A high-drape, stain-resistant curtain fabric and its production method

Abstract

This invention discloses a high-drape, stain-resistant curtain fabric and its production method, relating to the field of curtain fabric technology. It solves the problems of poor drape and stain resistance in curtain fabrics. The surface layer of the curtain fabric is made of polyester fiber and PCDT fiber, increasing the fabric's stiffness and weight, thus improving its drape. The stain-resistant isolation layer is made of silver fiber, polyurethane, polyester fiber, and bismuth powder, enhancing its stain resistance. A swarm tracking control algorithm model controls the threading speed, weaving speed, tension, and displacement, ensuring the production quality of the curtain fabric. A single-flow dual-channel heating plate uses a transverse magnetic flux induction heating optimization algorithm to achieve uniform temperature distribution in the material belt drying chamber. The automatic fabric ironing device uses a limit analysis upper limit control circuit to control the surface temperature of the hot rollers, reducing costs and production difficulty. This invention significantly improves the production quality of curtain fabrics, enhancing their drape and stain resistance.

Description

Technical Field

[0001] This invention relates to the field of curtain fabric technology, and more specifically to a high-drape, stain-resistant curtain fabric and its production method. Background Technology

[0002] The main functions of high-drape, stain-resistant curtain fabric and its production method are as follows: High drape: This fabric uses special materials and processing techniques to give the curtains excellent drape, making them more beautiful and comfortable. Stain resistance: The fabric undergoes special treatment, giving its surface a certain degree of stain resistance. During use, it effectively prevents bacteria and pollutants from adhering to the curtain surface, thus improving the hygiene of the curtains. The principle behind high-drape, stain-resistant curtain fabric lies in the use of specialized materials and manufacturing processes, achieving a dual improvement in both drape and stain resistance. During the manufacturing process, processes such as dyeing and anti-stain treatment give the fabric surface smoothness, softness, and anti-absorption properties, thereby improving the quality and lifespan of the curtains.

[0003] Existing technologies for high-drape, stain-resistant curtain fabrics and their production methods have many drawbacks. If unsuitable materials are used, resulting in insufficient stiffness and weight, the curtains will not drape properly and lack drape. Inadequate manufacturing processes can also negatively impact the drape. Furthermore, the fabric itself lacks good stain resistance, making it difficult to clean completely. The fabric preparation process requires multiple steps, is complex and time-consuming, increasing costs and production difficulty, and leading to inconsistent fabric quality. Therefore, this invention proposes a high-drape, stain-resistant curtain fabric and its production method, aiming to provide a high-quality, high-drape, stain-resistant curtain fabric. Summary of the Invention

[0004] To address the shortcomings of the aforementioned technologies, this invention provides a high-drape, stain-resistant curtain fabric and its production method. The surface layer of the curtain fabric is made of polyester fiber and PCDT fiber, increasing the curtain's stiffness and weight, thus solving the problem of insufficient drape. The stain-resistant isolation layer of the curtain fabric is made of silver fiber, polyurethane, polyester fiber, and bismuth powder, improving the fabric's stain resistance. A swarm tracking control algorithm model controls the heddle threading speed, weaving speed, tension, and displacement, achieving precise spinning control and ensuring production quality, thus solving the problem of inconsistent curtain fabric quality. A single-flow dual-channel heating plate uses a transverse magnetic flux induction heating optimization algorithm to stably generate hot air, achieving a uniform temperature distribution in the material belt drying chamber. The automatic fabric ironing device uses a limit analysis upper limit control circuit to control the surface temperature of the hot rollers, reducing costs and production difficulty.

[0005] The present invention adopts the following technical solution:

[0006] A high-drape, stain-resistant curtain fabric includes:

[0007] The surface layer is used to showcase the luster, feel, and drape of the curtain fabric. The warp of the surface layer is made of polyester fiber, and the weft is made of PCDT fiber. The polyester fiber has a diameter of 6.16 μm and a fineness of 112 dtex, while the PCDT fiber has a diameter of 34.11 μm and a fineness of 413 dtex. The warp density is 59.9 threads / cm, and the weft density is 48.1 threads / cm.

[0008] The anti-fouling isolation layer is used to prevent external dirt and light radiation from entering the curtain. The anti-fouling isolation layer is made of antibacterial, anti-radiation and stain-resistant fabric. The antibacterial, anti-radiation and stain-resistant fabric includes the following components by weight: 10-25 parts by weight of silver fiber, 13-20 parts by weight of polyurethane, 12-15 parts by weight of polyester fiber and 17-20 parts by weight of bismuth powder.

[0009] The intermediate insulation layer is used to improve the thermal insulation performance of the curtains. The intermediate insulation layer comprises the following components by weight: 18-20 parts by weight of rigid polyurethane foam, 15-18 parts by weight of glass fiber cotton, and 13-17 parts by weight of ceramic fiber cotton.

[0010] The intermediate light-blocking layer is used to block sunlight and protect the colors of indoor furniture and floors from being affected. The warp of the intermediate light-blocking layer is made of elastic synthetic fiber, and the weft is made of black polyethylene polyester elastic yarn. The diameter of the elastic synthetic fiber is 5.11μm and the fineness is 98dtex. The diameter of the black polyethylene polyester elastic yarn is 33.80μm and the fineness is 391dtex. The warp density is 51.9 threads / cm and the weft density is 52.1 threads / cm.

[0011] A protective layer is used to enhance the durability and stability of the curtain fabric. The protective layer is formed on the surface of the curtain fabric by polyurethane and siloxane to form a highly effective waterproof membrane. The polyurethane is 21-25 parts by weight and the siloxane is 18-23 parts by weight.

[0012] The protective layer is disposed on the outer surface of the surface layer, the anti-fouling isolation layer is disposed on the inner surface of the surface layer, the intermediate heat insulation layer is disposed inside the anti-fouling isolation layer, and the intermediate light-shielding layer is disposed inside the intermediate heat insulation layer.

[0013] As a further technical solution of the present invention, a method for producing a high-drape, stain-resistant curtain fabric is provided, applicable to the aforementioned high-drape, stain-resistant curtain fabric. The method includes the following steps:

[0014] Step 1: Soak the polyester fiber in an alkaline or acidic solution to remove impurities and oil from the surface of the polyester fiber. Use an automatic circulating steam processor to pre-treat the PCDT fiber. The automatic circulating steam processor uses a vacuum microwave processing mechanism to stably generate a temperature of 150°C and a pressure of 0.5MPa to obtain a compact PCDT fiber structure.

[0015] Step 2: A combined spinning machine is used to process polyester fiber as warp and PCDT fiber as weft. Both warp and weft are yarns from an automatic three-dimensional flatbed loom. The automatic three-dimensional flatbed loom uses a power guide mechanism to thread the heddles and trim the heddles, forming a surface layer fabric with structure and texture. The power guide mechanism includes at least a heddle guide frame, a control system, and a threading mechanism. The control system uses a swarm tracking control algorithm model to control the threading speed, weaving speed, tension, and displacement, achieving precise spinning control and ensuring production quality.

[0016] In step two, the swarm tracking control algorithm model includes a data sensing module, a data processing module, a swarm tracking module, and an integrated control module. The output of the data sensing module is connected to the input of the data processing module, the output of the data processing module is connected to the input of the swarm tracking module, and the output of the swarm tracking module is connected to the input of the integrated control module.

[0017] Step 3: The raw materials of the anti-fouling isolation layer, intermediate insulation layer, intermediate light-shielding layer and protective layer are respectively compressed into the anti-fouling isolation layer, intermediate insulation layer, intermediate light-shielding layer and protective layer by self-adhesive compression board. The self-adhesive compression board is then used to apply the anti-fouling isolation layer, intermediate insulation layer, intermediate light-shielding layer and protective layer to the back or surface of the surface layer fabric in sequence by network cross-linking and curing thermosensitive adhesive.

[0018] Step 4: The coated surface fabric is dried in a material belt drying chamber to ensure complete curing and fixation of the coating. The material belt drying chamber includes at least a conveyor belt, a heating zone, a cooling zone, and a discharge zone. The heating zone uses a single-flow dual-channel heating plate to generate 200°C high-temperature hot air that flows through the surface fabric, promoting coating cross-linking and achieving drying and curing of the surface fabric. The single-flow dual-channel heating plate uses a transverse magnetic flux induction heating optimization algorithm to stably generate hot air, achieving a uniform temperature distribution in the material belt drying chamber.

