Zhejiang Hengyi Petrochemical Co., Ltd.

Alibaba Group Holding Limited

Fujian Yijin Chemical Fiber Co., Ltd.

Dandan Wang

Peng Wang

Xiantao Peng

Mingyi Liu

Dake Li

Yibo Qiu

Jun Shen

Di Xu

Junliang Jin

Zheng Teng

Xiaofeng Pan

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 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 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

Attribute information of a pending data handling process to be performed is identified. Historical data handling processes for a same application scenario as the pending data handling process are identified based on the attribute information. A time that each identified historical data handling process is performed and methods used to perform the identified historical data handling processes are also identified. A weighting coefficient for each identified historical data handling process is determined. Weighted identified historical data handling processes are calculated by applying the weight coefficient to each identified historical data handling process. A recommended weight for each method used to perform the identified historical data handling processes is determined by combining weighted identified historical data handling processes that use the same method. A method that has the highest recommended weight to perform the pending data handling process is recommended.

Recommending a data handling method for a pending data handling process

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Hangzhou, China

Dandan Wang