Parameter optimization method and device of power transmission line steel-cored aluminum stranded wire, computer equipment, readable storage medium and program product

Abstract

This application relates to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for optimizing the parameters of steel-cored aluminum stranded wire for power transmission lines. The method includes: setting optimization parameters, optimization objectives, and allowable relative errors for the steel-cored aluminum stranded wire; iteratively optimizing the optimization parameters, and based on the parameter optimization results obtained in each iteration, establishing a steel-cored aluminum stranded wire model corresponding to the corresponding number of iterations using a parametric finite element modeling script; testing the steel-cored aluminum stranded wire model to obtain the axial ultimate tensile strength; determining whether a preset stopping condition has been met based on the axial ultimate tensile strength, optimization objective, and allowable relative error; if the preset stopping condition is met, ending the iterative optimization process, and using the parameter optimization results for the corresponding number of iterations as the completed parameter optimization result. This method can improve the efficiency and accuracy of the crimping process optimization.

Description

Technical Field

[0001] This application relates to the field of steel-cored aluminum stranded wire crimping technology for power transmission lines, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for optimizing the parameters of steel-cored aluminum stranded wires for power transmission lines. Background Technology

[0002] Overhead transmission lines are formed by crimping tension clamps, steel-cored aluminum stranded wire, steel anchors, and aluminum tubes. During on-site crimping, process parameters such as the length of the crimping aluminum tube and the width of the crimping die are largely controlled manually. Under non-national standard conditions, this can easily lead to uneven crimping quality, and the axial ultimate tensile strength of the steel-cored aluminum stranded wire may not meet the standards, posing safety hazards. The steel-cored aluminum stranded wire has a complex structure (outer aluminum stranded wire + inner spiral steel core, nested tension clamps and other components), making it difficult to inspect the crimping quality using non-destructive testing techniques. Currently, process parameter optimization mainly relies on traditional experimental methods, but there are many parameters and complex combinations. Moreover, existing simulation studies mostly focus on single characteristics such as transmission line tension and corrosion, and there are very few simulation methods for optimizing multiple parameters of the crimping process.

[0003] Traditional testing methods for optimizing the crimping process parameters of steel-cored aluminum stranded wires in power transmission lines suffer from numerous and complex combinations of crimping process parameters, resulting in a large workload, high operational difficulty, and an inability to quickly adapt to specification changes, severely impacting the efficiency of process optimization. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for optimizing the parameters of steel-cored aluminum stranded wires for power transmission lines, which can reduce the workload of process parameter optimization, reduce the difficulty of operation, and quickly adapt to changes in power transmission line specifications to improve the efficiency and accuracy of crimping process optimization.

[0005] Firstly, this application provides a method for optimizing the parameters of steel-cored aluminum stranded wire for power transmission lines, including:

[0006] Set the optimization parameters, optimization objectives, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines;

[0007] The optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script.

[0008] The axial ultimate tensile strength was obtained by testing the steel-cored aluminum stranded wire model of the transmission line. Based on the axial ultimate tensile strength, the optimization target and the allowable relative error, it was determined whether the preset stopping condition was met. If the preset stopping condition was met, the iterative optimization process was terminated, and the parameter optimization result of the corresponding number of iterations was taken as the parameter optimization result of the optimization.

[0009] In one embodiment, iterative optimization of the optimization parameters includes:

[0010] When the number of iterations is not less than three, for any iteration optimization process, obtain the number k of the current iteration. Based on the parameter optimization results obtained in k-1 iterations, k-2 iterations, the axial limit tensile strength obtained in k-1 iterations, and k-2 iterations, calculate the dimensionless sensitivity matrix of the current iteration using the dimensionless sensitivity function. Based on the optimization objective and the axial limit tensile strength obtained in k-1 iterations, calculate the residual vector of the current iteration. Based on the dimensionless sensitivity matrix, sensitivity attenuation coefficient, and residual vector of the current iteration, obtain the correction amount of the current iteration using the correction formula. Based on the parameter optimization results obtained in k-1 iterations and the correction amount of the current iteration, obtain the parameter optimization result of the current iteration.

[0011] In one embodiment, the method further includes:

[0012] When the number of iterations is one, the optimization parameters are initialized to obtain the parameter optimization result of the first iteration; when the number of iterations is two, the correction amount is initialized to obtain the correction amount of the second iteration; based on the parameter optimization result of the first iteration and the correction amount of the second iteration, the parameter optimization result of the second iteration is obtained.