[0019] Step 5: After coating and drying, the surface layer fabric is shaped by a setting device. The surface layer fabric is then ironed by an automatic fabric ironing device. The automatic fabric ironing device uses an automated cutting tooth horizontal cutting tool to trim the edges and corners. The automatic fabric ironing device uses a limit analysis upper limit control circuit to control the surface temperature of the hot roller to 160°C. The surface layer fabric is placed on it for heating treatment to obtain the final curtain fabric product.

[0020] As a further technical solution of the present invention, the vacuum microwave processing mechanism uses a microwave millimeter-wave generator to generate microwave energy. The microwave millimeter-wave generator enters the microwave cavity through a waveguide to heat and dry the PCDT fibers. The microwave millimeter-wave generator obtains microwave energy by modulating the microwave index and DC bias voltage through a frequency doubling factor tunable microwave circuit. The output spectrum of the microwave energy contains ±8th order optical sideband signals. The microwave millimeter-wave generator obtains ±24th order optical sidebands through a four-wave mixing semiconductor amplification protocol. Finally, the microwave millimeter-wave generator filters the optical sidebands through a filtering beat frequency engine to obtain a 48th order millimeter-wave signal. The vacuum microwave processing mechanism uses a vacuum valve to reduce the gas pressure in the microwave cavity to 0.9 kPa, and the water molecules are evaporated and discharged at a low temperature of 50℃-80℃.

[0021] As a further technical solution of the present invention, the data sensing module monitors simulated textile parameters through a sensor group. These simulated textile parameters include at least threading speed, weaving speed, tension, displacement, and processing ambient temperature. The sensor group includes at least a speed sensor, a tension sensor, a displacement sensor, and a temperature sensor. The data sensing module uses a distributed differential acquisition circuit to acquire and transmit the simulated textile parameters monitored by the sensor group to the data processing module. The distributed differential acquisition circuit acquires the measured simulated textile parameters through a sensor network and converts the acquired simulated textile parameters into digital signals using an analog-to-digital conversion protocol. The distributed differential acquisition circuit transmits the digital signal to the data processing module via a serial transmission network. The data processing module uses a time-frequency statistical analysis model to perform time-domain analysis on the digital signal. The time-frequency statistical analysis model analyzes the variation law of the digital signal with time and frequency through a periodogram analysis mechanism. The time-frequency statistical analysis model analyzes the center position, dispersion and correlation of the digital signal through a correlation coefficient statistical protocol. The correlation coefficient statistical protocol measures the linear correlation between two continuous digital signals based on the Pearson correlation coefficient. The correlation coefficient statistical protocol describes the positive or negative correlation between two discrete digital signals based on the Spearman rank correlation coefficient.

[0022] As a further technical solution of the present invention, the swarm tracking module uses a swarm tracking algorithm to accurately judge and analyze the status of the automatic three-dimensional flat loom. The working method of the swarm tracking algorithm is as follows:

[0023] S1. Tracking particles are randomly generated in the search space as a tracking population. The swarm tracking algorithm uses a fitness evaluation mechanism to input the position of each tracking particle into the evaluation function to obtain the fitness of the tracking particles. The evaluation function performs logical operations to analyze and judge the fitness based on the position of the tracking particles in the search space and the objective function.

[0024] S2. Then, the fitness function of the tracking particle is optimized by iteratively searching the multidimensional space to gradually approach the optimal solution. The iterative search of the multidimensional space sets the maximum iteration threshold through adaptive threshold segmentation. The adaptive threshold segmentation sets the maximum iteration threshold by analyzing the tracking particle characteristics through the mean drift protocol.

[0025] S3. The swarm tracking algorithm stops iterating based on the maximum iteration threshold and outputs the globally optimal automatic 3D flat loom state judgment result. The swarm tracking algorithm re-filters and selects the exploration region through the maximum inter-class variance tracking engine. The maximum inter-class variance tracking engine outputs the globally optimal segmentation based on the exploration region to obtain the globally optimal automatic 3D flat loom state judgment result.

[0026] As a further technical solution of the present invention, the integrated control module adopts a sliding mode control mechanism to output control signals. The sliding mode control mechanism tracks and controls the automatic three-dimensional flat loom based on the loom state judgment result. The sliding mode control mechanism designs the control law of the automatic three-dimensional flat loom through a sliding surface mechanism. The sliding surface mechanism performs logical analysis based on the control law to obtain the control quantity applied to the automatic three-dimensional flat loom. The sliding mode control mechanism performs control threshold analysis on the control quantity through network parameter gradient quantitative integral analysis to obtain the control signal. The integrated control module controls the heddle threading speed, weaving speed, tension, and displacement of the automatic three-dimensional flat loom according to the control signal. The integrated control module realizes the integrated control of the automatic three-dimensional flat loom through a four-channel width-term control chip. The four-channel width-term control chip controls the needle bar movement speed and guide needle frequency of the automatic three-dimensional flat loom through phase compensation to control the heddle threading speed and weaving speed. The four-channel width-term control chip controls the tension regulator of the fabric take-up mechanism through a channel gain compensation control mechanism to adjust the control tension. The channel gain compensation control mechanism controls the weaving mechanism inside the automatic three-dimensional flat loom through an attenuation additional phase shift circuit to adjust the fabric displacement.

[0027] As a further technical solution of the present invention, the working method of the transverse magnetic flux induction heating optimization algorithm is as follows:

[0028] S1. A permanent magnet synchronous magnetic field model is used to determine the magnetic field strength and direction, generating a transverse magnetic flux induction effect. The magnetic field direction is perpendicular to the magnetic fixing plate. The permanent magnet synchronous magnetic field model determines the magnetic field strength based on the permanent magnet vector and the electromagnetic vector. The permanent magnet synchronous magnetic field model measures heating parameters through thermocouples, humidity sensors, and differential pressure flow meters. The heating parameters include at least hot air temperature, humidity, and flow rate. The permanent magnet synchronous magnetic field model transmits the heating parameters to the transverse magnetic flux control model in real time via a wireless cognitive network. The formula for calculating the magnetic field strength is:

[0029] (1)

[0030] In formula (1), The magnetic field strength, The permanent magnet vector of the single-flow dual-channel heating plate. The electromagnetic vector of the single-flow dual-channel heating plate. The permeability of the single-flow dual-channel heating plate. The magnetic pressure of the permanent magnet in the single-flow dual-channel heating plate. This represents the number of turns of the magnetizing coil;

[0031] S2. The transverse magnetic flux control model obtains the current density distribution equation based on the heating parameters and the structure of the material belt drying chamber. The model then analyzes the magnetic flux intensity distribution using an inversion solution function to determine the current density distribution equation. The inversion solution function obtains the optimal solution for the current density distribution by solving the inversion equation. The model designs an optimization algorithm based on the magnetic field intensity distribution and the current density distribution function. This optimization algorithm optimizes the hot air temperature control, humidity control, and flow control through a weighted temperature control mechanism. The formula for calculating the optimal solution for the current density distribution is:

[0032] (2)

[0033] In formula (2), This is the optimal solution for the current density distribution. This refers to the free current variable in a single-current dual-channel heating plate power supply. The area unit is the single-current dual-channel heating plate power supply. To invert the solution function, the regularization variable is... To improve the convergence accuracy of the inversion solution function;

[0034] The formula for calculating the hot air temperature control quantity is as follows:

[0035] (3)

[0036] In formula (3), This is the amount for controlling the hot air temperature. This represents the initial temperature value of the single-flow dual-channel heating plate. This refers to the target temperature value for a single-flow dual-channel heating plate. The temperature weighting value is the weighted value of the weighted temperature control mechanism. Charge density is a function of current density distribution;

[0037] The formula for calculating the humidity control amount is:

[0038] (4)

[0039] In formula (4), For humidity control parameters, This represents the initial humidity value for the single-flow dual-channel heating plate. The target humidity value for a single-flow dual-channel heating plate. Let be the time step of the current density distribution function. The flux incident angle is the angle of incidence for the transverse flux control model.

[0040] The formula for calculating the flow control quantity is:

[0041] (5)

[0042] In formula (5), For flow control quantity, This is the initial flow rate of the single-flow dual-channel heating plate. The target flow rate for the single-flow dual-channel heating plate. To determine the number of iterations for optimizing the weighted temperature control mechanism, The flux scanning speed is the value of the transverse flux control model.

[0043] S3. The transverse magnetic flux induction heating optimization algorithm adjusts the current density distribution and magnetic field strength distribution of the single-flow dual-channel heating plate according to the hot air temperature control, humidity control, and flow control. The transverse magnetic flux induction heating optimization algorithm uses power coordination control to analyze the current density and magnetic field strength gain matrix and parameter matrix to realize the automated control and optimization of the drying process.

