feature information are obtained, thus being convenient for further classified detection, improving the accuracy of detection results, and being beneficial for maintaining the safety of blockchain.

Abstract: Disclosed is a method for detecting a block chain abnormal behavior based on graph embedding. The method comprises S100: data collection: acquiring public block chain abnormal behavior node data on the Internet, and acquiring normal nodes in a number equal to that of abnormal behavior nodes at the same time; S200: establishment of abnormal behavior recognition model: extracting features of all nodes, constructing the nodes subjected to feature extraction into a transaction graph, and forming the abnormal behavior recognition model based on a graph embedding technology according to the constructed transaction graph; and S300: transaction detection: determining a transaction risk according to the obtained abnormal behavior recognition model when a transaction occurs, and prompting a user of a risk level. According to the method for detecting the block chain abnormal behavior based on graph embedding, abnormal behaviors possibly existing in block chain transactions can be effectively detected and early warned.

Abstract: Disclosed is a method for detecting a block chain abnormal behavior based on graph embedding. The method comprises S100: data collection: acquiring public block chain abnormal behavior node data on the Internet, and acquiring normal nodes in a number equal to that of abnormal behavior nodes at the same time; S200: establishment of abnormal behavior recognition model: extracting features of all nodes, constructing the nodes subjected to feature extraction into a transaction graph, and forming the abnormal behavior recognition model based on a graph embedding technology according to the constructed transaction graph; and S300: transaction detection: determining a transaction risk according to the obtained abnormal behavior recognition model when a transaction occurs, and prompting a user of a risk level. According to the method for detecting the block chain abnormal behavior based on graph embedding, abnormal behaviors possibly existing in block chain transactions can be effectively detected and early warned.