Preparation method of high-performance intelligent gloves

Abstract

The invention relates to the technical field of information, and discloses a preparation method of a high-performance intelligent glove, which comprises the following steps: calendaring a woven knitted fabric, and controlling the calendaring pressure to be 2MPa, the calendaring temperature to be 120 DEG C and the calendaring speed to be 20m / min, so that the thickness of the knitted fabric is reduced by 20%, and the air permeability is improved by 30% from an initial value; the sensitivity and the air permeability of the glove are further improved; the knitted fabric subjected to hydrophobic finishing is cut into a glove shape, honeycomb-shaped breathable structures with the diameter being 3 mm and the depth being 1 mm are manufactured at the palm center and fingertip portions of the glove through the laser cutting technology, and the breathable area and sensitivity of the glove are further increased; the inner-layer fabric and the outer-layer fabric which are provided with the honeycomb-shaped breathable structures are sewn through a double-needle chain type sewing machine and high-strength sewing threads, it is guaranteed that the honeycomb-shaped breathable structures correspond to the outer-layer fabric accurately, and the intelligent gloves with high elasticity, breathable and sweat-releasing performance and hand feeling flexibility are manufactured.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a manufacturing process of gloves, and in particular to a method for preparing high-performance smart gloves. Background Art

[0002] In developing highly sensitive dispensing gloves, we faced a series of interrelated technical challenges. First, to achieve the softness and antistatic properties of the gloves, we needed to have precise control over the fiber selection and blending ratio. However, increasing the proportion of conductive fibers can improve antistatic properties, but it may reduce the softness and flexibility of the gloves. Secondly, in pursuit of breathability and fit, we used a special knitting structure and calendering treatment, but this may affect the wear resistance and strength of the gloves. Furthermore, in order to improve the anti-slip and wear resistance, we carried out hydrophobic finishing and dispensing treatment, but this may reduce the breathability and sensitivity of the gloves. Finally, making a honeycomb breathable structure in the palm and fingertips can improve sensitivity and breathability, but at the same time it may also reduce the protective performance of these key parts. These conflicting requirements require us to find the best balance between various properties to develop smart gloves that can meet the needs of dispensing process and have excellent feel and flexibility. Summary of the invention

[0003] The present invention provides a method for preparing high-performance smart gloves, the method comprising the following steps:

[0004] Step S1, according to the requirements of flexibility and wear resistance of the gloves, 120 denier / 48 fiber silk protein fiber is selected as the natural fiber, 40 denier spandex coated with 200 denier conductive yarn is selected as the conductive fiber, and the two fibers are blended in a ratio of 30:70 to prepare a high elastic blended yarn with both softness and antistatic properties;

[0005] Step S2, using a double-sided weft knitting process, designing a mesh structure with an aperture of 2 mm, using the above-mentioned high-elasticity blended yarn for weaving, and at the same time weaving 150 denier polyester moisture-absorbing quick-drying yarn into the inner layer of the knitted fabric to obtain a knitted fabric with excellent fit and breathability, the warp density is controlled at 40 strands / inch, the weft density is controlled at 38 strands / inch, and the surface density is controlled within the range of 200-250 g / m2 to ensure the sensitivity and softness of the gloves;

[0006] Step S3, calendering the knitted fabric after weaving, by controlling the calendering pressure to 2 MPa, the calendering temperature to 120 degrees Celsius, and the calendering speed to 20 m / min, the thickness of the knitted fabric is reduced by 20%, and the air permeability is increased by 30% from the initial value, thereby further improving the sensitivity and air permeability of the gloves;

[0007] Step S4, performing hydrophobic finishing on the surface of the knitted fabric after the calendering treatment, using an emulsion prepared with an environmentally friendly fluorocarbon resin, acrylate and deionized water in a ratio of 1:2:7, and performing a treatment at 25 degrees Celsius for 30 minutes by an immersion method, so that the contact angle of the fabric is increased to 120 degrees, thereby improving the anti-slip and wear resistance of the gloves;

[0008] Step S5, cutting the knitted fabric after the hydrophobic treatment into the shape of gloves, and using laser cutting technology to make a honeycomb air-permeable structure with a diameter of 3 mm and a depth of 1 mm on the palm and fingertips of the gloves, so as to further increase the air permeability area and sensitivity of the gloves;

[0009] Step S6, sewing the inner fabric with the honeycomb breathable structure and the outer fabric by using a double-needle chain sewing machine and high-strength sewing thread, ensuring that the honeycomb breathable structure and the outer fabric are precisely aligned, so as to obtain a smart glove with high elasticity, breathable and perspiration-wicking performance, and flexible feel;

[0010] Step S7, evaluating the performance of the manufactured smart gloves, testing the antistatic property of the gloves by a surface resistance tester, testing the air permeability of the gloves by a dynamic hot plate method, and testing the perspiration and quick-drying property of the gloves by an artificial climate chamber. When the surface resistance exceeds 1×10^9 ohm, the proportion of conductive fibers is increased by 5%. When the air permeability is lower than 80 mm / s, the aperture of the mesh structure is increased by 0.5 mm or the density of the honeycomb structure is increased by 10%. When the perspiration and quick-drying time exceeds 30 minutes, the content of polyester moisture-absorbing and quick-drying yarn is increased by 10% or the hydrophobic finishing process parameters are optimized.

[0011] Step S8, after adjusting the gloves accordingly according to the performance evaluation results, a uniform dispensing layer of glue is applied to the palm and fingertips of the smart gloves. The thickness of the glue layer is controlled at 0.1-0.2 mm by controlling the viscosity, coating speed and coating pressure of the glue to ensure the anti-slip and sensitivity of the gloves. A polyurethane glue with good flexibility and strong wear resistance is selected, and after UV curing treatment, the wear resistance and service life of the gloves are further improved. At the same time, a precisely controlled dispensing device is used during the gluing process to retain 30% of the uncoated microporous structure in the gluing area to maintain the breathability and perspiration performance of the gloves.

[0012] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0013] The invention discloses a method for preparing a high-performance smart glove. A high-elastic yarn is prepared by blending silk protein fiber and conductive yarn, a knitted fabric with a mesh structure is woven by a double-sided weft knitting process, and a moisture-absorbing quick-drying yarn is woven into the inner layer. The knitted fabric is calendered and hydrophobically finished to improve the sensitivity, air permeability and anti-slip properties of the glove. A honeycomb air-permeable structure is made by laser cutting technology at the palm and fingertips of the glove to further increase the air permeability area. The glove is optimized and adjusted through performance evaluation, and finally a dispensing layer is applied to the palm and fingertips, while retaining part of the microporous structure. The invention solves the comprehensive performance problems of the smart glove in terms of softness, antistatic property, air permeability and perspiration discharge, and sensitivity. Through the synergistic effect of multiple innovative processes and structural designs, the glove has high elasticity, excellent fit, good air permeability and perspiration discharge performance, and hand flexibility. At the same time, it has anti-slip and wear-resistant properties, and meets multiple performance requirements of the smart glove. DETAILED DESCRIPTION

[0014] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] The preparation method of a high-performance smart glove in this embodiment may specifically include:

[0016] Step S1, according to the requirements of flexibility and wear resistance of the gloves, 120 denier / 48 fiber silk protein fiber is selected as the natural fiber, and 40 denier spandex coated with 200 denier conductive yarn is selected as the conductive fiber, and they are blended in a ratio of 30:70 to prepare a high-elastic blended yarn with both softness and antistatic properties.