[0044] As a further technical solution of the present invention, the limit analysis upper limit control circuit controls the power supply voltage and current of the hot roller through an adjustable power supply model to achieve surface temperature control of the hot roller. The adjustable power supply model controls the current and voltage of the hot roller load through a neural network composite control power supply. The neural network composite control power supply outputs a control signal through a neural network learning algorithm and a feedback control mechanism to achieve control of the current and voltage of the hot roller load. The neural network learning algorithm performs fuzzification processing on the control signal through a neural network, converting the control signal into a fuzzy output. The feedback control mechanism monitors and adjusts the fuzzy output through a PID controller. The PID controller optimizes the current and voltage output of the hot roller load through a distributed control strategy.

[0045] The positive and beneficial effects of this invention compared to existing technologies are as follows:

[0046] This invention discloses a high-drape, stain-resistant curtain fabric and its production method. The surface layer of the curtain fabric is made of polyester fiber and PCDT fiber, increasing the stiffness and weight of the curtain and improving its drape. The stain-resistant isolation layer of the curtain fabric is made of silver fiber, polyurethane, polyester fiber, and bismuth powder, improving the stain resistance of the curtain fabric. A swarm tracking control algorithm model controls the threading speed, weaving speed, tension, and displacement through a data sensing module, a data processing module, a swarm tracking module, and an integrated control module, achieving precise control of the curtain fabric and ensuring production quality. A single-flow dual-channel heating plate uses a transverse magnetic flux induction heating optimization algorithm to stably generate hot air, achieving a uniform temperature distribution in the material belt drying chamber. The automatic fabric ironing device uses a limit analysis upper limit control circuit to control the surface temperature of the hot roller, reducing costs and production difficulty. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0048] Figure 1 This is a schematic diagram of the structure of a stain-resistant curtain fabric with high drape according to the present invention;

[0049] Figure 2 This is a flowchart of a method for producing a high-drape, stain-resistant curtain fabric according to the present invention;

[0050] Figure 3 This is a flowchart illustrating the process of the transverse magnetic flux induction heating optimization algorithm used in this invention.

[0051] Figure 4This is a schematic diagram of the congestion tracking control algorithm model structure used in this invention;

[0052] Figure 5 This is a flowchart illustrating the workflow of the swarm tracking algorithm used in this invention. Detailed Implementation

[0053] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0054] like Figures 1-5 As shown, Example 1:

[0055] A high-drape, stain-resistant curtain fabric includes:

[0056] Surface layer 1 is used to showcase the luster, feel, and drape of the curtain fabric. The warp of surface layer 1 is made of polyester fiber, and the weft is made of PCDT fiber. The diameter of the polyester fiber is 6.16μm and the fineness is 112dtex. The diameter of the PCDT fiber is 34.11μm and the fineness is 413dtex. The warp density is 59.9 threads / cm and the weft density is 48.1 threads / cm.

[0057] The anti-fouling isolation layer 2 is used to prevent external dirt and light radiation from entering the curtain. The anti-fouling isolation layer 2 is made of antibacterial, anti-radiation and stain-resistant fabric. The antibacterial, anti-radiation and stain-resistant fabric includes the following components by weight: 10 parts by weight of silver fiber, 13 parts by weight of polyurethane, 12 parts by weight of polyester fiber and 17 parts by weight of bismuth powder.

[0058] The intermediate insulation layer 3 is used to improve the thermal insulation performance of the curtains. The intermediate insulation layer 3 comprises the following components by weight: 18 parts by weight of rigid polyurethane foam, 15 parts by weight of glass fiber cotton and 13 parts by weight of ceramic fiber cotton.

[0059] The intermediate light-blocking layer 4 is used to block sunlight and protect the colors of indoor furniture and floors from being affected. The warp of the intermediate light-blocking layer 4 is made of elastic synthetic fiber, and the weft is made of black polyethylene polyester elastic yarn. The diameter of the elastic synthetic fiber is 5.11μm and the fineness is 98dtex. The diameter of the black polyethylene polyester elastic yarn is 33.80μm and the fineness is 391dtex. The warp density is 51.9 threads / cm and the weft density is 52.1 threads / cm.

[0060] Protective layer 5 is used to enhance the durability and stability of the curtain fabric. The protective layer 5 is formed on the surface of the curtain fabric by polyurethane and siloxane, with the polyurethane being 21 parts by weight and the siloxane being 18 parts by weight.

[0061] The protective layer 5 is disposed on the outer surface of the surface layer 1, the inner surface of the surface layer 1 is disposed on the anti-fouling isolation layer 2, the inner side of the anti-fouling isolation layer 2 is disposed on the middle heat insulation layer 3, and the inner side of the middle heat insulation layer 3 is disposed on the middle light-shielding layer 4.

[0062] In a specific embodiment, the high-drape, stain-resistant curtain fabric comprises a surface layer, a stain-resistant isolation layer, a middle insulation layer, a middle light-blocking layer, and a protective layer. The functions of each layer are as follows: Surface Layer: The surface layer is the outermost layer of the curtain fabric, generally made of fiber materials. Its main functions are aesthetics and tactile feel. The surface layer can give the curtains rich colors and patterns, making them more beautiful, fashionable, and personalized. Stain-Resistant Isolation Layer: The stain-resistant isolation layer is the second layer of the curtain fabric, generally using special coating or spraying treatment technology, possessing excellent stain-resistant properties. The stain-resistant isolation layer can effectively isolate external dust and stains, keeping the curtains clean for a long time. Middle Insulation Layer: The middle insulation layer is the third layer of the curtain fabric, generally made of special insulation materials, possessing good insulation properties. The middle insulation layer can effectively reduce indoor heat loss, maintain temperature stability, and reduce indoor energy consumption. Middle Light-Blocking Layer: The middle light-blocking layer is the fourth layer of the curtain fabric, generally made of light-blocking materials, possessing good light-blocking effects. The middle blackout layer effectively blocks direct sunlight from entering the room, regulates indoor light, protects people's eyes and skin, and maintains indoor comfort and privacy. The protective layer, the innermost layer of the curtain fabric, is generally made of POF or other non-combustible materials and has excellent fire-resistant properties. In the event of a fire, the protective layer effectively protects people and property and prevents the spread of fire. In summary, the different layers of high-drape, stain-resistant curtain fabric each play a different role, working together to provide users with a comfortable, safe, and healthy indoor environment.

[0063] The drape and stain resistance test of high-drape stain-resistant curtain fabric requires an experimental procedure, as follows: Drape test experimental procedure: (1) Measure the length and width of the curtain fabric and hang it vertically on the test column. (2) Fix the curtain fabric to the test column with metal wire so that it does not tilt or swing. (3) Measure the drape length of the curtain fabric and record it. (4) Measure the drape angle of the curtain fabric with an angle measuring instrument and record it. (5) Use a timer to measure the drape time of the curtain fabric for 3 seconds and record it. Stain resistance test experimental procedure: (1) Place the test sample on the laboratory platform and perform standard pretreatment. (2) Set the parameters such as contact time, contact pressure and contact liquid for the sample. (3) Drop different types of liquid directly onto the sample, keep the drip volume consistent, and then record the drip time. (4) Measure the remaining stain or stain area with a standard measuring instrument, and observe and record the color, shape, size, stability and other information of the stain with a special stain observation instrument. (5) Perform washing or cleaning treatment, and observe and record the degradation and disappearance of stains again using a stain observation instrument. It is important to note that the experiment must be conducted according to standard operating procedures to ensure the accuracy and reliability of the test results. Furthermore, to avoid experimental errors, it is recommended to repeat the test multiple times and statistically analyze the results. The statistical analysis of the test results is shown in Table 1:

[0064]

[0065] As shown in Table 1, standard testing methods were used in the actual tests. For example, the drape test adopted the ASTM D6413-02 standard method, and the stain resistance test adopted AATCC 138 and AATCC 147 standard methods. The test results met the relevant national standards, ensuring the quality and performance of the curtain fabric.

[0066] Example 2:

[0067] A high-drape, stain-resistant curtain fabric includes:

[0068] Surface layer 1 is used to showcase the luster, feel, and drape of the curtain fabric. The warp of surface layer 1 is made of polyester fiber, and the weft is made of PCDT fiber. The diameter of the polyester fiber is 6.16μm and the fineness is 112dtex. The diameter of the PCDT fiber is 34.11μm and the fineness is 413dtex. The warp density is 59.9 threads / cm and the weft density is 48.1 threads / cm.

