Abstract:
This application discloses an alarm processing method and apparatus, a device, and a readable storage medium. An example method includes generating an alarm attribute graph based on alarm records in a target network and topology data of the target network. The alarm attribute graph includes identifiers of a plurality of devices, a communication connection relationship between the plurality of devices, type information of the plurality of devices, and alarm attribute information of the plurality of devices. The alarm attribute information of each device includes a name of an alarm occurring in the device and a time point at which each alarm occurs in the device.
Abstract:
A wavelength alignment method includes: emitting a first optical signal by using a laser; filtering the first optical signal by using a filter, and then transmitting a second optical signal; monitoring an extinction ratio of the second optical signal and an optical power of the second optical signal; and adjusting a working temperature of the laser and/or a working temperature of the filter to a target working temperature when the extinction ratio of the second optical signal exceeds an upper limit of a first extinction ratio threshold range and the optical power of the second optical signal exceeds a lower limit of a first optical power threshold range or when the extinction ratio of the second optical signal exceeds a lower limit of a first extinction ratio threshold range and the optical power of the second optical signal exceeds an upper limit of a first optical power threshold range.
Abstract:
A method for predicting a node state, including: obtaining static graphs and dynamic graphs of a plurality of nodes in a target network, where the static graphs and the dynamic graphs are all topology views; generating spatial feature data of the plurality of nodes based on the static graphs and the dynamic graphs; obtaining time feature data of the plurality of nodes; and obtaining a predicted state of a target node in a target time range based on the spatial feature data and the time feature data, where the target node is any node in the plurality of nodes. The method for predicting a node state provided in this application is applied to the field of node state prediction in a network, and uses a dynamic spatial feature in addition to a time feature and a static spatial feature. FIG. 8