Frame type stranded wire process parameter optimization simulation method

Abstract

The invention relates to the technical field of data processing, in particular to a frame type stranded wire process parameter optimization simulation method, which comprises the following steps: presetting the number of particles in a particle swarm, randomly generating the positions of the particles, and iteratively updating the positions of the particles according to the fitness value of each position; according to the ratio of the maximum value of the position change after the # imgabs 0 # iteration of the particles to the displacement accumulated sum before and after all the iteration after the # imgabs 1 # iteration of the particles, the route optimization degree of the particles after the # imgabs 2 # iteration is obtained; the optimal solution trend degree of the particles after the # imgabs3 # iteration is calculated; determining the optimization degree of the #-imgabs 5 # iteration of the particle through the product of the route optimization degree of the particle after the #-imgabs 4 # iteration and the optimal solution trend degree; and after the particles are screened in response to the comparison result of the optimization degree of the # imgabs6 # iteration of the particles and the preset threshold value, iteration updating is continued, so that optimization of the frame-type stranded wire process parameters is realized, and the optimization efficiency of the frame-type stranded wire process parameters is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing. More specifically, the present invention relates to a simulation method for optimizing the process parameters of frame stranding. Background Art

[0002] The frame stranding process can be applied to industries such as cables, communications, and automobiles. Through the stranding and compacting of copper and aluminum cable core sector conductors and the stranding and drawing and compacting of round conductors by a frame stranding machine, a large number of wire rods can be wound in a short time, improving the flexibility and tensile strength of the wire rods. In order to ensure the stability and safety of electrical connections, it is necessary to continuously optimize the process parameters of frame stranding.

[0003] In order to improve the process technology, there is a lot of research content on the optimization of process parameters in the prior art. For example, the patent application document with the publication number CN111069328A discloses an optimization method for isothermal extrusion process parameters based on a particle swarm algorithm. This method uses a support vector machine to establish a prediction model of process parameters and the energy consumption of isothermal extrusion forming and the temperature of the profile outlet surface, constructs a multi-objective optimization model, and uses the particle swarm algorithm to solve the multi-objective optimization model, thereby optimizing the isothermal extrusion process parameters to achieve an optimization scheme with the best profile quality and the minimum forming energy consumption.

[0004] Although the above prior art can optimize the process parameters to reduce energy consumption to a certain extent, there are many types of process parameters involved in the production process, resulting in some particles not reaching the global optimal solution. Iterating such particles not only wastes the computational amount but also affects the acquisition of the global optimal solution, reducing the computational efficiency.

[0005] Based on this, how to improve the optimization efficiency of frame stranding process parameters is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] To solve the above technical problem of how to improve the optimization efficiency of frame stranding process parameters, the present invention proposes a simulation method for optimizing frame stranding process parameters, which includes the following steps: Obtain the parameter values corresponding to each data point in the dataset of frame stranding process parameters; preset the number of particles in the particle swarm, randomly generate the positions of the particles through the parameter value range in the dataset, determine the fitness values of each position, and iteratively update the positions of the particles according to the fitness values; according to the maximum value of the position change of the particle after the th iteration and the cumulative sum of all displacements before and after iteration of the particle after the th iteration, obtain the route preference degree of the particle after the th iteration; according to the distance between the particle after the th iteration and the global optimal solution in the historical iteration process, calculate the particle after the The degree of approximation of the optimal solution after the th iteration; By multiplying the route optimization degree after the th iteration of the particle by the degree of approximation of the optimal solution, determine the optimization degree of the th iteration of the particle; After screening the particles in response to the comparison result between the optimization degree of the

[0007] th iteration of the particle and a preset threshold, continue the iterative update to achieve the optimization of the frame stranding process parameters.

[0008] According to the frame stranding process parameter optimization simulation method provided by the present invention, obtaining the parameter values corresponding to each data point in the dataset of the frame stranding process parameters includes: taking the parameter values corresponding to each batch of stranded wires as a data point, and after preprocessing all batches of data points, forming a dataset of the frame stranding process parameters.

[0009] The present invention takes into account that there may be noise interference and other situations in the originally collected data. Therefore, the overall quality of the process parameters is improved through preprocessing to prepare for the subsequent optimization of the process parameters.

[0010] According to the frame stranding process parameter optimization simulation method provided by the present invention, determining the fitness value at each position includes: determining a fitness function according to the process parameters of the frame stranding; substituting the process parameters in the particle position into the fitness function to obtain the fitness value at this position.

