204 results about "Textile industry" patented technology

The invention provides a textile industry broken yarn identification method and system based on a neural network, and the method comprises the steps: collecting yarn multi-modal data, including a surface image, a fracture sound wave signal and tension change data; preprocessing the data, and extracting an image ROI region, sound wave spectrum features and a tension mutation sequence; performing space-time alignment and feature extraction on the extracted content to obtain a joint feature vector; inputting the broken yarn into a trained broken yarn identification model, wherein the model can identify broken yarn features; judging whether broken yarns exist or not according to the output result and outputting an identification result; and updating the model through online incremental learning. According to the method, multiple types of data are combined, the adaptive preprocessing and feature extraction technology is used, environmental noise is inhibited, key features are focused, and the problem of false alarm of a traditional sensor is solved; multi-modal features are fused through space-time alignment and a self-attention mechanism, and bidirectional LSTM modeling is combined, so that a broken yarn dynamic rule is accurately captured, and the problems of missing detection and delay of manual inspection are avoided.

The invention relates to the field of artificial intelligence and computer vision, discloses an AI vision-driven polyester fabric pilling detection method and system, and aims at solving the problems that traditional manual visual inspection is low in efficiency and high in subjectivity, and existing machine vision is insufficient in sensitivity to low-contrast tiny defects. The method comprises the steps that fabric images are collected through a high-resolution linear array camera and a multi-channel self-adaptive LED light source, after non-uniform correction and self-adaptive filtering, the fabric images are input into a three-level convolutional network to extract multi-scale texture features, and then unified representation is generated through cross-scale attention fusion; according to the technical scheme, high-precision recognition of pilling with the diameter smaller than 0.5 mm is achieved, the detection rate is increased by 42%, the false alarm rate is lower than 3%, full inspection of a 60-meter / minute production line is supported, structured quality data are output, and the intelligent and quantitative level of online quality inspection in the textile industry is remarkably improved.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a multi-sensor fusion textile machinery fault diagnosis method, which comprises the following steps of S1, capturing a vibration signal, a sound waveform and a visual image in equipment operation; s2, executing preprocessing operation on the data collected in the S1; s3, integrating heterogeneous data of vibration, sound and vision to generate comprehensive high-dimensional feature representation; s4, performing fault feature extraction and classification by using a preset AGNN; s5, when the AGNN identifies a specific fault type, automatically generating a detailed fault report; and S6, continuously optimizing the DMFN model and the AGNN model by using the feedback information. According to the invention, by combining DMFN and AGNN technologies, efficient, accurate and comprehensive diagnosis of textile machinery faults is realized, the fault early warning capability is significantly improved, the maintenance process is optimized, and powerful support is provided for intelligent development of the textile industry.

The invention relates to the technical field of intelligent control of an air-jet vortex spinning yarn-forming process in the textile industry, in particular to an air-jet vortex spinning yarn-forming process control model based on a semi-supervised stacked auto-encoder, which comprises the following steps of: (1) analyzing data hidden variable distribution by adopting a Gaussian mixture model, and marking unlabeled data to expand data resources; (2) constructing a semi-supervised auto-encoder, and synchronously realizing data reconstruction and multi-quality characteristic'balance point 'prediction through weight sharing design; (3) stacking layer by layer to form a depth model, and strengthening multi-characteristic correlation modeling; and (4) realizing automatic disturbance detection in combination with a sequence auto-encoder containing an LSTM layer, an attention mechanism and a Tukey rule. The model reduces data dependence, improves the quality stability of resultant yarn and the anti-interference performance of production, adapts to industrial intelligent requirements, and assists the application upgrade of the air-jet vortex spinning technology.

The invention relates to the technical field of automatic control in the textile industry, and discloses an automatic control system of a doffer, which comprises a sensing module, a decision control module, an execution module, an energy consumption management module and a fault monitoring and maintenance module. Through a multi-dimensional data acquisition and intelligent control technology, each link of operation of the doffer can be comprehensively covered, conventional mechanical action execution is concerned, factors such as spindle posture, yarn tension, equipment energy consumption and vibration state are also included, and an intelligent algorithm is deeply integrated to carry out working condition analysis and instruction generation; doffing control can meet production requirements more accurately, and powerful support is provided for efficient development of doffing operation.

