A method and system for optimizing antistatic properties of textiles

Abstract

The invention provides a method and system for optimizing the antistatic performance of textiles, and relates to the technical field of data processing. The method comprises: constructing a textile electrostatic test model; setting fabric parameters of a textile sample placed in the textile electrostatic test model; performing grid division; setting boundary conditions; calculating the maximum electric field strength, resistivity and electrostatic decay time of the textile sample under various fabric parameters by solving the description equation of the textile electrostatic test model; comprehensively evaluating the antistatic performance of the textile sample under various fabric parameters according to the maximum electric field strength, resistivity and electrostatic decay time; taking the optimal antistatic performance as the goal, determining a suboptimal solution of a combination of fabric parameters with good antistatic performance effect by improving a fireworks algorithm; using the suboptimal solution of a combination of fabric parameters with good antistatic performance effect to actually manufacture textiles, and determining a combination of fabric parameters with the best antistatic performance from the suboptimal solution by actual testing.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a method and system for optimizing the antistatic performance of textiles. Background Art

[0002] Static electricity accumulation may lead to dangerous electrostatic discharge, especially in flammable and explosive environments such as petrochemical industry and electronic manufacturing. Antistatic textiles can reduce the risk of static electricity accumulation and discharge, ensuring the safety of personnel and equipment. At the same time, textiles are prone to static electricity due to friction, resulting in discomfort such as clothing adsorption, clothing sticking, and hair electrification. Antistatic textiles can effectively prevent these problems and improve wearing comfort.

[0003] At present, the anti-static effect of textiles is mainly achieved by anti-static fibers, anti-static coatings or finishing agents, and optimizing the structural design of textiles. The specific anti-static structure and process of textiles mainly rely on experienced clothing designers to complete, and require a lot of practical manufacturing and testing, and determine the final anti-static structure and process of textiles through performance comparison. This requires a lot of time and material costs, a long design cycle, and increased manufacturing costs. Experience-driven designs tend to rely on past experience, and it is difficult to ultimately find the best anti-static structure and process for textiles. More importantly, when market demand changes or customers put forward new demands, repeated testing is required, making it difficult to respond quickly to market demand. Summary of the invention

[0004] In order to solve the problem that the current technology mainly relies on experienced clothing designers to complete, a large amount of practical manufacturing and testing is required, and the final antistatic structure and process of textiles are determined through performance comparison, which requires a lot of time and material costs, a long design cycle, and increased manufacturing costs. Experience-driven design tends to rely on past experience, and it is difficult to ultimately find the best antistatic structure and process of textiles. More importantly, when market demand changes or customers put forward new demands, repeated testing is required, and it is difficult to quickly respond to market demand. The technical problem, the present invention provides a method and system for optimizing the antistatic performance of textiles.

[0005] The technical solution provided by the embodiment of the present invention is as follows:

[0006] First aspect:

[0007] An embodiment of the present invention provides a method for optimizing the antistatic properties of a textile, comprising:

[0008] S1: Construct a textile electrostatic test model;

[0009] S2: setting fabric parameters of the textile sample placed in the textile electrostatic test model;

[0010] S3: Meshing the textile electrostatic test model;

[0011] S4: setting boundary conditions of the textile electrostatic test model;

[0012] S5: calculating the maximum electric field strength, resistivity and electrostatic decay time of the textile sample under various fabric parameters by solving the descriptive equation of the textile electrostatic test model;

[0013] S6: comprehensively evaluating the antistatic performance of the textile sample under various fabric parameters according to the maximum electric field strength, the resistivity and the static decay time;

[0014] S7: Taking the optimal antistatic performance as the goal, determining a suboptimal solution of a combination of fabric parameters with good antistatic performance by improving the fireworks algorithm;

[0015] S8: using the suboptimal solutions of the fabric parameter combinations with good antistatic performance effects to actually manufacture textiles, and determining the fabric parameter combination with the best antistatic performance from the suboptimal solutions through actual testing.