[0011] According to the frame stranding process parameter optimization simulation method provided by the present invention, iteratively updating the position of the particle according to the fitness value includes: if the fitness value of the position of the particle after iteration is greater than the fitness value of the position before iteration, then take the position after iteration as the latest position of the particle, otherwise take the position before iteration as the latest position of the particle.

[0012] By comparing the fitness values before and after particle iteration, the present invention moves the particles continuously towards the position with a higher fitness value, making the entire particle swarm gradually tend to the global optimal solution, thereby accurately obtaining the parameter values corresponding to the global optimal solution.

[0013] According to the frame stranding process parameter optimization simulation method provided by the present invention, through the maximum value of the position change of the particle after the th iteration and the ratio of the cumulative sum of all displacements before and after iteration of the particle after the th iteration, the route preference degree of the particle after the th iteration is obtained.

[0014] According to the frame stranding process parameter optimization simulation method provided by the present invention, the degree of tendency of the optimal solution after the th iteration of the particle satisfies the relational expression: ; In the formula, represents the degree of tendency of the optimal solution of the th iteration of the i-th particle, , respectively represent the distances between the th and th iterations of the i-th particle and the global optimal solution,

[0015] According to the frame stranding process parameter optimization simulation method provided by the present invention, the method for obtaining the maximum value of the position change of the particle after the th iteration includes: obtaining the positions after all historical iterations of the particle after the th iteration, determining the maximum displacement between all positions, and recording it as the maximum value of the position change of the particle after the th iteration.

[0016] According to the frame stranding process parameter optimization simulation method provided by the present invention, screening particles in response to the comparison result between the preference degree of the particle after the th iteration and a preset threshold includes: if the preference degree of the particle after the th iteration is not greater than the preset threshold, then eliminating the particle; otherwise, retaining the particle.

[0017] By screening by obtaining the preference degree of the particle, the present invention can reduce the influence of particles with lower quality on the iterative convergence process and effectively improve the efficiency of particle iteration.

[0018] According to the frame stranding process parameter optimization simulation method provided by the present invention, the continuous iterative update to achieve the optimization of the frame stranding process parameters includes: in response to the particle iteration termination condition, outputting the maximum fitness value during all particle iterations as the target fitness; adjusting the frame stranding process parameters to the parameter values corresponding to the target fitness; wherein, the iteration termination condition includes a preset number of particle iterations and / or the fitness difference before and after particle iteration within a preset number of convergence times is less than a preset difference threshold.

[0019] According to the frame stranding process parameter optimization simulation method provided by the present invention, after achieving the optimization of the frame stranding process parameters, it further includes: performing abnormal monitoring on the process parameters during the frame stranding process.

[0020] The present invention takes into account that there may be potential safety hazards when the frame stranding process parameters are abnormal, so abnormal monitoring is used to remind the staff to handle it in a timely manner.

[0021] The present invention has the following beneficial effects: Based on the above technical solutions, when the present invention optimizes the frame stranding process parameters through the particle swarm algorithm, the degree of route optimization is obtained by obtaining the displacement change during each particle iteration, and particles that do not reciprocate near the local optimal solution are used for iterative convergence to improve the efficiency of process parameter optimization; on this basis, the present invention also obtains the degree of tendency of the particle after iteration to the optimal solution, and corrects the degree of route optimization of the particle through the degree of tendency of the optimal solution, reducing the influence of particles that reciprocate near the global optimal solution on particle screening, so that high-quality particles can be accurately screened for iterative convergence, effectively improving the efficiency of optimizing the frame stranding process parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a schematic flowchart of a frame stranding process parameter optimization simulation method provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0025] It should be noted that the frame stranding process can be applied to industries such as cables, communications, and automobiles. Through the stranding and compacting of copper and aluminum cable core sector conductors and the stranding and drawing compacting of round conductors by a frame stranding machine, a large number of wire rods can be wound in a short time, improving the flexibility and tensile strength of the wire rods. In order to ensure the stability and safety of electrical connections, it is necessary to continuously optimize the frame stranding process parameters.

[0026] The particle swarm optimization algorithm is an optimization method based on swarm intelligence, which finds the optimal solution through the cooperation of individuals in the swarm. Each particle updates its position according to its own historical experience and the experience of other particles in the swarm, enabling the algorithm to quickly approach the global optimal solution with a relatively fast convergence speed. At the same time, through the velocity and position update mechanism of the particles, the algorithm has strong global search capabilities.

