Zhejiang Hengyi Petrochemical Co., Ltd.

Fujian Yijin Chemical Fiber Co., Ltd.

Mingyi Liu

Peng Wang

Xiantao Peng

Dake Li

Yibo Qiu

Feng Xu

Dandan Wang

Jun Shen

Junliang Jin

Zheng Teng

Xuan Wu

Haifeng Wang

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 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

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

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.

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Hangzhou, China

Mingyi Liu