8419 results about "Textile" patented technology

The invention discloses an automatic testing device and method for multi-directional tensile strength of textiles based on constant tension, and relates to the technical field of tensile strength detection.The automatic testing device comprises a rigid rack system, and a high-elastic fabric optical response mechanism is integrated on a double-stand-column portal frame of the rigid rack system; the fiber damage process is reflected through the light transmittance of light to the fabric and the light spot change; the high-elastic fabric optical response mechanism comprises a sealing light insulation assembly, a light source assembly and an imaging assembly. The light source assembly comprises pulse LED light source arrays arranged at equal intervals in the fabric stretching direction, a lampshade and a transverse movement driving unit. The problems that in the prior art, when the tensile strength of a fabric is detected, only the limit value when a sample is completely broken can be recorded, and microcracks generated by step-by-step failure of fibers in the stretching process cannot be captured are solved. The device has the advantages that when the tensile strength of the fabric is detected, the fiber fracture process in the fabric is directly reflected through the change of light spots formed by light penetrating through the fabric, and the like.

The invention relates to the technical field of biomechanical measurement and analysis, in particular to a home textile recommendation method and system based on child health data monitoring and analysis. The method comprises the following steps: collecting an original point cloud coordinate set of a sleeping posture of a child; extracting a cervical curvature parameter group according to the extracted cervical curvature parameter group of the skeleton key point spatial distribution diagram; performing cervical vertebra and body position analysis according to the cervical vertebra curvature parameter group to obtain cervical vertebra curvature-body position holographic data; collecting a static pressure baseline data set according to the cervical curvature-body position holographic data; performing body position-pressure correlation analysis according to the static pressure baseline data set to obtain a body position-pressure mapping relation table; and generating a pressure distribution index map according to the body position-pressure mapping relation table. According to the method, the physical sign data, the pressure distribution and the physiological curvature change of the children in the natural sleep state are comprehensively collected and analyzed, so that high-precision, whole-process and personalized healthy home textile recommendation is realized.

The invention relates to the technical field of polyester fibers, in particular to a high-strength wear-resistant polyester fiber and a preparation method thereof. The invention aims to solve the problem that the mechanical strength and wear resistance of the existing polyester fiber are difficult to consider at the same time. The preparation method comprises the following steps: carrying out esterification and polycondensation on terephthalic acid, ethylene glycol, hydroxyl-terminated polydimethylsiloxane and isophthalic acid to obtain modified PET; the preparation method comprises the following steps: respectively carrying out oxidation treatment on graphene and a multi-walled carbon nanotube, and then adding polyether amine for grafting; carrying out melt blending with PET slices to obtain nano master batches; carrying out melt blending on the chopped glass fibers and the PET slices to prepare micron master batches; and mixing the modified PET, the nano master batch and the micron master batch, carrying out ultrasonic dispersion, adding an auxiliary agent, and carrying out melt spinning, three-section stretching and three-section heat setting treatment to obtain the high-strength wear-resistant polyester fiber. Through the synergistic effect of the components and the process, the prepared fiber has excellent mechanical properties and wear resistance, and has wide application prospects in the fields of clothing, home textiles, industry and the like.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

The invention provides a method for recovering a BHET monomer from a waste polyester textile alcoholysis solution, and is characterized in that the recovery method comprises the following steps: (1) filtering: mixing the waste polyester textile alcoholysis solution with water under a stirring condition, and filtering at 55-70 DEG C; and (2) cooling crystallization: under a stirring condition, carrying out primary cooling on the filtrate obtained by filtering in the step (1) from 55-70 DEG C to 45-55 DEG C, then adding a BHET seed crystal, then carrying out secondary cooling to 5-15 DEG C, and maintaining the temperature for 20-60 min to obtain the BHET monomer. The mixed solution cooling crystallization method is developed on the basis of complexity of components of the waste polyester textile fabric, the problems that in the conventional production process, a BHET crystal product is low in quality, prone to scaling, difficult to filter and dry and the like are solved, and the obtained BHET product is large in particle size, narrow in particle size distribution, high in purity, good in fluidity and high in stability.