[0016] Second aspect:

[0017] An embodiment of the present invention provides a system for optimizing antistatic properties of textiles, comprising:

[0018] processor;

[0019] A memory having computer-readable instructions stored therein, wherein when the computer-readable instructions are executed by the processor, the method for optimizing the antistatic properties of textiles as described in the first aspect is implemented.

[0020] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0021] In the present invention, with the optimal anti-static performance as the goal, the suboptimal solution of the fabric parameter combination with good anti-static performance is determined by improving the Fireworks algorithm, and the suboptimal solution of the fabric parameter combination with good anti-static performance is adopted, and then the textile is actually manufactured, which greatly reduces the repeated test steps. The fabric parameter combination with the best anti-static performance is determined from the suboptimal solution through actual testing, which improves the design efficiency, reduces the waste of test resources, shortens the design cycle, and quickly responds to market demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of a process for optimizing the antistatic properties of a textile provided by an embodiment of the present invention;

[0024] Figure 2 A schematic structural diagram of a method for optimizing antistatic properties of textiles provided by an embodiment of the present invention;

[0025] Figure 3 A schematic structural diagram of a system for optimizing antistatic properties of textiles provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0027] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0028] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0029] Reference Manual Attached Figure 1 , showing a schematic flow chart of a method for optimizing antistatic properties of textiles provided in an embodiment of the present invention.

[0030] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a method for optimizing antistatic properties of textiles provided by an embodiment of the present invention.

[0031] The embodiment of the present invention provides a method for optimizing the antistatic performance of textiles, which can be implemented by a device for optimizing the antistatic performance of textiles, and the device for optimizing the antistatic performance of textiles can be a terminal or a server. The processing flow of the method for optimizing the antistatic performance of textiles can include the following steps:

[0032] S1: Construct a textile electrostatic testing model.

[0033] Optionally, COMSOL is used to construct a textile electrostatic test model.

[0034] Optionally, the textile electrostatic test model specifically includes: an electric field generating electrode, a measuring electrode, a charge amplifier, a guard ring and a clamping ring.

[0035] Optionally, the geometrical models of the electrodes and test specimens are constructed to scale with standard test equipment.

[0036] Further, the electric field generating electrode is used to generate the test electric field. The measuring electrode is used to sense the change of the test electric field strength. The charge amplifier is used to obtain the change of the electric field strength measured by the measuring electrode. The guard ring is used to avoid the interference of the edge effect on the measurement result. The clamping ring is used to place the textile sample.

[0037] S2: Setting fabric parameters of the textile sample placed in the textile electrostatic test model.

[0038] Optionally, fabric parameters include, but are not limited to: fiber type, fiber content, yarn fineness, yarn twist, yarn structure, yarn moisture content, fabric type, warp and weft density, fabric thickness and porosity.

[0039] It should be noted that the above fabric parameters may affect the antistatic properties of the textile.

[0040] S3: Mesh the textile electrostatic test model.

[0041] Optionally, a triangular, square or hexagonal grid may be used in the grid division process.

[0042] In a possible implementation, S3 specifically includes sub-steps S301 to S303:

[0043] S301: Using triangular meshes, perform preliminary mesh division on the textile electrostatic test model.

[0044] It should be noted that triangular meshes are very flexible and suitable for handling complex geometric shapes. In the textile electrostatic test model, the structure of the conductive fibers and substrates may have irregular or complex shapes, and triangular meshes can adapt well to these complex areas, thereby ensuring the accuracy of mesh division and the stability of calculation.

[0045] S302: After the initial grid division, the electric field distribution of the textile electrostatic test model is calculated.

[0046] S303: Adaptively adjust the grid size at each position according to the electric field intensity gradient:

[0047]

[0048] in, h represents the grid size, x Indicates location, h 0 represents the initial grid size, α represents the refinement coefficient, represents the gradient operation, E Indicates the electric field strength.

