Beijing University of Posts and Telecommunications

Huawei Technologies Co., Limited

Chinese Academy of Sciences

Xiaolong Zheng

Liang Liu

Huadong Ma

Yinben Xia

Zhigang Ji

Yifan Zhang

Di Qu

Siyu Yan

Dan Xia

Yunhao Xie

Desheng Wu

Tianchi Zhao

Chengling Jiang

Zihui Luo

Fuxing Chen

Weishan Deng

A network parameter configuration method, where in the method, network running data corresponding to a first period is input into a prediction model, so that the prediction model predicts, based on the input network running data, a value of a parameter of a network device in a second period, and the parameter of the network device in the second period is configured to the value predicted by the prediction model.

Network parameter configuration method and apparatus, computer device, and storage medium

The embodiments of the present invention provide a dynamic production scheduling method, apparatus and electronic device based on deep reinforcement learning, which relate to the technical field of Industrial Internet of Things, and can reduce the overall processing time of jobs on the basis of not exceeding the processing capacity of production device. The embodiments of the present invention includes: acquiring static characteristics, dynamic characteristics of each of jobs and system dynamic characteristics, inputting the static characteristics, dynamic characteristics of each of jobs to be scheduled and system dynamic characteristics into a scheduling model to obtain a job execution sequence or batch execution sequence of the jobs in each production stage, wherein, the static characteristics of the job include an amount of tasks and time required for completion, the dynamic characteristics of the job include reception moment, and the system dynamic characteristics include a remaining amount of tasks that can be performed by the device in each production stage. The scheduling model is a model obtained after training a first actor network based on static characteristics and dynamic characteristics of a sample job, system dynamic characteristics, and a first critic network.

Dynamic production scheduling method and apparatus based on deep reinforcement learning, and electronic device

Disclosed is an intelligent information parsing method based on cross-modal data fusion, including: step 1, acquiring information multi-modal data, step 2, processing the multi-modal data, step 3, fusing data features, step 4, establishing and training a parsing model, step 5, parsing fused cross-modal data, and step 6, viewing parsed data.

Intelligent information parsing method based on cross-modal data fusion

The embodiments of the present invention provide a method, apparatus for cross-protocol opportunistic routing, an electronic device, and a storage medium, the method includes: when there is a first data packet in a low-power wireless network, simulating the first data packet to generate a second data packet including to-be-transmitted data in the first data packet; obtaining identification information of a destination node in the first data packet, and selecting a low-power node with the lowest delay to the destination node in the low-power wireless network, except the first low-power node, as a forwarding low-power node based on the identification information of the destination node; sending the generated second data packet to the forwarding low-power node, so that the forwarding low-power node forwards the to-be-transmitted data to the destination node. By using high-power nodes, when there is a data packet in the low-power node, the data packet can be sent in time without being transmitted in a reserved idle channel, thereby reducing the transmission delay of the data packet from the source node to the destination node in the low-power wireless network.

Method, apparatus for cross-protocol opportunistic routing, electronic device and storage medium

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Beijing, China

Xiaolong Zheng