Abstract: Various embodiments of the teachings herein include anomaly detection methods for a dynamic control system. An example method includes: using a g network to initialize a hidden state distribution of the system; receiving a measurement value of a sensor and a state value of a trigger at a current point of time; receiving a sampling point into an f network to perform a prediction to obtain a second sampling point; using an h network to map the second sampling point into a sensor measurement value space to perform a prediction to obtain a probability distribution of a measurement value of the sensor in the dynamic control system at the current point of time; and determining whether an anomaly exists in the dynamic control system by comparing the measurement value obtained from real-time monitoring and the probability distribution obtained from a prediction.

Abstract: Various embodiments include a method for product quality inspection on a group of products. The method may include: getting for each product in the group of products: image, value for each known fabrication parameter affecting quality of the group of products, and quality evaluation result; training a neural network. A layer of the neural network comprises at least one first neuron and at least one second neuron; each first neuron represents a known fabrication parameter affecting quality of the group of products and each second neuron represents an unknown fabrication parameter affecting quality of the group of products; and the images of the group of products are input to the neural network, the quality evaluation results are output of the neural network, and the value of each first neuron is set to the value for the known fabrication parameter the first neuron representing.

Abstract: Various embodiments include a method for product quality inspection on a group of products. The method may include: getting for each product in the group of products: image, value for each known fabrication parameter affecting quality of the group of products, and quality evaluation result; training a neural network. A layer of the neural network comprises at least one first neuron and at least one second neuron; each first neuron represents a known fabrication parameter affecting quality of the group of products and each second neuron represents an unknown fabrication parameter affecting quality of the group of products; and the images of the group of products are input to the neural network, the quality evaluation results are output of the neural network, and the value of each first neuron is set to the value for the known fabrication parameter the first neuron representing.

Abstract: An industrial device matching method and apparatus are used for acquiring a corresponding relationship between industrial devices in different industrial data sources to provide basis for industrial data analysis. The method, in an embodiment, includes collecting data of at least two industrial data sources; determining a first relationship between various industrial devices in each industrial data source, and determining a first relationship topology between the industrial devices in the industrial data source; and comparing the first relationship topologies corresponding to various industrial data sources, to determine a first corresponding relationship between industrial devices in industrial data sources, the first corresponding relationship enabling the first relationship topologies corresponding to at least two industrial data sources to be similar.

Abstract: An industrial device matching method and apparatus are used for acquiring a corresponding relationship between industrial devices in different industrial data sources to provide basis for industrial data analysis. The method, in an embodiment, includes collecting data of at least two industrial data sources; determining a first relationship between various industrial devices in each industrial data source, and determining a first relationship topology between the industrial devices in the industrial data source; and comparing the first relationship topologies corresponding to various industrial data sources, to determine a first corresponding relationship between industrial devices in industrial data sources, the first corresponding relationship enabling the first relationship topologies corresponding to at least two industrial data sources to be similar.