[0049] Among them, those skilled in the art can set the refinement coefficient according to actual conditions. α The present invention does not limit the size.

[0050] In the present invention, adaptive meshing can automatically adjust the mesh density as needed, ensuring that finer meshes are used in areas where the electric field changes dramatically (such as the junction of the conductive fiber and the substrate), so as to more accurately capture the electric field distribution and charge transfer process in these areas. In areas where the electric field changes less, larger meshes are used to reduce the number of meshes, thereby reducing the overall amount of calculation. This method can significantly reduce unnecessary computing resource consumption and improve computing efficiency while ensuring accuracy.

[0051] S4: Set the boundary conditions of the textile electrostatic test model.

[0052] In a possible implementation, S4 specifically includes: applying a constant voltage at the electric field generating electrode as a fixed potential boundary condition (also called a Dirichlet boundary condition).

[0053] Optionally, those skilled in the art may set the constant voltage according to actual conditions, which is not limited in the present invention. For example, the constant voltage may be 1200V.

[0054] In the present invention, by applying a constant voltage to the electric field generating electrode, the stability and consistency of the electric field are ensured, which helps to generate a constant electric field during the test, thereby ensuring that the electric field is uniformly applied to the entire textile sample, which is convenient for evaluating the electrostatic properties. At the same time, under a constant voltage, the actual performance of the textile in an electrostatic environment can be more accurately simulated. This stable test environment helps to evaluate the effects of different fabric parameters (such as yarn type, fiber content, etc.) on antistatic performance, ensuring that the experimental results have high repeatability.

[0055] S5: By solving the descriptive equation of the textile electrostatic test model, the maximum electric field strength, resistivity and electrostatic decay time of the textile sample under various fabric parameters are calculated.

[0056] In a possible implementation, the descriptive equations of the textile electrostatic test model include: Poisson's equation, electric field equation and current density equation.

[0057] The Poisson equation is specifically:

[0058]

[0059] in, represents the gradient operation, s represents the conductivity, Indicates electric potential.

[0060] The electric field equation is specifically:

[0061]

[0062] in, E Represents the electric field.

[0063] Optionally, the electric field distribution is determined according to the electric field equation, and then the maximum electric field intensity can be solved.

[0064] The current density equation is:

[0065]

[0066] in, J Represents the current density.

[0067] Optionally, Newton's automatic method is usually used as the nonlinear solution method, and the maximum number of iterations and tolerance are set to ensure the accuracy of the results.

[0068] Optionally, the resistivity is determined as follows:

[0069] First, the transmission current is solved by integrating the current density over the transmission path between the electrodes I . Then, according to the applied voltage V , transmission current I and the fabric dimensions determine the resistivity:

[0070]

[0071] in, r represents the resistivity, R Represents resistance, A represents the cross-sectional area of ​​the textile, L The transmission path length between the electrodes, V represents a constant voltage, I Indicates the transmission current.

[0072] Optionally, the electrostatic decay time is determined by: determining an electrostatic decay curve:

[0073]

[0074] in,E represents the electric field strength, t Indicates time, E ( t ) represents the electrostatic decay curve, E max It represents the maximum electric field strength and also the initial electric field strength. exp represents the natural constant e The exponential function with base , A 0 , A 1 , A 2 , A 3 represents the fitting coefficient, t 1 , t 2 , t 3 Represents the time constant component.

[0075] In the electrostatic decay curve, the time required for the electric field strength to dissipate to half of the initial electric field strength is taken as the electrostatic decay time.

[0076] S6: Comprehensively evaluate the antistatic performance of textile samples under various fabric parameters based on maximum electric field strength, resistivity and static decay time.

[0077] Among them, the maximum electric field intensity describes the peak value of the electric field on the surface or inside of the textile under the action of an external electric field. The smaller the maximum electric field intensity, the better the textile shields the electric field and can reduce static electricity accumulation. This is an important criterion for evaluating the antistatic performance of textiles.