[0027] However, there are many types of process parameters involved in the frame stranding production process, resulting in some particles being unable to reach the global optimal solution. Iterating such particles not only wastes computational effort but also affects the acquisition of the global optimal solution, reducing the computational efficiency.

[0028] Based on this, the embodiments of the present invention disclose an optimization simulation method for frame stranding process parameters. By obtaining the preference degree of each particle during the iteration process and then screening, particles with lower quality are eliminated, and particles with better iteration effects are retained, effectively improving the efficiency of optimizing frame stranding process parameters.

[0029] Specifically, please refer to Figure 1 as shown in Figure 1 which is a schematic flowchart of an optimization simulation method for frame stranding process parameters provided by an embodiment of the present invention. The method specifically includes the following steps.

[0030] S1: Obtain the parameter values corresponding to each data point in the dataset of frame stranding process parameters; preset the number of particles in the particle swarm, randomly generate the positions of the particles based on the parameter value range in the dataset, determine the fitness values of each position, and iteratively update the positions of the particles according to the fitness values.

[0031] Among them, the frame stranding process parameters may include the diameter of the wire, material tensile strength, material yield strength, stranding angle, stranding rate, tension during the stranding process, production environment temperature, humidity, production efficiency, and insulation layer thickness, etc. Specifically, it can be set according to actual needs, and the embodiments of the present invention do not limit this too much here. The parameter values of the frame stranding process parameters jointly determine the stranding strength.

[0032] Exemplarily, the number of particles in the particle swarm can be preset as , where represents the particle coefficient, , represents the number of parameter values corresponding to each data point; the number of particles in the particle swarm can be specifically set according to actual needs, and the embodiments of the present invention do not limit this too much here.

[0033] Exemplarily, in the embodiments of the present invention, obtaining the parameter values corresponding to each data point in the dataset of the frame stranding process parameters includes: taking the parameter values corresponding to each batch of stranding as a data point, and after preprocessing all batches of data points, forming a dataset of the stranding process parameters.

[0034] Among them, the preprocessing can be data cleaning, data format conversion, dimension elimination, etc., which can be specifically set according to actual needs.

[0035] It can be understood that the parameter values corresponding to each data point are the values of each process parameter, and all the parameter values of each data point correspond to a stranding strength. The position of a particle is randomly composed of a set of parameter values, and each particle represents a possible solution. Therefore, the particle swarm is a set of solutions, and the maximum stranding strength corresponding to the particle parameter values obtained through iterative calculation is the global optimal solution of the particle swarm, that is, the maximum fitness value of the particle position.

[0036] Exemplarily, in the embodiments of the present invention, determining the fitness value of each position includes: determining a fitness function according to the process parameters of the frame stranding; substituting the process parameters in the particle position into the fitness function to obtain the fitness value of this position.

[0037] Exemplarily, when determining the fitness function according to the process parameters of the frame stranding, each process parameter is an evaluation index, and a set of evaluation indexes includes all process parameter categories; by setting corresponding weights for the normalized parameter values of each group of evaluation indexes, the corresponding stranding strength, that is, the fitness value, can be obtained; through each group of evaluation indexes and the corresponding fitness values, a fitness function for evaluating the stranding strength can be obtained.

[0038] Among them, the specific steps of obtaining the fitness function through each group of evaluation indexes and the corresponding fitness values can be set according to actual needs, and the embodiments of the present invention do not limit this too much here.

[0039] Exemplarily, in the embodiments of the present invention, iteratively updating the position of a particle according to the fitness value includes: if the fitness value of the position of the particle after iteration is greater than the fitness value of the position before iteration, then taking the position after iteration as the latest position of the particle, otherwise taking the position before iteration as the latest position of the particle.

[0040] In this way, the embodiments of the present invention iteratively update the position of the particle, so that the updated particle position gradually approaches the position of the global optimal solution, that is, the parameter value corresponding to the maximum stranding strength.

[0041] For ease of understanding, in the embodiments of the present invention, the analysis of the particle in the

[0042] n-th iteration process is taken as an example for illustration, but it does not mean that the embodiments of the present invention are only limited to this. S2: Obtain the route preference degree of the particle after the n-th iteration by taking the ratio of the maximum value of the position change of the particle after the n-th iteration to the cumulative sum of all displacements before and after iteration of the particle after the

[0043] It should be noted that for any particle, if it keeps moving repeatedly near a position during the iteration process, it indicates that the particle may be trapped in a local optimal solution. Such particles with local optimal solutions will hinder the process of the particle swarm approaching the global optimal solution, thus affecting the optimization effect of process parameters.

