Huawei Technologies Co., Limited

Zhenhua Dong

Xiuqiang He

Ruiming Tang

Li Qian

Hong Zhu

Zhirong Liu

Xiaolong Dong

Gong Zhang

Biao Qi

Guoxiang Cao

Yuzhou Zhang

Gang Wang

Qi Zhang

Fei Chen

Zhiyong Feng

Zhenguo Li

Chao Lv

Liangwei Wang

Shuaihua Peng

Yanzhao Lu

Xu Zhou

Xiaoguang Li

Chao Pan

Weiwen Liu

Chao Jin

Yanjie Li

Bogdan Cautis

Jinkai Yu

Guohao Cai

Chih Yao Chang

Bowen Yuan

Alan Lau

This application provides a port identification method. The method includes: obtaining reflection information of reflection peaks formed by each ONT by separately reflecting test light when the test light provided by a wavelength-tunable device is transmitted in the PON; determining, based on the reflection information of the reflection peaks, a port of a splitter corresponding to each reflection peak group, where each reflection peak group includes reflection peaks formed by a same ONT by reflecting the test light; and determining, based on first transmission information of at least one reflection peak in each reflection peak group, the port of the splitter corresponding to each reflection peak group, and second transmission information between each ONT and an OLT, a port corresponding to each ONT in the splitter. According to this application, port identification efficiency can be improved.

Port identification method and apparatus

A recommendation model training method includes selecting a positive sample in a sample set, and adding the positive sample to a training set, where the sample set includes the positive sample and negative samples, each sample includes n sample features, n≥1, and the sample features of each sample include a feature used to represent whether the sample is a positive sample or a negative sample, calculating sampling probabilities of the negative samples in the sample set by using a preset algorithm, selecting a negative sample from the sample set based on the sampling probability, and adding the negative sample to the training set, and performing training by using the samples in the training set, to obtain a recommendation model.

Recommendation model training method and related apparatus

This application relates to the field of fiber-optic communication technologies, and provides a fault locating method, apparatus, and system. The method includes: obtaining a first correspondence between a receive time and receive power during reverse backhaul that occurs when a test optical signal that can be reflected by a reflection component disposed at each port of at least one stage of optical splitter is in downlink transmission in an ODN, where reverse backhaul includes backscatter and reflection or includes backscatter; determining, based on the first correspondence, a second correspondence between a transmission distance and receive power during reverse backhaul that occurs when a test optical signal is in downlink transmission in an optical fiber between each port of the at least one stage of optical splitter and a component connected to the port; and locating a fault in the ODN based on the second correspondence.

Fault locating method, apparatus, and system

A method and an apparatus for training a search recommendation model, and a method and an apparatus for sorting search results are provided. The training method includes: obtaining a training sample set including a sample user behavior group sequence and a masked sample user behavior group sequence; and using the training sample set as input data, and training a search recommendation model, to obtain a trained search recommendation model, where a target of the training is to obtain the object of the response operation of the sample user after the mask processing, the search recommendation model is used to predict a label of a candidate recommendation object in search results corresponding to a query field when a target user inputs the query field, and the label is used to indicate a probability that the target user performs a response operation on the candidate recommendation object.

Method and apparatus for training search recommendation model, and method and apparatus for sorting search results

This application provides a port identification method, apparatus, and system, and belongs to the field of optical communications technologies. According to this application, a connection relationship between the ONT and the optical splitter and a connection relationship between the ONT and the port of the optical splitter can be accurately identified.

Port identification method, apparatus, and system

A splitter includes an optical input section, N optical branch sections, and at least (N−1) optical filter structures. Each optical filter structure reflects an optical signal of one wavelength. The at least (N−1) optical filter structures include a special optical filter structure and at least (N−3) common optical filter structures, and a wavelength of an optical signal reflected by each of the common optical filter structures is a common wavelength. A wavelength of an optical signal reflected by a first/second special optical filter structure is a first/second special wavelength. At least (N−3) common wavelengths constitute an arithmetic sequence, a difference between the first special wavelength and a largest common wavelength is greater than a tolerance of the arithmetic sequence, and a difference between the second special wavelength and a smallest common wavelength is greater than the tolerance of the arithmetic sequence.