[0017] Step S1 includes: obtaining the initial mixing ratio data of silk protein fiber and conductive fiber, setting the initial mixing ratio to 30:70 through a computer program, and obtaining an initial mixing ratio model. Obtaining data of silk protein fiber fineness of 120 denier / 48 fiber and conductive fiber fineness of 40 denier spandex coated 200 denier, using the fineness data as the input parameter of the mixing ratio model, and obtaining a mixing ratio adjustment model for a specific fineness. According to the yarn denier requirement, the mixing ratio adjustment model is iteratively optimized. If the yarn denier does not meet the preset range, the fineness of the silk protein fiber and the conductive fiber is adjusted to obtain the optimal fineness combination that meets the yarn denier requirement. A preliminary blending test is carried out using the optimal fineness combination, and the fiber distribution image after blending is observed by an electron microscope to obtain fiber distribution uniformity data, and it is judged whether the fiber distribution uniformity data reaches the set threshold. According to the fiber distribution uniformity data, the blending process parameters are optimized. If the fiber distribution uniformity data is lower than the set threshold, the blending process parameters are adjusted to obtain the optimized blending process parameters and obtain new fiber distribution uniformity data. The sensor detects the yarn static electricity and yarn elasticity of the blended yarn, obtains the yarn static electricity data and the yarn elasticity data, and determines whether the yarn static electricity data and the yarn elasticity data meet the performance requirements of the gloves. According to the yarn static electricity data and the yarn elasticity data, if the yarn static electricity data or the yarn elasticity data does not meet the requirements, the ratio of the silk protein fiber and the conductive fiber is adjusted to obtain a high-elasticity antistatic blended yarn that meets the flexibility and wear resistance requirements of the gloves.

[0018] Specifically, the mixing ratio of silk protein fiber and conductive fiber is crucial for making high-performance antistatic gloves. The initial mixing ratio is set to 30:70, which is determined based on the excellent softness of silk protein fiber and the antistatic performance of conductive fiber. For example, 30% silk protein fiber content can provide sufficient comfort, while 70% conductive fiber ensures good static dissipation ability. Denier is a key factor affecting yarn performance. The denier of silk protein fiber is 120 denier / 48 fiber, which means that the fineness of each fiber is about 2.5 denier. The conductive fiber adopts a 40 denier spandex coated with 200 denier structure. This design not only ensures conductivity but also improves the elasticity of the yarn. Inputting these denier data into the mixing ratio model can more accurately predict the physical properties of the final yarn. The adjustment of yarn denier is an iterative optimization process. Assuming the target yarn denier is 300 denier, the initial model may produce a result of 280 denier. At this point, the desired yarn denier can be achieved by increasing the fineness of the silk protein fiber to 130 denier / 48 fiber, or adjusting the conductive fiber to 45 denier spandex coated with 220 denier. This process may require multiple adjustments until the best fineness combination is obtained. The choice of blending method has a significant impact on the uniformity of fiber distribution. Preliminary tests can be carried out using the air-flow blending method, which can effectively disperse different types of fibers. Quantitative data on fiber distribution can be obtained by observing the cross-section of the blended fibers through an electron microscope. For example, if the uniformity threshold is set to 90%, and the observation results show that the uniformity is only 85%, the blending process parameters need to be optimized. Optimizing the blending process parameters may include adjusting the air flow speed, feeding speed, etc. For example, increasing the air flow speed from the initial 20 m / s to 25 m / s may significantly improve the dispersion of the fibers. At the same time, reducing the feeding speed can give the fibers more time to be fully mixed. Through these adjustments, the fiber distribution uniformity can be increased to 92%, exceeding the set threshold. Yarn static electricity and elasticity are important indicators for evaluating glove performance. The surface resistance of the yarn can be measured using an electrostatic tester, for example, the target value may be set to 10^6-10^9 ohms. If the measured resistance value is 10^10 ohms, it means that the electrostatic dissipation ability is insufficient. Elasticity testing can be performed through a stretch recovery experiment, such as setting the recovery rate at 50% elongation to be no less than 95%. If the actual measured recovery rate is only 90%, it indicates insufficient elasticity. If the electrostatic or elastic data does not meet the requirements, the fiber ratio needs to be readjusted. For example, to improve the electrostatic dissipation ability, the conductive fiber ratio can be increased to 75%. To improve elasticity, a more elastic spandex fiber can be selected, such as 50 denier spandex coated with 180 denier conductive fiber. This adjustment may affect other properties, so multiple iterations are required to find the best balance. The entire process embodies a closed-loop optimization system, where each step from initial design to final product is adjusted based on the results of the previous step.This method can effectively balance multiple performance indicators, such as static dissipation, elasticity, comfort, etc., and finally obtain a highly elastic antistatic blended yarn that meets the flexibility and wear resistance requirements of gloves. Through this systematic approach, the efficiency and success rate of product development can be greatly improved, the cost of trial and error can be reduced, and the speed of new products to market can be accelerated.

[0019] Step S2, using a double-sided weft knitting process, designing a mesh structure with an aperture of 2 mm, using the above-mentioned high-elasticity blended yarn for weaving, and at the same time weaving 150 denier polyester moisture-absorbing quick-drying yarn into the inner layer of the knitted fabric to obtain a knitted fabric with excellent fit and breathability, the warp density is controlled at 40 strands / inch, the weft density is controlled at 38 strands / inch, and the surface density is controlled within the range of 200-250 g / m2 to ensure the sensitivity and softness of the gloves.

[0020] Step S2 includes: obtaining real-time operating parameters of the double-sided weft knitting machine, the operating parameters including rotation speed, yarn feeding amount and pulling tension data; constructing a high-elastic yarn tension feature vector set according to the operating parameters; judging whether the operating parameters exceed a preset range, if so, triggering an alarm mechanism and controlling the equipment to stop running; according to the alarm mechanism, using a control system to adjust the yarn feeding amount and pulling tension to obtain operating parameters that meet the preset range, determining that the equipment is operating normally, and generating a normal operating equipment number; retrieving corresponding process parameters from a database through the equipment number, the process parameters including aperture size, warp density and weft density data; using a photoelectric sensor to detect actual weaving parameters, judging whether the deviation between the actual weaving parameters and the process parameters exceeds a threshold, if so, generating deviation data; according to the deviation The method comprises the following steps: obtaining the mesh structure that meets the process parameters and determining the image information of the mesh structure; collecting the image information of the mesh structure, graying and binarizing the image information; extracting the mesh edge contour by the edge detection algorithm, calculating the area of ​​each mesh, and judging whether the mesh area is within the set range; if the mesh area is not within the set range, repeating steps 6 and 7 until the mesh area is qualified, obtaining a fabric with a qualified mesh structure, and generating a fabric number; extracting the fabric weight data from the database by the fabric number, scanning the fabric thickness by an infrared sensor, and obtaining the thickness data; fusing the thickness data and the weight data, calculating the fabric surface density, and obtaining the result that the fabric surface density data is within the preset range.

