382 results about "Textile industry" patented technology

The invention discloses an electronic jacquard machine control method and system based on multi-channel collaboration, and the method comprises the steps: calculating a delay compensation value of each channel in real time according to the hardware performance difference and signal transmission delay data of each channel, and dynamically adjusting the time sequence offset of a control instruction of an electronic jacquard machine by using the delay compensation value, generating a multi-channel cooperative control signal; according to input fabric pattern data, decomposing the fabric pattern data to generate a layered instruction set, and binding the layered instruction set to a multi-channel cooperative control signal to form a time-space synchronization instruction set; according to channel operation state data acquired in real time, identifying an abnormal channel and a fault type thereof, and generating a reconstruction instruction set after fault immunization; and dynamically allocating computing resources and storage bandwidth according to the reconstruction instruction set, generating an optimization execution instruction set, and issuing the optimization execution instruction set to each channel for execution in real time. By utilizing the embodiment of the invention, the control capability of the electronic jacquard machine and the fabric production efficiency can be improved, and the intelligent development of the textile industry is promoted.

The invention discloses a fiber raw material traceability method and monitoring system based on big data, and the method comprises the steps: collecting the multi-source data of a raw material through an Internet of Things device, including image data, sensor data and a unique identity; performing standardization processing on the collected data, and combining to generate a comprehensive feature vector; inputting the comprehensive feature vector into an improved convolutional neural network, and outputting parameters of the fiber raw material, including crimpness, length, color feature, region and generation time; the traceability information is encrypted and stored through a block chain technology; a user retrieves and displays fiber raw material parameters in real time through the query module. By introducing the big data analysis and block chain technology, the problems of data splitting, easy tampering and insufficient intelligence in the existing fiber raw material traceability and quality monitoring method are solved, the traceability transparency, data security and monitoring efficiency are significantly improved, and the method is suitable for supply chain management and quality control in the textile industry.

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 discloses a yarn weak twist and hairiness intelligent detection system based on visual stroboscopic synchronization, particularly relates to the field of quality control in the textile industry, is used for solving the problem of accurate detection of weak twist and hairiness defects in the yarn production process, and dynamically adjusts the flash frequency of a stroboscope according to the real-time speed and acceleration data of yarn, so that the detection accuracy is improved. The light source and the yarn are ensured to move synchronously, and a high-resolution image is obtained. In combination with yarn motion spectrum analysis and image quality evaluation, a complexity score and a matching degree score are generated and are used for dynamically regulating and controlling image noise reduction and edge detection parameters, and characteristic extraction requirements under different yarn motion conditions are met. The weak twist characteristic and the hairiness texture of the yarn are accurately extracted through a multi-scale edge detection and wavelet transform algorithm, a twist distribution model is constructed, the boundary range is corrected in combination with hairiness distribution data, characteristic interference is reduced, and the detection precision and reliability are improved.

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 wind tunnel experiments and textile carding, and discloses a guide plate system for a card clothing test wind tunnel and a test method thereof, the system comprises a mounting clamp, a guide plate, a driving mechanism and a tail airflow measurement and analysis system; the guide plate is located in the wind tunnel, forms a sealing structure with the inner wall surface of the test section of the wind tunnel, and is continuously controlled to move along the direction vertical to the test airflow through the driving mechanism; the length of the wind tunnel is 200-1000 mm, the height of the wind tunnel is 10-150 mm, and the width of the wind tunnel The guide plate is provided with static pressure measuring points which are continuously arranged; card clothing is arranged below the flow guide plate and is arranged on the mounting clamp; the mounting fixture is arranged on the bottom surface in the wind tunnel; an airflow measurement and analysis system is arranged behind the flow guide plate and installed on the inner bottom face of the wind tunnel. The actual carding process in the textile industry is simulated, the optimal numerical values of the gauge, the needle shape, the carding airflow and other characteristics in the experiment are determined, and the test precision of the wind tunnel is improved.

The invention provides a cloth defect detection method and device and a readable storage medium. The method comprises the following steps: acquiring a cloth image; carrying out image feature extraction and quantitative analysis processing on the basis of the cloth image to obtain a periodic rule of a pattern in the cloth image; splicing the cloth image according to the periodic rule of the pattern in the cloth image, and determining a corresponding template image according to the obtained spliced image; and performing template comparison detection according to the spliced image and the corresponding template image to obtain a cloth defect detection result. Through an efficient algorithm and an advanced image analysis technology, the problems that the manual quality inspection efficiency is low, the omission ratio is high and the detection precision of the cloth with the complex patterns is insufficient in the textile industry are powerfully solved, the quality inspection efficiency is greatly improved, the omission ratio is remarkably reduced, and high-precision detection of the cloth with the complex patterns is achieved.

