Zhejiang Hengyi Petrochemical Co., Ltd.

Haining Hengyi New Materials Co., Ltd.

Fujian Yijin Chemical Fiber Co., Ltd.

Yibo Qiu

Xiantao Peng

Peng Wang

Dake Li

Mingyi Liu

Feng Xu

Junliang Jin

Dandan Wang

Junwei Zhang

Jun Shen

Jianjun Sheng

Zhangzhong Cheng

Xuan Wu

Haifeng Wang

Guorong Jian

Zhongliang Wu

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

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

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.

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Hangzhou, China

Yibo Qiu