Abstract: A model update system, which may be applied to the network control field, includes a site analysis device and a first analysis device. The site analysis device is configured to: receive a first model sent by the first analysis device; train the first model by using a first training sample to obtain a second model, where the first training sample includes first feature data of a network device in a site network corresponding to the site analysis device; obtain differential data between the first model and the second model; and send the differential data to the first analysis device. The first analysis device is configured to: send the first model to the site analysis device; receive the differential data sent by the site analysis device; and update the first model based on the differential data to obtain a third model.

Abstract: This application provides a method for determining an access device type, a device, and a system. An access device type is determined by obtaining one or more packet pairs and a time difference of each packet pair. Each packet pair includes a first packet and a second packet, where the second packet is a response to the first packet, and the one or more packet pairs pass through a same access device. Based on this solution, maintenance manpower required for determining an access device type can be reduced, and O&M personnel management complexity of operators or service providers can be reduced.

Abstract: A fault locating method includes: receiving, by a first network device, a fault information packet flooded by another network device in a network, where each fault information packet includes statistical information about an interior gateway protocol packet of the network device sending the fault information packet, and the statistical information of each network device includes a statistical result on one or more key performance indicators KPIs of the network device; and determining, based on statistical information of the first network device and the statistical information of the another network device, a network device on which a fault occurs in the network. According to the method, the fault information packet flooded by the network device is received, so that information required for fault locating, that is, the KPI of the network device, can be quickly collected. This facilitates a fault locating process and reduces a fault locating time.

Abstract: This application discloses a model training method, apparatus, and system, and belongs to the AI field. The method includes: receiving a machine learning model sent by a first analysis device; and performing incremental training on the machine learning model based on a first training sample set, where feature data in the first training sample set is feature data from a local network corresponding to a local analysis device. In this application, a problem that the machine learning model obtained through offline training cannot be effectively adapted to a requirement of the local analysis device is resolved. Embodiments of this application are used to predict a classification result.

Abstract: A label determining method includes: obtaining a target feature vector of a first time series, where a time series is a set of a group of data arranged in a time sequence; obtaining a similarity between the target feature vector and a reference feature vector in a reference feature vector set, where the reference feature vector is a feature vector of a second time series with a determined label; and when a similarity between the target feature vector and a first reference feature vector is greater than a similarity threshold, determining that a label corresponding to the first reference feature vector is a label of the first time series, where the first reference feature vector is a reference feature vector in the reference feature vector set.

Abstract: A packet processing method includes receiving, by an aggregation gateway, a first tunnel establishment request message sent by a home gateway, and sending a first tunnel establishment success message to the home gateway; receiving, by the aggregation gateway, a second tunnel establishment request message sent by the home gateway, and sending a second tunnel establishment success message to the home gateway; associating, by the aggregation gateway, a first tunnel with a second tunnel according to an identifier of the home gateway; and sending a downlink packet to the home gateway by using the first tunnel and/or the second tunnel. The embodiments of the present application may increase bandwidth.

Abstract: A traffic anomaly detection method includes obtaining a target time series including N elements; obtaining a target parameter of the target time series, where the target parameter includes at least one of a periodic factor or a jitter density, the periodic factor represents a wave-shaped change that is presented in the target time series and that is about a long-term trend, and the jitter density represents a deviation between an actual value and a target value of the target time series within a target time; determining, from a plurality of types based on the target parameter, a first type to which the target time series belongs, where each of the types corresponds to one parameter set, and the target parameter belongs to a parameter set corresponding to the first type; and detecting an anomaly of the target time series based on a first-type decision model corresponding to the first type.

Abstract: A sampling frequency recommendation method, apparatus, and device, and a storage medium relating to the field of communications technologies are disclosed.

Abstract: A video quality assessment method includes obtaining a video quality assessment parameter of a to-be-assessed video, where the video quality assessment parameter of the to-be-assessed video includes an average packet loss gap of the to-be-assessed video, determining packet loss dispersion of the to-be-assessed video based on the video quality assessment parameter of the to-be-assessed video, and determining quality of the to-be-assessed video based on a packet loss rate of the to-be-assessed video, an average consecutive packet loss length of the to-be-assessed video, the packet loss dispersion of the to-be-assessed video, and attribute information of the to-be-assessed video.

Abstract: A fault localization method includes: obtaining user experience data, network topology data, and resource management data that are of a video service; where the network topology data is used to represent a connection relationship between network devices, and the resource management data is used to represent a connection relationship between user equipment and the network devices; determining a QoE experience indicator of a network device based on the user experience data, the network topology data, and the resource management data; and when QoE represented by the QoE experience indicator of the network device is lower than QoE represented by a device screening threshold, determining the network device as a possible questionable device.

Abstract: A fault localization method includes: obtaining user experience data, network topology data, and resource management data that are of a video service; where the network topology data is used to represent a connection relationship between network devices, and the resource management data is used to represent a connection relationship between user equipment and the network devices; determining a QoE experience indicator of a network device based on the user experience data, the network topology data, and the resource management data; and when QoE represented by the QoE experience indicator of the network device is lower than QoE represented by a device screening threshold, determining the network device as a possible questionable device.

