Abstract: Disclosed is a complex device key position vibration characteristic parameter verification method, comprising following steps: 1) constructing a model of complex device parts; 2) establishing a complex device dynamic model in a simulation software; 3) in the advancing process of a physical complex device, obtaining connection modes and constraint relationships among parts; 4) pre-simulating a complex device model in a dynamic simulation software; 5) determining a vibration characteristic parameter of a complex device key position needing to be verified, and carrying out post-processing on the vibration characteristic parameter for different levels of pavement spectra and vehicle speeds; 6) using a neural network model, training a selected key position rigidity damping coefficient and the vibration characteristic parameter; 7) comparing and verifying the vibration characteristic parameter obtained in the simulation process of the complex device dynamic model with the vibration characteristic parameter obtained

Abstract: Disclosed are an ergonomic evaluation method and simulation system based on virtual-real fusion. The system includes a user and device module, a virtual scene building module, and a data processing and analysis module. The method includes: obtaining real-time human joint point position data by means of a human motion capturing device, generating a character model of a current posture, and integrating the character model into a visual device and a personal computer (PC) terminal that are implanted with a virtual scene; obtaining comprehensive human joint point data through computation according to the real-time human joint point position data, and obtaining virtual scene data; and determining a human motion according to the comprehensive human joint point data and the virtual scene data, obtaining human posture data, obtaining human posture evaluation information through computation, and conducting analysis to determine whether an ergonomic evaluation index is rational.

Abstract: Disclosed is a characterization method based on deep reinforcement learning for discrete manufacturing industry data. The method includes: collecting discrete manufacturing industry data, and creating a spatio-temporal database; dividing the discrete manufacturing industry data into a discrete feature and a continuous feature, creating a data coupling coding network, converting a coding vector in the coding network into a characterization vector, and creating a data characterization model; quantitatively characterizing discrimination of a data category by means of cluster evaluation indexes; and using weights of cluster evaluation indexes of different dimensions as dynamic rewards, creating a deep reinforcement learning model, and updating a neural network parameter of deep reinforcement learning through characterization of an interactive relation between a model and a discrete manufacturing decision-making analysis system.