The invention relates to the field of yarn spinning, in particular to a real-time quality rating method and system for yarn spinning. The method comprises the following steps: acquiring a space-time charge data set, and generating a yarn electrostatic state data set through a multi-mode sensing fusion strategy; on the basis of the yarn static state data set, yarn spinning quality defects are recognized by combining yarn form data collected in real time, two-dimensional dynamic rating is executed according to defect types and severity, and yarn real-time quality rating is generated; according to the yarn real-time quality rating, carrying out change trend analysis on the yarn real-time quality rating to generate a yarn subsequent quality change trend; and executing a corresponding process regulation and control instruction according to the subsequent quality change trend of the yarn, and outputting a quality rating log. In the yarn spinning process, process look-ahead optimization is achieved, quality tracing is perfected, and the accuracy and real-time performance of quality rating are improved.

Devices and methods of harvesting textile hemp are presented. The methods comprise mowing the hemp and wherein the cut stems are held between crossed conveyor belts and then rotated 90° from vertical to horizontal, taken over one or more times by other belts, to be deposited in parallel swaths on the field.

The invention relates to a textile product defect identification method based on improved YOLOv11. The method comprises the following steps: acquiring a textile product defect image data set; performing pretreatment; dividing into a training set and a verification set; the method comprises the following steps: introducing MConv into a YOLOv11 backbone network, adding a CCIAP module behind a C2PSA module, and applying BiFPN in a path aggregation network; performing prediction through YOLO Head to obtain N prediction feature maps; the overall loss of the network is calculated, and network parameters are optimized through back propagation; predicting the verification set image through a network to output AP values of various categories; repeating the above steps to obtain a trained YOLOv11 network; and detecting the test image or video by using the trained detector to obtain a detection result. According to the method, the MConv is introduced into the YOLOv11 network to enlarge the receptive field, the CCIAP module is added behind the C2PSA to improve the feature extraction capability, and the BiFPN is applied to the Neck layer to enhance the feature fusion capability, so that the target detection precision is improved and the real-time detection of textile product flaws is realized under the condition that the reasoning speed is not influenced.

A textile fabric is provided, including: a main body and at least one film layer. The main body includes two surfaces opposite to each other, and each of the two surfaces has a surface texture being concave and convex. The at least one film layer is coveringly disposed on at least one of the two surfaces, and each said film layer is concave and convex corresponding to the surface texture. Therefore, the at least one film layer has an appearance corresponding to the surface texture, and the textile fabric has good toughness, is waterproof and is not easily damaged, which solves the problems of insufficient structural strength and poor durability of a conventional textile fabric.

The invention relates to the technical field of textile machinery, and discloses a covering yarn machine spindle speed and twist control system which comprises a data acquisition module, a global control module, an individual diagnosis module, a power execution module and a man-machine interaction module. The actual average speed of the whole machine is calculated by collecting the speed of each spindle in real time, and PID closed-loop control is carried out on the actual average speed and a set value, so that the systematic speed deviation of the whole machine is accurately corrected. Meanwhile, the individual diagnosis module calculates spindle difference signals of the speeds of the spindles and the average speed of the whole machine in parallel, time domain and frequency domain analysis is carried out on the signals, and hidden faults such as steady-state deviation, periodic fluctuation and transient impact existing in a single spindle are accurately recognized. And finally, the system carries out visual early warning through a human-computer interaction interface and provides a predictive maintenance instruction. According to the method, high-precision control of the twist and high consistency of products are achieved, an effective predictive maintenance mechanism is established, and the production quality and the intelligent level are remarkably improved.

A textile fabric is provided, including: a main body and a film layer. The main body includes two surfaces opposite to each other, and the film layer is coveringly disposed on one of the two surfaces. A side of the film layer opposite to the main body includes at least one first surface portion and at least one second surface portion, and a glossiness of the at least one first surface portion is different from a glossiness of the at least one second surface portion.