[0013] In one embodiment, based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script, including:

[0014] For any iteration of parameter optimization, based on the parameter optimization results, the steel anchor pressing die is controlled to press down at a uniform speed along a preset path perpendicular to the steel core axis, squeezing the overlapping section of the steel anchor and the steel core aluminum stranded wire to obtain a stable pressing structure between the steel anchor and the steel core; based on the parameter optimization results, the anti-slip groove pressing die is controlled to press down in alignment with the anti-slip groove area of ​​the steel anchor, squeezing the anti-slip groove area of ​​the steel anchor and the corresponding contact section of the aluminum tube to obtain a mechanical interlocking structure between the steel anchor and the aluminum tube; based on the parameter optimization results, the aluminum tube pressing die is controlled to press down step by step at preset intervals, uniformly squeezing the mating section between the aluminum tube and the steel core aluminum stranded wire to complete the pressing process of the steel anchor, aluminum tube and steel core aluminum stranded wire, and obtain the steel core aluminum stranded wire model of the transmission line.

[0015] In one embodiment, based on the axial ultimate tensile strength, the optimization target, and the allowable relative error, it is determined whether a preset stopping condition has been met, including:

[0016] Based on the axial ultimate tensile strength and the optimization target, the relative error is obtained; if the absolute value of the relative error is greater than the allowable relative error, it is determined that the preset condition has not been met; if the absolute value of the relative error is not greater than the allowable relative error, it is determined that the preset condition has been met.

[0017] In one embodiment, the method further includes:

[0018] If the preset stopping condition is not met, the optimization parameters will be optimized in the next iteration.

[0019] Secondly, this application also provides a parameter optimization device for steel-cored aluminum stranded wire of a power transmission line, comprising:

[0020] The setting module is used to set the optimization parameters, optimization targets, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines.

[0021] The optimization module is used to iteratively optimize the optimization parameters and, based on the parameter optimization results obtained in each iteration, to establish the steel-cored aluminum stranded wire model of the transmission line corresponding to the number of iterations through the parametric finite element modeling script.

[0022] The testing module is used to test the steel-cored aluminum stranded wire model of the transmission line to obtain the axial ultimate tensile strength. Based on the axial ultimate tensile strength, the optimization target and the allowable relative error, it is determined whether the preset stopping condition has been met. If the preset stopping condition is met, the iterative optimization process ends and the parameter optimization result of the corresponding number of iterations is taken as the parameter optimization result of the optimization.

[0023] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0024] Set the optimization parameters, optimization objectives, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines;

[0025] The optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script.

[0026] The axial ultimate tensile strength was obtained by testing the steel-cored aluminum stranded wire model of the transmission line. Based on the axial ultimate tensile strength, the optimization target and the allowable relative error, it was determined whether the preset stopping condition was met. If the preset stopping condition was met, the iterative optimization process was terminated, and the parameter optimization result of the corresponding number of iterations was taken as the parameter optimization result of the optimization.

[0027] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0028] Set the optimization parameters, optimization objectives, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines;

[0029] The optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script.

[0030] The axial ultimate tensile strength was obtained by testing the steel-cored aluminum stranded wire model of the transmission line. Based on the axial ultimate tensile strength, the optimization target and the allowable relative error, it was determined whether the preset stopping condition was met. If the preset stopping condition was met, the iterative optimization process was terminated, and the parameter optimization result of the corresponding number of iterations was taken as the parameter optimization result of the optimization.

[0031] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0032] Set the optimization parameters, optimization objectives, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines;

[0033] The optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script.

[0034] The axial ultimate tensile strength was obtained by testing the steel-cored aluminum stranded wire model of the transmission line. Based on the axial ultimate tensile strength, the optimization target and the allowable relative error, it was determined whether the preset stopping condition was met. If the preset stopping condition was met, the iterative optimization process was terminated, and the parameter optimization result of the corresponding number of iterations was taken as the parameter optimization result of the optimization.