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 discloses a single-component polyurethane liquid glue which comprises a hydroxyl-terminated polyurethane prepolymer, polyol and isocyanate, and the molar ratio of an isocyanate group to a hydroxyl group is (1.01-5): 1; the hydroxyl-terminated polyurethane prepolymer comprises aliphatic or / and alicyclic isocyanate and polyol, and the molar ratio of an isocyanate group to a hydroxyl group is (0.5-0.99): 1. The preparation method comprises the following steps: firstly, synthesizing a hydroxyl-terminated polyurethane prepolymer, and then taking the hydroxyl-terminated polyurethane prepolymer as a main material to react with a small part of polyhydric alcohols and isocyanate. The polyurethane liquid adhesive synthesized by the two-step method has the performance advantages of high initial adhesion and low hardness, meets the process requirement of normal-temperature coating, can replace an adhesive film product and a hot melt adhesive product, and is applied to composite bonding of fabrics in 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 utility model discloses automation equipment for silk coil feeding, which relates to the technical field of textile industry and comprises an unstacking station and an automatic carrying trolley, guide rails are arranged on the front end face and the rear end face of the top of a frame, and a supporting frame is vertically arranged in the middle of an inner cavity of the frame. Matched automatic carrying trolley material taking points are arranged between the left side of the supporting frame and the left side of the inner cavity of the frame, and an electric control cabinet is arranged on the left side of the frame. Manual work is replaced by a machine; according to the automatic wire coil feeding device, traditional manual operation is replaced with mechanical automation, the production efficiency can be remarkably improved, quality control in the wire coil feeding process can be guaranteed, in a wire coil storage area, neatly-arranged wire coils wait to be treated, then two sets of automatic clamping jaws are arranged, and the automatic wire coil feeding device is used for feeding the wire coils. The first four-degree-of-freedom clamping jaw and the second four-degree-of-freedom clamping jaw work cooperatively, the wire coil is carried to a material taking point of the automatic carrying trolley from a storage area, and the automatic carrying trolley appears on time at the point and prepares to take materials.

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 provides a spatial feature fused colored spun yarn color characterization method, and the method comprises the steps: constructing a multi-level architecture of 1D-CNN time sequence feature extraction, DNN aperture internal fusion, Bi-LSTM spatial sequence modeling, ResNet image color feature extraction, pyramid pooling layer image spatial feature extraction, and Transform cross-modal fusion through five aperture multi-position spectrum collection. The method is characterized by comprising the steps of hierarchical feature extraction of a multi-aperture spectrum, bidirectional LSTM space dependence modeling and Transform spectrum depth fusion. Experiments show that the method is obviously superior to the prior art in color prediction precision, material generalization and calculation efficiency, and is suitable for intelligent color control and formula design in the textile industry.

The invention provides a dynamic feature contribution degree-based color textile fabric image retrieval method. Innovation and improvement are carried out aiming at single feature limitation and fixed weight fusion defects existing in a traditional retrieval technology. Color, texture and shape features are separated through a multi-modal feature decoupling technology, and coupling interference between the features is eliminated; a dynamic contribution degree quantification mechanism is innovatively constructed, gradient back propagation is used for calculating the contribution proportion of features to retrieval results in real time, and weight self-adaptive adjustment is achieved. According to the method, the problem of retrieval deviation caused by complex characteristics such as color gradient and blending structure difference of the colored spun yarn fabric is solved in a breakthrough mode, weight distribution can be intelligently matched according to inquired image characteristics (for example, the color gradient fabric focuses on color characteristics, and the jacquard fabric focuses on shape edges), and the retrieval result better fits human visual perception. According to the technical scheme, the image retrieval precision of complex fabrics in the textile industry is effectively improved, and an intelligent solution is provided for production process data association.

The invention relates to the technical field of production raw material dehydration equipment, and provides a mud pressing machine for the textile industry, which comprises a rack, and a sliding main beam extending along the length direction is arranged on the rack; the number of the filter plates is multiple, two sliding supporting assemblies are symmetrically arranged on the two sides of each filter plate in the width direction of the filter plate in a sliding mode, and the multiple filter plates are arranged on the sliding main beam in a sliding mode through the sliding supporting assemblies; the bottom and the side part of the sliding support assembly are respectively and correspondingly propped against the top and the side edge of the sliding main beam in a sliding manner, and the sliding support assembly is configured in a manner that after the filter plate deflects along the vertical direction by the deflection shaft, the side wall of the sliding support assembly is extruded with the side edge of the sliding main beam, and then the sliding support assembly is driven to slide in a direction far away from the filter plate; the device is used for reducing extrusion force between the side wall of the sliding supporting assembly and the side edge of the sliding main beam. According to the technical scheme, the technical problem that the main beam is scratched or deformed in the filter pressing working process of the plate-type membrane filter press in the prior art is solved.

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 provides alkaline laccase WCotA and application thereof, and belongs to the technical field of biological engineering. According to the invention, assumed protein codons from bacillus amyloliquefaciens are optimized by a bioengineering technology, then are heterologously expressed on a carrier, and are transferred into escherichia coli with clear genetic background and fast growth cycle for amplification and expression to obtain a protein product; the amino acid sequence of the protein product is as shown in SEQ ID NO.1, and the nucleotide sequence for coding the protein product is as shown in SEQ ID NO.2; according to the present invention, the characterization results show that the protein product has high laccase activity, and is marked as alkaline laccase WCotA; the alkaline laccase WCotA has the outstanding advantages of alkali resistance, high temperature resistance, wide pH action range and the like, and has good application in the fields of textile industry, food processing, pulping processing, industrial wastewater treatment and the like.

