Zhejiang Hengyi Petrochemical Co., 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.

Xiantao Peng

Peng Wang

Dake Li

Feng Xu

Yibo Qiu

Mingyi Liu

Junliang Jin

Dandan Wang

Jun Shen

Zheng Teng

Junwei Zhang

Zhangzhong Cheng

Xuan Wu

Jianjun Sheng

Jifei Chen

Yanjun Jiang

Haifeng Wang

Jiabo Gao

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.

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

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

Provided is a control method, an electronic device and a storage medium. The method comprises: when a first yarn spindle with a first identifier is transported on a transport channel and determining that the number of first yarn spindles located in a preset area needs to be detected, detecting the first number of first yarn spindles located in the preset area; and when the first number of first yarn spindles is less than the total number of yarn spindles that the first mechanical arm can grab, determining the target number of first yarn spindles to be replenished, and generating a first control instruction to instruct a second mechanical arm to grab the target number of first yarn spindles from a target trolley associated with the first identifier of the first yarn spindle, and place the target number of first yarn spindles in the transport channel or preset area.

Provided is a control method, an electronic device and a storage medium. The method includes: obtaining a first target image collected by a first collection device and a second target image collected by a second collection device, in a case of it is determined that characteristic information of a mechanical gripper satisfies a first preset requirement; detecting a first center position of the mechanical gripper in the first target image and a second center position of a target spindle with yarn in the second target image; and generating a calibration instruction, in a case of it is determined that a target position relationship between the first center position and the second center position does not satisfy a second preset requirement, where the calibration instruction is used to calibrate a center position of the mechanical gripper or to calibrate a center position of the target spindle with yarn.

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Hangzhou, China

Xiantao Peng