[0069] The anti-fouling isolation layer 2 is used to prevent external dirt and light radiation from entering the curtain. The anti-fouling isolation layer 2 is made of antibacterial, anti-radiation and stain-resistant fabric. The antibacterial, anti-radiation and stain-resistant fabric includes the following components by weight: 25 parts by weight of silver fiber, 20 parts by weight of polyurethane, 15 parts by weight of polyester fiber and 20 parts by weight of bismuth powder.

[0070] The intermediate insulation layer 3 is used to improve the thermal insulation performance of the curtain. The intermediate insulation layer 3 comprises the following components by weight: 20 parts by weight of rigid polyurethane foam, 18 parts by weight of glass fiber cotton and 17 parts by weight of ceramic fiber cotton.

[0071] The intermediate light-blocking layer 4 is used to block sunlight and protect the colors of indoor furniture and floors from being affected. The warp of the intermediate light-blocking layer 4 is made of elastic synthetic fiber, and the weft is made of black polyethylene polyester elastic yarn. The diameter of the elastic synthetic fiber is 5.11μm and the fineness is 98dtex. The diameter of the black polyethylene polyester elastic yarn is 33.80μm and the fineness is 391dtex. The warp density is 51.9 threads / cm and the weft density is 52.1 threads / cm.

[0072] Protective layer 5 is used to enhance the durability and stability of the curtain fabric. The protective layer 5 is formed on the surface of the curtain fabric by polyurethane and siloxane, with the polyurethane being 25 parts by weight and the siloxane being 23 parts by weight.

[0073] The protective layer 5 is disposed on the outer surface of the surface layer 1, the inner surface of the surface layer 1 is disposed on the anti-fouling isolation layer 2, the inner side of the anti-fouling isolation layer 2 is disposed on the middle heat insulation layer 3, and the inner side of the middle heat insulation layer 3 is disposed on the middle light-shielding layer 4.

[0074] In a specific embodiment, the antibacterial, radiation-resistant, and stain-resistant fabric comprises the following components by weight: 25 parts by weight of silver fiber, 20 parts by weight of polyurethane, 15 parts by weight of polyester fiber, and 20 parts by weight of bismuth powder. The functions of these components are as follows: Silver fiber: possesses strong antibacterial properties, capable of killing bacteria and fungi attached to the surface, effectively preventing cross-infection and bacterial growth. Polyurethane: possesses good elasticity and abrasion resistance, enhancing the fabric's durability and resistance to wear, making it more durable. Polyester fiber: possesses good weaving properties and fade resistance, making the fabric more aesthetically pleasing, while also improving its strength and durability. Bismuth powder: possesses strong radiation-resistant properties, effectively blocking electromagnetic radiation and other radiation, protecting human health. Therefore, this antibacterial, radiation-resistant, and stain-resistant fabric combines multiple properties, possessing antibacterial, radiation-resistant, stain-resistant, and aesthetically pleasing effects, making it ideal for use in curtains, drapes, and other decorative materials in medical, industrial, and home applications. The thermal insulation and light-blocking tests of curtain fabrics typically include the following steps: Thermal insulation test procedure: (1) Prepare a closed test chamber, lay a thin film at the bottom of the test chamber, and place a certain amount of water on the film. (2) Hang the curtain fabric to be tested inside the test chamber so that it covers the water surface. (3) Measure the temperature inside and outside the test chamber with a thermometer and record it. (4) Measure the water temperature inside the test chamber at certain time intervals and record it. (5) Repeat the above steps several times and calculate the thermal insulation performance of the curtain fabric. Light-blocking test procedure: (1) Prepare a closed laboratory, hang the curtain fabric to be tested and keep the fabric flat. (2) Place a light source outside the laboratory and shine light into the laboratory. (3) Place a light meter inside the laboratory and record the light intensity after the curtain fabric blocks the light. (4) Gradually add adhesive tape, fabric, and sunshade fabric to the laboratory and record the light intensity after each addition. (5) Repeat the above steps several times and calculate the light-blocking performance of the curtain fabric. The above test requires the support of instruments and equipment, and strict control of experimental conditions is necessary to ensure the accuracy and reliability of the test results. The test results are shown in Table 2:

[0075]

[0076] As shown in Table 2, the light-blocking performance of curtain fabrics is generally expressed by light transmittance or light-blocking coefficient. Typically, the light-blocking coefficient ranges from 0 to 1, with a lower value indicating better light-blocking performance. The curtain fabric of this invention achieves excellent light blocking and heat insulation. Its light-blocking performance is attributed to the use of black polyethylene polyester elastic yarn and elastic synthetic fibers. These materials and processes effectively block light transmission, thus achieving the light-blocking effect. The heat insulation performance is due to the intermediate insulation layer, which effectively prevents the exchange of indoor and outdoor temperatures, thereby achieving the heat insulation effect.

[0077] Example 3:

[0078] In the above embodiments, a method for producing a high-drape, stain-resistant curtain fabric, applied to the aforementioned high-drape, stain-resistant curtain fabric, includes the following steps:

[0079] Step 1: Soak the polyester fiber in an alkaline or acidic solution to remove impurities and oil from the surface of the polyester fiber. Use an automatic circulating steam processor to pre-treat the PCDT fiber. The automatic circulating steam processor uses a vacuum microwave processing mechanism to stably generate a temperature of 150°C and a pressure of 0.5MPa to obtain a compact PCDT fiber structure.

[0080] Step 2: A combined spinning machine is used to process polyester fibers as warp and PCDT fibers as weft. Both warp and weft are yarns from an automatic three-dimensional flatbed loom. The automatic three-dimensional flatbed loom uses a power guide mechanism for heddle threading and heddle edge trimming to form a surface layer fabric with structure and texture. The power guide mechanism includes at least a heddle guide frame, a control system, and a heddle threading mechanism. The control system uses a swarm tracking control algorithm model to control the heddle threading speed, weaving speed, tension, and displacement to achieve precise spinning control and production quality assurance.

[0081] In step two, the swarm tracking control algorithm model includes a data sensing module, a data processing module, a swarm tracking module, and an integrated control module. The output of the data sensing module is connected to the input of the data processing module, the output of the data processing module is connected to the input of the swarm tracking module, and the output of the swarm tracking module is connected to the input of the integrated control module.

[0082] Step 3: The raw materials of the anti-fouling isolation layer 2, intermediate insulation layer 3, intermediate light-shielding layer 4 and protective layer 5 are respectively compressed into anti-fouling isolation layer 2, intermediate insulation layer 3, intermediate light-shielding layer 4 and protective layer 5 by self-adhesive compression board. The self-adhesive compression board is then used to bond the anti-fouling isolation layer 2, intermediate insulation layer 3, intermediate light-shielding layer 4 and protective layer 5 to the back or surface of the surface layer fabric in sequence using a network cross-linking and curing thermosensitive adhesive.

[0083] Step 4: The coated surface fabric is dried in a material belt drying chamber to ensure complete curing and fixation of the coating. The material belt drying chamber includes at least a conveyor belt, a heating zone, a cooling zone, and a discharge zone. The heating zone uses a single-flow dual-channel heating plate to generate 200°C high-temperature hot air that flows through the surface fabric, promoting coating cross-linking and achieving drying and curing of the surface fabric. The single-flow dual-channel heating plate uses a transverse magnetic flux induction heating optimization algorithm to stably generate hot air, achieving a uniform temperature distribution in the material belt drying chamber.

[0084] Step 5: After coating and drying, the surface layer fabric is shaped by a setting device. The surface layer fabric is then ironed by an automatic fabric ironing device. The automatic fabric ironing device uses an automated cutting tooth horizontal cutting tool to trim the edges and corners. The automatic fabric ironing device uses a limit analysis upper limit control circuit to control the surface temperature of the hot roller to 160°C. The surface layer fabric is placed on it for heating treatment to obtain the final curtain fabric product.

