51results about How to "Make full use of computing resources" patented technology

The present invention provides a voice recognition method, device, electronic equipment and storage medium; the method includes: acquiring the acoustic features of a plurality of voice frames of the voice signal to be recognized; Coding conversion to obtain the state corresponding to each of the speech frames; classify and combine the states corresponding to each of the speech frames through the central processing unit to obtain a phoneme sequence corresponding to the speech signal to be recognized; decode and convert the phoneme sequence , to obtain a text sequence corresponding to the speech signal to be recognized. In this way, speech recognition efficiency and resource utilization can be improved.

A method, device, and system for self-maintenance of massive equipment status, wherein the method includes: communication between equipment in each group of massive equipment with the group as the self-maintenance unit through the Gossip protocol, and judging whether there is any equipment in the group that is in Power-off state; among them, each device in a large number of devices is assigned a unique number; for each group, if all devices in the group are in the power-on state, the device with the smallest or largest unique number in the group will set the state of all devices in the group The normal message notifies the centralized control platform; if there are devices in the group that are in the shutdown state, and there are still devices in the power-on state in the group, the device with the smallest or largest unique number among the devices in the The unique number of the device and its shutdown status notify the centralized control platform. The technical solution proposed by the embodiment of the present invention greatly reduces the load pressure of the centralized control platform, fully utilizes the computing resources of each device, and improves the computing efficiency.

The invention discloses an artificial intelligence processing method and system for batch simulation of a digital aircraft working condition set. The method includes the following steps: accessing the digital aircraft simulation working condition set, and storing the working condition information included in the working condition set in a batch processing task database; Receive at least one working condition information from the batch task database, and record the receiving situation; download the configuration file corresponding to the working condition information according to the received at least one working condition information, and start the digital aircraft simulation program to perform digital simulation according to the configuration file; Archive all simulation data generated after digital simulation to a batch archive database. The invention realizes artificial intelligence processing for batch simulation of digital aircraft simulation working conditions, can automatically simulate and archive data for a large number of working conditions, reduces human workload, and can be processed when a large number of working conditions need to be processed. Realize multi-machine parallel simulation processing, make full use of computing resources, and save simulation time.

The present invention relates to a kind of intelligent seismic data anti-aliasing method and system based on U-Net network, the method comprises the following steps: (1) build the U-Net network f for seismic data anti-aliasing; (2) obtain Simulated data training pair: including simulated non-aliased seismic data and aliased seismic data; (3) using simulated aliased seismic data as input and non-aliased seismic data as training labels to train the U‑Net network to obtain the trained Network parameter θ * (4) Based on the transfer learning method, using part of the actual aliasing seismic data with labels, the trained network parameters θ * Perform fine-tuning to obtain optimized network parameters (5) use the optimized U-Net network to perform cyclic iterations on the seismic data to be processed to obtain separated seismic data. Compared with the prior art, the present invention avoids assumptions about data linearity, sparsity, and low rank, and has high anti-aliasing processing efficiency, good stability, and high precision.