[0044] Based on this, in order to evaluate the performance of each particle during the iteration process, the embodiments of the present invention obtain the historical iteration data of each particle after each iteration, and obtain the route preference degree of the particle through the position change situation in the historical iteration data. If the particle keeps moving repeatedly near the same position, the route preference degree of the particle is relatively low.

[0045] Exemplarily, in the embodiments of the present invention, when determining the position change situation of the particle after the n-th iteration, the maximum value of the position change of the particle after the n-th iteration and all displacements before and after iteration of the particle after the n-th iteration can be obtained to get the position change situation of the particle.

[0046] Exemplarily, in the embodiments of the present invention, the method for obtaining the maximum value of the position change of the particle after the n-th iteration includes: obtaining all the positions of the particle after all historical iterations of the n-th iteration, determining the maximum displacement value between all positions, and denoting it as the maximum value of the position change of the particle after the n-th iteration.

[0047] Exemplarily, to determine the route preference degree of the particle after the n-th iteration, the following relational expression can be specifically referred to: ; In the formula, represents the route preference degree of the i-th particle after the n-th iteration, represents the maximum value of the position change of the i-th particle after the n-th iteration, represents the number of iterations, represents the displacement of the i-th particle before and after the th iteration.

[0048] In the above formula, represents the cumulative sum of displacements before and after all iterations after the th iteration of the i-th particle. The smaller the ratio of the maximum value of the position change of the particle after iteration to the displacements before and after all iterations, the smaller the degree of displacement change of the particle in the historical displacements, the greater the possibility that the particle makes reciprocating motions at the same position, and the smaller the corresponding route preference degree.

[0049] Illustrate the parameters in the above formula by an example: If , then are 1, 2, and 3 respectively. The displacement before and after the first iteration is the displacement between the initial position and the position after the first iteration, the displacement before and after the second iteration is the displacement between the position after the first iteration and the position after the second iteration, and the displacement before and after the third iteration is the displacement between the position after the second iteration and the position after the third iteration; the cumulative sum of displacements in the three iterations is used as the cumulative sum of displacements before and after all iterations after the third iteration of the particle. Obtain the maximum value among the displacements between the position after the first iteration and the position after the second iteration, the displacements between the position after the first iteration and the position after the third iteration, and the displacements between the position after the second iteration and the position after the third iteration, as the maximum value of the position change of the particle after the third iteration.

[0050] After obtaining the route preference degrees of each particle after the th iteration based on the above steps, continue to execute the following steps.

[0051] S3: Calculate the degree of approaching the optimal solution of the particle after the th iteration.

[0052] It should be noted that the reciprocating motion of the particle at the same position may also be that the particle is already near the global optimal solution, and the reciprocating motion of the particle is around the global optimal solution. If the preference degree of the particle after iteration obtained directly based on the above steps is used to determine the preference degree of the particle, the particles near the global optimal solution may be misjudged as particles with low preference degrees.

[0053] It can be understood that there will be an individual optimal solution of the particle and a global optimal solution among all particles after each iteration of each particle in the particle swarm.

[0054] Based on this, the embodiment of the present invention further determines whether the reciprocating motion of the particle is near the global optimal solution by obtaining the position between the particle and the global optimal solution. The closer the particle position is to the global optimal solution, the higher its preference degree.

[0055] Specifically, according to the distance between the particle and the global optimal solution in the historical iteration process after the -th iteration, calculate the degree of tendency of the optimal solution of the particle after the -th iteration.

[0056] Exemplarily, in the embodiment of the present invention, to determine the degree of tendency of the optimal solution of the particle after the -th iteration, the following relational expression can be specifically referred to: ; In the formula, represents the degree of tendency of the optimal solution of the i-th particle after the -th iteration, represents the number of iterations, , respectively represent the distances between the i-th particle and the global optimal solution after the -th and the -th iterations, represents the exponential function with base e; In the above formula, represents the distance deviation between the i-th particle and the global optimal solution after the -th and the -th iterations, that is, the distance deviation between the positions before and after the -th iteration and the global optimal solution. Since the overall direction of particle iteration is towards the global optimal solution, the larger this value is, the closer the particle is to the global optimal solution through iteration.

[0057] Based on the above steps to analyze the distance deviation between the particle before and after iteration and the global optimal solution, the degree of tendency of the particle towards the optimal solution can be accurately obtained, and the following steps can be continued.