The present application discloses a shapewear garment comprising an upper, middle, and lower portion, along with an abdominal compression panel. The upper portion wraps around the wearer's chest and upper back. The middle portion, positioned below the upper, comprises an abdominal and lower back covering. The lower portion extends around the wearer's front crotch area and buttocks. The abdominal compression panel, combined with the abdominal covering, has a higher modulus of elasticity than the other portions. A bust support area on the upper portion's outer surface features greater textile tension than surrounding areas.

The invention provides a textile cloth defect real-time detection method and system based on multi-modal feature fusion, and the method comprises the steps: collecting visual image data, infrared thermal imaging data and ultrasonic acoustic data of textile cloth through a multi-sensor array, and forming multi-modal input; performing time sequence alignment and noise filtering preprocessing on the multi-modal data to eliminate motion artifacts and environmental interference; a parallel feature extraction module is used for extracting texture features from the visual data, extracting temperature distribution features from the thermal imaging data and extracting acoustic impedance features from the acoustic data. By deeply fusing complementary information of three modes of vision, thermal imaging and ultrasonic wave, the detection capability is improved, the vision mode captures surface texture details, the thermal imaging mode reveals thermodynamic anomalies related to friction and materials, the ultrasonic wave mode perceives subcutaneous structure defects, hidden flaws which cannot be recognized by a single mode can be found, and the detection efficiency is improved. Therefore, the omission ratio is greatly reduced, and flaw types are distinguished more accurately.

The invention relates to the technical field of textile detection, in particular to a computer-aided textile color fastness prediction method, which comprises the following steps: acquiring multi-source data before and after a textile color fastness test through computer control equipment, including digital images, reflection spectrum data, process parameters and dye characteristic component data, carrying out pretreatment; extracting color difference features and spectrum similarity features, and screening by adopting a dimension reduction algorithm to form a core feature vector; a machine learning algorithm is adopted to construct a color fastness prediction model, parameters are optimized in combination with cross validation and an early stop mechanism, model fine tuning is executed, and a final optimization model is obtained; and outputting a color fastness grade prediction result, and carrying out spectrum similarity secondary verification. According to the method, objective, efficient and high-precision prediction of the color fastness grade of the textile is realized, and the problems of high subjectivity and low efficiency of traditional manual evaluation are solved.

The invention discloses a method and a system for identifying a spectrogram of a quaternary ammonium salt extracting solution in a textile, and relates to the technical field of material detection. Comprising the following steps: collecting different textile samples without quaternary ammonium salts and samples containing quaternary ammonium salts with different concentrations, determining extraction conditions, and extracting the quaternary ammonium salts to prepare a sample extracting solution; raman spectrums of all sample extracting solutions are collected, the spectrum containing the quaternary ammonium salt sample and the spectrum not containing the sample are divided into a plurality of sub-waveband areas, feature data are extracted, and the similarity is calculated. Based on the similarity change of the samples with different concentrations, defining the weight coefficient of the sub-band region; and performing similarity calculation on the spectrum of the to-be-detected textile extracting solution, and judging whether the to-be-detected textile extracting solution contains quaternary ammonium salt. And if not, further detecting by using Fourier transform infrared spectroscopy, and determining the quaternary ammonium salt concentration of the to-be-detected textile through the quaternary ammonium salt concentration prediction model. The calculation resources are reduced, and the detection precision and efficiency of the quaternary ammonium salt are improved.

The invention provides a textile surface defect detection method and system based on image features, and relates to the technical field of textile surface defect detection.The method comprises the steps that an RGB image of a to-be-detected textile is obtained and zoomed to 512 * 512 pixels, and then at least five semantic tags are output through a semantic segmentation model; the method comprises the following steps: adopting hyperspectral imaging with the wavelength of 400-1700 nm and the resolution of 10 nm, calculating the pixel reflectivity through a reflectivity formula, comparing the reflectivity of 5-8 characteristic wavebands, marking as a pollutant if the average deviation exceeds a threshold value, matching the type and position of a stain with a spectral angular distance, and after a standard sample and a semantic region are converted into an LAB color space, extracting a minimum rotation enclosing rectangle and dividing a grid, invalid pixels are filled with 3 * 3 neighborhood effective pixel weighted mean values, and a grid LAB mean value is calculated. A grid pollutant pixel corresponding to an image to be detected is filled with a standard sample in a mean value mode, then channels A and B are clustered through a sliding window, a pilling area is preliminarily judged according to the standard deviation of a channel L, and the pilling area is confirmed by combining the structured light three-dimensional point cloud and comparing with a threshold value.