[0021] Specifically, the double-sided weft knitting machine is a key equipment for producing highly elastic mesh fabrics. Obtaining its real-time operating parameters is crucial to ensure fabric quality. The speed, yarn feed and drawing tension are three core parameters that together determine the structure and performance of the fabric. For example, too high a speed may cause yarn breakage, insufficient yarn feed will make the fabric too tight, and excessive drawing tension may cause the fabric to deform. By constructing a set of feature vectors of these parameters, the machine operation status can be fully monitored. When the parameters exceed the preset range, triggering an alarm mechanism and shutting down the machine is a protective measure. For example, if it is detected that the yarn feed suddenly drops below 50% of the normal value, the system will immediately alarm and shut down to prevent the production of a large number of unqualified products. Subsequently, the operator can adjust the parameters according to the alarm information, such as increasing the yarn feed or reducing the drawing tension to restore it to the normal range. Each device has a unique number, which is associated with its corresponding process parameters. For example, a machine numbered WM001 may be set to produce mesh fabrics with a pore size of 2mm, a warp density of 40 strands / cm, and a weft density of 35 strands / cm. By detecting these parameters in real time through photoelectric sensors, deviations can be detected in time. If the warp density is detected to be 45 strands / cm, which exceeds the preset range of 40±2 strands / cm, the system will generate deviation data. PID control algorithm is widely used in the textile industry for parameter adjustment. It can quickly and accurately adjust the machine state through the combination of three links: proportional, integral and differential. For example, if the warp density is detected to be too high, the PID controller will calculate the amount of warp that needs to be reduced and accurately adjust the speed of the warp let-off motor through the actuator to return the warp density to the set value. The mesh structure is a key feature of high-elasticity gloves. The quality of the mesh can be accurately evaluated through image processing technology. First, the collected image is grayed to eliminate the interference of color information. Then, the image is converted into black and white through binarization to highlight the mesh outline. Using edge detection algorithms such as Canny, the mesh edge can be accurately extracted. By calculating the pixel area of ​​each mesh and comparing it with the preset standard, it can be determined whether the mesh size is qualified. If the mesh area is found to be unqualified, such as exceeding the preset range of 4±0.5 square millimeters, it is necessary to return to adjust the process parameters. This may involve changing the amount of yarn fed, adjusting the pulling tension, and other operations. This process may require multiple iterations until a qualified mesh structure is produced. The surface density of the fabric is an important indicator for evaluating its quality. The thickness of the fabric is scanned by an infrared sensor, and the surface density can be calculated in combination with the known weight of the fabric. For example, if a 1 square meter piece of fabric weighs 220 grams and is 1 mm thick, then its surface density is 220 grams per square meter, which falls within the target range of 200 to 250 grams per square meter. This range of surface density ensures that the fabric has sufficient strength without being too thick and heavy to affect the flexibility of the gloves. Real-time data collection and analysis play a key role in the entire production process.From the original operating parameters to the final fabric performance, each step requires accurate data support. This data-driven production method can not only improve product quality, but also significantly improve production efficiency. By establishing a complete data chain, the entire production process can be traced, which is conducive to the rapid location and resolution of problems. At the same time, the large amount of accumulated data also provides a valuable foundation for future process optimization and product innovation.

[0022] Step S3, calendering the knitted fabric after weaving. By controlling the calendering pressure to 2 MPa, the calendering temperature to 120 degrees Celsius, and the calendering speed to 20 m / min, the thickness of the knitted fabric is reduced by 20%, and the air permeability is increased by 30% from the initial value, thereby further improving the sensitivity and air permeability of the gloves.

[0023] Specifically, fabric calendering is an important process to improve fabric performance. First, collect the initial state data of the fabric, including thickness and air permeability. For example, the initial thickness of a piece of cotton fabric is 0.8mm and the air permeability is 150cm 3 / cm 2 / s. These data are recorded by the sensor network system to form the initial fabric performance data set. Based on the initial data, a priori knowledge base and mapping model are established. The support vector machine algorithm is used to establish the relationship between calendering parameters and fabric performance. For example, input thickness 0.8mm and air permeability 150cm 3 / cm 2 / s, the model may output calendering pressure 2MPa, temperature 120℃, speed 5m / min. This model can predict the optimal calendering parameters under different fabric properties. After obtaining the target fabric performance data, calculate the required performance improvement. Assume that the target thickness is 0.6mm and the target air permeability is 200cm 3 / cm 2 / s, the thickness needs to be reduced by 25% and the air permeability needs to be increased by 33%. These data are standardized for subsequent model adjustments. When the performance improvement demand is large, the support vector machine model needs to be adjusted. For example, if the thickness reduction demand exceeds 20% and the air permeability improvement demand exceeds 30%, it may be necessary to increase the training samples or adjust the kernel function parameters to improve the prediction accuracy of the model. According to the updated calendering parameters, the lathe is controlled by the CNC system to perform calendering. For example, the system may set the calendering pressure to 2.5MPa, the temperature to 130℃, and the speed to 4m / min. These parameters are precisely controlled by the actuator to ensure the stability and consistency of the calendering process. After calendering, the fabric performance data is collected again. Assume that the thickness after treatment is 0.62mm and the air permeability is 195cm 3 / cm 2 / s. These data are conditioned by the data acquisition card and converted into digital signals for subsequent analysis and comparison. Finally, the processed data are compared with the target data. If the error between the treated thickness and air permeability and the target value is within an acceptable range (such as ±5%), the calendering parameters are considered to be the optimal parameters. These parameters will be recorded in the prior knowledge base to guide the calendering of similar fabrics in the future. This process reflects the refined control and continuous optimization of the calendering process. Through data-driven and machine learning methods, the calendering effect can be continuously improved, material waste can be reduced, and production efficiency can be improved. At the same time, this method also provides the possibility for personalized processing of different types of fabrics. It can flexibly adjust the calendering parameters according to the characteristics and target performance of different fabrics to achieve the best processing effect.

[0024] Step S4, performing hydrophobic finishing on the surface of the knitted fabric after the calendering treatment, using an emulsion prepared with environmentally friendly fluorocarbon resin, acrylate and deionized water in a ratio of 1:2:7, and treating it at 25 degrees Celsius for 30 minutes by an immersion method, so that the contact angle of the fabric is increased to 120 degrees, thereby improving the anti-slip and wear resistance of the gloves.

[0025] Step S4 includes: obtaining a knitted fabric after calendering as a substrate for hydrophobic finishing. According to the formula ratio of 1:2:7, environmentally friendly fluorocarbon resin, acrylate and deionized water are mixed to prepare a hydrophobic finishing emulsion. The knitted fabric is immersed in the hydrophobic finishing emulsion, the immersion temperature is controlled to be 25°C, and the immersion time is 30 minutes. The knitted fabric after immersion is taken out and deliquored to remove excess hydrophobic finishing emulsion. The knitted fabric after deliquoring is dried and shaped to fully combine the hydrophobic finishing agent with the knitted fabric. A contact angle meter is used to measure the contact angle of the knitted fabric surface after hydrophobic finishing to determine whether the target value of 120° is reached. If not, steps 3-5 are repeated until the requirements are met. The knitted fabric after hydrophobic finishing is used to make gloves, and the anti-slip and wear resistance of the gloves are evaluated through friction coefficient test and wear resistance test to ensure that the expected performance requirements are met.