The invention relates to the technical field of textile industry, in particular to a textile fabric winding tension prediction system based on machine learning, which comprises an environment sensing module, a dynamic data acquisition module, a data preprocessing enhancement module, a deep learning model training module, a self-adaptive control module and a fault diagnosis early warning module. Wherein the environment sensing module is used for monitoring spinning environment data in real time; the dynamic data acquisition module is used for comprehensively monitoring the running state and the physical environment change of the textile machinery; the data preprocessing enhancement module is used for standardizing the data acquired by the acquisition module; and the deep learning model training module is used for carrying out model training on tension change in the textile fabric winding process. According to the invention, real-time and accurate tension control is realized, the production automation and intelligence levels are improved, and fault diagnosis early warning is started, so that the production efficiency and quality of textiles are remarkably improved, and meanwhile, the resource waste and the production cost are reduced.

The invention discloses a vortex spinning yarn defect detection method and system based on computer vision, and relates to the technical field of textile industry quality control and image recognition, and the method comprises the steps: obtaining a bright field image sequence and a dark field image sequence, and collecting a yarn position signal and an air pressure pulsation signal of a spindle vortex field; performing track modeling and inter-frame displacement compensation on the bright field image sequence to generate a yarn structured displacement image; constructing an airflow fiber-carrying feature map; performing spatial registration and fusion on the yarn structured displacement image and the airflow fiber-carrying feature map to obtain a dual-channel feature map; generating a defect cause directivity judgment instruction; marking a defect position based on defect information of the defect cause directivity judgment instruction, and generating a process parameter adjustment suggestion signal; and constructing a defect-process feedback sample set. The air vortex spinning yarn defect detection system effectively solves the key problems that an existing air vortex spinning yarn defect detection system is single in detection dimension, defect causes cannot be distinguished, and real-time process closed-loop feedback is lacked.

The invention provides a special static electricity removing device matched with a twisting machine, a working method and an installation method of the special static electricity removing device. The special static electricity removing device comprises an arc-shaped static electricity removing device body, a dustproof flange arranged on the outer edge of the body, a plurality of electrode tungsten needles distributed on the body, an integrated circuit board and a high-voltage pack. The device adopts a low-voltage power supply to supply power, adopts a highly integrated circuit board to replace a traditional high-voltage generator and a switch, cancels a high-voltage line, simplifies a line structure, compresses a traditional device assembly into a few core components, reduces the manufacturing cost, improves the reliability and safety of equipment, eliminates potential safety hazards of a high-voltage line, and improves the reliability and safety of the equipment. Production safety is guaranteed, high-quality yarn guarantee is provided for subsequent weaving, dyeing and finishing and other procedures, the core problems of complex installation, poor effect, potential safety hazards and the like in the prior art are solved, and an efficient, safe and economical static elimination solution is provided for the textile industry.

The application discloses a preparation method of recycled cotton viscose fiber and belongs to the technical field of waste recycling. The preparation method comprises the following processes: obtaining spinning viscose by mixing pulp, impregnating, squeezing, aging, yellowing, dissolving, ripening and defoaming of recycled cotton pulp; and obtaining recycled cotton viscose fiber by spinning, refining and drying of the spinning viscose. The application aims to realize the recycling of recycled cotton pulp formed by discarded cotton clothes or floor cotton of textile mills, to prepare spinning viscose and then prepare cotton viscose fiber, and to obtain pure cotton products with excellent quality, which can be used in the textile industry, so as to realize environmental protection, regeneration and recycling in the textile industry.

The invention discloses a realistic simulation method for a fluffing and pilling process of a knitted fabric under a fiber scale, and relates to the technical field of realistic simulation of the fluffing and pilling process of the knitted fabric in the textile industry. The method aims at solving the problems that an existing knitted fabric fuzzing and pilling simulation method is difficult to accurately simulate a complex process on the fiber scale, and a simulation result lacks a sense of reality. The method comprises the following steps: constructing a mass point-spring model of the knitted fabric, discretizing yarn sections into mass points and connecting the mass points through a virtual spring, integrating a fiber entanglement calculation module on the basis of the model, simulating complex entanglement behaviors among fibers by adopting a diffusion limitation aggregation model, and calculating normal contact force and tangential friction force in real time. When the tangential friction force exceeds a fiber cohesive force threshold value, triggering a fuzzy ball to form an initial condition; defining and calculating local friction energy, and generating a fuzzy ball surface texture in combination with a Perlin noise function and a fractal texture technology; and finally, realizing high-quality rendering and developing a user interaction interface by utilizing a high-level graphics library.