The invention belongs to the technical field of textile materials, and particularly relates to an antibacterial regenerated wool blended yarn and a production process thereof.The antibacterial regenerated wool blended yarn is prepared from, by weight, 35 parts of regenerated wool fibers, 25-33 parts of LF tencel, 15-27 parts of cellulosic fibers, 8-12 parts of antibacterial chinlon 6 and 5 parts of an antibacterial complexing agent; according to the regenerated wool fiber prepared from the waste wool product through a synergistic improvement process of biological enzyme targeted cleaning and plasma etching, the natural properties of the wool fiber are reserved, and meanwhile, a surface pore structure capable of being combined with an antibacterial complexing agent is also formed; through a Z / S double-twist asynchronous spinning process, dynamic balance of a multilayer structure and hygroscopicity and strength of the blended yarn is achieved, and environment friendliness and commercial feasibility are both achieved.

The invention belongs to the field of functional textile materials, and provides a directional moisture-conducting, dry, comfortable, breathable and quick-dry fabric and a preparation method thereof. A three-layer composite structure design is adopted, the outer layer is a gradient wetting structure formed by attaching a thermoplastic polyurethane film subjected to single-face thermal orientation treatment and knitted fabric, the middle layer is a hydrophilic non-woven fabric, the inner layer is a knitted fabric subjected to hydrophilic finishing, and the two layers are compounded through a hot melt adhesive dot-shaped net. Hydrophilic blocks of the thermoplastic polyurethane film are directionally enriched through single-face contact heating, gradient distribution with one hydrophilic side and one hydrophobic side is formed, and the hydrophobic surface is coated with a low-surface-energy coating. The fabric has excellent one-way moisture conduction performance, moisture can be rapidly transmitted from the inner layer to the outer layer and rapidly volatilized, dryness and comfort of the close-fitting fabric are kept, meanwhile, the fabric has good air permeability and quick-drying performance, the moisture conduction rate is increased by 30% or above compared with that of traditional fabric, and the fabric is suitable for being popularized and applied. The fabric has wide application value in the fields of sportswear, outdoor equipment, leakage-proof underpants and the like.

The invention belongs to the field of functional textile materials, and provides a cool-feeling anti-ultraviolet nylon fabric and a preparation method thereof, and the cool-feeling anti-ultraviolet nylon fabric adopts a design scheme of combining a boron nitride / titanium dioxide composite filler with a beta-hydroxyl ether type ultraviolet absorption silane coupling agent surface modification technology. Uniform loading of hexagonal boron nitride on the surface of anatase type titanium dioxide is realized through an in-situ heat treatment process, a composite structure with heat conduction and ultraviolet shielding functions is constructed, and a complete process flow of master batch preparation, melt spinning, weaving, after-treatment and the like is matched; the ultraviolet protection coefficient UPF of the fabric is not lower than 40, the UPF retention rate after 20 times of household washing cycles is not lower than 80%, and the cool feeling qmax at the contact moment is not lower than 0.20 W / cm, the technical problem that an existing nylon fabric is insufficient in ultraviolet resistance and cool feeling function is solved, and the fabric has wide industrial application value.

The invention belongs to the field of flame-retardant finishing of textiles, and discloses a phytic acid-based flame retardant and a preparation method of a flame-retardant cotton fabric. Natural phytic acid, polyethylene glycol, melamine and metal salt are used as main raw materials, the environment-friendly characteristic of a biomass material is combined, an efficient and environment-friendly flame retardant is prepared, the prepared flame retardant is arranged on a cotton fabric through a traditional impregnation method, and the flame retardance and smoke suppression performance of the arranged cotton fabric are greatly improved. The method not only has a good flame retardant effect, but also avoids potential hazards of a traditional flame retardant to the environment and human health, and has a wide application prospect. The flame-retardant cotton fabric can be widely applied to the fields of household textiles, protective clothing, fire-fighting equipment and the like, and has remarkable economic benefits and social benefits. The invention not only provides an efficient and environment-friendly flame retardant preparation method, but also opens up a new way for application of biomass materials in the field of flame retardance, and has important scientific significance and practical value.