[0078] Among them, resistivity reflects the conductivity of textile materials. In the design of antistatic textiles, resistivity is a key parameter to evaluate whether the material is easy to conduct charge. If the resistivity is too low, the material is too conductive and is not suitable for use as antistatic textiles.

[0079] Among them, the static decay time refers to the time required for the static charge on the surface or inside of the textile to decay to half of the initial charge, reflecting the textile's ability to dissipate static electricity. The shorter the static decay time, the faster the textile can dissipate the charge after static electricity accumulates, reducing the retention time of static electricity, thereby reducing the potential hazards caused by static electricity (such as static discharge, sparks, etc.).

[0080] Optionally, the antistatic performance is specifically:

[0081]

[0082] in, d Indicates antistatic properties, rrepresents the resistivity, E max represents the maximum electric field strength, t 50 Indicates the static decay time, l 1 represents the weight coefficient of resistivity, l 2 represents the weight coefficient of the maximum electric field strength, l 3 Indicates the weight coefficient of the electrostatic decay time.

[0083] Optionally, before calculating the antistatic performance, the maximum electric field intensity, resistivity, and electrostatic decay time may be normalized to eliminate the differences between different parameter dimensions.

[0084] Among them, those skilled in the art can set the resistivity weight coefficient according to actual conditions. l 1 , weight coefficient of maximum electric field strength l 2 And the weight coefficient of electrostatic decay time l 3 The present invention does not limit the size.

[0085] It should be noted that by simultaneously considering the three key parameters of resistivity, maximum electric field strength and electrostatic decay time, the antistatic properties of textiles can be evaluated from multiple angles, avoiding the limitations of a single indicator evaluation.

[0086] S7: With the optimal antistatic performance as the goal, the suboptimal solution of fabric parameter combination with good antistatic performance is determined by improving the fireworks algorithm.

[0087] Among them, the Fireworks Algorithm (FWA) is an intelligent optimization algorithm that can efficiently find the global optimal or suboptimal solution in a multi-dimensional parameter space by simulating the generation, diffusion and search of sparks during the explosion of fireworks. The core idea of ​​the Fireworks Algorithm is to generate sparks to search different areas of the solution space by simulating the process of fireworks explosion, and to perform global and local searches based on the number and distribution of explosion sparks to find the optimal solution to the objective function.

[0088] In a possible implementation, S7 specifically includes sub-steps S701 to S709:

[0089] S701: Using the parameter value of the anti-static performance as the fitness function of the fireworks algorithm.

[0090] S702: Initialize firework individuals, each firework individual is composed of multiple dimensional components, each firework individual represents a feasible fabric parameter set, and each component represents a fabric parameter.

[0091] S703: Explode each firework:

[0092]

[0093] in, A i Indicates i The explosion radius of each firework, x i Indicates i Fireworks individual, f represents the fitness function, N Indicates the total number of fireworks individuals, min means taking the minimum value, e represents a hyperparameter to avoid division by zero, C A Indicates the explosion radius adjustment factor. S i Indicates i The number of explosion sparks of each firework. max means taking the maximum value. C S Indicates the explosion spark number adjustment factor.

[0094] In the present invention, individuals with poor fitness (i.e., the current solution is poor) have a larger search radius to expand the exploration space and find new solutions. Individuals with good fitness (i.e., the current solution is good) have more sparks and conduct more refined searches for further optimization. This resource allocation mechanism ensures the effective use of computing resources. Individuals with good fitness can concentrate resources for local optimization, and individuals with poor fitness will not consume too many resources, thereby maintaining the efficiency of the algorithm.

[0095] S704: Limiting the number of explosion sparks generated by the explosion operation:

[0096]

[0097] in, Indicates the number of i The number of explosion sparks of each firework, S max Indicates the maximum number of explosion sparks, S min Indicates the minimum number of explosion sparks.