[0085] In specific embodiments, the self-adhesive compression board is typically made of materials such as polyester or plastic film. The network cross-linking curing thermosensitive adhesive is an adhesive with thermosensitive curing function, capable of pressing and curing with the fabric at high temperatures. Through the above manufacturing process and material selection, the curtain fabric of this invention can simultaneously achieve stain resistance, heat insulation, and light blocking functions, providing consumers with a more modern and practical curtain option. The network cross-linking curing thermosensitive adhesive is a special adhesive whose main function is to fix materials together through a cross-linking reaction at high temperatures. Its main principle is to utilize the reaction between nucleophilic and electrophilic groups in polymer compounds to achieve polymer cross-linking. Typically, the network cross-linking curing thermosensitive adhesive is used in conjunction with a co-crosslinking agent. At high temperatures, the network cross-linking curing thermosensitive adhesive first undergoes a one-step reaction, reacting the co-crosslinking agent with the electrophilic groups on the material surface to form chemical bonds. Subsequently, the high-temperature conditions cause the cross-linking reaction to continue, forming a three-dimensional network structure, thereby achieving material fixation and reinforcement. Therefore, the network cross-linking curing thermosensitive adhesive is mainly used in the processing of materials requiring high-strength bonding, such as curtain fabrics. It possesses special properties such as high viscosity, high strength, high temperature resistance, and oil resistance, ensuring that the material remains fixed and stable at high temperatures, without loosening or falling off. In general, the main function of the network cross-linked curing thermosensitive adhesive is to achieve high-strength bonding of the material, making it more robust and stable during manufacturing. After coating and drying, the surface layer fabric needs to undergo a shaping process to conform to the design requirements. The shaping device typically uses heat and pressure to treat the surface layer fabric, stabilizing and fixing its shape. Furthermore, automated ironing processes are required to better maintain the flatness and aesthetics of the curtain fabric. To achieve more efficient automated trimming and ironing, the automatic fabric ironing device needs to employ state-of-the-art control technology and equipment. The automated cutting tooth horizontal cutter can quickly and accurately complete the trimming process, improving production efficiency and reducing waste. The use of a limit analysis upper limit control circuit to control the surface temperature of the hot roller at 160℃ avoids overheating and insufficient heating, thus ensuring the best processing effect for the surface layer fabric. After the above processing, the final curtain fabric product can achieve good functions such as light blocking, heat preservation, and stain prevention, while maintaining a flat and beautiful appearance, making it suitable for curtain use in various home decoration and commercial venues.

[0086] Example 4:

[0087] In the above embodiments, the vacuum microwave processing mechanism uses a microwave millimeter-wave generator to generate microwave energy. The microwave millimeter-wave generator enters the microwave cavity through a waveguide to heat and dry the PCDT fibers. The microwave millimeter-wave generator obtains microwave energy by modulating the microwave index and DC bias through a frequency doubling factor tunable microwave circuit. The output spectrum of the microwave energy contains ±8th-order optical sideband signals. The microwave millimeter-wave generator obtains ±24th-order optical sidebands through a four-wave mixing semiconductor amplification protocol. Finally, the microwave millimeter-wave generator filters the optical sidebands through a filtering beat engine to obtain a 48th-order millimeter-wave signal. The vacuum microwave processing mechanism uses a vacuum valve to reduce the gas pressure in the microwave cavity to 0.9 kPa, and water molecules evaporate and are discharged at a low temperature of 50℃-80℃.

[0088] In the above embodiments, the data sensing module monitors simulated textile parameters through a sensor array. These simulated signals include at least heddle threading speed, weaving speed, tension, displacement, and ambient temperature. The sensor array includes at least a speed sensor, a tension sensor, a displacement sensor, and a temperature sensor. The data sensing module uses a distributed differential acquisition circuit to acquire and transmit the simulated textile parameters monitored by the sensor array to the data processing module. The distributed differential acquisition circuit acquires the measured simulated textile parameters through a sensor network and converts the acquired simulated textile parameters into digital signals using an analog-to-digital conversion protocol. The distributed differential acquisition circuit transmits the digital signal to the data processing module via a serial transmission network. The data processing module uses a time-frequency statistical analysis model to perform time-domain analysis on the digital signal. The time-frequency statistical analysis model analyzes the variation law of the digital signal with time and frequency through a periodogram analysis mechanism. The time-frequency statistical analysis model analyzes the center position, dispersion and correlation of the digital signal through a correlation coefficient statistical protocol. The correlation coefficient statistical protocol measures the linear correlation between two continuous digital signals based on the Pearson correlation coefficient. The correlation coefficient statistical protocol describes the positive or negative correlation between two discrete digital signals based on the Spearman rank correlation coefficient.

[0089] In a specific embodiment, the vacuum microwave processor is a device that uses a microwave millimeter-wave generator to produce microwave energy. In this device, the microwave millimeter-wave generator enters the microwave cavity through a waveguide to heat and dry PCDT fibers. The output spectrum of the microwave millimeter-wave generator contains ±8-order optical sideband signals, and microwave energy is obtained by modulating the microwave index and DC bias voltage through a frequency-multiplying factor-tunable microwave circuit. In the vacuum microwave processor, the microwave millimeter-wave generator can obtain ±24-order optical sidebands through a four-wave mixing semiconductor amplification protocol. Finally, the microwave millimeter-wave generator filters the optical sidebands through a filtering beat engine to obtain a 48th-harmonic millimeter-wave signal. Thus, the vacuum microwave processor can provide high-frequency (48th harmonic), high-efficiency, high-power, and stable microwave drying energy. In the vacuum microwave processor, a vacuum valve is used to reduce the gas pressure inside the microwave cavity to 0.9 kPa, which allows moisture molecules to evaporate and be expelled more easily at a low temperature of 50℃-80℃. This enables the vacuum microwave processor to achieve rapid drying and processing, avoids the problem of excessive moisture, and improves work efficiency and product quality.

[0090] The data sensing module is a device used to monitor textile parameters. This device uses a sensor array to monitor analog signals of textile parameters, including heddle speed, weaving speed, tension, displacement, and ambient temperature. The sensor array includes various sensor types such as speed sensors, tension sensors, displacement sensors, and temperature sensors. The data sensing module employs a distributed differential acquisition circuit to acquire and transmit the analog signals of the textile parameters monitored by the sensor array. The distributed differential acquisition circuit acquires the measured analog signals of the textile parameters through a sensor network and converts the acquired analog signals into digital signals through an analog-to-digital conversion protocol. Finally, the distributed differential acquisition circuit transmits the digital signals to the data processing module through a serial transmission network. In the data processing module, a time-frequency statistical analysis model is used to perform time-domain analysis on the digital signals. The time-frequency analysis model analyzes the variation of the digital signals with time and frequency through a periodogram analysis mechanism. Based on this, the time-frequency statistical analysis model analyzes the center position, dispersion, and correlation of the digital signals through a correlation coefficient statistical protocol. The correlation coefficient statistical protocol can be used to measure the linear correlation between two continuous digital signals, using the Pearson correlation coefficient for measurement. Meanwhile, the Spearman rank correlation coefficient can describe the positive or negative correlation between two discrete digital signals. These protocols can effectively help the data processing module analyze and process the digital signals monitored by the sensor array, thereby achieving precise monitoring and control of textile parameters.

[0091] Example 5:

[0092] In the above embodiments, the swarm tracking module uses a swarm tracking algorithm to accurately determine and analyze the status of the automatic three-dimensional flat loom. The working method of the swarm tracking algorithm is as follows:

[0093] S1. Tracking particles are randomly generated in the search space as a tracking population. The swarm tracking algorithm uses a fitness evaluation mechanism to input the position of each tracking particle into the evaluation function to obtain the fitness of the tracking particles. The evaluation function performs logical operations to analyze and judge the fitness based on the position of the tracking particles in the search space and the objective function.

[0094] S2. Then, the fitness function of the tracking particle is optimized by iteratively searching the multidimensional space to gradually approach the optimal solution. The iterative search of the multidimensional space sets the maximum iteration threshold through adaptive threshold segmentation. The adaptive threshold segmentation sets the maximum iteration threshold by analyzing the tracking particle characteristics through the mean drift protocol.

[0095] S3. The swarm tracking algorithm stops iterating based on the maximum iteration threshold and outputs the globally optimal automatic 3D flat loom state judgment result. The swarm tracking algorithm re-filters and selects the exploration region through the maximum inter-class variance tracking engine. The maximum inter-class variance tracking engine outputs the globally optimal segmentation based on the exploration region to obtain the globally optimal automatic 3D flat loom state judgment result.