A textile fabric is provided, including: a main body, a film layer and a covering layer. The main body includes two surfaces opposite to each other, and each of the two surfaces has a surface texture being concave and convex. The film layer is coveringly disposed on one of the two surfaces and is concave and convex corresponding to the surface texture, and the covering layer is coveringly disposed on the other of the two surfaces. Therefore, the film layer has an appearance corresponding to the surface texture, and the textile fabric has good toughness, is waterproof and is not easily damaged, and the covering layer provides comfortable feeling of touching.

The invention relates to the technical field of knowledge maps, and particularly discloses a fabric cross-modal image-text retrieval method based on a knowledge map and a storage medium, and the method comprises the following steps: constructing a textile fabric structured knowledge map; performing semantic modeling on the structured knowledge graph of the textile fabric according to a TransR model; semantic structure extraction is carried out on the structured semantic model of the textile fabric according to the graph attention network; extracting deep semantic features of the textile fabric image; constructing an image and entity bidirectional contrast loss function according to the textile fabric entity semantic structure and the image deep semantic features; constructing a multi-task joint loss function according to the bidirectional comparison loss function and the structured semantic model; and training the spatial alignment matching degree of the entity semantic structure and the deep semantic features of the image according to a multi-task joint loss function to obtain a fabric cross-modal image-text retrieval result. The fabric cross-modal image-text retrieval method based on the knowledge graph can be suitable for fabric cross-modal image-text retrieval.

A method for forming a nanomaterial coating through solute-assisted assembly is provided. The method includes steps of: providing a mixture comprising a solvent, a solute, and a nanomaterial or particle; applying sonication to the mixture; and contacting a substrate with the mixture so as to form a coating of the nanomaterial or the particle onto the substrate. The solute is selected from a salt, a sugar, an acid, a base, or a combination thereof. The present disclosure also provides the resulting products comprising the nanomaterial coating for flexible electronics and functional textiles.

Athletic and / or work wear garment can have textile regions of differing friction to facilitate maintaining a grip between high friction textile regions and objects pressed against the high friction textile regions. In some embodiments, the high friction textile regions can facilitate carrying objects. In some embodiments, the garment can be a shirt. In some embodiments, the garment can be pants. In some embodiments, the garment can be an undergarment and the high friction textile regions can reduce shifting of an outer garment in contact with the undergarment.

The invention relates to the technical field of intelligent manufacturing and management of textiles and garments, and discloses an automatic sewing and cutting management system for underwear processing. The system comprises a central control module, a material conveying and positioning sub-module, a machine vision acquisition sub-module, a multi-axis linkage sewing mechanical arm sub-module and a cutting execution sub-module. According to the system, image data of raw materials and semi-finished products are collected through a machine vision collection sub-module before sewing and after sewing respectively, the initial deformation quantity and the secondary deformation quantity are calculated through a central control module, then a dynamic sewing path and an actual cutting path are generated, and a mechanical arm is controlled to execute self-adaptive sewing and accurate cutting; a sensing-decision-execution closed-loop intelligent control strategy is adopted, the problem of deformation accumulation of the high-elasticity fabric in the processing process is effectively solved, the precision and efficiency of underwear production and the material utilization rate are further improved, and small-batch multi-variety flexible automatic production is achieved.

The invention provides a waterproof and windproof warm-keeping fabric and a production process thereof, and relates to the technical field of textile fabrics, the waterproof and windproof warm-keeping fabric comprises a protective surface layer, a windproof temperature-locking middle layer and a skin-friendly warm-keeping inner layer which are sequentially arranged from outside to inside, and the three layers are compounded through a gradient hot melt adhesive; the protective surface layer is a graphene oxide modified polyester fiber fabric treated by a composite hydrophobic finishing liquid; the windproof temperature-locking middle layer sequentially comprises a non-swelling environment-friendly TPU film, a polytetrafluoroethylene microporous film and a Cs0. 33WO3 modified PA6 / 66 hollow fiber net from outside to inside, and the three are laminated and compounded into an integrated structure through hot pressing; and the skin-friendly warm-keeping inner layer is a bamboo pulp fiber-cotton-skin-friendly antibacterial polyester fiber blended fabric loaded with phase change microcapsules. The fabric disclosed by the invention integrates excellent waterproof, windproof and warm-keeping properties, is suitable for outdoor and cold-proof clothes, and has both functions and comfort.