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 relates to the technical field of dust explosion prevention in the textile industry, in particular to a dust explosion prevention method and system in the textile industry and a storage medium. The explosion-proof method comprises the steps that a bus communication architecture based on a two-wire system power line carrier is constructed, a multi-host communication mode adopting a private protocol is established between a controller and a plurality of detection probes, and a message structure of the private protocol comprises a function code field, a number field, a data field and a CRC verification field, wherein the function code field definition comprises 01H alarm coding, 02H parameter modification coding and 03H heartbeat request coding. According to the invention, a multi-host communication architecture based on a private protocol is constructed, dynamic distribution of bus control right and dust flow feature detection are deeply coupled, and a zero-latency master-slave switching mechanism when an alarm event is triggered is realized.

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 discloses a preparation method of jatropha curcas oil-based waterborne polyurethane emulsion, and relates to a preparation method of a high polymer material polyurethane emulsion, the method is a synthesis method of amino silicon oil (KH550) modified bio-based polyurethane used in the textile field, jatropha curcas oil is used as a raw material, and functional molecular design is adopted to prepare the jatropha curcas oil-based waterborne polyurethane emulsion. KH550 is introduced by virtue of a catalyst-free green chemical process to synthesize controllable jatropha curcas oil-based polyol, and the controllable jatropha curcas oil-based polyol is further polymerized with isophorone diisocyanate (IPDI) through chain structure design to prepare a jatropha curcas oil-based waterborne polyurethane coating dispersion which is subsequently applied to the surface of a fabric. The preparation method aims at preparing the high-performance textile coating and enhancing the hydrophobic and antifouling properties of the fabric. The work not only has important significance on design and application of a sustainable bio-based waterborne polyurethane textile finishing agent in the textile industry, but also widens the application field of non-grain bio-based materials.

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 discloses a rapid yarn doubling device, and relates to the technical field of the textile industry, the rapid yarn doubling device comprises a bottom plate, one side of the top end of the bottom plate is fixedly connected with a yarn doubling mechanism, the fixing mechanism comprises two fixing plates fixedly connected with the bottom plate, the opposite ends of the two fixing plates are fixedly connected with outer rods, and the outer rods are fixedly connected with the bottom plate. Inner rods are slidably connected to the inner surfaces of the two outer rods, supporting rods are fixedly connected to the opposite ends of the two inner rods, springs are jointly arranged on the outer surfaces of the outer rods and the outer surfaces of the corresponding inner rods, one ends of the two springs are fixedly connected with one ends of the two supporting rods, and the other ends of the two springs are fixedly connected with the other ends of the two supporting rods. The opposite ends of the two supporting rods are both rotationally connected with rotating plates, the opposite ends of the two rotating plates are both fixedly connected with top tray rods, and the device can fix top trays of different shapes or different sizes by arranging the fixing mechanism, so that the applicability of the device can be effectively improved.

The present invention relates to a new material, in particular to the technical field of coatings, and more particularly to a preparation process for a novel liquid mildew and antibacterial agent. The preparation process comprises the following steps: preparing an ionic liquid, preparing a microcapsule mildew-proof material, preparing a molecular sieve MCM-41-NH2, preparing a modified molecular sieve Ag / MCM-41-NH2, preparing a nanocomposite material, preparing a liquid antibacterial agent containing nanosilver, and the like. The novel liquid mildew and antibacterial agent prepared by the present invention, which creatively combines a solvent purification technology and other preparation methods, has the advantages of a simple preparation process, being environmentally friendly, having excellent mildew-proof performance, and having a long antibacterial effect, can be used in multiple fields such as the textile industry, the construction industry, and the medical protection industry.

The utility model provides a textile fabric wetting device for the textile industry, and belongs to the technical field of textile fabric wetting equipment. The textile fabric wetting device for the textile industry comprises a supporting assembly and a wetting assembly. A wetting box is installed on the base, the bottom of the wetting box is a hollow part, a feeding port and a discharging port are formed in the two sides of the wetting box respectively, and a guide roller piece is arranged in the wetting box; the two water containing tanks are arranged at the two ends of the base in a sliding mode, two air cylinders are installed on the two sides of the base respectively, and the cylinder rod ends of the two air cylinders are connected with the two water containing tanks respectively and drive the two water containing tanks to be sequentially located in the hollow part of the wetting tank respectively. Through the arrangement of the two water containing tanks, the two water containing tanks can be alternately used when the textile cloth is wetted, in this way, long-time stopping of textile cloth wetting is avoided during water changing, the textile cloth wetting efficiency is improved, and then the production efficiency of the textile cloth is improved.