[0096] In the above embodiments, the integrated control module outputs control signals using a sliding mode control mechanism. This mechanism tracks and controls the automatic three-dimensional flat loom based on the loom's state judgment. The sliding mode control mechanism designs the control law for the automatic three-dimensional flat loom using a sliding surface mechanism. The sliding surface mechanism performs logical analysis based on the control law to obtain the control quantity applied to the automatic three-dimensional flat loom. The sliding mode control mechanism performs control threshold analysis on the control quantity using network parameter gradient integral analysis to obtain the control signal. The integrated control module controls the heddle threading speed, weaving speed, tension, and displacement of the automatic three-dimensional flat loom based on the control signal. The integrated control module achieves integrated control of the automatic three-dimensional flat loom through a four-channel width-term control chip. The four-channel width-term control chip controls the needle bar movement speed and guide needle frequency of the automatic three-dimensional flat loom through phase compensation, thereby controlling the heddle threading speed and weaving speed. The four-channel width-term control chip controls the tension regulator of the fabric take-up mechanism through a channel gain compensation control mechanism to adjust the control tension. The channel gain compensation control mechanism controls the weaving mechanism inside the automatic three-dimensional flat loom through an attenuation-addition phase shift circuit, thereby adjusting the fabric displacement.

[0097] In a specific embodiment, the swarm tracking algorithm is an optimization algorithm used to find the optimal solution in a multidimensional space, typically for solving complex optimization problems. The algorithm's working method includes the following steps: S1: Generating a tracking population: The swarm tracking algorithm randomly generates a batch of tracking particles as the initial tracking population. The position of each tracking particle is evaluated for fitness by combining a fitness evaluation mechanism with the objective function. The evaluation function performs logical operations to analyze and determine the fitness based on the tracking particle's position in the search space and the objective function. S2: Multidimensional space iterative search: The swarm tracking algorithm uses a multidimensional space iterative search method to optimize the fitness function of the tracking particles, gradually approaching the optimal solution. During this iterative search process, an adaptive threshold segmentation is used to set the maximum iteration threshold. The adaptive threshold segmentation analyzes the tracking particle characteristics using a mean-shift protocol to calculate the maximum iteration threshold. S3: Outputting the globally optimal result: Finally, the swarm tracking algorithm stops iterating based on the maximum iteration threshold and outputs the globally optimal automatic 3D flat loom state judgment result. In this process, a maximum inter-class variance tracking engine is also used to re-filter and select the exploration area to better explore and discover the globally optimal solution. The maximum inter-class variance tracking engine analyzes the explored region to output the globally optimal segmentation and obtain the best state judgment result of the automatic 3D flat loom.

[0098] The integrated control module employs a sliding mode control mechanism to track and control the automatic 3D flat loom. The key to sliding mode control lies in designing a suitable sliding surface. Through the sliding surface mechanism, a control law is used to logically analyze and obtain the control quantity applied to the automatic 3D flat loom. This control quantity is then subjected to control threshold analysis to obtain the control signal. The integrated control module controls the heddle threading speed, weaving speed, tension, and displacement of the automatic 3D flat loom based on the control signal. To achieve integrated control, the integrated control module uses a four-channel amplitude control chip to control the automatic 3D flat loom. This chip can control the needle bar movement speed and guide needle frequency of the automatic 3D flat loom through phase compensation, thereby controlling the heddle threading speed and weaving speed. Simultaneously, a channel gain compensation control mechanism can control the tension regulator of the fabric take-up mechanism to adjust the control tension. This control mechanism can also control the weaving mechanism inside the automatic 3D flat loom through attenuation and additional phase shift circuits to adjust the fabric displacement. Through these control methods, the integrated control module can achieve comprehensive control of the automatic 3D flat loom, ensuring it operates according to a preset mode and speed.

[0099] Example 6:

[0100] In the above embodiments, the working method of the transverse magnetic flux induction heating optimization algorithm is as follows:

[0101] S1. A permanent magnet synchronous magnetic field model is used to determine the magnetic field strength and direction, generating a transverse magnetic flux induction effect. The magnetic field direction is perpendicular to the magnetic fixing plate. The permanent magnet synchronous magnetic field model determines the magnetic field strength based on the permanent magnet vector and the electromagnetic vector. The permanent magnet synchronous magnetic field model measures heating parameters through thermocouples, humidity sensors, and differential pressure flow meters. The heating parameters include at least hot air temperature, humidity, and flow rate. The permanent magnet synchronous magnetic field model transmits the heating parameters to the transverse magnetic flux control model in real time via a wireless cognitive network. The formula for calculating the magnetic field strength is:

[0102] (1)

[0103] In formula (1), The magnetic field strength, The permanent magnet vector of the single-flow dual-channel heating plate. The electromagnetic vector of the single-flow dual-channel heating plate. The permeability of the single-flow dual-channel heating plate. The magnetic pressure of the permanent magnet in the single-flow dual-channel heating plate. This represents the number of turns of the magnetizing coil;

[0104] S2. The transverse magnetic flux control model obtains the current density distribution equation based on the heating parameters and the structure of the material belt drying chamber. The model then analyzes the magnetic flux intensity distribution using an inversion solution function to determine the current density distribution equation. The inversion solution function obtains the optimal solution for the current density distribution by solving the inversion equation. The model designs an optimization algorithm based on the magnetic field intensity distribution and the current density distribution function. This optimization algorithm optimizes the hot air temperature control, humidity control, and flow control through a weighted temperature control mechanism. The formula for calculating the optimal solution for the current density distribution is:

[0105] (2)

[0106] In formula (2), This is the optimal solution for the current density distribution. This refers to the free current variable in a single-current dual-channel heating plate power supply. The area unit is the single-current dual-channel heating plate power supply. To invert the solution function, the regularization variable is... To improve the convergence accuracy of the inversion solution function;

[0107] The formula for calculating the hot air temperature control quantity is as follows:

[0108] (3)

[0109] In formula (3), This is the amount for controlling the hot air temperature. This represents the initial temperature value of the single-flow dual-channel heating plate. This refers to the target temperature value for a single-flow dual-channel heating plate. The temperature weighting value is the weighted value of the weighted temperature control mechanism. Charge density is a function of current density distribution;

[0110] The formula for calculating the humidity control amount is:

[0111] (4)

[0112] In formula (4), For humidity control parameters, This represents the initial humidity value for the single-flow dual-channel heating plate. The target humidity value for a single-flow dual-channel heating plate. Let be the time step of the current density distribution function. The flux incident angle is the angle of incidence for the transverse flux control model.

[0113] The formula for calculating the flow control quantity is:

[0114] (5)

[0115] In formula (5), For flow control quantity, This is the initial flow rate of the single-flow dual-channel heating plate. The target flow rate for the single-flow dual-channel heating plate. To determine the number of iterations for optimizing the weighted temperature control mechanism. The flux scanning speed is the value of the transverse flux control model.

[0116] S3. The transverse magnetic flux induction heating optimization algorithm adjusts the current density distribution and magnetic field strength distribution of the single-flow dual-channel heating plate according to the hot air temperature control, humidity control, and flow control. The transverse magnetic flux induction heating optimization algorithm uses power coordination control to analyze the current density and magnetic field strength gain matrix and parameter matrix to realize the automated control and optimization of the drying process.

[0117] In the above embodiments, the limit analysis upper limit control circuit controls the power supply voltage and current of the hot roller through an adjustable power supply model to achieve surface temperature control of the hot roller. The adjustable power supply model controls the current and voltage of the hot roller load through a neural network composite control power supply. The neural network composite control power supply outputs a control signal through a neural network learning algorithm and a feedback control mechanism to achieve control of the current and voltage of the hot roller load. The neural network learning algorithm performs fuzzification processing on the control signal through a neural network, converting the control signal into a fuzzy output. The feedback control mechanism monitors and adjusts the fuzzy output through a PID controller. The PID controller optimizes the current and voltage output of the hot roller load through a distributed control strategy.

[0118] In a specific embodiment, an application platform for the lateral magnetic flux induction heating optimization algorithm can be constructed. During the operation of this platform, hardware and software platforms can be built, for example, by constructing the following components for processing. The following is a more detailed description of the implementation method:

[0119] Transverse flux induction heating equipment: This equipment is the core component for achieving transverse flux induction heating. It can heat metal materials through high-frequency induced electromagnetic fields. This equipment must have stable output power and frequency, and be able to ensure heating uniformity and repeatability.

[0120] Temperature sensor: A temperature sensor is used to monitor the temperature changes of a heated object in real time. In an application platform, multiple temperature sensors can be used to monitor the temperature of various parts of the heated object and feed it back to the controller.

[0121] Controller: The controller is the core control component of the application platform. It can adjust the heating power and frequency in real time based on the collected temperature signal, thereby ensuring that the temperature of the heated object remains within the set range. The controller needs to have high-speed computing and response capabilities, and be able to achieve high-precision control.

[0122] Data acquisition equipment: Data acquisition equipment is mainly used to collect real-time temperature and control signals and transmit them to a host computer for processing and analysis. In the application platform, high-performance data acquisition cards can be used to achieve data acquisition and processing.