[0058] S4: Determine the preference degree of the particle in the -th iteration by multiplying the route preference degree of the particle after the -th iteration and the degree of tendency towards the optimal solution; after screening the particles in response to the comparison result between the preference degree of the particle in the -th iteration and the preset threshold, continue the iterative update to optimize the process parameters of the frame stranding.

[0059] It should be noted that based on the above steps, the route preference degree of the particle and the degree of tendency of the particle towards the optimal solution can be obtained respectively. The route preference degree of the particle is used to characterize the quality of the particle route, and the degree of tendency of the particle towards the optimal solution is used to characterize the tendency degree between the particle and the global optimal solution. By combining the two, the preference degree of the particle can be accurately obtained, and thus the particles with low preference degree can be accurately screened out based on this.

[0060] Exemplarily, in the embodiment of the present invention, in response to the particle in the Screen particles according to the comparison result between the preference level of the current iteration and a preset threshold, including: if the preference level of the particle in the current iteration is not greater than the preset threshold, then eliminate the particle; otherwise, retain the particle.

[0061] Among them, the preset threshold can be set to 0.3; the preset threshold can be specifically set according to actual needs, and the embodiments of the present invention do not impose too many restrictions here.

[0062] It can be understood that if the preference level of the particle in the current iteration is not greater than the preset threshold, it means that the quality performance of the particle after the current iteration is poor. After eliminating the particles with poor quality performance, the particle swarm iteration can converge better based on other particles with better quality performance.

[0063] Exemplarily, in the embodiments of the present invention, continue to iterate and update to optimize the process parameters of the frame stranding process, including: in response to the particle iteration termination condition, output the maximum fitness value during the iteration process of all particles as the target fitness; adjust the frame stranding process parameters to the parameter values corresponding to the target fitness.

[0064] Among them, the iteration termination condition includes a preset number of particle iterations and / or the fitness difference before and after particle iteration within a preset number of convergence times is less than a preset difference threshold.

[0065] Exemplarily, the number of particle iterations, the number of convergence times, and the preset difference threshold can be specifically set according to actual needs, and the embodiments of the present invention do not impose too many restrictions here.

[0066] Based on the above steps, the embodiments of the present invention screen particles with better quality performance for iterative convergence, and can accurately obtain the parameter values corresponding to the maximum stranding strength. During this process, the embodiments of the present invention also consider that there may be potential safety hazards when the process parameters of the frame stranding process are abnormal, so it is necessary to monitor its abnormalities.

[0067] Exemplarily, in the embodiments of the present invention, after optimizing the process parameters of the frame stranding process, it further includes: monitoring the abnormalities of the process parameters during the frame stranding process.

[0068] Among them, the steps for monitoring the abnormalities of the process parameters during the frame stranding process can be specifically set according to actual needs, and the embodiments of the present invention do not impose too many restrictions here.

[0069] It can be seen that in the embodiment of the present invention, when optimizing the process parameters of frame stranding, the parameter values corresponding to each data point in the dataset of the frame stranding process parameters can be obtained; the number of particles in the preset particle swarm is set, the positions of the particles are randomly generated through the parameter value range in the dataset, the fitness values of each position are determined, and the positions of the particles are iteratively updated according to the fitness values; according to the maximum value of the position change of the particle after the -th iteration and the cumulative sum of all displacements before and after iteration of the particle after the -th iteration, the route preference degree of the particle after the -th iteration is obtained; according to the distance between the particle after the -th iteration and the global optimal solution in the historical iteration process, the optimal solution tendency degree of the particle after the -th iteration is calculated; through the product of the route preference degree and the optimal solution tendency degree of the particle after the -th iteration, the preference degree of the particle after the -th iteration is determined; in response to the comparison result between the preference degree of the particle after the -th iteration and the preset threshold, the particles are screened and then the iteration is continued to update to realize the optimization of the frame stranding process parameters.

[0070] In this way, the embodiment of the present invention can realize the optimization of the frame stranding process parameters through the particle swarm algorithm. In this process, the embodiment of the present invention considers that the particles may reciprocate near the local optimal solution, which affects the efficiency of the particle swarm to obtain the global optimal solution. Based on this, the embodiment of the present invention obtains the route preference degree of each particle by obtaining the displacement change situation during the iteration process, so as to obtain the particles that do not reciprocate near the local optimal solution for iterative convergence, improving the efficiency of process parameter optimization; on this basis, the embodiment of the present invention also considers that the particles may also reciprocate near the global optimal solution. Directly screening based on the route preference degree may classify such particles as particles with low route preference degree. Based on this, the embodiment of the present invention obtains the tendency degree of the particle after iteration with respect to the optimal solution, and corrects the route preference degree of the particle through the tendency degree of the optimal solution, accurately screening out high-quality particles that do not reciprocate near the local optimal solution for iterative convergence, effectively improving the optimization efficiency of the frame stranding process parameters.