[0026] Specifically, hydrophobic finishing is a key process to improve the waterproof performance of knitted fabrics. First, the knitted fabric after calendering is obtained as the substrate, which ensures the surface flatness and is conducive to the uniform adhesion of the subsequent hydrophobic agent. The formula ratio of the hydrophobic finishing emulsion is 1:2:7, in which environmentally friendly fluorocarbon resin is the main hydrophobic component, acrylate provides adhesion, and deionized water is used as the dispersion medium. This ratio can reduce environmental impact while ensuring the hydrophobic effect. The temperature of the immersion process is controlled at 25°C and the time is 30 minutes. Such conditions are conducive to the full penetration of the hydrophobic agent into the fabric fibers. For example, if the temperature is too high, the emulsion may be unstable, and if the time is too short, the penetration is insufficient. The dehydration treatment is to remove excess emulsion to avoid wasting resources and affecting the subsequent shaping effect. Drying and shaping is a key step. It not only allows the hydrophobic agent to fully combine with the fabric, but also solidifies the hydrophobic layer and enhances its durability. Contact angle measurement is an important indicator for evaluating the hydrophobic effect. The target value of 120° indicates good hydrophobicity. If it is not achieved, repeated treatment is required to improve the effect. For example, if the contact angle is 110° after the first treatment, it may be necessary to immerse again or adjust the emulsion ratio to improve the hydrophobicity. This iterative optimization ensures the stable performance of the final product. The treated knitted fabrics are used to make gloves and tested for coefficient of friction and abrasion resistance. The coefficient of friction test can evaluate the anti-slip properties of the gloves, for example, using the inclined plane method to measure the inclination angle when the gloves start to slide. The abrasion resistance test can use the Martindale method to record the number of frictions before the fabric breaks. These test results are directly related to the actual performance of the gloves. The entire hydrophobic finishing process embodies the concept of refined control and quality assurance. From raw material selection to process parameter setting to final performance evaluation, each step is carefully designed. For example, the use of environmentally friendly fluorocarbon resins not only ensures the hydrophobic effect, but also reduces the environmental burden. The precise control of the impregnation temperature and time ensures the uniform distribution of the hydrophobic agent. This comprehensive quality control system not only improves product performance, but also enhances the repeatability and stability of the production process. Through this systematic hydrophobic finishing process, the waterproof performance and service life of knitted gloves can be significantly improved. For example, the treated gloves can still maintain good grip in wet environments, reducing the risk of slipping. At the same time, the hydrophobic treatment can also reduce the water absorption of the fabric, reduce the weight increase of the gloves after they are soaked, and improve wearing comfort. These improvements directly affect the practicality and safety of gloves in various working environments, especially in wet environments that require delicate operations.

[0027] Step S5, cutting the knitted fabric after hydrophobic finishing into the shape of gloves, and using laser cutting technology to produce a honeycomb breathable structure with a diameter of 3 mm and a depth of 1 mm on the palm and fingertips of the gloves, so as to further increase the breathable area and sensitivity of the gloves.

[0028] Specifically, first, obtain a knitted fabric that has been hydrophobically finished. The surface of this knitted fabric has good hydrophobicity and a contact angle of 120 degrees, which can effectively prevent water stains from penetrating. This is the basis for making high-performance gloves. For example, in the medical field, doctors need to wear gloves for surgical operations. If the hydrophobic performance of the gloves is poor, blood or other body fluids may penetrate into the inside of the gloves and cause contamination. The use of knitted fabrics after hydrophobic finishing to make gloves can effectively avoid this situation and ensure the safety and hygiene of the surgical process. After obtaining the knitted fabric after hydrophobic finishing, the knitted fabric is cut according to a pre-designed glove shape template, for example, a common glove template with five fingers separated, or a mitten glove template for a specific working environment. This step can use automated cutting equipment, such as a computer cutting bed. By pre-programming the cutting path, the machine can accurately cut the knitted fabric into the corresponding shape according to the template shape, ensuring that each piece of cut knitted fabric meets the shape requirements of the gloves and prepares for the subsequent suturing process. In order to improve the breathability of the gloves, laser cutting technology is used to create tiny honeycomb air holes in the palm and fingertips of the gloves. Laser cutting technology uses a high-energy-density laser beam to locally heat the material, causing it to melt or vaporize, thereby achieving the purpose of cutting. This technology has the advantages of high precision, smooth incision, and small heat-affected zone, and is very suitable for processing delicate breathable structures. For example, for workers who need to wear gloves for a long time, the breathability of the gloves is very important. If the breathability is poor, the palms are prone to sweating, which not only affects comfort but also may reduce work efficiency. By setting honeycomb air holes with a diameter of 3 mm and a depth of 1 mm in the palm and fingertips, the air permeability area can be effectively increased while ensuring the structural strength of the gloves, the air circulation inside the gloves can be improved, the sweating of the palms can be reduced, and the wearing comfort can be improved. The knitted fabrics that have been cut and processed with air holes are sewn. The sewing process can use automated sewing equipment to sew the knitted fabrics of various parts together according to the preset sewing template, for example, different sewing methods such as flat sewing, overlock sewing, and stretch sewing to form a complete glove shape. This step requires ensuring that the sewing stitches are firm, uniform, and beautiful, avoiding problems such as leaking seams and skipping seams, and ensuring the durability of the gloves. For example, in industrial production, workers need to wear gloves for handling, assembly and other operations. If the stitching quality of the gloves is poor, problems such as open thread and damage are prone to occur, which will affect the service life of the gloves. The use of high-quality stitching technology can ensure the durability of the gloves and extend their service life. After the stitching is completed, the air permeability and sensitivity of the finished gloves need to be evaluated. The determination of the air permeability area can be completed by a professional air permeability tester. The instrument will simulate the process of air circulation, measure the air flow through the gloves per unit time, and thus calculate the air permeability area.For example, if the breathable area threshold is set to 10 square centimeters, if the test results show that the breathable area of ​​a certain glove is 12 square centimeters, which is greater than the threshold, it means that the breathability of the glove meets the requirements. Sensitivity can be evaluated by simulating an experiment of touching an object with a finger. For example, a sensor can be used to measure the pressure change generated when a finger touches an object through a glove to determine the degree of obstruction of the glove to tactile perception. For example, if the sensitivity threshold is set to 90%, if the test results show that the sensitivity of a certain glove is 95%, which is greater than the threshold, it means that the sensitivity of the glove meets the requirements. Finally, the finished gloves are sorted according to the test results of breathability and sensitivity. Only when both the breathability and sensitivity meet the requirements will the gloves be sent to the next process, such as packaging, disinfection, etc. Unqualified gloves will be sent to the waste recycling process to prevent unqualified products from entering the market. For example, for electronic assembly workers who need to perform delicate operations, the sensitivity of gloves is very important. If the sensitivity of the gloves is poor, it will affect the accuracy of the operation, reduce work efficiency, and even cause product defects. Through strict quality testing, it can be ensured that only gloves that meet the sensitivity standards can leave the factory to ensure product quality.

[0029] Step S6, sew the inner fabric with the honeycomb breathable structure and the outer fabric by using a double-needle chain sewing machine and high-strength sewing thread to ensure that the honeycomb breathable structure corresponds precisely to the outer fabric, thereby obtaining a smart glove with high elasticity, breathable and perspiration-wicking properties, and flexible feel.