Abstract: A network element health status detection method and device, where the method includes: determining sampled data of at least one key performance indicator (KPI) of a target network element in a first time window; obtaining a fluctuation score of any KPI in the at least one KPI according to sampled data of the any KPI in the first time window and a steady state value of the any KPI; and determining a health status of the target network element based on a fluctuation score of each KPI. Therefore, a network element health status is determined using single-point performance data of a network element and performance data in a network element time window.

Abstract: method data collection method includes: collecting, by a data collection device, performance indicator data of a target device based on a collection periodicity; detecting change amplitude of the collected performance indicator data that is in a change detection window, where the change detection window includes multiple collection periodicities; and when it is detected that change amplitude of the performance indicator data that is in the change detection window is greater than or equal to a change detection threshold, sending the performance indicator data that is in the change detection window to a data analysis device.

Abstract: A method includes: obtaining an indicator value of a user experience quality reference indicator and an indicator value of a network performance indicator that are corresponding to a target network device at a different moment in a same time period, where a user experience quality reference indicator is used to represent user experience quality, and the network performance indicator is used to represent network quality; and determining a network performance bottleneck value based on the obtained indicator value of the user experience quality reference indicator and the obtained indicator value of the network performance indicator in the same time period, where the network performance bottleneck value is used to be compared with an indicator value of a network performance indicator of a network device to determine network quality of the network device.

Abstract: In a method for implementing a GRE tunnel, an access device obtains an address of an aggregation gateway group including at least one aggregation gateway. The access device sends a tunnel setup request to the aggregation gateway for setting up a GRE tunnel, and the request contains an address of the access device encapsulated by using the address of the aggregation gateway group as a destination address. The access device receives a tunnel setup accept response sent back by an aggregation gateway and obtains an address of the aggregation gateway from the response. The aggregation gateway belongs to the aggregation gateway group. The access device configures the address of the aggregation gateway as a network side destination address of the GRE tunnel.

Abstract: A method for implementing a GRE tunnel, an access point (AP), and a gateway (GW). The method includes: the AP receives a first packet, where the first packet carries an address of the GW; configures a GRE tunnel to the GW, where a source destination of the GRE tunnel is an address of the AP, and a destination address of the GRE tunnel is the address of the GW; the AP receives a second packet sent by user equipment; performs GRE tunnel encapsulation for the second packet to form a third packet; and the AP sends the third packet to the GW by using the GRE tunnel, where the third packet carries the address of the AP. The embodiments of the present application enable efficient establishment of the GRE tunnel between the AP and the GW if there are a large quantity of APs.

Abstract: A packet processing method includes receiving, by an aggregation gateway, a first tunnel establishment request message sent by a home gateway, and sending a first tunnel establishment success message to the home gateway; receiving, by the aggregation gateway, a second tunnel establishment request message sent by the home gateway, and sending a second tunnel establishment success message to the home gateway; associating, by the aggregation gateway, a first tunnel with a second tunnel according to an identifier of the home gateway; and sending a downlink packet to the home gateway by using the first tunnel and/or the second tunnel. The embodiments of the present application may increase bandwidth.

Abstract: A video quality assessment method includes obtaining a video quality assessment parameter of a to-be-assessed video, where the video quality assessment parameter of the to-be-assessed video includes an average packet loss gap of the to-be-assessed video, determining packet loss dispersion of the to-be-assessed video based on the video quality assessment parameter of the to-be-assessed video, and determining quality of the to-be-assessed video based on a packet loss rate of the to-be-assessed video, an average consecutive packet loss length of the to-be-assessed video, the packet loss dispersion of the to-be-assessed video, and attribute information of the to-be-assessed video. Therefore, during video quality assessment, impact of packet loss distribution indicated by the packet loss dispersion on the video quality is considered.

Abstract: A method for implementing a GRE tunnel, an access point (AP), and a gateway (GW). The method includes: the AP receives a first packet, where the first packet carries an address of the GW; configures a GRE tunnel to the GW, where a source destination of the GRE tunnel is an address of the AP, and a destination address of the GRE tunnel is the address of the GW; the AP receives a second packet sent by user equipment; performs GRE tunnel encapsulation for the second packet to form a third packet; and the AP sends the third packet to the GW by using the GRE tunnel, where the third packet carries the address of the AP. The embodiments of the present application enable efficient establishment of the GRE tunnel between the AP and the GW if there are a large quantity of APs.

Abstract: A video quality assessment method includes obtaining a video quality assessment parameter of a to-be-assessed video, where the video quality assessment parameter of the to-be-assessed video includes an average packet loss gap of the to-be-assessed video, determining packet loss dispersion of the to-be-assessed video based on the video quality assessment parameter of the to-be-assessed video, and determining quality of the to-be-assessed video based on a packet loss rate of the to-be-assessed video, an average consecutive packet loss length of the to-be-assessed video, the packet loss dispersion of the to-be-assessed video, and attribute information of the to-be-assessed video.