Abstract: A fault root cause identification method, apparatus, and device. For a failure flow that occurs when a connectivity fault for access in a network occurs, a target success flow that has a high similarity with the failure flow is determined from a plurality of success flows in the network based on the failure flow. Then, a target fault root cause of the failure flow is obtained based on the failure flow, the target success flow, and a trained first machine learning model. In this way, the target success flow related to the failure flow is determined from the plurality of success flows in the network, and the first machine learning model trained by using a large quantity of success flows and failure flows whose feature indicators are slightly different from each other is used.

Abstract: A method and an apparatus for determining a coronary heart disease probability are provided. The method includes: obtaining ECG signals of one or more leads of a user; determining, based on the ECG signals of the one or more leads, reconstructed signals respectively corresponding to the ECG signals of the one or more leads; and determining a coronary heart disease probability of the user based on the ECG signals of the one or more leads and the reconstructed signals respectively corresponding to the ECG signals of the one or more leads.

Abstract: A data stream classification method includes obtaining, based on a packet feature of a current data stream and a behavior classification model, at least one first confidence that is of the current data stream and that corresponds to at least one data stream class, where the behavior classification model is based on a plurality of data stream samples; obtaining, based on feature information of the current data stream and a content classification model, at least one second confidence that is of the current data stream and that corresponds to the at least one data stream class, where the content classification model is based on one or more historical data streams; and determining a data stream class of the current data stream based on the at least one first confidence and the at least one second confidence.

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 method and an apparatus for updating an application identification model, and a storage medium are provided. A client device may determine a plurality of training samples based on identification results of a plurality of pieces of data traffic, and train an application identification model using the training samples. Then, the client device may upload model data of the trained application identification model to a server, and the server performs joint update based on the model data uploaded by a plurality of client devices. Then, the client device may obtain a jointly updated application identification model based on jointly updated model data delivered by the server.

Abstract: A forwarding device receives at least one service flow; the forwarding device obtains service information of the at least one service flow, where the service information of the service flow includes identification information of a network object to which the service flow belongs and M key performance indicators KPIs of the service flow, M is an integer greater than 0, and the network object includes one or more devices; and the forwarding device sends training information to a first device, where the training information includes the service information of the at least one service flow or a feature set obtained based on the service information of the at least one service flow, the training information is used to train a fault detection model, and the fault detection model is used to detect whether the network object is in a faulty state.

Abstract: A method for implementing model update is provided and used in a federated update framework. The method includes: a service device receives difference information uploaded by at least two clients. The service device performs calculation based on the difference information uploaded by the at least two clients, to obtain first difference consistency information, where the first difference consistency information indicates a consistency degree of the difference information uploaded by the at least two clients. The service device performs calculation based on the first difference consistency information, to obtain first training information, where the first training information is used to train a third model, and the third model is obtained by updating the first model by the service device based on the difference information uploaded by the at least two clients. The service device sends the first training information to the at least two clients.

Abstract: In certain embodiments, a forwarding device receives at least one service flow. The forwarding device obtains service information of the at least one service flow, where the service information of the service flow includes identification information of a network object to which the service flow belongs and M key performance indicators KPIs of the service flow. M is an integer greater than o, and the network object includes one or more devices. The forwarding device sends detection information to a first device, where the detection information includes the service information of the at least one service flow or a feature set obtained based on the service information of the at least one service flow. The detection information is used to detect whether the network object is in a faulty state.

Abstract: A data stream classification method includes obtaining, based on a packet feature of a current data stream and a behavior classification model, at least one first confidence that is of the current data stream and that corresponds to at least one data stream class, where the behavior classification model is based on a plurality of data stream samples; obtaining, based on feature information of the current data stream and a content classification model, at least one second confidence that is of the current data stream and that corresponds to the at least one data stream class, where the content classification model is based on one or more historical data streams; and determining a data stream class of the current data stream based on the at least one first confidence and the at least one second confidence.

Abstract: A fault root cause identification method, apparatus, and device. For a failure flow that occurs when a connectivity fault for access in a network occurs, a target success flow that has a high similarity with the failure flow is determined from a plurality of success flows in the network based on the failure flow. Then, a target fault root cause of the failure flow is obtained based on the failure flow, the target success flow, and a trained first machine learning model. In this way, the target success flow related to the failure flow is determined from the plurality of success flows in the network, and the first machine learning model trained by using a large quantity of success flows and failure flows whose feature indicators are slightly different from each other is used.