[0030] Step S6 includes: collecting three-dimensional image information of the outer layer fabric to determine the curvature distribution map of the outer layer fabric; identifying the area to be sewn of the outer layer fabric through a deep learning algorithm based on the curvature distribution map of the outer layer fabric to obtain the contour of the area to be sewn; and matching a pre-established inner layer fabric database based on the contour of the area to be sewn to obtain the three-dimensional model data of the inner layer fabric to be sewn.

[0031] Specifically, first, the cut outer fabric is photographed from multiple angles using a high-resolution industrial camera, for example, from the front, side, and top view angles, and no less than 100 images are collected each time to ensure that every detail of the fabric surface is captured. These image data constitute the three-dimensional image information of the outer fabric, and then these two-dimensional image information are synthesized into a complete three-dimensional model through professional three-dimensional reconstruction software. This model can accurately reflect the spatial position information of the outer fabric surface, including the spatial coordinates (x, y, z) of each point. Using these coordinate data, the curvature distribution map of the outer fabric can be drawn. For example, by calculating the curvature radius of the area around each point, the area with a curvature radius of less than 5 cm is marked as a high curvature area, which usually corresponds to the fingertips and joints of the gloves, while the area with a curvature radius of more than 20 cm is marked as a low curvature area, corresponding to the palm of the glove and other relatively flat parts. Based on the curvature distribution map of the outer fabric, a deep learning algorithm is used to identify the area to be stitched. For example, a convolutional neural network model can be trained, which takes the curvature distribution map as input and outputs the contour of the area to be stitched. The training data set contains 10,000 glove curvature distribution maps with marked areas to be stitched. Through a large number of training samples, the model can learn the mapping relationship between different curvature features and areas to be stitched. The trained model can accurately identify the areas to be stitched, for example, the connection between the fingertips and the palm of the glove, and the connection between the fingers, and output the precise contours of these areas. After obtaining the contour of the area to be stitched, the image matching algorithm is used to search and match in the pre-established honeycomb breathable structure inner fabric database. This database contains three-dimensional model data of honeycomb breathable structures of various sizes and shapes. For example, the database can contain three sizes of S, M, and L glove inner fabric models for different hand shapes, and each size contains 10 models of different honeycomb breathable structure arrangements. By calculating the similarity between the contour of the area to be stitched and each model in the database, the best matching model can be found. For example, a shape context-based matching algorithm can be used to calculate the shape features of each point on the contour and compare them, and finally determine the model with the highest similarity as the three-dimensional model data of the inner fabric to be stitched. According to the matched three-dimensional model data of the inner fabric to be stitched, a three-dimensional modeling algorithm is used to generate a spatial fitting model of the inner fabric and the outer fabric. For example, the inner fabric model and the outer fabric model can be spatially registered so that the two can achieve the best fitting state in three-dimensional space. Then, the fitting model is discretized into a large number of tiny units through the finite element analysis method. For example, the edge of each honeycomb structure is divided into 10 units, and the size of each unit is about 0.3 mm. Each unit is mechanically analyzed to calculate its force during the stitching process and determine the precise spatial coordinates of each point to be stitched.For example, the coordinates of a certain point to be sutured at the fingertip can be determined to be (10.2, 25.8, 5.3). Based on the spatial coordinates of each point to be sutured, the motion trajectory information of the sewing machine is generated by a path planning algorithm. For example, the A* algorithm can be used to take the point to be sutured as a path node to calculate a path from the starting point to the end point, passing through all the points to be sutured and with the shortest path. This path is the trajectory that the sewing machine needle needs to move. Then, this trajectory information is converted into a control signal, for example, the coordinates of each point to be sutured are converted into a motion instruction of the robot arm, and the robot arm is controlled to drive the sewing machine to move along a predetermined trajectory. During the sewing process, the robot arm controls the double-needle chain sewing machine to move according to the generated control signal. At the same time, the position information of the sewing machine needle is obtained in real time through a high-precision position sensor, for example, the position coordinates of the needle are collected every 0.1 seconds. The real-time position collected is compared with the predetermined spatial coordinates of the point to be sutured. If the deviation is greater than the preset threshold, such as greater than 0.5 mm, the PID control algorithm is used to adjust the motion parameters of the robot arm, such as adjusting the motion speed and angle of the robot arm, so that the needle can accurately return to the predetermined trajectory. In addition, during the sewing process, a tension sensor is used to collect the tension data of the high-strength sewing thread in real time, for example, the tension value is collected every 0.05 seconds. Through the fuzzy control algorithm, the tension of the sewing thread is dynamically adjusted according to the real-time tension data and the preset tension target value. For example, if the tension value is detected to be too large and exceeds the preset target value of 2 Newtons, the sewing thread is relaxed by controlling the motor to reduce the tension; if the tension value is too small, the sewing thread is tightened to increase the tension. In this way, the optimal sewing thread tension value can be obtained to ensure the quality of the suture. Finally, the suture of the smart gloves is completed. This suture method can ensure the strength and durability of the gloves while maintaining good air permeability and sensitivity.

[0032] Step S7, the performance of the finished smart gloves is evaluated, the antistatic property of the gloves is tested by a surface resistance tester, the air permeability of the gloves is tested by a dynamic hot plate method, and the perspiration and quick-drying property of the gloves is tested by an artificial climate chamber. When the surface resistance exceeds 1×10^9 ohms, the proportion of conductive fibers is increased by 5%, when the air permeability is lower than 80 mm / s, the aperture of the mesh structure is increased by 0.5 mm or the density of the honeycomb structure is increased by 10%, and when the perspiration and quick-drying time exceeds 30 minutes, the content of polyester moisture-absorbing and quick-drying yarn is increased by 10% or the hydrophobic finishing process parameters are optimized.

[0033] Step S7 includes: obtaining a manufactured smart glove sample, and testing the surface resistance value of the smart glove sample by a surface resistance tester; if the surface resistance value exceeds a preset resistance threshold, increasing the proportion of conductive fibers by a preset proportion, remaking the smart glove sample, until the surface resistance value is less than or equal to the preset resistance threshold, and obtaining a smart glove sample that meets the antistatic performance requirements; for the smart glove sample that meets the antistatic performance requirements, testing its air permeability by a dynamic hot plate method; if the air permeability is lower than the preset air permeability threshold, increasing the aperture of the mesh structure by a first preset length or the density of the honeycomb structure by a first preset density according to the structural characteristics of the smart glove sample, remaking the smart glove sample, until the air permeability is greater than or equal to the preset air permeability threshold, and obtaining a smart glove sample that meets the antistatic performance and air permeability requirements; for the smart glove sample that meets the antistatic performance and air permeability requirements, testing its perspiration and quick-drying performance by an artificial climate chamber; if the surface humidity of the smart glove sample does not reach a preset dryness within a preset time, removing the polyester moisture-absorbing quick-drying yarn The content of the smart gloves is increased by a second preset ratio, or the temperature and time parameters of the hydrophobic finishing process are optimized, and the smart glove sample is remade until the preset dryness is reached within the preset time, so as to obtain a smart glove sample that meets the requirements of antistatic performance, air permeability and perspiration quick-drying performance; a support vector machine algorithm is used, and the conductive fiber ratio, mesh aperture, honeycomb density, and moisture-absorbing and quick-drying yarn content are used as input features, and the antistatic, air permeability, and perspiration quick-drying test results of the smart glove sample are used as output to train and optimize the smart glove performance prediction model; in the subsequent smart glove production process, according to the raw material characteristics and process parameters, the smart glove performance prediction model is used to predict whether the comprehensive performance of the smart gloves meets the requirements; if not, the material ratio and process parameters are adjusted with reference to the optimization suggestions given by the smart glove performance prediction model; the actual performance test data of each batch of smart gloves is continuously collected for continuous optimization and iterative update of the smart glove performance prediction model, so as to improve the generalization ability and prediction accuracy of the smart glove performance prediction model and guide the formulation design and production of smart gloves.