Splitter, optical distribution network, and method for determining wavelength corresponding to optical filter structure

This application provides a recommendation model training method in the artificial intelligence (AI) field. The training method includes: obtaining a first training sample; processing attribute information of a first user and information about a first recommended object based on an interpolation model, to obtain an interpolation prediction label of the first training sample; and performing training by using the attribute information of the first user and the information about the first recommended object as an input to a recommendation model and using the interpolation prediction label of the first training sample as a target output value of the recommendation model, to obtain a trained recommendation model. According to the technical solutions of this application, impact of training data bias on recommendation model training can be alleviated, and recommendation model accuracy can be improved.

Recommendation model training method, recommendation method, apparatus, and computer-readable medium

The method includes: collecting historical operations of sample users for M items, and predicting a preference value of a target user for each of the M items according to historical operations of the sample users for each of the M items, collecting classification data of N to-be-recommended items, and classifying the N to-be-recommended items according to the classification data of the N to-be-recommended items, to obtain X themes, where each of the X themes includes at least one of the N to-be-recommended items, and the N to-be-recommended items are some or all of the M items; calculating a preference value of the target user for each of the X themes according to a preference value of the target user for a to-be-recommended item included in each of the X themes; and pushing a target theme to the target user.

Theme recommendation method and apparatus

A recommendation method includes generating a feature sequence based on to-be-predicted data of a user for a target object and according to a preset encoding rule, obtaining probability distribution information corresponding to each feature in the feature sequence, and obtaining, through calculation, a feature vector corresponding to each feature, obtaining a predicted score of the user for the target object based on values of N features and a feature vector corresponding to each of the N features, and recommending the target object to the user when the predicted score is greater than or equal to a preset threshold.

Recommendation method and apparatus

Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

Prediction method, terminal, and server

An application sorting method and apparatus are provided. The method includes: obtaining, a positive operation probability and positive operation feedback information of each of at least two data samples; calculating an uncertainty parameter of a positive operation probability of a first data sample based on the positive operation probabilities and the positive operation feedback information of the at least two data samples and feature indication information of at least one same feature in a plurality of features in the at least two data samples; and correcting the positive operation probability of the first data sample by using the uncertainty parameter of the positive operation probability; and sorting, based on corrected positive operation probabilities, application programs corresponding to the at least two data samples.

Application program sorting method and apparatus

This application discloses an optical signal transceiver apparatus, and belongs to the communications field. The apparatus includes: an optical signal generation module, configured to generate a to-be-sent optical signal and a local oscillator optical signal, where the to-be-sent optical signal includes an OTDR signal; an optical combining/splitting module, configured to: receive a to-be-processed optical signal from an optical fiber; and input the to-be-processed optical signal into an coherent receiving module; the coherent receiving module, configured to coherently receive the local oscillator optical signal and the to-be-processed optical signal to obtain a to-be-processed electrical signal; a signal processing module, configured to: obtain a first digital signal and a second digital signal from the to-be-processed electrical signal based on a signal frequency; process the first digital signal to obtain a communications code stream; and process the second digital signal to obtain information used to reflect a feature of the optical fiber.

Optical signal transceiver apparatus

A route planning method includes obtaining exercise capability information of a wearer and one or more candidate routes, where the candidate routes include attribute features that comprise historical exercise capability information, where the historical exercise capability information is information calculated according to a first preset rule and based on obtained exercise capability information of a plurality of users having exercised along the candidate routes; determining a target route based on the attribute features of the candidate routes and the exercise capability information of the wearer; and outputting the target route information.

Route planning method and wearable device

An application program recommending method may include acquiring current context information of the terminal, acquiring an amount of context information generated when the terminal runs a first application program, where the first application program refers to an application program stored in the terminal, determining a to-be-used recommending mechanism according to the amount of the context information generated when the terminal runs the first application program, and determining, according to the to-be-used recommending mechanism, a second application program corresponding to the current context information, where the second application program refers to a to-be-recommended application program; and displaying the second application program. In this way, accuracy of predicting an application program to be used by a user is improved. Moreover, when historical information of using an application program by the user is insufficient, a to-be-recommended application program can also be accurately determined.

System and method for recommending applications based on historical usage

A method and an apparatus for generating a journal, which can implement automatic generation of a journal based on data from various sources. The method includes: obtaining a source data set and a journal description data set corresponding to the source data set; calculating an alignment probability between each source data sequence and each journal description data sequence to obtain an alignment probability set; calculating a probability that each journal description data sequence occurs in the journal description data set to obtain an occurrence probability set; determining, according to the alignment probability set and the occurrence probability set and from each journal description data sequence, a target journal description data sequence corresponding to a source data sequence to be translated, and translating the target journal description data sequence into a journal description text.

Growing community of inventors

Shenzhen, China

Zhenhua Dong