[0071] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A simulation method for optimizing process parameters of frame-type stranded wire, characterized in that: include: Obtain parameter values ​​corresponding to each data point in a data set of frame-type stranding process parameters; The number of particles in the particle swarm is preset, the positions of the particles are randomly generated according to the parameter value range in the data set, the fitness value of each position is determined, and the positions of the particles are iteratively updated according to the fitness value; According to the particle The maximum value of the position change after the iteration is the same as the particle After the iteration, the displacement accumulation sum of all iterations before and after is obtained. The degree of route optimization after iterations; According to the particle The distance between the particle and the global optimal solution in the historical iteration process after the iteration is calculated. The degree of trend towards the optimal solution after iterations; Through the particle The product of the route optimization degree after the iteration and the degree of tendency to the optimal solution determines the particle The preferred degree of the iteration; in response to the particle After the particles are screened by comparing the optimization degree of the iteration with the preset threshold, the iterative update is continued to achieve the optimization of the process parameters of the frame stranding wire.

2. The frame-type stranding wire process parameter optimization simulation method according to claim 1 is characterized in that: The parameter value corresponding to each data point in the data set of obtaining the frame-type stranding process parameters includes: The parameter value corresponding to each batch of stranded wire is taken as a data point, and the data points of all batches are preprocessed to form a data set of stranded wire process parameters.

3. The frame-type stranding wire process parameter optimization simulation method according to claim 1 is characterized in that: Determining the fitness value of each position includes: The fitness function is determined according to the process parameters of the frame-type stranded wire; the process parameters at the particle position are substituted into the fitness function to obtain the fitness value of the position.

4. The frame-type stranding process parameter optimization simulation method according to claim 1 is characterized in that: The iterative updating of the particle position according to the fitness value comprises: If the fitness value of the particle's position after iteration is greater than the fitness value of the position before iteration, the position after iteration is used as the particle's latest position, otherwise the position before iteration is used as the particle's latest position.

5. The frame-type stranding wire process parameter optimization simulation method according to claim 1 is characterized in that: Through the particle The maximum value of the position change after the iteration is the same as the particle The ratio of the cumulative sum of displacements before and after all iterations after the iteration is obtained. The route optimization degree after iteration.

6. The frame-type stranding wire process parameter optimization simulation method according to claim 1 is characterized in that: The particle The degree of trend toward the optimal solution after iterations satisfies the relationship: ； In the formula, represents the i-th particle The degree of trend towards the optimal solution after iterations, represents the number of iterations, , represents the number of particles i and sequence The distance between the solution and the global optimal solution after iterations is Represents an exponential function with base e.

7. The frame-type stranding wire process parameter optimization simulation method according to claim 1 is characterized in that: The particle The method for obtaining the maximum value of the position change after iterations includes: Get the particle The positions of all historical iterations after the iteration are determined to determine the maximum displacement between all positions, which is recorded as the particle The maximum value of the position change after iterations.

8. The frame-type stranding wire process parameter optimization simulation method according to claim 1 is characterized in that: The response to the particle The comparison result between the optimization degree of the iteration and the preset threshold is used to select particles, including: If the particle If the optimization degree of the iteration is not greater than the preset threshold, the particle is eliminated; otherwise, the particle is retained.

9. The frame-type stranding wire process parameter optimization simulation method according to claim 1, characterized in that: The continued iterative update to achieve optimization of frame-type stranding process parameters includes: In response to the particle iteration termination condition, the maximum fitness value of all particle iteration processes is output as the target fitness value; the frame stranding process parameters are adjusted to the parameter values ​​corresponding to the target fitness value; The iteration termination condition includes that the fitness difference before and after the particle iteration within a preset number of particle iterations and / or a preset number of convergences is less than a preset difference threshold.

10. The frame-type stranding wire process parameter optimization simulation method according to claim 1, characterized in that: The method for optimizing the process parameters of the frame-type stranded wire further includes: Abnormal monitoring of process parameters during frame stranding.

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