[0034] Specifically, the performance testing and optimization of smart gloves is a multi-step, iterative process. First, the antistatic performance test uses a surface resistance tester, which calculates the resistance value by applying voltage to the surface of the glove and measuring the current. For example, if the measured resistance value is 2×10^9 ohms, which exceeds the standard of 1×10^9 ohms, the conductive fiber ratio needs to be increased. Here, the original 20% conductive fiber content can be increased to 25%, and the sample can be remade and tested until it meets the standard. The breathability test uses the dynamic hot plate method, which simulates the heat dissipation process of human skin and evaluates the breathability of the material by measuring the heat transfer rate. Assuming that the measured air permeability is 60 mm / s, which is lower than the requirement of 80 mm / s, the glove structure needs to be optimized. For the mesh structure, the original 2 mm pore size can be increased to 7 mm; for the honeycomb structure, the density can be increased from 50 to 55 per square centimeter to improve breathability. The perspiration and quick-drying performance test uses an artificial climate chamber to evaluate the moisture absorption and perspiration ability of the gloves by simulating different environmental conditions. For example, if the temperature is set to 35°C and the relative humidity is 80%, if the humidity on the surface of the gloves is still over 60% after 30 minutes, it is considered as not meeting the standard.

[0035] 40%, or increase the temperature of the hydrophobic finishing process from 120°C to 130°C and extend the time from 30 minutes to 40 minutes to enhance the perspiration and quick-drying effect. The support vector machine algorithm is used to establish a smart glove performance prediction model. The algorithm classifies data points by constructing hyperplanes in high-dimensional space, which is suitable for dealing with multivariate nonlinear problems. Input features may include the proportion of conductive fibers (such as 25%), mesh aperture (such as 7 mm), honeycomb density (such as 55 per square centimeter), moisture-absorbing and quick-drying yarn content (such as 40%), etc. The output is the comprehensive performance score of the gloves, such as the weighted average of antistatic properties, breathability, and perspiration and quick-drying properties. In actual production, the model can pre-evaluate the effects of different material ratios and process parameters. For example, if the model predicts that the comprehensive performance score of a formula is 85 points (out of 100 points), which is lower than the requirement of 90 points, it may be recommended to increase the proportion of conductive fibers by 2% or the honeycomb density by 5%. This predictive ability helps reduce trial and error costs and improve production efficiency. Continuous optimization of the model is the key to ensuring its accuracy. The actual test data of each batch of products will be used to update the model. For example, if the model predicts that the air permeability of a batch of gloves is 85 mm / s, and the actual test result is 82 mm / s, this error will be used to fine-tune the model parameters and improve its prediction accuracy. As the amount of data increases, the generalization ability of the model will continue to improve, and it will be able to better adapt to changes in different raw materials and process conditions. Through this data-driven approach, the performance optimization process of smart gloves becomes more scientific and efficient. It not only ensures the stability of product quality, but also provides strong support for new product development and promotes the continuous advancement of smart glove technology.

[0036] Step S8, after adjusting the gloves accordingly according to the performance evaluation results, apply a uniform dispensing layer on the palm and fingertips of the smart gloves. By controlling the viscosity, coating speed and coating pressure of the glue, the thickness of the glue layer is controlled to be 0.1-0.2 mm to ensure the anti-slip and sensitivity of the gloves. Use polyurethane glue with good flexibility and strong wear resistance, and after UV curing treatment, further improve the wear resistance and service life of the gloves. At the same time, use precision-controlled dispensing equipment during the glue coating process to retain 30% of the uncoated microporous structure in the glue coating area to maintain the breathability and perspiration performance of the gloves.

[0037] Step S8 includes: obtaining glove performance data, the glove performance data including the anti-slip property, sensitivity, wear resistance, air permeability and perspiration property of the gloves under different working conditions, and corresponding glue dispensing layer parameters, the glue dispensing layer parameters including glue viscosity, coating speed, coating pressure, micropore size and curing degree; training a smart glove performance prediction model using a support vector machine algorithm according to the glove performance data, the input of the smart glove performance prediction model is the glue dispensing layer parameters, and the output is the predicted value of the glove performance parameters; obtaining performance requirement data of the target glove, inputting the performance requirement data into the smart glove performance prediction model, and optimizing the particle swarm algorithm to obtain the optimal glue dispensing layer parameters; configuring the glue viscosity, coating speed and coating pressure of the glue dispensing device according to the optimal glue dispensing layer parameters, controlling the glue dispensing device to coat the glue on the palm and fingertips of the glove to form an initial glue dispensing layer, and the thickness of the initial glue dispensing layer The degree is 1 to 2 mm; a microporous structure is created on the initial glue-dispensing layer by using laser etching technology, the pore size and distribution of the microporous structure are determined according to the micropore size parameter in the optimal glue-dispensing layer parameters, the proportion of uncoated microporous structure is 30%, and a primary glue-dispensing layer with a microporous structure is obtained; the primary glue-dispensing layer with a microporous structure is placed in an ultraviolet light curing device, and the intensity and irradiation time of the ultraviolet light are controlled according to the curing degree parameter in the optimal glue-dispensing layer parameters to obtain a secondary glue-dispensing layer after ultraviolet light curing treatment; the image information of the secondary glue-dispensing layer is collected by an image sensor, and the thickness and uniformity characteristics of the secondary glue-dispensing layer are extracted; if the thickness and uniformity characteristics meet the preset range, a final glue-dispensing layer is obtained; if the thickness and uniformity characteristics do not meet the preset range, the thickness and uniformity characteristics are input into the smart glove performance prediction model, and the glue-dispensing layer parameters are optimized again.