The invention relates to the field of textile control, in particular to a tension control method and system of a high-speed loom. The method comprises the following steps: firstly, obtaining the tension fluctuation degree of each moment according to the tension data difference of the loom at each moment and the tension data distribution of each moment in a preset time domain of each moment, clustering all moments to obtain a first cluster, and obtaining the tension fluctuation degree of each moment according to the tension data distribution of each moment in each first cluster; the method comprises the steps of obtaining a change state value of each first clustering cluster according to the tension fluctuation degree of each first clustering cluster and the difference of the tension fluctuation degree of each first clustering cluster at each moment, further clustering all the first clustering clusters to obtain second clustering clusters, and obtaining the tension change degree of the loom according to the position distribution of each first clustering cluster in each second clustering cluster. And performing PID control on the loom according to the deviation condition of the tension data of the loom at the current moment and the tension change degree. The control effect on the yarn tension of the high-speed loom can be improved.

The invention discloses a textile workshop optimization algorithm recommendation method and system based on a large language model, and relates to the technical field of textile workshop scheduling, and the method comprises the steps: S1, constructing a textile knowledge vector database comprising a first-stage textile modeling knowledge base and a second-stage algorithm code knowledge base; s2, taking the spinning knowledge vector database as a fine tuning data set, and performing spinning field adaptive fine tuning on the large language model to obtain a spinning field model; and S3, based on the textile knowledge vector database, taking the obtained textile workshop scheduling demand as input, and using a retrieval enhancement generation method to generate a recommendation algorithm code for textile workshop scheduling. The textile field model is obtained by establishing the textile knowledge vector database and performing efficient fine tuning in the textile field on the large language model, the executable code is automatically generated through the retrieval enhancement generation method, the scheduling requirements and constraint conditions of different textile workshops can be adapted, the manual intervention cost is reduced, and the scheduling efficiency is improved. And the reusability and the scheduling scheme solving efficiency are improved.

The invention discloses a preparation method and application of washable tin-based perovskite quantum dots, and the preparation method comprises the following steps: mixing cesium iodide powder, tin iodide powder and mesoporous silica powder, and then placing the mixture in a plasma ball mill for ball-milling diffusion, cesium iodide and tin iodide react in a confinement space of mesoporous silica to generate a CsSn I < 3 >-coated SiO2 composite structure, CsSn I < 3 > quantum dots are dispersed and coated in the mesoporous silica, and the application of the tin-based perovskite quantum dots in the anti-ultraviolet textile fabric is provided. The method has the following beneficial effects that the purpose of remarkably improving the stability of the tin-based perovskite quantum dots and the washing resistance of the textile is achieved.

The invention relates to the technical field of textile fabric storage, and discloses a textile fabric storage rack for an automatic stereoscopic warehouse, which comprises an electric sliding rail, a rack body is arranged at the rear part of the electric sliding rail, a transmission mechanism is arranged at the left part of the rack body, and a storage rack is arranged on the outer wall of a bearing column. The transmission mechanism comprises an electric base, a sliding frame, a first screw rod, an electric conveying column, a transmission wheel, an isolation frame, a buffer plate and an output shaft, the electric base is slidably connected to the top of an electric sliding rail, and the sliding frame is fixedly connected to the top of the electric base. And a transmission rack is in engaged transmission with a fourth spiral rod through a bevel gear set in the moving process, so that a clamping plate is driven to move under limiting of a conveying plate, the clamping plate clamps and fixes objects on the top of the goods moving frame in the moving process, and the situation that the objects fall off when the goods moving frame moves in the conveying process is avoided.