Huawei Technologies Co., Limited

China Construction Industrial & Energy Engineering Group Co., Ltd.

Yanfang Zhang

Jiaojiao Ni

Anhui Wang

Zhanwei Huang

Jian Mg Cheng

Liang Zhang

Li Xue

Zengxiao Gao

Yan Bai

Li Ni

Tianyu Zhou

Fan Xia

Wei Dong Chen

Wei Lin

Jie Xiong

Rui Zhang

Xiaoyun Si

Jian Cheng

Gang Li

Ligang Feng

Qiwei Zhan

Wanying Dong

Changhao Fu

Jiao Lin

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 0, 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.

Fault detection method, apparatus, and system

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.

Traffic anomaly detection method, and model training method and apparatus

Disclosed is a real-time intelligent monitoring system for long-distance curved pipe jacking, which includes a pipe jacking mechanism and a working platform, wherein the working platform includes a control console and a main jacking hydraulic oil cylinder, the control console is arranged at one side of the main jacking hydraulic oil cylinder and is electrically connected with the main jacking hydraulic oil cylinder, the pipe jacking mechanism includes a pipe jacking machine, a curved jacking pipe and a jacking pipe, an arcing assembly is arranged inside the curved jacking pipe, a soil settlement monitoring assembly is arranged at the top of the jacking pipe, and laser induction assemblies are arranged inside the pipe jacking machine, the curved jacking pipe and the jacking pipe correspondingly.

Real-time intelligent monitoring system for long-distance curved pipe jacking

A method for implementing transmission performance detection includes: after successively receiving data packets whose sequence numbers are N1 and N2, determining, by a detection apparatus, that N2 is greater than N1 and that N1 and N2 are inconsecutive; after receiving the data packet whose sequence number is N2, receiving a data packet whose sequence number is M1; and when determining N1<M1<N2 and T2−T1>=RTT, determining that the data packet whose sequence number is M1 is a retransmitted packet corresponding to an upstream packet loss, where T1 is a time for receiving the data packet whose sequence number is N2, T2 is a time for receiving the data packet whose sequence number is M1.

Data transmission performance detection

Provided is an inorganic solid waste-microbial composite curing agent and its preparation method and application. The inorganic solid waste-microbial composite curing agent consists of the following raw materials in parts by weight: steel slag, active magnesium oxide, recycled powder, stearic acid, borax, carbonic anhydrase bacteria,, and soluble calcium salts. The preparation method includes performing granulation on steel slag, active magnesium oxide, recycled powder, stearic acid, borax, etc. into porous materials through dry rolling extrusion and compression, and loading carbonic anhydrase bacteria andinto the porous materials. By adding inorganic solid waste-microbial composite curing agent into the sludge and introducing CO, the sludge is quickly solidified through the carbonization and microbial mineralization of inorganic solid waste. At the same time, microorganisms can effectively decompose organic pollutants in the sludge, achieving the goal of harmless, stabilized and resourceful utilization of sludge.

Inorganic solid waste-microbial composite curing agent and its preparation method and application

A fault detection method includes: obtaining a video quality parameter of a monitored video stream, where the video quality parameter is determined according to a packet loss recovery method of the monitored video stream, the video quality parameter includes an effective packet loss factor, and the effective packet loss factor is used to indicate effectiveness of network packet loss recovery performed by using the packet loss recovery method of the monitored video stream; and performing fault detection based on the video quality parameter of the monitored video stream.

Video stream fault detection

The present disclosure discloses a method for improving the performance of concrete aggregates, comprising the following steps: (1) adding composite microbial powders, recycled aggregates and water into a stirring pot in a certain ratio to be continuously and uniformly stirred; (2) placing the recycled aggregate obtained in step (1) and another microbial powder in a vacuum device so as to fill the microbial powder into the cracks of the recycled aggregate in a negative-pressure environment; and (3) spraying a calcium source solution to the surface of the recycled aggregate obtained in step (2) at an interval of 5-6 hours for repeating 3-5 times to maintain the wet surface of the recycled aggregate.

Method for improving performance of concrete aggregates

A soil solidification material based on solid waste and bioenzyme, and a preparation method thereof are disclosed. The soil solidification material is composed of the following components in parts by weight: recycled aggregate 22-35 parts, steel slag 20-30 parts, high-calcium fly ash 16-24 parts, the bioenzyme 5-15 parts, an inorganic adsorbent 10-18 parts, an organic adsorbent 8-20 parts, industrial waste gypsum 25-35 parts, an activator 20-30 parts, sodium citrate 1-3 parts, and slaked lime 0.02-0.2 parts. The present disclosure adopts the recycled aggregate, the steel slag, the industrial waste gypsum and the high-calcium fly ash as the main components of the soil solidification material to reduce the cost. The soil solidification material of the present disclosure prepared by optimizing the proportion is capable of significantly improving the engineering properties of the soil or the mixed contaminated soil, and has significant economic and environmental benefits.

Soil stabilization material based on solid waste and bioenzyme, and preparation method thereof

A video quality assessment method and device are provided. The video quality assessment method includes: obtaining a to-be-assessed video, where the to-be-assessed video includes a forward error correction (FEC) redundancy data packet; when a quantity of lost data packets of a first source block in the to-be-assessed video is less than or equal to a quantity of FEC redundancy data packets of the first source block, generating a first summary packet for a non-lost data packet of the first source block, and generating a second summary packet for a lost data packet of the first source block; and calculating a mean opinion score of video (MOSV) of the to-be-assessed video based on the first summary packet and the second summary packet. The MOSV calculated according to the method is more consistent with real video experience of a user, so accuracy of video quality assessment can be improved.

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Nanjing, China

Yanfang Zhang