[0038] Specifically, building a glove performance database is a key step in the development of smart gloves. By collecting performance parameters under different working conditions, such as anti-slip properties, sensitivity, etc., and recording the corresponding dispensing layer parameters, a comprehensive data foundation can be established. For example, the anti-slip properties of gloves are tested in a low temperature environment, the friction coefficient of gloves on slippery surfaces is recorded, and the parameters such as the viscosity of the dispensing layer and the coating speed are recorded at the same time. Such a data collection process can cover a variety of working environments, such as high temperature, high humidity, oil pollution, etc., to ensure the comprehensiveness and representativeness of the database. Using the support vector machine algorithm to train the smart glove performance prediction model, the mapping from the dispensing layer parameters to the glove performance can be achieved. For example, by inputting the dispensing layer parameters of 500cP glue viscosity, 10mm / s coating speed, 0.5MPa coating pressure, 50μm micropore size, and 95% curing degree, the model can predict that the anti-slip properties of the gloves are 0.8 (friction coefficient), 98% sensitivity, 5000 times (abrasion times), 85mm / s air permeability, and 20min perspiration (drying time). This predictive capability is very valuable for quickly evaluating the effects of different dispensing parameter combinations. After obtaining the target performance requirement data, the dispensing layer parameters are optimized by the particle swarm algorithm. Assuming the target requirements are anti-slip 0.9, sensitivity 99%, wear resistance 6000 times, air permeability 90mm / s, and perspiration 15min, the algorithm may give the optimized dispensing layer parameters: glue viscosity 550cP, coating speed 8mm / s, coating pressure 0.6MPa, micropore size 45μm, and curing degree 97%. This optimization process can quickly find the best parameter combination that meets the target requirements. Configure the dispensing equipment according to the optimized parameters, and apply the glue to the palm and fingertips of the glove to form an initial dispensing layer. Controlling the initial dispensing layer thickness between 1 and 2 mm can be achieved by adjusting the coating speed and pressure. For example, when the coating speed is 8mm / s and the pressure is 0.6MPa, a uniform dispensing layer of 1.5 mm thick may be formed. Laser etching technology is used to create a microporous structure, and the laser power and scanning speed are set according to the micropore size of 45μm in the optimization parameters. The uncoated microporous structure accounts for 30%, which can be achieved by controlling the density and distribution of laser etching. This microporous structure can significantly improve the breathability and perspiration wicking of the gloves while maintaining good anti-slip properties. UV curing is a key step to ensure the performance of the glue layer. According to the curing degree parameter of 97%, it may be necessary to set the UV intensity to 500mW / cm 2 , the irradiation time is 30 seconds. This precise control can ensure that the dispensing layer achieves the best physical and chemical properties. Finally, the image information of the secondary dispensing layer is collected by the image sensor to extract the thickness and uniformity features. If the features do not meet the preset range, such as the thickness variation exceeds ±0.2 mm or the uniformity deviation exceeds 5%, the dispensing layer parameters need to be re-optimized. This closed-loop control can ensure the quality stability of the final dispensing layer, thereby ensuring the overall performance of the smart glove.

[0039] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of the present application. For example, the above features are replaced with the technical features with similar functions disclosed in the present application (but not limited to) to form a technical solution.

Claims

1. A method for preparing high-performance smart gloves, characterized in that: The method comprises the following steps: Step S1, according to the requirements of flexibility and wear resistance of the gloves, 120 denier / 48 fiber silk protein fiber is selected as the natural fiber, 40 denier spandex coated with 200 denier conductive yarn is selected as the conductive fiber, and the two fibers are blended in a ratio of 30:70 to prepare a high elastic blended yarn with both softness and antistatic properties; Step S2, using a double-sided weft knitting process, designing a mesh structure with an aperture of 2 mm, using the above-mentioned high-elasticity blended yarn for weaving, and at the same time weaving 150 denier polyester moisture-absorbing quick-drying yarn into the inner layer of the knitted fabric to obtain a knitted fabric with excellent fit and breathability, the warp density is controlled at 40 strands / inch, the weft density is controlled at 38 strands / inch, and the surface density is controlled within the range of 200-250 g / m2 to ensure the sensitivity and softness of the gloves; Step S3, calendering the knitted fabric after weaving, by controlling the calendering pressure to 2 MPa, the calendering temperature to 120 degrees Celsius, and the calendering speed to 20 m / min, the thickness of the knitted fabric is reduced by 20%, and the air permeability is increased by 30% from the initial value, thereby further improving the sensitivity and air permeability of the gloves; Step S4, performing hydrophobic finishing on the surface of the knitted fabric after the calendering treatment, using an emulsion prepared with an environmentally friendly fluorocarbon resin, acrylate and deionized water in a ratio of 1:2:7, and performing a treatment at 25 degrees Celsius for 30 minutes by an immersion method, so that the contact angle of the fabric is increased to 120 degrees, thereby improving the anti-slip and wear resistance of the gloves; Step S5, cutting the knitted fabric after the hydrophobic treatment into the shape of gloves, and using laser cutting technology to make a honeycomb air-permeable structure with a diameter of 3 mm and a depth of 1 mm on the palm and fingertips of the gloves, so as to further increase the air permeability area and sensitivity of the gloves; Step S6, sewing the inner fabric with the honeycomb breathable structure and the outer fabric by using a double-needle chain sewing machine and high-strength sewing thread, ensuring that the honeycomb breathable structure and the outer fabric are precisely aligned, so as to obtain a smart glove with high elasticity, breathable and perspiration-wicking performance, and flexible feel; Step S7, evaluating the performance of the manufactured smart gloves, testing the antistatic property of the gloves by a surface resistance tester, testing the air permeability of the gloves by a dynamic hot plate method, and testing the perspiration and quick-drying property of the gloves by an artificial climate chamber. When the surface resistance exceeds 1×10^9 ohm, the proportion of conductive fibers is increased by 5%. When the air permeability is lower than 80 mm / s, the aperture of the mesh structure is increased by 0.5 mm or the density of the honeycomb structure is increased by 10%. When the perspiration and quick-drying time exceeds 30 minutes, the content of polyester moisture-absorbing and quick-drying yarn is increased by 10% or the hydrophobic finishing process parameters are optimized. Step S8, after adjusting the gloves accordingly according to the performance evaluation results, a uniform dispensing layer of glue is applied to the palm and fingertips of the smart gloves. The thickness of the glue layer is controlled at 0.1-0.2 mm by controlling the viscosity, coating speed and coating pressure of the glue to ensure the anti-slip and sensitivity of the gloves. A polyurethane glue with good flexibility and strong wear resistance is selected, and after UV curing treatment, the wear resistance and service life of the gloves are further improved. At the same time, a precisely controlled dispensing device is used during the gluing process to retain 30% of the uncoated microporous structure in the gluing area to maintain the breathability and perspiration performance of the gloves.

2. The method for preparing a high-performance smart glove according to claim 1, characterized in that: The step S1 comprises: The initial mixing ratio data of the silk protein fiber and the conductive fiber are obtained, and the initial mixing ratio is set to 30:70 by a computer program to obtain an initial mixing ratio model; The data of silk protein fiber fineness of 120 denier / 48 fiber and conductive fiber fineness of 40 denier spandex coated with 200 denier are obtained, and the fineness data are used as input parameters of the mixing ratio model to obtain a mixing ratio adjustment model for a specific fineness; According to the yarn denier requirement, the mixing ratio adjustment model is iteratively optimized, and if the yarn denier does not meet the preset range, the fineness of the silk protein fiber and the conductive fiber is adjusted to obtain the optimal fineness combination that meets the yarn denier requirement; A preliminary blending test is carried out using the optimal fineness combination, and the fiber distribution image after blending is observed through an electron microscope to obtain fiber distribution uniformity data, and determine whether the fiber distribution uniformity data reaches a set threshold; Optimizing blending process parameters according to the fiber distribution uniformity data; if the fiber distribution uniformity data is lower than a set threshold, adjusting the blending process parameters to obtain optimized blending process parameters and obtain new fiber distribution uniformity data; Detecting the yarn static electricity and yarn elasticity of the blended yarn by means of a sensor, obtaining the yarn static electricity data and the yarn elasticity data, and determining whether the yarn static electricity data and the yarn elasticity data meet the performance requirements of the gloves; According to the yarn electrostatic data and the yarn elastic data, if the yarn electrostatic data or the yarn elastic data does not meet the requirements, the ratio of the silk protein fiber and the conductive fiber is adjusted to obtain a high-elasticity antistatic blended yarn that meets the flexibility and wear resistance requirements of the gloves.