[0098] In the present invention, when the number of sparks of some fireworks is too large, a large number of sparks will be generated, thereby increasing the amount of calculation and causing a waste of computing resources. By setting the maximum number of sparks, the number of sparks generated can be limited to avoid concentrating computing resources on too many local searches.

[0099] S705: Displace the fireworks explosion according to the fireworks explosion radius:

[0100]

[0101] in, y ijk Indicates i The first k Explosion sparks j Dimensional components, x ij Indicates i Fireworks individuals j Dimensional components, Indicates that between 0 and A ij Pick a random number between A ij Indicates i The explosion radius of each firework j Dimensional components.

[0102]

[0103] in, x rj represents the number of randomly selected fireworks individuals j Dimensional components, x gsj represents the global optimal fireworks individual j Dimensional components, x lsj represents the optimal fireworks individual in the previous iteration j Dimensional components, l 1 represents the weight of the random firework individual, l 2 represents the weight of the global optimal fireworks individual, l 3 represents the weight of the best firework individual in the previous iteration, c represents the displacement coefficient, f s represents the variance of the fitness values ​​of all fireworks individuals, f 0 Indicates the preset variance value.

[0104] Among them, those skilled in the art can set the preset variance value according to actual conditions. f 0 The present invention does not limit the size.

[0105] In the present invention, the variance of the fitness value is used for conditional judgment, and the algorithm can automatically adjust the search strategy according to the distribution of the solution. f 0 When the solution distribution is relatively wide, a global search strategy is used (introducing the influence of random individuals and global optimal individuals). f 0 When the solution gradually converges, it mainly uses local search (optimization along the direction of the previous optimal solution), thereby ensuring that the algorithm can adaptively adjust the search intensity when the complexity of the problem changes.

[0106] S706: Randomly select some fireworks individuals, generate a random number, and determine whether the random number is less than the mutation probability. If so, perform Gaussian mutation operation:

[0107]

[0108] in, It means the Gaussian mutation i Fireworks individuals j Dimensional components, x ij Indicates i Fireworks individuals j Dimensional components, e Represents a random number that satisfies a Gaussian distribution with mean 1 and variance 1.

[0109] Among them, those skilled in the art can set the size of the mutation probability according to actual conditions, and the present invention does not limit it.

[0110] In the present invention, Gaussian mutation can explore new search spaces based on existing solutions by randomly adjusting the dimensions of individuals.

[0111] S707: Mapping operations are performed on each individual firework:

[0112]

[0113] in, Indicates the first i The first of the fireworks j Dimensional components, L j Indicates j The lower limit of the dimension, Uj Indicates j The upper limit of each dimension, % indicates the modulo operation.

[0114] In the present invention, through mapping operations, it is ensured that each dimensional component of each firework individual is restricted between defined upper and lower limits, avoiding invalid solutions in the solution space and ensuring that the algorithm always searches in the feasible solution space.

[0115] S708: Perform a selection operation on each firework individual. The probability of each firework individual being selected is:

[0116]

[0117] in, P represents the probability of selection, D Represents the sum of the distances between the current individual and other individuals except itself, d represents the Euclidean distance between two individuals, N Represents the total number of fireworks individuals.

[0118] In the present invention, individuals with larger distances are relatively far away from other individuals, indicating that they explore different areas in the solution space. Through this distance-driven selection mechanism, individuals with larger distances have a higher probability of selection, thereby encouraging the retention of these diverse solutions. This helps maintain the diversity of solutions and avoids solutions concentrating in the same area, thereby better performing global search and preventing the algorithm from falling into local optimality.

[0119] S709: Determine whether the current number of iterations has reached the maximum number of iterations. If so, sort the fireworks individuals in descending order of fitness, and output the fabric parameter combinations represented by the top 10 fireworks individuals as the suboptimal solution for the fabric parameter combination with good antistatic performance. Otherwise, return to continue iteration.

