Abstract: Provided is a yarn box, a yarn stripping method, an electronic device and a storage media. The yarn box includes a support, a yarn stripping rod and a yarn receiving rod; where a first end of the yarn receiving rod is connected to the support, a first end of the yarn stripping rod is slidably connected to the support, the yarn stripping rod is configured to slide to a yarn stripping position corresponding to a first yarn receiving rod among the yarn receiving rod when multiple yarn spindles are sleeved outside the first yarn receiving rod and the number of yarn spindles of the first yarn receiving rod reaches a set condition, the yarn stripping rod is provided with multiple yarn strippers corresponding to the multiple yarn spindles one by one, and the yarn strippers are configured to strip fiber yarn on outer surfaces of the corresponding yarn spindles.

Abstract: Provided is a transfer vehicle, including: a vehicle body; a rotating mechanism, including an annular track vertically arranged above the vehicle body, a crawler belt arranged along an end surface of the annular track, and a first driving device connected to the crawler belt; a plurality of material receiving rods arranged at intervals on a side of the rotating mechanism, wherein each material receiving rod includes an inner rod and an outer rod sleeved on the inner rod, and the inner rod is connected to the crawler belt; a docking auxiliary mechanism arranged beside a docking station on the side of the rotating mechanism, including a pushing device and a positioning device, wherein the positioning device is configured to determine an orientation of a target docking position; and a control mechanism configured to control movement of the vehicle body.

Abstract: 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.

Abstract: Provided is a yarn spindle quality control method based on a knowledge graph, an electronic device, and a storage medium. The method includes determining whether there is an abnormal parameter in a production parameter and/or a process parameter of a group of yarn spindles based on a quality inspection result, the production parameter and the process parameter of the group of yarn spindles; searching an adjustment manner corresponding to the production parameter and/or the process parameter from a knowledge graph for yarn spindle production and management, in a case where there is the abnormal parameter; and sending the adjustment manner corresponding to the production parameter and/or the process parameter to a related device of the group of yarn spindles.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.