4 results about "Continuous interaction" patented technology

Embodiments of the present application provide a continuous interaction method of voice and related products, the method comprising: a terminal obtaining first voice data input by a target object, and sending preset data in a cache to a voice activity detection (VAD) engine of the terminal after determining that the first voice data is a wake-up voice; the VAD engine of the terminal continuously monitoring a conversation between the target object and a vehicle machine, and processing the conversation; and the VAD engine of the terminal sending the preset data in the cache to the VAD engine of the terminal again after detecting that the target object has finished speaking, so as to realize continuous voice interaction. The technical solution provided by the present application has the advantages of realizing continuous interaction and improving user experience.

The application discloses a shuttle frame component interaction method and an electronic device, relates to the technical field of front-end development user interface components, and comprises the following steps: performing a target operation on at least one of a search box subcomponent, a first list subcomponent and a second list subcomponent; if the target operation is a selection operation or a deletion operation, synchronously updating the first list subcomponent displayed in a first view and the second list subcomponent displayed in a second view; if the target operation is a search operation or a page turning operation, obtaining new element configuration information from a preconfigured data interface, rendering the new element configuration information, updating the first list subcomponent displayed in the first view according to the rendered new element configuration information, and keeping the second list subcomponent displayed in the second view unchanged during the search operation and the page turning operation. The method solves the problems of page freezing and interaction delay caused by full rendering in the prior art, and realizes smooth component operation and continuous interaction improvement under massive data.

The embodiment of the application provides a device safety risk identification method and system based on continuous interaction of mine agents, after the mine agent receives and analyzes device abnormal natural language description, calls analysis multi-source time sequence signal flow, outputs an initial fault hypothesis set and an initial fault confidence set, according to the confidence deviation of the initial fault confidence set relative to a preset confidence threshold, constructs a fault hypothesis space convergence strategy of the initial fault hypothesis set to call multi-source compensation signal flow and iteratively update fault root cause, until the updated fault confidence set meets the confidence threshold, and outputs a structured device fault diagnosis report. The problems that the existing mine device fault diagnosis exists cross-monitoring system data island and relies on artificial experience, which leads to deviation of fault diagnosis and delay of diagnosis result feedback response are solved. The mine device fault diagnosis achieves a second-level response, completely eliminates the artificial experience blind area and cross-system data island, and greatly reduces the operation and maintenance cost.

The application discloses a manufacturing system reconfiguration planning method based on deep reinforcement learning, and the implementation process of the method is as follows: analyzing an existing reconfigurable machine tool and a product to be processed to obtain a machine tool configuration set and a product feature set to be processed; a Markov decision process is used to model a manufacturing system reconfiguration planning problem, a Markov model of the reconfiguration planning is formed, a state space and an action space are determined according to the machine tool configuration set and the product feature set to be processed, and a reward function and a transition probability matrix are determined according to a determined optimization target; based on the Markov model of the reconfiguration planning, a neural network of a DQN algorithm is trained by using sample data obtained through continuous interaction between a reconfiguration planning intelligent agent and an environment; and the trained DQN network is used to input workpiece features, and an optimization scheme of the manufacturing system reconfiguration planning is quickly solved. The application can solve the manufacturing system reconfiguration planning problem.