[0120] In the present invention, by using the improved fireworks algorithm for parameter optimization, multiple suboptimal solutions with good antistatic performance can be determined in the simulation or calculation stage, thereby greatly reducing the number of physical experiments in traditional design. This reduces the waste of experimental resources, speeds up the product development cycle, reduces design costs, and reduces the time for repeated experiments.

[0121] S8: using the suboptimal solutions of the fabric parameter combinations with good antistatic performance effects to actually manufacture textiles, and determining the fabric parameter combination with the best antistatic performance from the suboptimal solutions through actual testing.

[0122] In the present invention, the actual manufacturing and testing steps can ensure that the antistatic performance of the final product is stable and reliable, and avoid the problem of substandard performance due to the difference between the model and the actual environment.

[0123] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0124] In the present invention, with the optimal anti-static performance as the goal, the suboptimal solution of the fabric parameter combination with good anti-static performance is determined by improving the Fireworks algorithm, and the suboptimal solution of the fabric parameter combination with good anti-static performance is adopted, and then the textile is actually manufactured, which greatly reduces the repeated test steps. The fabric parameter combination with the best anti-static performance is determined from the suboptimal solution through actual testing, which improves the design efficiency, reduces the waste of test resources, shortens the design cycle, and quickly responds to market demand.

[0125] Reference Manual Attached Figure 3 , showing a schematic structural diagram of a textile antistatic performance optimization system provided by the present invention.

[0126] The present invention also provides a textile antistatic performance optimization system 20, comprising:

[0127] Processor 201;

[0128] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the method for optimizing the antistatic properties of textiles as described in the method embodiment is implemented.

[0129] The textile antistatic performance optimization system 20 provided by the present invention can execute the above-mentioned textile antistatic performance optimization method and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0130] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0131] In the present invention, with the optimal anti-static performance as the goal, the suboptimal solution of the fabric parameter combination with good anti-static performance is determined by improving the Fireworks algorithm, and the suboptimal solution of the fabric parameter combination with good anti-static performance is adopted, and then the textile is actually manufactured, which greatly reduces the repeated test steps. The fabric parameter combination with the best anti-static performance is determined from the suboptimal solution through actual testing, which improves the design efficiency, reduces the waste of test resources, shortens the design cycle, and quickly responds to market demand.

[0132] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0133] There are a few points to note:

[0134] (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0135] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the film or region is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a film, a region or a substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0136] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0137] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for optimizing the antistatic properties of textiles, characterized in that: include: S1: Construct a textile electrostatic test model; S2: setting fabric parameters of the textile sample placed in the textile electrostatic test model; S3: Meshing the textile electrostatic test model; S4: setting boundary conditions of the textile electrostatic test model; S5: calculating the maximum electric field strength, resistivity and electrostatic decay time of the textile sample under various fabric parameters by solving the descriptive equation of the textile electrostatic test model; S6: comprehensively evaluating the antistatic performance of the textile sample under various fabric parameters according to the maximum electric field strength, the resistivity and the static decay time; S7: Taking the optimal antistatic performance as the goal, determining a suboptimal solution of a combination of fabric parameters with good antistatic performance by improving the fireworks algorithm; S8: using the suboptimal solutions of the fabric parameter combinations with good antistatic performance effects to actually manufacture textiles, and determining the fabric parameter combination with the best antistatic performance from the suboptimal solutions through actual testing.

2. The method for optimizing the antistatic properties of textiles according to claim 1, characterized in that: The textile electrostatic test model specifically comprises: an electric field generating electrode, a measuring electrode, a charge amplifier, a guard ring and a clamping ring; The electric field generating electrode is used to generate a test electric field; The measuring electrode is used to sense the change of the test electric field strength; The charge amplifier is used to obtain the change of electric field strength measured by the measuring electrode; The guard ring is used to prevent edge effects from interfering with the measurement results; The clamping ring is used to place the textile sample.

3. The method for optimizing the antistatic properties of textiles according to claim 1, characterized in that: The fabric parameters include: fiber type, fiber content, yarn fineness, yarn twist, yarn structure, yarn moisture content, fabric type, warp and weft density, fabric thickness and porosity.