[0035] The aforementioned parameter optimization method, apparatus, computer equipment, computer-readable storage medium, and computer program product for steel-cored aluminum stranded wire transmission lines first sets the optimization parameters, optimization objectives, and allowable relative errors for the steel-cored aluminum stranded wire transmission lines. Then, the optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model corresponding to the corresponding number of iterations is established using a parametric finite element modeling script. Finally, the steel-cored aluminum stranded wire model is tested to obtain the axial ultimate tensile strength. Based on the axial ultimate tensile strength, optimization objectives, and allowable relative errors, it is determined whether a preset stopping condition has been met. If the preset stopping condition is met, the iterative optimization process ends, and the parameter optimization results for the corresponding number of iterations are taken as the completed parameter optimization results. This application enables rapid model construction through parametric modeling scripts, allowing for adaptation to different specifications of steel-cored aluminum stranded wires for power transmission lines without the need for repeated experimental setups. This reduces the workload of optimizing process parameters, lowers operational difficulty, and facilitates rapid adaptation to changes in power transmission line specifications. Simultaneously, through iterative optimization and precise error control, it ensures that the optimized process parameters meet the requirements for safe service due to the axial ultimate tensile strength, significantly improving the efficiency and accuracy of crimping process optimization and eliminating potential safety hazards to the lines. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating a parameter optimization method for steel-cored aluminum stranded wire in one embodiment.

[0038] Figure 2 This is a schematic diagram illustrating the creation of a steel-cored aluminum stranded wire model for a power transmission line using a parametric finite element modeling script in one embodiment.

[0039] Figure 3 This is a structural block diagram of a parameter optimization device for steel-cored aluminum stranded wire in one embodiment;

[0040] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0042] In one embodiment, such as Figure 1 As shown, a parameter optimization method for steel-cored aluminum stranded wire in power transmission lines is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, and to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0043] Step 102: Set the optimization parameters, optimization target, and allowable relative error for the steel-cored aluminum stranded wire of the transmission line.

[0044] The optimization parameters include the length of the aluminum tube to be crimped, the width of the crimping die, the width of the overlapping die, and the number of crimping cycles. Each parameter needs to be set with a reasonable optimization range (e.g., aluminum tube length 80-120mm, crimping die width 15-25mm, overlapping die width 5-15mm, number of crimping cycles 6-12). The number of parameters, n, is determined based on actual process requirements. Multiple optimization objectives are possible; this application selects the axial ultimate tensile strength of the transmission line as optimization objective I, with a value referencing the safety service standard for transmission lines (e.g., 120kN). It must be ensured that this indicator after optimization is not lower than the safety threshold specified in the national standard. Allowable relative error: This is the preset maximum deviation threshold R (e.g., 2%) between the calculated result and the optimization objective, used to judge whether the iterative optimization has achieved the expected effect and to avoid substandard crimping quality due to excessive error.

[0045] For example, the optimization parameters for the crimping process of steel-cored aluminum stranded wire in power transmission lines are defined. The optimization objectives I (i.e., the axial ultimate tensile strength of the transmission line) and the allowable relative error R are as follows:

[0046]

[0047]

[0048]

[0049] In the formula: n is the number of design parameters; m is the number of optimization objectives and allowable relative errors. In this application, only the axial ultimate tensile strength of the transmission line is selected as the optimization objective, so the value of m in this application is 1; the superscript (k) indicates the number of iterations of the current optimization; R is the allowable relative error.

[0050] Step 104: Iteratively optimize the optimization parameters, and based on the parameter optimization results obtained from each iteration, establish the steel-cored aluminum stranded wire model of the transmission line corresponding to the number of iterations using a parametric finite element modeling script.

[0051] In this process, after each iteration of optimization yields the corresponding parameter optimization results, these results are used to build a corresponding steel-cored aluminum stranded wire model for power transmission lines using a parametric finite element modeling script. Testing is then conducted, and if the preset stopping condition is not met after testing, the next iteration of optimization is performed. The parametric finite element modeling script binds the geometric dimensions, material properties, contact relationships, boundary conditions, and other elements of the steel-cored aluminum stranded wire crimping model to optimization parameters (such as the length of the crimped aluminum tube and the width of the crimping die), achieving full automation of the entire process from "input parameters → automatic modeling → output of a computable model".

[0052] Step 106: Test the steel-cored aluminum stranded wire model of the transmission line to obtain the axial ultimate tensile strength; based on the axial ultimate tensile strength, the optimization target and the allowable relative error, determine whether the preset stopping condition has been met; if the preset stopping condition has been met, end the iterative optimization process and take the parameter optimization result of the corresponding number of iterations as the parameter optimization result of the optimization completed.

[0053] In this process, after obtaining the steel-cored aluminum stranded wire model of the transmission line, all degrees of freedom of the steel anchor end are fixed. A uniformly increasing axial tensile load is applied to the free end of the steel-cored aluminum stranded wire away from the crimping end. The load-displacement curve and component contact stress and relative displacement data are monitored in real time through the finite element software solution module. When the curve shows an obvious inflection point (the load no longer increases with the displacement or drops sharply), that is, when the component is about to detach, slip or break, the load value at this time is recorded, which is the axial ultimate tensile pull force corresponding to this iteration.

