Abstract: Provided is a model training method, a data processing method and related apparatuses, relating to the technical fields of large model, image processing, and computer vision. The method includes: obtaining a historical process flow card set for spinning process; extracting a handwritten area from each historical process flow card; classifying the handwritten area to obtain a handwritten digit image block and a handwritten text image block; constructing a digit recognition model of different handwritten digit categories based on the handwritten digit image block, to extract a target digit from a newly-added process flow card; and constructing a text recognition model of different handwritten text categories based on the handwritten text image block, to extract a target text from the newly-added process flow card; wherein the target digit and the target text are used to construct a process flow database for the spinning process.

Abstract: Provided is a yarn spindle packaging system, a control method thereof, an electronic device and a storage medium, relating to technical field of chemical fiber intellectualization.

Abstract: Provided is a yarn spindle packaging system, a control method therefor, an electronic device and a storage medium, relating to the field of chemical fiber intelligent technology. The yarn spindle packaging system includes: a control device and a plurality of stand-alone devices, where the control device is configured to generate a packaging task and a device control parameter of each stand-alone device for a target type of yarn spindles according to a packaging requirement; and adjust the packaging task and the device control parameter of each stand-alone device in combination with work data returned by each stand-alone device; and each stand-alone device is configured to receive the corresponding packaging task and device control parameter sent by the control device; execute the corresponding packaging task based on the corresponding device control parameter; and return the work data corresponding to the corresponding packaging task to the control device.

Abstract: Provided is a spinning workshop inspection method, an electronic device and a storage medium, relating to technical fields of computers. The spinning workshop inspection method includes: acquiring first image information of an inspection passage when an inspection robot inspects a first position corresponding to the first spinning box in the inspection passage; determining a second spinning box from spinning boxes when the first image information includes a first detection object; acquiring second image information of the inspection passage when the inspection robot inspects a second position corresponding to the second spinning box in the inspection passage; and determining state information of the first detection object based on the first and second image information.

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 parameter processing method, an electronic device and a storage medium. The method includes: determining T full-winding rates of a yarn spindle wound by a winding machine; and under the condition where it is determined that M full-winding rates in the T full-winding rates do not meet a requirement of a preset full-winding rate, determining a candidate characteristic parameter set, wherein a candidate characteristic parameter included in the candidate characteristic parameter set is the characteristic parameter that is selected from N first characteristic parameters corresponding to a full-winding rate not meeting the requirement of the preset full-winding rate and that causes the full-winding rate not meeting not to meet the requirement of the preset full-winding rate.

Abstract: Provided is a method for measuring a color difference, an electronic device and a storage medium. The method includes: conveying respective fabric segments of the fabric to be detected sequentially to a measurement port of a detection portion, so as to collect color values at a plurality of sampling points; determining a time range in which a target segment of the fabric to be detected passes through the measurement port of the detection portion; the target segment being any one fabric segment of the fabric to be detected; selecting an effective sampling point of the target segment based on the time range and sampling time points of the plurality of sampling points; and determining a corresponding color difference value of a target yarn spindle based on a color value and standard value of the effective sampling point.

Abstract: Provided is an order processing including pre-checking a first product order on an online shopping platform to obtain a second product order; determining a target production factory from a plurality of production factories based on product parameters of a target product or an estimated cost; generating a production scheduling order of the target product and sending the production scheduling order to the target production factory; updating the second product order on the online shopping platform based on latest progress information of the production scheduling order obtained from the target production factory; generating a delivery order based on inventory information of the target product after completion of the production scheduling order and sending the delivery order to a logistics platform; and updating the second product order on the online shopping platform based on latest progress information of the delivery order obtained from the logistics platform.

Abstract: Provided is a method and apparatus for processing a logistics waybill and a storage media. The method includes: acquiring a sales order to be delivered; splitting the sales order into multiple sub-orders according to the sales order and inventory information of multiple plant areas when the sales order needs to be split and arranged for shipment; planning and obtaining the logistics waybill according to shipment quantities and shipping addresses of the multiple sub-orders and a delivery address of the sales order; binding vehicle information of a delivery vehicle receiving the logistics waybill with the logistics waybill; generating a first instruction and sending the first instruction to a current pickup plant area based on position information of the delivery vehicle before the delivery vehicle arrives at the current pickup plant area; and generating (S106) reconciliation settlement information when the logistics waybill is signed for.

Abstract: Provided is a doffing scheduling method and apparatus for yarn spindle product, and storage medium, applied in an automatic doffing system, relating to the field of computer technology, and particularly to the field of automation control. The automatic doffing system is connected to an automated guided vehicle, (AGV) and an overhead rail doffing vehicle, the AGV and the overhead rail doffing vehicle being deployed in different vertical spaces within a target workshop. The method includes: determining a target doffing vehicle corresponding to a first winding machine in the target workshop from the AGV and the overhead rail doffing vehicle based on time-related information of doffing the yarn spindle product in all or part of winding machines in the target workshop; scheduling the target doffing vehicle to complete a doffing task of the yarn spindle product in the first winding machine.

Abstract: Provided is a method for processing sample data, an electronic device and a storage medium, relating to the field of artificial intelligence technology, and especially to the fields of image processing and computer vision. The method includes: obtaining a plurality of first working images of a spinning box; for each first working image in the plurality of first working images, determining the first working image as a defect image of the spinning box when a difference between the first working image and a normal image of the spinning box meets a first condition; and obtaining a first sample data set based on all defect images in the plurality of first working images.

Abstract: Provided is an automatic doffing communication method, a control system, a communication system and a network device. The method includes: receiving a doffing monitoring image from a terminal device; sending the doffing monitoring image to a text recognition system; receiving an automatic doffing scheduling task from an automatic doffing system; wherein the automatic doffing scheduling task comprises a task generated by the automatic doffing system according to a recognition result of the doffing monitoring image by the text recognition system; and sending the automatic doffing scheduling task to an automatic doffing device.

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 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 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.