The invention relates to the technical field of visual monitoring, and discloses a yarn defect intelligent identification method and system based on visual monitoring, and the method comprises the steps: firstly obtaining a yarn image, and carrying out the feature extraction of a shared shallow layer, so as to obtain a yarn visual shallow layer shared feature map; performing double-flow feature decoupling on the yarn visual shallow shared feature map to obtain a yarn texture feature map and a yarn defect feature map, and then performing texture residual gating modulation on the yarn texture feature map and the yarn defect feature map to obtain a refined yarn defect feature map; and finally, carrying out decoding and defect segmentation on the refined yarn defect feature map to obtain a defect mask. Thus, accurate extraction and segmentation of yarn defect features are achieved, low-efficiency and unreliable manual detection is expected to be replaced, the performance of an existing automatic detection system is improved, and an efficient, stable and accurate online yarn quality control solution is provided for the textile industry, so that the product quality is guaranteed, and the production cost is reduced.

The present application relates to water recycling equipment technical field, specifically to a kind of textile water circulation collection equipment;Water collection part, including a plurality of conducting installation in the first liquid warehouse above flow guide structure, flow guide structure is installed with liquid collection warehouse, liquid collection warehouse is installed with waste liquid pipe and effluent pipe on conducting, and effluent pipe water outlet and first liquid warehouse between conducting installation;Inner processing part, including installation in the first liquid warehouse filtrate inner warehouse, and filtrate inner warehouse is installed with gas injection pipe, stirring blade and water pump;Water collection part can gather production wastewater or rainwater and other water resources, can be introduced into first liquid warehouse by flow guide structure and effluent pipe with water resources;While provided with inner processing part can be extracted to second liquid warehouse after water resources are handled, this collection equipment can be recycled by water collection part with inner processing part after the water resources such as textile wastewater of slight pollution and collected rainwater are handled, can improve water resource utilization, applicable to textile industry and other large water demand industry.

This utility model relates to the field of fabric dyeing technology and discloses a fabric dyeing device for garment processing, including a dye pool. A first motor is fixedly installed on the right side of the dye pool. A dyeing roller is rotatably connected to the inner side of the dye pool via bearings. A transmission mechanism is arranged above the left side of the dye pool. The transmission mechanism includes a first mounting plate, a second motor, an upper pressure roller, a driving gear, a driven gear, and a lower pressure roller. The bottom of the first mounting plate is fixedly installed above the dye pool. Two first mounting plates are symmetrically arranged on both sides above the dye pool. The second motor is fixedly installed on the outer side of the first mounting plate, and the output end of the second motor is fixedly connected to the upper pressure roller. This textile fabric dyeing device, with its transmission mechanism, eliminates the need for manual pulling of the fabric during dyeing, improving the efficiency of the dyeing process. The clamping mechanism prevents the fabric from shifting during the dyeing process, thus affecting the dyeing quality.

The application discloses a multi-lamp-bar type lamp strip structure and relates to the field of mechanical equipment for textile industry, which comprises a long-strip-shaped mounting plate and an LED lamp bar, the LED lamp bar comprises a strip-shaped conductive substrate and a plurality of lamp beads arranged on the conductive substrate, the conductive substrate is arranged on one side of the mounting plate and detachably fixedly connected with the mounting plate through a threaded part, a plurality of the lamp beads are arranged in multiple rows along the width direction of the conductive substrate; and the lamp beads comprise white light lamp beads and purple light lamp beads, a plurality of the purple light lamp beads are uniformly arranged along the length direction of the conductive substrate and form a purple light lamp strip, and a plurality of the white light lamp beads are uniformly arranged along the length direction of the conductive substrate and form a white light lamp strip. Through the dual-light-source space cooperation, the end brightness compensation, the modular heat dissipation and the quick maintenance architecture, the application precisely matches the strict requirements of the cotton foreign fiber removing equipment on the light source stability and the imaging uniformity, finally realizes efficient and durable lighting support, and improves the comprehensiveness and sensitivity of the foreign fiber machine in detecting foreign fibers.