[0054] The aforementioned parameter optimization method, apparatus, computer equipment, computer-readable storage medium, and computer program product for steel-cored aluminum stranded wire transmission lines first sets the optimization parameters, optimization objectives, and allowable relative errors for the steel-cored aluminum stranded wire transmission lines. Then, the optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model corresponding to the corresponding number of iterations is established using a parametric finite element modeling script. Finally, the steel-cored aluminum stranded wire model is tested to obtain the axial ultimate tensile strength. Based on the axial ultimate tensile strength, optimization objectives, and allowable relative errors, it is determined whether a preset stopping condition has been met. If the preset stopping condition is met, the iterative optimization process ends, and the parameter optimization results for the corresponding number of iterations are taken as the completed parameter optimization results. This application enables rapid model construction through parametric modeling scripts, allowing for adaptation to different specifications of steel-cored aluminum stranded wires for power transmission lines without the need for repeated experimental setups. This reduces the workload of optimizing process parameters, lowers operational difficulty, and facilitates rapid adaptation to changes in power transmission line specifications. Simultaneously, through iterative optimization and precise error control, it ensures that the optimized process parameters meet the requirements for safe service due to the axial ultimate tensile strength, significantly improving the efficiency and accuracy of crimping process optimization and eliminating potential safety hazards to the lines.

[0055] In one exemplary embodiment, iterative optimization of the optimization parameters includes:

[0056] When the number of iterations is not less than three, for any iteration optimization process, obtain the number k of the current iteration. Based on the parameter optimization results obtained in k-1 iterations, k-2 iterations, the axial limit tensile strength obtained in k-1 iterations, and k-2 iterations, calculate the dimensionless sensitivity matrix of the current iteration using the dimensionless sensitivity function. Based on the optimization objective and the axial limit tensile strength obtained in k-1 iterations, calculate the residual vector of the current iteration. Based on the dimensionless sensitivity matrix, sensitivity attenuation coefficient, and residual vector of the current iteration, obtain the correction amount of the current iteration using the correction formula. Based on the parameter optimization results obtained in k-1 iterations and the correction amount of the current iteration, obtain the parameter optimization result of the current iteration.

[0057] For example, when the number of iterations is not less than three, for any iteration optimization process, obtain the current iteration number k. Based on the parameter optimization results obtained from k-1 iterations, k-2 iterations, the axial ultimate tensile strength obtained from k-1 iterations, and k-2 iterations, calculate the dimensionless sensitivity matrix of the current iteration using the dimensionless sensitivity function. The calculation formula is as follows:

[0058]

[0059] In this application, m is 1, corresponding to the axial ultimate tensile strength. It is a dimensionless sensitivity matrix. It is the actual result corresponding to the optimization objective, specifically: In this application, the corresponding axial ultimate tensile strength is obtained based on the parameter optimization results of the current iteration. This is the result of parameter optimization. Based on the optimization objective and the axial ultimate tensile strength obtained from k-1 iterations, the residual vector of the current iteration is calculated using the following formula:

[0060]

[0061] in, The optimization objective is as follows. Based on the dimensionless sensitivity matrix, sensitivity attenuation coefficient, and residual vector of the current iteration, the correction amount for the current iteration is obtained through the correction formula, which is as follows:

[0062]

[0063] in, It is a correction amount. It is a sensitivity attenuation coefficient vector, used to reduce the impact of sensitivity on a specific optimization objective, thereby achieving precise control over the optimization direction or magnitude. The calculation process is as follows:

[0064]

[0065]

[0066] Among them, t m The sensitivity attenuation coefficient W (k) The constant term, It is the allowable relative error of the m-th optimization objective. It is the relative error of the m-th optimization objective, calculated using the following formula:

[0067]

[0068] Based on the parameter optimization results obtained from k-1 iterations and the correction amount in the current iteration, the parameter optimization result for the current iteration is obtained, calculated using the following formula:

[0069]

[0070] Among them, the updated parameter optimization results The parameter must be within the optimization interval [a, b], as shown below:

[0071] .