[0123] Host computer software: This software is used to implement the transverse magnetic flux induction heating algorithm and provides a user interface, allowing users to set parameters and monitor the heating process in real time. This software needs to be efficient, stable, and user-friendly. By building the above hardware and software platform, optimized control of transverse magnetic flux induction heating can be achieved, improving heating efficiency and heating quality.

[0124] The optimization algorithm for transverse flux induction heating uses a transverse flux control model to optimize the hot air temperature control quantity. The specific implementation is as follows: Inversion of the current density distribution function: The core of transverse flux induction heating lies in controlling the current density distribution to heat the object. Therefore, an inversion algorithm needs to be designed to derive the current density distribution by measuring the temperature distribution on the surface of the heated object, thereby controlling the heating uniformity. This inversion algorithm can be obtained through numerical simulation or experimental measurement. Optimization algorithm design: Based on the magnetic field strength distribution and current density distribution function, an optimization algorithm can be designed to solve for a suitable control quantity through numerical calculation, ensuring that the heated object remains within the set temperature range. This optimization algorithm is mainly implemented through a weighted control mechanism, dynamically adjusting the control quantity according to the temperature distribution to optimize the hot air temperature control. Weighted temperature control mechanism: The weighted temperature control mechanism is the core part of the optimization algorithm. It obtains a weighted average temperature by weighting the temperature distribution, thereby dynamically adjusting the control quantity. The weighted temperature control mechanism can be implemented using proportional-random control algorithms or genetic algorithms, and the specific implementation method can be adjusted according to the actual situation. Through the above implementation methods, the horizontal magnetic flux induction heating optimization algorithm can optimize the hot air temperature control, thereby improving heating efficiency and heating quality. The statistical results of the hot air temperature control calculation are shown in Table 3.

[0125]

[0126] As shown in Table 3, four test groups were set up, and two methods were used to calculate the hot air temperature control quantity. Method 1 is the cross-correlation method, which performs cross-correlation calculations on the clock signal and the ideal clock signal to find the maximum correlation peak between them, and then determines the hot air temperature control quantity. Method 2 uses the transverse magnetic flux induction heating optimization algorithm to optimize the hot air temperature control quantity using the transverse magnetic flux control model. The error of Method 1 is greater than that of Method 2. It can be seen that the transverse magnetic flux induction heating optimization algorithm of the present invention has outstanding technical effect in optimizing the hot air temperature control quantity using the transverse magnetic flux control model.

[0127] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are merely illustrative. Various omissions, substitutions, and changes can be made to the details of the methods and systems described above without departing from the principles and essence of the present invention. For example, combining the above method steps to perform substantially the same function and achieve substantially the same result using substantially the same method falls within the scope of the present invention. Therefore, the scope of the present invention is defined only by the appended claims.

Claims

1. A high-drape, stain-resistant curtain fabric, characterized in that: The curtain fabric comprises: The surface layer (1) has polyester fiber as the warp and PCDT fiber as the weft. The polyester fiber has a diameter of 6.16 μm and a fineness of 112 dtex. The PCDT fiber has a diameter of 34.11 μm and a fineness of 413 dtex. The warp density is 59.9 threads / cm and the weft density is 48.1 threads / cm. The anti-fouling isolation layer (2) is used to prevent external dirt and light radiation from entering the curtain. The anti-fouling isolation layer (2) is made of antibacterial, anti-radiation and stain-resistant fabric. The antibacterial, anti-radiation and stain-resistant fabric includes the following components by weight: 10-25 parts by weight of silver fiber, 13-20 parts by weight of polyurethane, 12-15 parts by weight of polyester fiber and 17-20 parts by weight of bismuth powder. The intermediate insulation layer (3) is used to improve the thermal insulation performance of the curtain. The intermediate insulation layer (3) includes the following components by weight: 18-20 parts by weight of rigid polyurethane foam, 15-18 parts by weight of glass fiber cotton and 13-17 parts by weight of ceramic fiber cotton. The intermediate light-blocking layer (4) is used to block sunlight and protect the colors of indoor furniture and floor from being affected. The warp of the intermediate light-blocking layer (4) is made of elastic synthetic fiber and the weft is made of black polyethylene polyester elastic yarn. The diameter of the elastic synthetic fiber is 5.11 μm and the fineness is 98 dtex. The diameter of the black polyethylene polyester elastic yarn is 33.80 μm and the fineness is 391 dtex. The warp density is 51.9 threads / cm and the weft density is 52.1 threads / cm. The protective layer (5) is used to enhance the durability and stability of the curtain fabric. The protective layer (5) is formed on the surface of the curtain fabric by polyurethane and siloxane to form a highly efficient waterproof membrane. The polyurethane is 21-25 parts by weight and the siloxane is 18-23 parts by weight. The protective layer (5) is disposed on the outer surface of the surface layer (1), the inner surface of the surface layer (1) is provided with a dirt-proof isolation layer (2), the inner side of the dirt-proof isolation layer (2) is provided with an intermediate heat insulation layer (3), and the inner side of the intermediate heat insulation layer (3) is provided with an intermediate light-shielding layer (4).

2. A method for producing a high-drape, stain-resistant curtain fabric, characterized in that: The method for manufacturing a high-drape, stain-resistant curtain fabric as described in claim 1 includes the following steps: Step 1: Soak the polyester fiber in an alkaline or acidic solution to remove impurities and oil from the surface of the polyester fiber. Use an automatic circulating steam processor to pre-treat the PCDT fiber. The automatic circulating steam processor uses a vacuum microwave processing mechanism to stably generate a temperature of 150°C and a pressure of 0.5MPa to obtain a compact PCDT fiber structure. Step 2: A combined spinning machine is used to process polyester fiber as warp and PCDT fiber as weft. Both warp and weft are yarns from an automatic three-dimensional flatbed loom. The automatic three-dimensional flatbed loom uses a power guide mechanism to thread the heddles and trim the heddles, forming a surface layer fabric with structure and texture. The power guide mechanism includes at least a heddle guide frame, a control system, and a threading mechanism. The control system uses a swarm tracking control algorithm model to control the threading speed, weaving speed, tension, and displacement, achieving precise spinning control and ensuring production quality. In step two, the swarm tracking control algorithm model includes a data sensing module, a data processing module, a swarm tracking module, and an integrated control module. The output of the data sensing module is connected to the input of the data processing module, the output of the data processing module is connected to the input of the swarm tracking module, and the output of the swarm tracking module is connected to the input of the integrated control module. Step 3: The raw materials of the anti-fouling isolation layer (2), intermediate insulation layer (3), intermediate light-shielding layer (4) and protective layer (5) are respectively compressed into the anti-fouling isolation layer (2), intermediate insulation layer (3), intermediate light-shielding layer (4) and protective layer (5) by a self-adhesive compression board. The self-adhesive compression board is used to apply the anti-fouling isolation layer (2), intermediate insulation layer (3), intermediate light-shielding layer (4) and protective layer (5) to the back or surface of the surface layer fabric in sequence by a network cross-linking and curing thermosensitive adhesive. Step 4: The coated surface fabric is dried in a material belt drying chamber to ensure complete curing and fixation of the coating. The material belt drying chamber includes at least a conveyor belt, a heating zone, a cooling zone, and a discharge zone. The heating zone uses a single-flow dual-channel heating plate to generate 200°C high-temperature hot air that flows through the surface fabric, promoting coating cross-linking and achieving drying and curing of the surface fabric. The single-flow dual-channel heating plate uses a transverse magnetic flux induction heating optimization algorithm to stably generate hot air, achieving a uniform temperature distribution in the material belt drying chamber. + Step 5: After coating and drying, the surface layer fabric is shaped by a setting device. The surface layer fabric is then ironed by an automatic fabric ironing device. The automatic fabric ironing device uses an automated cutting tooth horizontal cutting tool to trim the edges and corners. The automatic fabric ironing device uses a limit analysis upper limit control circuit to control the surface temperature of the hot roller to 160°C. The surface layer fabric is placed on it for heating treatment to obtain the final curtain fabric product.

3. The method for producing a high-drape, stain-resistant curtain fabric according to claim 2, characterized in that: The vacuum microwave processing mechanism uses a microwave millimeter-wave generator to generate microwave energy. The microwave millimeter-wave generator enters the microwave cavity through a waveguide to heat and dry the PCDT fibers. The microwave millimeter-wave generator obtains microwave energy by modulating the microwave index and DC bias through a frequency doubling factor tunable microwave circuit. The output spectrum of the microwave energy contains ±8th order optical sideband signals. The microwave millimeter-wave generator obtains ±24th order optical sidebands through a four-wave mixing semiconductor amplification protocol. Finally, the microwave millimeter-wave generator filters the optical sidebands through a filtering beat engine to obtain a 48th order millimeter-wave signal. The vacuum microwave processing mechanism uses a vacuum valve to reduce the gas pressure in the microwave cavity to 0.9 kPa, and water molecules evaporate and are discharged at a low temperature of 50℃-80℃.

