Abstract: A multi-dimensional data prediction method and system for a discrete manufacturing workshop. The method comprises the following steps: obtaining a raw data set; using a DBSCAN algorithm to perform clustering on the raw data set, so as to obtain a plurality of data clusters, and establishing corresponding labels for the data clusters; searching for association relationships between the labels, so as to constitute data knowledge; deleting redundant data from all the data knowledge, so as to form a knowledge network; obtaining raw data which is cleaned in step S1 and corresponds to labels included in the data knowledge; initializing the weight of a BP network structure, so as to obtain an initial model; obtaining a final prediction model; and acquiring raw data of discrete manufacturing to be processed, and inputting same into the prediction model, and the prediction model outputting a prediction result.

Abstract: Disclosed in the present invention are a multimodal industrial data fusion method and system for discrete manufacturing. The method comprises: collecting discrete manufacturing industrial multimodal data in real time, performing feature extraction to obtain feature data, and inputting the feature data into a differentiable computer network to obtain a data fusion result, wherein a differentiable computer is further used for accomplishing a prediction objective on the basis of the data fusion result, and the differentiable computer network comprises a data fusion module and a memory system, the data fusion module fusing the feature data in an agent, and the memory system being used for storing and reading/writing re-fused data in the agent; and inputting the data in the memory system into an evaluation system to calculate a reward value, guiding the agent in data fusion on the basis of the reward value, and updating the memory system.

Abstract: An industrial data hybrid model construction method and system for discrete manufacturing. The method comprises: firstly, collecting discrete manufacturing industrial data in real time, and performing feature extraction to obtain a feature vector; then, separately inputting feature data into a first model and a second model to obtain a first output and a second output; and performing weighted summation on the first output and the second output to obtain an industrial data hybrid model. The first model is used for obtaining a fitting curve of the feature data in a discrete space, and the second model is used for using a decision tree as a weak learner to perform ensemble learning on the feature data. Regarding the characteristic of service scenarios in the discrete manufacturing industry being complex and variable, the flexibility of intelligent data inference is improved.

Abstract: Disclosed are a three-dimensional modeling system and modeling method based on multimodal fusion. The method includes: separately collecting feedback data of an electroencephalogram sensor, an electromyography sensor, an eye movement sensor, a gesture sensor, and a voice sensor, conducting multimodal fusion on the feedback data, obtaining multimodal-fused model data, matching the model data with a database instruction, obtaining and analyzing an instruction set, obtaining and identifying a relevant modeling parameter, obtaining a modeling method, automatically conducting modeling according to the modeling method, and obtaining a visual entity model.

Abstract: Disclosed is an intelligent interactive decision-making method for a discrete manufacturing system. The method includes the following steps: step 1, establishing a production scheduling optimization model and strategy for discrete manufacturing for an actual application scene; step 2, training the scheduling strategy with existing production data on the basis of a deep reinforcement learning algorithm, and storing a state having a high reward in a training process in a memory; step 3, updating the state according to prior knowledge in the memory; step 4, inputting the updated state into a deep reinforcement learning network, obtaining a corresponding reward, and updating the memory according to the reward; and step 5, repeating step 4 until model parameters converge, and saving and putting the model into an actual production scene.

Abstract: The present invention discloses a method for constructing an intelligent computation engine of an artificial intelligence cross-platform model based on knowledge self-evolution. The method comprises: determining source and target moments; dividing a discrete manufacturing system data set; initializing a dynamic discrete manufacturing system model; preprocessing data and constructing a task pool; constructing a meta learning framework; migrating a trained neural network to new tasks; iterating until convergence and storing model parameters; and testing in a new environment. This invention shortens the convergence time of model parameters, significantly benefiting the training of dynamic discrete manufacturing models subject to temporal disturbances in actual production.

Abstract: Parameter optimization method for nonlinear vibration model of complex device, comprising: 1) constructing various structures of complex device into tree structure, to form tree-shaped complex device model subsystem, and carrying out sign convention for dynamic analysis; 2) establishing complex device dynamic model to obtain dynamic relationships among all parts of complex device; 3) according to contact and collision conditions in advancing process of physical complex device, adding constraint relationships among parts in dynamic simulation software; 4) on basis of dynamic simulation software, establishing virtual prototype model of complex device, and determining target parameter and optimization target; 5) simulating vibration characteristics of complex device for different levels of pavement spectrums and different vehicle speeds; 6) adding required input point and output point for virtual prototype model; 7) on basis of optimization algorithm of numerical solution in small sample deep learning, obtaining

Abstract: Disclosed is an intelligent interactive decision-making method for a discrete manufacturing system. The method includes the following steps: step 1, establishing a production scheduling optimization model and strategy for discrete manufacturing for an actual application scene; step 2, training the scheduling strategy with existing production data on the basis of a deep reinforcement learning algorithm, and storing a state having a high reward in a training process in a memory; step 3, updating the state according to prior knowledge in the memory; step 4, inputting the updated state into a deep reinforcement learning network, obtaining a corresponding reward, and updating the memory according to the reward; and step 5, repeating step 4 until model parameters converge, and saving and putting the model into an actual production scene.

Abstract: Disclosed are a three-dimensional modeling system and modeling method based on multimodal fusion. The method includes: separately collecting feedback data of an electroencephalogram sensor, an electromyography sensor, an eye movement sensor, a gesture sensor, and a voice sensor, conducting multimodal fusion on the feedback data, obtaining multimodal-fused model data, matching the model data with a database instruction, obtaining and analyzing an instruction set, obtaining and identifying a relevant modeling parameter, obtaining a modeling method, automatically conducting modeling according to the modeling method, and obtaining a visual entity model.

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.