The utility model relates to the technical field of textile industry, and discloses a modified polyester staple fiber automatic bundling and yarn guiding mechanism which comprises a bottom plate, a supporting frame is fixedly connected to the top of the bottom plate, a sliding groove is formed in the top of the supporting frame, and a plurality of conveying rollers are rotationally connected to the interior of the sliding groove. And the tops of the multiple conveying rollers are fixedly connected with rotating shafts, the top of the supporting frame is slidably connected with a cover plate, the tops of the multiple rotating shafts are rotatably connected to the bottom of the cover plate, the interior of a sliding groove is slidably connected with a clamping plate, and the front side of the exterior of the clamping plate is fixedly connected with multiple alignment blocks. According to the conveying roller, the clamping plates drive the alignment blocks to smoothly slide in the sliding grooves, meanwhile, when the conveying roller needs to be maintained, the clamping plates slide out, the conveying roller can be rapidly maintained, the dismounting time for maintenance is saved, and the working efficiency is improved.

The invention discloses a temperature-variable-stress-resistant self-feedback yarn anti-corrosion drying method, and relates to the technical field of intelligent control and material protection of a yarn drying process in the textile industry, and the method comprises the following steps: S1, establishing a digital twinning stress prediction model and a dynamic anti-corrosion performance evaluation model of yarns; the digital twinning stress prediction model is used for predicting transient thermal stress distribution in the yarn according to the drying process parameters. According to the temperature change resistant stress self-feedback yarn anti-corrosion drying method, synchronous and coordinated regulation and control of internal thermal stress, the surface anti-corrosion state and the moisture regain of the yarn in the drying process are achieved; on the premise of stabilizing the vehicle speed, the yarn structure damage risk caused by temperature fluctuation can be actively inhibited, continuous and effective corrosion protection is provided for the yarn in the drying process through intelligent management of the slow-release anti-corrosion medium, and the physical integrity and durability of the yarn are overall improved.

The invention belongs to the technical field of water-based curing agents in the textile industry, and provides a water-based closed isocyanate curing agent, a preparation method thereof and application of the water-based closed isocyanate curing agent in fabric waterproof finishing. The preparation method comprises the following steps: mixing polyisocyanate, hydroxyl acrylate and a solvent, carrying out a polymerization reaction, adding a sealing agent into the obtained polymerization reaction system, and carrying out an end-capping reaction to obtain blocked isocyanate; the preparation method comprises the following steps: mixing blocked isocyanate, long-chain alkyl acrylate, a POSS (polyhedral oligomeric silsesquioxane) hydrophobic modifier, a chain transfer agent, an initiator and a solvent, and carrying out free radical addition reaction to obtain hydrophobic modified blocked isocyanate; and mixing the hydrophobic modified blocked isocyanate, an emulsifier, a pH regulator, water and a cosolvent, and carrying out high-pressure homogenization to obtain the water-based blocked isocyanate curing agent. The water-based closed isocyanate curing agent disclosed by the invention has waterproof synergism, and is compounded with a fluoride-free waterproof agent, so that the waterproofness and washability of the fluoride-free waterproof agent can be improved.

The utility model relates to the field of textile industry, and discloses a cooling mechanism which comprises a rubber pipe arranged between a cotton suction air bellow on a spinning frame and a frequency converter and used for leading air blown out by a turbofan to the frequency converter to cool the frequency converter; the mounting mechanism is arranged on an air outlet pipe of the turbofan and is used for mounting the rubber pipe and controlling the mounting position of the rubber pipe; the fixing mechanism is arranged on the mounting mechanism and used for fixing the rubber hose, the rubber hose directly leads air blown out by the turbofan to the frequency converter, cooling of the frequency converter is achieved, cooling efficiency is greatly improved, the problem that the frequency converter is damaged due to overheating is effectively avoided, and the service life of the frequency converter is prolonged. And the frequency converter is cooled through existing equipment on the spinning frame, so that energy is saved, and space can be saved.

The invention belongs to the technical field of genetic engineering and protein engineering modification, and particularly relates to a high-specific-activity cellulase mutant and application thereof. Compared with wild cellulase, the specific activity of the mutant is improved by 51.9% and reaches 319 U / mg, and an unexpected technical effect is achieved. The mutant can be widely applied to the field of textile industry, is uniform in flower formation, small in flower point and strong in stereoscopic impression, most importantly, the strength loss of the fabric is very small, the strength retention rate is improved by 10-15% compared with that of the fabric treated by wild type cellulase, the use amount of the cellulase can be greatly reduced, the cost is reduced, and the application prospect is wide.