4. The method for producing a high-drape, stain-resistant curtain fabric according to claim 2, characterized in that: The data sensing module monitors simulated textile parameters via a sensor array. These simulated signals include at least heddle threading speed, weaving speed, tension, displacement, and ambient temperature. The sensor array includes at least a speed sensor, a tension sensor, a displacement sensor, and a temperature sensor. The data sensing module uses a distributed differential acquisition circuit to acquire and transmit the simulated textile parameters monitored by the sensor array to the data processing module. The distributed differential acquisition circuit acquires the measured simulated textile parameters via a sensor network and converts the acquired simulated textile parameters into digital signals using an analog-to-digital conversion protocol. The distributed differential acquisition circuit transmits the digital signal to the data processing module via a serial transmission network. The data processing module uses a time-frequency statistical analysis model to perform time-domain analysis on the digital signal. The time-frequency statistical analysis model analyzes the variation law of the digital signal with time and frequency through a periodogram analysis mechanism. The time-frequency statistical analysis model analyzes the center position, dispersion and correlation of the digital signal through a correlation coefficient statistical protocol. The correlation coefficient statistical protocol measures the linear correlation between two continuous digital signals based on the Pearson correlation coefficient. The correlation coefficient statistical protocol describes the positive or negative correlation between two discrete digital signals based on the Spearman rank correlation coefficient.

5. The method for producing a high-drape, stain-resistant curtain fabric according to claim 2, characterized in that: The swarm tracking module uses a swarm tracking algorithm to accurately determine and analyze the status of the automatic three-dimensional flat loom. The working method of the swarm tracking algorithm is as follows: S1. Tracking particles are randomly generated in the search space as a tracking population. The swarm tracking algorithm uses a fitness evaluation mechanism to input the position of each tracking particle into the evaluation function to obtain the fitness of the tracking particles. The evaluation function performs logical operations to analyze and judge the fitness based on the position of the tracking particles in the search space and the objective function. S2. Then, the fitness function of the tracking particle is optimized by iteratively searching the multidimensional space to gradually approach the optimal solution. The iterative search of the multidimensional space sets the maximum iteration threshold through adaptive threshold segmentation. The adaptive threshold segmentation sets the maximum iteration threshold by analyzing the tracking particle characteristics through the mean drift protocol. S3. The swarm tracking algorithm stops iterating based on the maximum iteration threshold and outputs the globally optimal automatic 3D flat loom state judgment result. The swarm tracking algorithm re-filters and selects the exploration region through the maximum inter-class variance tracking engine. The maximum inter-class variance tracking engine outputs the globally optimal segmentation based on the exploration region to obtain the globally optimal automatic 3D flat loom state judgment result.

6. The method for producing a high-drape, stain-resistant curtain fabric according to claim 2, characterized in that: The integrated control module outputs control signals using a sliding mode control mechanism. This mechanism tracks and controls the automatic three-dimensional flat loom based on the loom's state. The sliding mode control mechanism designs the control law for the automatic three-dimensional flat loom using a sliding surface mechanism. The sliding surface mechanism performs logical analysis based on the control law to obtain the control quantity applied to the automatic three-dimensional flat loom. The sliding mode control mechanism performs control threshold analysis on the control quantity using network parameter gradient integral analysis to obtain the control signal. The integrated control module controls the heddle threading speed, weaving speed, tension, and displacement of the automatic three-dimensional flat loom based on the control signal. The integrated control module achieves integrated control of the automatic three-dimensional flat loom through a four-channel width-term control chip. This four-channel width-term control chip controls the needle bar movement speed and guide needle frequency of the automatic three-dimensional flat loom through phase compensation, thereby controlling the heddle threading speed and weaving speed. The four-channel width-term control chip controls the tension regulator of the fabric take-up mechanism through a channel gain compensation control mechanism to adjust the control tension. This channel gain compensation control mechanism controls the weaving mechanism inside the automatic three-dimensional flat loom through an attenuation-addition phase shift circuit, adjusting the fabric displacement.

7. The method for producing a high-drape, stain-resistant curtain fabric according to claim 2, characterized in that: The working method of the lateral magnetic flux induction heating optimization algorithm is as follows: S1. A permanent magnet synchronous magnetic field model is used to determine the magnetic field strength and direction, generating a transverse magnetic flux induction effect. The magnetic field direction is perpendicular to the magnetic fixing plate. The permanent magnet synchronous magnetic field model determines the magnetic field strength based on the permanent magnet vector and the electromagnetic vector. The permanent magnet synchronous magnetic field model measures heating parameters through thermocouples, humidity sensors, and differential pressure flow meters. The heating parameters include at least hot air temperature, humidity, and flow rate. The permanent magnet synchronous magnetic field model transmits the heating parameters to the transverse magnetic flux control model in real time via a wireless cognitive network. The formula for calculating the magnetic field strength is: (1) In formula (1), The magnetic field strength, The permanent magnet vector of the single-flow dual-channel heating plate. The electromagnetic vector of the single-flow dual-channel heating plate. The permeability of the single-flow dual-channel heating plate. The magnetic pressure of the permanent magnet in the single-flow dual-channel heating plate. This represents the number of turns of the magnetizing coil; S2. The transverse magnetic flux control model obtains the current density distribution equation based on the heating parameters and the structure of the material belt drying chamber. The model then analyzes the magnetic flux intensity distribution using an inversion solution function to determine the current density distribution equation. The inversion solution function obtains the optimal solution for the current density distribution by solving the inversion equation. The model designs an optimization algorithm based on the magnetic field intensity distribution and the current density distribution function. This optimization algorithm optimizes the hot air temperature control, humidity control, and flow control through a weighted temperature control mechanism. The formula for calculating the optimal solution for the current density distribution is: （2） In formula (2), This is the optimal solution for the current density distribution. This refers to the free current variable in a single-current dual-channel heating plate power supply. The area unit is the single-current dual-channel heating plate power supply. To invert the solution function, the regularization variable is... To improve the convergence accuracy of the inversion solution function; The formula for calculating the hot air temperature control quantity is as follows: （3） In formula (3), This is the amount for controlling the hot air temperature. This represents the initial temperature value of the single-flow dual-channel heating plate. This refers to the target temperature value for a single-flow dual-channel heating plate. The temperature weighting value is the weighted value of the weighted temperature control mechanism. Charge density is a function of current density distribution; The formula for calculating the humidity control amount is: （4） In formula (4), For humidity control parameters, This represents the initial humidity value for the single-flow dual-channel heating plate. The target humidity value for a single-flow dual-channel heating plate. Let be the time step of the current density distribution function. The flux incident angle is the angle of incidence for the transverse flux control model. The formula for calculating the flow control quantity is: （5） In formula (5), For flow control quantity, This is the initial flow rate of the single-flow dual-channel heating plate. The target flow rate for the single-flow dual-channel heating plate. To determine the number of iterations for optimizing the weighted temperature control mechanism, The flux scanning speed is the value of the transverse flux control model. S3. The transverse magnetic flux induction heating optimization algorithm adjusts the current density distribution and magnetic field strength distribution of the single-flow dual-channel heating plate according to the hot air temperature control, humidity control, and flow control. The transverse magnetic flux induction heating optimization algorithm uses power coordination control to analyze the current density and magnetic field strength gain matrix and parameter matrix to realize the automated control and optimization of the drying process.

8. The method for producing a high-drape, stain-resistant curtain fabric according to claim 2, characterized in that: The limit analysis upper limit control circuit controls the power supply voltage and current of the hot roller through an adjustable power supply model to achieve surface temperature control of the hot roller. The adjustable power supply model controls the current and voltage of the hot roller load through a neural network composite control power supply. The output of the neural network composite control power supply is converted into a control signal through a neural network learning algorithm and a feedback control mechanism to control the current and voltage of the hot roller load. The neural network learning algorithm performs fuzzification processing on the control signal through a neural network, converting the control signal into a fuzzy output. The feedback control mechanism monitors and adjusts the fuzzy output through a PID controller. The PID controller optimizes the current and voltage output of the hot roller load through a distributed control strategy.

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