4. The method for optimizing the antistatic properties of textiles according to claim 1, characterized in that: The S3 specifically includes: S301: using a triangular grid to perform preliminary grid division on the textile electrostatic test model; S302: After the initial grid division, calculating the electric field distribution of the textile electrostatic test model; S303: Adaptively adjust the grid size at each position according to the electric field intensity gradient: in, h represents the grid size, x Indicates location, h 0 means the initial grid size, α represents the refinement coefficient, represents the gradient operation, E Indicates the electric field strength.

5. The method for optimizing the antistatic properties of textiles according to claim 2, characterized in that: The S4 is specifically: A constant voltage is applied at the electric field generating electrodes as a fixed potential boundary condition.

6. The method for optimizing the antistatic properties of textiles according to claim 1, characterized in that: The descriptive equations of the textile electrostatic test model include: Poisson's equation, electric field equation and current density equation.

7. The method for optimizing the antistatic properties of textiles according to claim 1, characterized in that: The antistatic properties are specifically: in, Indicates antistatic properties, represents the resistivity, represents the maximum electric field strength, Indicates the static decay time, represents the weight coefficient of resistivity, represents the weight coefficient of the maximum electric field strength, Represents the weight coefficient of electrostatic decay time; Before calculating the antistatic performance, the maximum electric field strength, resistivity and electrostatic decay time are normalized to eliminate the differences between different parameter dimensions.

8. The method for optimizing the antistatic properties of textiles according to claim 1, characterized in that: The electrostatic decay time is determined specifically as follows: Determine the electrostatic decay curve: in, E represents the electric field strength, t Indicates time, E ( t ) represents the electrostatic decay curve, E max It represents the maximum electric field strength and also the initial electric field strength. exp represents the natural constant e The exponential function with base , A 0. A 1. A 2. A 3 represents the fitting coefficient, , , represents the time constant component; In the electrostatic decay curve, the time required for the electric field intensity to dissipate to half of the initial electric field intensity is taken as the electrostatic decay time.

9. The method for optimizing the antistatic properties of textiles according to claim 1, characterized in that: The S7 specifically includes: S701: Using the parameter value of the antistatic performance as the fitness function of the fireworks algorithm; S702: Initialize firework individuals, each firework individual is composed of multiple dimensional components, each firework individual represents a feasible fabric parameter set, and each component represents a fabric parameter; S703: performing explosion operation on each individual firework; S704: Limiting the number of explosion sparks generated by the explosion operation; S705: Displace the fireworks explosion according to the fireworks explosion radius: in, Indicates i The first k Explosion sparks j Dimensional components, Indicates i Fireworks individuals j Dimensional components, Indicates that between 0 and Pick a random number between Indicates i The explosion radius of each firework j Dimensional components; in, represents the number of randomly selected fireworks individuals j Dimensional components, represents the global optimal fireworks individual j Dimensional components, represents the optimal fireworks individual in the previous iteration j Dimensional components, represents the weight of the random firework individual, represents the weight of the global optimal fireworks individual, represents the weight of the best firework individual in the previous iteration, represents the displacement coefficient, represents the variance of the fitness values ​​of all fireworks individuals, Indicates the preset variance value; S706: Randomly select some fireworks individuals, generate a random number, and determine whether the random number is less than the mutation probability. If so, perform Gaussian mutation operation; S707: Perform mapping operation on each individual firework; S708: performing selection operations on each individual firework; S709: Determine whether the current number of iterations has reached the maximum number of iterations; if so, sort each firework individual in descending order of fitness, and output the fabric parameter combinations represented by the top 10 firework individuals as the suboptimal solution for the fabric parameter combination with good antistatic performance; otherwise, return to continue iteration.

10. A system for optimizing the antistatic properties of textiles, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for optimizing the antistatic properties of a textile as claimed in any one of claims 1 to 9 is implemented.

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