Abstract: 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.

Abstract: An interference source identification method includes obtaining a first parameter, where the first parameter includes a co-channel interference rate of a first access point in a preset time period, a receive channel utilization rate and a transmit channel utilization rate of a second access point in the preset time period, and a receive frame rate of data received from a first station by the second access point in the preset time period, and determining that the data causes co-channel interference to the first access point when the first parameter meets a preset condition.

Abstract: A method, an apparatus, and a device for obtaining an artificial intelligence model, and a storage medium are provided. A client receives a first artificial intelligence AI model sent by a service end (303). The first AI model includes a plurality of neurons. The client determines, from the plurality of neurons, a target neuron participating in a current round of training, where the current round of training is a non-first round of training, and a quantity of target neurons is less than a total quantity of the plurality of neurons (304). The client trains the target neuron based on local data (305). The client returns parameter data corresponding to the target neuron to the service end (306). The parameter data corresponding to the target neuron is used by the service end to obtain a converged target AI model.

Abstract: 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.

Abstract: An interference source identification method includes obtaining a first parameter, where the first parameter includes a co-channel interference rate of a first access point in a preset time period, a receive channel utilization rate and a transmit channel utilization rate of a second access point in the preset time period, and a receive frame rate of data received from a first station by the second access point in the preset time period, and determining that the data causes co-channel interference to the first access point when the first parameter meets a preset condition.

Abstract: A multi-domain centralized content-centric networking (MCCN), including: a management layer; a control layer; and a data layer. The management layer communicates with the data layer through the control layer. The management layer is configured to acquire application transmission requests, network resource allocation, and network running status, and give network operating commands to a control plane according to reconfiguration of management strategies. The control layer is configured to carry out routing establishment, maintain network topology of domains, inform the management layer of network status, and execute commands of the management layer. The data layer is configured to process data packet according to commands of the control layer. The task of the data layer is completed by a router and link of the bottom layer.

Abstract: A content-based routing method, including: a) performing routing topology discovery and maintenance, centralized routing computation, and routing information inquiry by a name routing center of a controller; b) caching an active routing information by a forwarding information base; and c) acquiring inquired information of a routing node and updating the forwarding information base by the name routing center of the controller.

Abstract: The invention relates to a basic self-routing unit for multicast. The basic self-routing unit includes two input ports and two output ports. The input signal of the input ports includes the route signal and the following data priority control section and data content. The route signal has an algebraic lattice structure. The route signal includes a bicast signal, a unicast signal and an idle signal. When the route signals of the two input ports are the bicast signal and the idle signal respectively, the output value of the first output port is a Boolean product of the two input route signals and the output value of the second output port is a Boolean sum of the two input route signals. The invention relates to the use of a concentrator built by the basic self-routing unit and its network-forming method.