Zhejiang Hengyi Petrochemical Co., Ltd.

Zhejiang University

Netease (hangzhou) Network Co., Ltd.

Hangzhou Bigfish Bio-tech Co., Ltd.

Alibaba Cloud Computing Ltd.

Zhejiang Shuangtu New Materials Co., Ltd.

Zhejiang Hengyi Polyamide Co., Ltd.

Zhejiang Hengyi High-tech Materials Co., Ltd.

Haining Hengyi New Materials Co., Ltd.

Fujian Yijin Chemical Fiber Co., Ltd.

Hangzhou Hikvision Digital Technology Co., Ltd.

Jiangsu Contemporary Amperex Technology Limited

Peng Wang

Xiantao Peng

Dake Li

Feng Xu

Bin Guo

Yibo Qiu

Mingyi Liu

Junliang Jin

Dandan Wang

Jun Shen

Xuan Wu

Jianjun Sheng

Zhangzhong Cheng

Feifei Li

Fei Wu

Jing Zhang

Xi Chen

Yulian Zheng

Zheng Teng

Junwei Zhang

Lianyi Xie

Xinxiang Chen

Ning Chen

Renguang Feng

Ming Lei

Ya Lin

Zhushi Cheng

Jiabo Gao

Jiale Fang

Xiaojuan Xu

Wenyu Ren

Xing Zheng

Haifeng Wang

Guorong Jian

Zhongliang Wu

Shuai Qiao

Tingqun Ye

Shouwei Lin

Yuefang Wei

Yi Yuan

Yiming Wang

Jianfeng Peng

Yanjun Jiang

Jifei Chen

Zhiqiang Zhou

Shoujun Huang

Huasheng Su

Provided is a control method, system and apparatus for drone inspection of a chemical production plant, relating to the field of chemical fiber intelligent technology. The control method includes: determining a first drone for performing a preset inspection task in a target scene, wherein the first drone is responsible for the preset inspection task of the chemical production plant; obtaining data of a target object collected by the first drone in the target scene; determining a state of the target object in the target scene based on the data of the target object; and outputting prompt information matching a preset state in the target scene when the state represents that the target object is in the preset state of the target scene.

Control method for drone inspection of chemical production plant, electronic device and storage medium

The present application relates to technical field of organic semiconductors and provides an arylamine compound and use thereof in photoelectric devices. The compound is composed of a N,N-dialkyl-dicarbazole amine functional group modified unit with larger conjugated system. The semiconductor material provided by the present application has high glass transition temperature, high solubility, good film forming property and high thermal stability of film morphology, and can be used as a hole transport material in photoelectric devices to realize excellent thermal stability. The compound has simple preparation process, easily available raw materials and low price, and is suitable for industrial production. High glass transition temperature and good thermal stability are beneficial to prolong the working life of optoelectronic devices. High solubility, can form amorphous film with excellent quality, and is beneficial to application in photoelectric devices; The device has high photoelectric conversion efficiency and is a hole transport material with excellent performance.

Arylamine compound and use thereof in photoelectric devices

Provided in the present disclosure is a message sending method, including: providing at least one virtual surveillance character in a preset game scene; determining that a first virtual character is located in a preset range of the virtual surveillance character in the preset game scene and generating a message notification command; and sending the message notification command to a server, such that the server generates a support notification on the basis of the message notification command, the support notification being used for providing a transfer interface to at least one second client terminal controlling a second virtual character, and the transfer interface being used for triggering a command for transferring the second virtual character to the preset game scene in which the first virtual character is located.

Method and device for triggering teleporting operation

Provided is a noise processing method, relating to the field of data processing. The method includes: obtaining a first noise signal at a position of a target person in a target workshop in a first time period; predicting an overall control parameter of multiple signal transmitters in a second time period based on the first noise signal to obtain a current parameter prediction result; determining one or more target transmitters that need to work in the second time period from the multiple signal transmitters based on the result, and obtaining a parameter prediction value of each target transmitter in the second time period; and controlling each target transmitter to transmit a noise interference signal in the second time period according to a corresponding parameter prediction value to weaken a second noise signal at the position of the target person in the target workshop in the second time period.

Noise processing method, electronic device and storage medium

Provided is a spinning box detection method, an electronic device and a storage medium. The method includes: obtaining an image to be detected of a spinning box when determining that the spinning box meets a preset defect detection condition; and inputting the image to be detected into a target detection model to obtain a target detection result of the spinning box; wherein the target detection model is used to detect whether there is a defect in the spinning box to obtain the target detection result; and the target detection result comprises at least one of: a total quantity of defects, a defect position or a defect type.

Spinning box detection method, electronic device and storage medium

Provided is a yarn spindle coding method including: when determining that yarn spindles are fully wound, sleeving the yarn spindles in the winding machine sequentially outside a yarn connecting rod of a doffer, and updating the number of unloading times of the winding machine and recording first doffing time of each yarn spindle; generating a first information code of each yarn spindle based on identification information of the winding machine, the updated number of unloading times, the first doffing time and position information of each yarn spindle; and sending each first information code to the doffer, so that the doffer determines a corresponding target yarn spindle among the yarn spindles according to a matching degree between the first doffing time and second doffing time of each yarn spindle recorded by the doffer and code the information code on a corresponding target yarn spindle.