This invention relates to a method for improving the quality reproducibility of small-batch yarn-dyed fabrics compared to mass production, belonging to the field of yarn-dyed fabric technology in the textile industry. The production process for small-batch yarn-dyed fabrics in the warp direction employs a process of warping the raw yarn – dyeing the warp beam – dehydration – sizing the yarn onto the small beam – sizing machine assembly, achieving consistency with the mass production process. This results in consistent dyeing reproducibility, yarn elasticity, and sizing effect. Comparative tests show a color reproducibility level of 4 or higher, a yarn elongation deviation within 3%, and a sizing rate deviation within 1%. This avoids the differences in color, elasticity, and yarn absorbency between the warp-dyed yarn in small-batch yarn-dyed fabrics and the warp beams in mass production, which can lead to differences in sizing rates.

The invention relates to the technical field of automation of the textile industry, in particular to an automatic optimization scheduling method and system for head tasks between ring spinning wheels. The method comprises the following steps: fusing multi-source heterogeneous data to generate risk potential energy; introducing an environment modulation factor, establishing a space-time diffusion model of the risk potential energy, and obtaining a risk potential energy field; based on the risk potential energy field, analyzing a dynamic responsibility domain through a comprehensive anti-risk capability index; and in the dynamic responsibility domain, a three-layer distributed negotiation protocol is utilized to realize automatic optimization scheduling of the joint task between the ring spinning wheels. According to the method, discrete end breaking events are converted into a continuous space risk field, responsibility domains are dynamically divided according to the real-time capability of equipment, and finally, a plurality of joint machines are driven to autonomously cooperate through a three-layer negotiation protocol, so that dynamic and balanced distribution of tasks of a plurality of joint robots is realized; and the time interval from yarn breaking to robot processing is obviously reduced.

The present invention relates to a method for air jet texturizing or air entangling a silk filament or multifilament. Further, the present invention relates to a texturized or entangled silk filament or multifilament obtained by this method. Furthermore, the present invention relates to a yarn comprising the texturized or entangled silk filament or multifilament. In addition, the present invention relates to the use of the silk filament, multifilament, or yarn in the textile industry.

This invention provides a quality traceability system and data interaction method for the entire textile and apparel industry chain, comprising: a physical fingerprint marking unit for providing composite marking information of at least two different physical detection modes; a multi-node data acquisition unit for collecting process parameters of each process node and physical fingerprint information of the physical fingerprint marking units in semi-finished products; a data fusion and map construction unit for receiving and processing multi-source heterogeneous data including physical fingerprint information to construct a traceability map of the entire industry chain characterizing the relationships between fiber raw materials, semi-finished products, finished products, and various processes; and a distributed traceability application unit for responding to traceability requests, generating traceability paths based on the traceability map of the entire industry chain and physical fingerprint information, and providing them to the requesting party. This invention can adapt to the special process environment of the textile industry, solve the problems of blending ratio verification and feature attenuation, while also taking into account data privacy protection, thus forming a quality traceability system for the entire industry chain.

The present application relates to the field of textile printing and dyeing technology, and more particularly to an environmentally-friendly dyeing process for nylon fibers, comprising the following steps: S1, pretreatment; S2, ultrasonic-assisted low-temperature dyeing; S3, integrated treatment of phased temperature control and color fixing; S4, post-treatment; S5, drying and setting. The present application adopts the combination of ultrasonic-assisted dispersion, composite levelling agent synergy and phased temperature control, solves the long-standing problems of color mottle and color difference in nylon low-temperature dyeing, increases the one-time success rate of dyeing, almost eliminates the need for rework, and greatly improves the production efficiency. At the same time, the core dyeing temperature is reduced from the traditional high-temperature process of 95-100 DEG C to 65-75 DEG C, with a cooling range of 20-30 DEG C, which reduces the comprehensive energy consumption, greatly reduces the steam and power consumption, and fully meets the development of the textile industry.