3. The method for preparing a high-performance smart glove according to claim 1, characterized in that: The step S2 comprises: Acquire real-time operating parameters of the double-sided weft knitting machine, wherein the operating parameters include rotation speed, yarn feeding amount and pulling tension data; Constructing a set of high-elastic yarn tension feature vectors according to the operating parameters; Determine whether the operating parameter exceeds a preset range. If so, trigger an alarm mechanism and control the device to stop running; According to the alarm mechanism, the control system is used to adjust the yarn feeding amount and the pulling tension to obtain the operating parameters that meet the preset range, determine the normal operation of the equipment, and generate the normal operation equipment number; Retrieving corresponding process parameters from a database through the equipment number, wherein the process parameters include aperture size, warp density, and weft density data; Using a photoelectric sensor to detect actual weaving parameters, determining whether a deviation between the actual weaving parameters and the process parameters exceeds a threshold, and if so, generating deviation data; According to the deviation data, a PID control algorithm is used to calculate an adjustment signal, and the machine operation state is adjusted through an actuator to obtain a mesh structure that meets the process parameters and determine the image information of the mesh structure; Collecting image information of the mesh structure, and performing grayscale and binarization processing on the image information; Extract the mesh edge contour through the edge detection algorithm, calculate the area of ​​each mesh, and determine whether the mesh area is within the set range; If the mesh area is not within the set range, repeat steps 6 and 7 until the mesh area is qualified, obtain a fabric with a qualified mesh structure, and generate a fabric number; Extract fabric weight data from a database using the fabric number, and use an infrared sensor to scan fabric thickness to obtain thickness data; The thickness data and the weight data are integrated to calculate the fabric surface density, and obtain a result that the fabric surface density data is within a preset range.

4. The method for preparing a high-performance smart glove according to claim 1, characterized in that: Described step S4 comprises: Obtaining a knitted fabric after calendering as a base material for hydrophobic finishing; Environmentally friendly fluorocarbon resin, acrylate and deionized water were mixed according to a formula ratio of 1:2:7 to prepare a hydrophobic finishing emulsion; The knitted fabric is immersed in the hydrophobic finishing emulsion, and the immersion temperature is controlled to be 25°C and the immersion time is 30 minutes; The impregnated knitted fabric is taken out and subjected to a de-liquidation treatment to remove excess hydrophobic finishing emulsion; The knitted fabric after dehydration is dried and shaped to fully combine the hydrophobic finishing agent with the knitted fabric; Use a contact angle meter to measure the contact angle of the knitted fabric surface after hydrophobic finishing to determine whether it reaches the target value of 120°. If not, repeat steps 3-5 until the requirement is met. The knitted fabric after hydrophobic finishing is used to make gloves. The anti-slip and wear resistance of the gloves are evaluated through friction coefficient test and abrasion resistance test to ensure that the expected performance requirements are met.

5. The method for preparing a high-performance smart glove according to any one of claims 1 to 4, characterized in that: The step S7 comprises: Obtaining a manufactured smart glove sample, and testing the surface resistance value of the smart glove sample using a surface resistance tester; If the surface resistance value exceeds the preset resistance threshold, the proportion of the conductive fiber is increased by a preset proportion, and the smart glove sample is remade until the surface resistance value is less than or equal to the preset resistance threshold, so as to obtain a smart glove sample that meets the antistatic performance requirements; For the smart glove samples that meet the antistatic performance requirements, the air permeability is tested by a dynamic hot plate method; If the air permeability is lower than the preset air permeability threshold, according to the structural characteristics of the smart glove sample, the aperture of the mesh structure is increased by a first preset length or the density of the honeycomb structure is increased by a first preset density, and the smart glove sample is remade until the air permeability is greater than or equal to the preset air permeability threshold, so as to obtain a smart glove sample that meets the requirements of antistatic performance and air permeability performance; For the smart glove samples that meet the requirements of antistatic performance and breathability, an artificial climate chamber is used to test their perspiration and quick-drying performance; If the surface humidity of the smart glove sample does not reach the preset dryness within the preset time, the content of the polyester moisture-absorbing quick-drying yarn is increased by a second preset ratio, or the temperature and time parameters of the hydrophobic finishing process are optimized, and the smart glove sample is remade until the preset dryness is reached within the preset time, so as to obtain a smart glove sample that meets the requirements of antistatic performance, air permeability, and perspiration-wicking quick-drying performance; A support vector machine algorithm is used, with the conductive fiber ratio, mesh aperture, honeycomb density and moisture-absorbing and quick-drying yarn content as input features, and the antistatic property, air permeability and perspiration-wicking and quick-drying test results of the smart glove samples as outputs, to train and optimize the smart glove performance prediction model; In the subsequent smart glove production process, the smart glove performance prediction model is used to predict whether the comprehensive performance of the smart gloves meets the requirements according to the raw material characteristics and process parameters; If not, refer to the optimization suggestions given by the smart glove performance prediction model to adjust the material ratio and process parameters; The actual performance test data of each batch of smart gloves is continuously collected for continuous optimization and iterative update of the smart glove performance prediction model, thereby improving the generalization ability and prediction accuracy of the smart glove performance prediction model and guiding the formulation design and production of smart gloves.

6. The method for preparing a high-performance smart glove according to any one of claims 1 to 4, characterized in that: The step S8 comprises: Obtaining glove performance data, wherein the glove performance data includes anti-slip property, sensitivity, wear resistance, air permeability and perspiration wicking of the gloves under different working conditions, and corresponding dispensing layer parameters, wherein the dispensing layer parameters include glue viscosity, coating speed, coating pressure, micropore size and curing degree; According to the glove performance data, a support vector machine algorithm is used to train a smart glove performance prediction model, wherein the input of the smart glove performance prediction model is a dispensing layer parameter, and the output is a predicted value of the glove performance parameter; Acquire performance requirement data of the target gloves, input the performance requirement data into the smart glove performance prediction model, and obtain the optimal dispensing layer parameters through particle swarm algorithm optimization; According to the optimal dispensing layer parameters, the glue viscosity, coating speed and coating pressure of the dispensing device are configured, and the dispensing device is controlled to coat the glue on the palm and fingertips of the glove to form an initial dispensing layer, wherein the thickness of the initial dispensing layer is 1 to 2 mm; A microporous structure is created on the initial dispensing layer by using laser etching technology, wherein the pore size and distribution of the microporous structure are determined according to the micropore size parameters in the optimal dispensing layer parameters, and the proportion of the uncoated microporous structure is 30%, thereby obtaining a primary dispensing layer with a microporous structure; Placing the primary glue layer with the microporous structure in an ultraviolet light curing device, and controlling the intensity and irradiation time of ultraviolet light according to the curing degree parameter in the optimal glue layer parameter to obtain a secondary glue layer after ultraviolet light curing treatment; Collecting image information of the secondary glue dispensing layer through an image sensor, and extracting thickness and uniformity characteristics of the secondary glue dispensing layer; If the thickness and uniformity characteristics meet the preset range, a final dispensing layer is obtained; If the thickness and uniformity characteristics do not meet the preset range, the thickness and uniformity characteristics are input into the smart glove performance prediction model, and the dispensing layer parameters are optimized again.