Yarn spindle coding method, electronic device and storage medium

Provided is an audio-based device fault detection method, an electronic device, and a storage medium, relating to the field of data processing and in particular to technical fields of deep learning and voice technology. The method includes: obtaining initial audio data collected by a drone for a target device; preprocessing the initial audio data to obtain audio data to be detected; performing feature extraction on the audio data to be detected to obtain an audio feature of the audio data to be detected; constructing an information graph based on the audio feature; and obtaining a fault detection result for the target device based on the information graph and a graph neural network model.

Audio-based device fault detection method, electronic device and storage medium

Provided is a detection method for a spinning workshop, an electronic device and a storage medium; relating to the field of data processing. The method includes: performing image collection on a process control device of a spinning box in the spinning workshop to obtain an image to be processed; extracting a first image feature from the image to be processed; and processing the first image feature based on a decoder network to obtain a fault detection result. The decoder network comprises a plurality of decoder modules connected in series in sequence. Each decoder module includes a decoder layer and an adaptive classification head. The adaptive classification head is configured to perform classified prediction on an output feature of the decoder layer to obtain a first fault classification result. The fault detection result output by the decoder network includes a second fault classification result and a fault position.

Detection method for spinning workshop, electronic device and storage medium

Medical instrument

Provided is a training method for an image recognition model, a spinneret plate detection method, a spinneret plate detection device, and a storage medium, relating to the fields of image recognition and deep learning technologies. The training method includes: processing a spinneret plate sample image based on a first image recognition model to obtain annotation state information of each micro hole in the spinneret plate sample image, wherein the spinneret plate sample image is a projection image formed on an imaging member after light emitted by a light source passes through each micro hole of a spinneret plate sample and is amplified by an amplifying member, the annotation state information is used to characterize a shape state of a micro hole corresponding thereto; and training a preset image recognition model based on the spinneret plate sample image and the annotation state information to obtain a target image recognition model.

Training method for image recognition model, spinneret plate detection method and apparatus

A method and an apparatus for processing Bitmap data are provided by the embodiments of the present disclosure. The method for processing Bitmap data includes: dividing a Bitmap region in a disk into a plurality of partitions in advance and setting an update region in the disk; obtaining a respective amount of dirty data corresponding to each of the plurality of partitions in memory in response to a condition for writing back to the disk being satisfied; finding multiple second partitions with an amount of dirty data satisfying to be merged into the update region from the plurality of partitions according to the respective amount of dirty data corresponding to each of the plurality of partitions; and recording dirty data corresponding to the multiple second partitions in the memory into the update region in the disk through one or more I/O operations after merging.

Method and apparatus for processing Bitmap data

Provided is a control method, an electronic device and a storage medium. The method comprises: when determining that a yarn spindle transported on a transport channel enters a detection area, detecting the yarn spindle located in the detection area to obtain an appearance feature of the detected yarn spindle; wherein the transport channel is a channel for transporting yarn spindles in an automatic packaging workshop; and the detection area is at least a partial area on the transport channel; when the appearance feature of the detected yarn spindle does not meet a first preset requirement, determining the yarn spindle with the appearance feature not meeting the first preset requirement as a target yarn spindle; and detecting a physical attribute feature of the target yarn spindle.

Control method, electronic device and storage medium

Provided is a control method for a yarn route inspection equipment, an electronic device and a storage medium. The method includes: detecting whether a first workstation in an initial yarn route inspection path is in a shoveling state; when the first workstation is not in the shoveling state, controlling the yarn route inspection equipment to inspect the first workstation; when the first workstation is in the shoveling state, controlling the yarn route inspection equipment to bypass the first workstation to inspect a second workstation and mark the first workstation as an uninspected state; and when inspection of the initial yarn route inspection path is completed and there is a workstation in the uninspected state, controlling the yarn route inspection equipment to perform supplementary inspection on the workstation in the uninspected state in the initial yarn route inspection path.

Control method for yarn route inspection equipment, electronic device and storage medium

Provided is a channel monitoring method, an electronic device, and a storage medium. The method includes: obtaining scan data collected by a vehicle body collection component of an automated guided vehicle (AGV) in an area around a vehicle body; obtaining video data collected by a camera in a video collection area, where the camera is one of a plurality of cameras and is used to collect video of at least a partial area of the channel to be monitored of the plurality of channels to be monitored; in a case of determining an existence of a target object based on the scan data collected by the vehicle body collection component and/or the video data collected by the camera, obtaining a target channel where the target object is located; and generating target warning information for the target channel where the target object is located.

Channel monitoring method, electronic device, and storage medium

Provided is a dyeing effect prediction method, a training method of a dyeing effect prediction model, an electronic device, and a storage medium, relating to the field of computers, and in particular to artificial intelligence technologies, neural network model technologies and model training technologies. The dyeing effect prediction method includes decomposing Raman spectrum data of a to-be-detected yarn spindle into a plurality of sub-signal data by using a wavelet basis function; determining a feature of the to-be-detected yarn spindle according to at least a part of target sub-signal data in the plurality of sub-signal data; and predicting to obtain a first dyeing label according to the feature of the to-be-detected yarn spindle by using a dyeing effect prediction model, wherein the first dyeing label is used for representing a dyeing effect grade.

Dyeing effect prediction method, training method of dyeing effect prediction model, electronic device and storage medium

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Hangzhou, China

Peng Wang