The utility model discloses a screw air compressor suitable for the textile industry. The screw air compressor comprises a box body, a filter assembly, an air compression assembly, a cooling assembly and an oil mist separation assembly, an air outlet and a plurality of air inlets are formed in the box body, and a containing space is formed in the box body; the filtering assembly, the air compression assembly, the cooling assembly and the oil mist separation assembly are arranged in the containing space. The filter assembly is connected with the air compression assembly, the air compression assembly is connected with the oil mist separation assembly, and the oil mist separation assembly is connected with the air outlet; and the cooling assembly is arranged on the inner wall of the box body and is communicated with the compression assembly to form a parallel cooling and filtering structure. Through the arrangement, the air compressor is simpler in structure and lower in maintenance and operation cost. Meanwhile, an after-cooler is omitted, the complexity and the manufacturing cost of the equipment structure are reduced, and the actual requirements of the textile industry are effectively met.

The application relates to a textile defect detection method based on an improved YOLOv11 model. The method comprises data preprocessing, adding a small detection head on the basis of the YOLOv11 model, introducing an SAHI model to process large-size images, and defect detection. A loss function composed of a boundary box regression loss, a classification loss and a confidence loss is used for model training. The model test and evaluation use the accuracy, recall rate, average accuracy and intersection over union (IoU) indexes. The optimized model is applied to an actual production line, real-time detection, marking and classification processing of textile defects can be realized, the detection accuracy and efficiency are improved, and technical support is provided for quality control of the textile industry.

The invention discloses a filter capable of realizing on-line backwashing and screen replacement at the same station and improving the backwashing effect, and relates to the technical field of cellulose fiber stock solution filtration in the textile industry. The invention relates to an improved and upgraded improvement on the patent 201913038298, in particular to a novel high-efficiency and high-efficiency energy-saving and environment- According to the present invention, most of the main body structure, the filtration chamber, the screen changing oil cylinder and other components of the filtration base body in the filtration unit are mainly retained, the backwashing station is changed from the built-in state to the external state, and the online backwashing function component structure is improved; according to the invention, on-line backwashing and on-line screen replacement and exhaust can be alternately realized on the same external station, impurities on the filter screen can be efficiently, thoroughly and effectively removed in the backwashing process, the backwashing effect is greatly improved, the backwashing efficiency is improved, the backwashing uniformity is improved, the existence of backwashing dead angles is avoided, and the backwashing efficiency is improved. And repeated backwashing is avoided, so that the filtering quality of the cellulose filtrate is improved, and a good foundation is laid for the quality of subsequent fiber spinning.

The invention discloses a multi-channel image data driven fabric analysis system, which relates to the technical field of intelligent detection in the textile industry, and comprises an image sampling unit, a data processing unit and a component prediction unit, a sampling detection belt is arranged on a detected fabric, a multi-channel sensor group comprising visible light, near-infrared and polarization imaging sensors is arranged at the detection belt, multi-dimensional image information of the fabric is collected and preprocessed, an image feature vector group is extracted, and then component categories and proportions in a sampling area are analyzed; a texture trend reasoning mechanism is combined to realize high-precision prediction of component distribution and proportion of the whole fabric image; the precision and robustness of component recognition are effectively improved, the data processing burden and required computing resources are remarkably reduced, and the system response speed and the engineering application feasibility are improved.

The utility model relates to the technical field of textile industry discloses a silk cake diameter detection device, including frame, the inner wall bottom end front side of frame is provided with winding mechanism, the winding mechanism is used to collect the work piece of equipment processing, increases the output of equipment, the outer wall top rear side of frame is installed with processing mechanism, the processing mechanism is used for the processing of equipment raw material, makes the output of equipment increase, the winding mechanism includes protective housing, the protective housing sets up in the inner wall bottom end front side of frame, the inner wall rear side middle upper portion of protective housing is fixedly connected with motor. In the utility model, the winding hook of product winding is arranged in the order of anti -winding, after the processing of pressure roller, motor no.

The application relates to the fields of textile chemical engineering and green material technology, and discloses a pH-responsive recyclable starch sizing material as well as a preparation method and application thereof. Through molecular structure design, an intelligent starch sizing material is prepared, which can intelligently convert between cationic state and zwitterionic state in response to changes in environmental pH value, is excellent in sizing performance, can realize function switching and multiple recycling through simple pH adjustment, realizes efficient recovery and recycling of the sizing material, and based on the starch sizing material, a closed-loop application process is designed, the traditional mode that the starch sizing material is abandoned after one-time use is changed, and the problems of sizing material resource waste and desizing wastewater pollution existing in the textile industry for a long time are solved.