[0072] In this embodiment, by constructing the dimensionless sensitivity matrix of the current iteration, and combining the optimization objective with the residual vector calculated by the tensile strength of the k-1th iteration, and by dynamically adjusting the direction and amplitude of the parameters through the sensitivity attenuation coefficient vector and constant terms, the limitations of single-iteration data optimization can be overcome, making the sensitivity matrix more in line with the actual process law, greatly improving the convergence speed and accuracy of iterative optimization, and avoiding iterative oscillation or slow convergence.

[0073] In one exemplary embodiment, the method further includes:

[0074] When the number of iterations is one, the optimization parameters are initialized to obtain the parameter optimization result of the first iteration; when the number of iterations is two, the correction amount is initialized to obtain the correction amount of the second iteration; based on the parameter optimization result of the first iteration and the correction amount of the second iteration, the parameter optimization result of the second iteration is obtained.

[0075] For example, when the number of iterations is one, i.e., in the first iteration, the optimization parameters are initialized to obtain the parameter optimization result of the first iteration. In addition, the correction amount of the first iteration is set to 0. When the number of iterations is two, the correction amount is randomly initialized to obtain the correction amount of the second iteration. The parameter optimization results of the first iteration and the correction amount of the second iteration are added together to obtain the parameter optimization results of the second iteration.

[0076] In this embodiment, the optimization parameters are directly initialized during the first iteration to obtain the corresponding parameter optimization results, and the correction amount is set to 0 to ensure that the initial parameters meet the basic engineering requirements. During the second iteration, the correction amount is randomly initialized and added to the parameter optimization results of the first iteration to obtain the parameters for the second iteration, quickly constructing two rounds of basic iteration data. This design can avoid the deviation in optimization direction caused by insufficient data in the initial iteration stage, providing effective support for calculating the dimensionless sensitivity matrix based on the data from the first two rounds when k≥3, ensuring that the entire iterative optimization process can proceed smoothly from the initial stage, and further improving the coherence and reliability of the overall optimization.

[0077] In an exemplary embodiment, based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script, including:

[0078] For any iteration of parameter optimization, based on the parameter optimization results, the steel anchor pressing die is controlled to press down at a uniform speed along a preset path perpendicular to the steel core axis, squeezing the overlapping section of the steel anchor and the steel core aluminum stranded wire to obtain a stable pressing structure between the steel anchor and the steel core; based on the parameter optimization results, the anti-slip groove pressing die is controlled to press down in alignment with the anti-slip groove area of ​​the steel anchor, squeezing the anti-slip groove area of ​​the steel anchor and the corresponding contact section of the aluminum tube to obtain a mechanical interlocking structure between the steel anchor and the aluminum tube; based on the parameter optimization results, the aluminum tube pressing die is controlled to press down step by step at preset intervals, uniformly squeezing the mating section between the aluminum tube and the steel core aluminum stranded wire to complete the pressing process of the steel anchor, aluminum tube and steel core aluminum stranded wire, and obtain the steel core aluminum stranded wire model of the transmission line.

[0079] like Figure 2As shown, taking the kth iteration as an example, the parameter optimization results include the aluminum tube length (aluminum tube) of 105mm, the die width (aluminum tube die) of 20mm, the stacking die width of 8mm, and the number of crimping operations of 8. After the parametric finite element modeling script reads this set of parameters, it first controls the special steel anchor extrusion die (steel anchor die) for crimping transmission line steel core aluminum stranded wire to press down uniformly at a speed of 5mm / s along a vertical path perpendicular to the steel core axis. The pressing stroke is determined by the die width parameter (20mm in this case). The steel anchor (steel anchor) used for crimping and fixing transmission lines overlaps with the steel-cored aluminum stranded wire (60mm in length). This causes the inner wall of the steel anchor to plastically deform and tightly wrap the steel core inside the steel-cored aluminum stranded wire, forming a stable crimping structure between the steel anchor and the steel core. Then, keeping other parameters such as model constraints and material properties unchanged, the special extrusion die (anti-slip groove die) adapted to the anti-slip groove structure of the steel anchor is aligned with the pre-set groove area (anti-slip groove of the steel anchor) on the outer surface of the steel anchor, and the section is pressed down in 8 steps according to the crimping number parameters. The compression process involves pressing the anti-slip groove area of ​​the steel anchor and the corresponding contact section of the aluminum sleeve (aluminum tube) used for power transmission line crimping, causing the inner wall of the aluminum tube to embed into the anti-slip groove, forming a mechanical interlocking structure between the steel anchor and the aluminum tube. Finally, based on the parameters of the aluminum tube length (axial dimension of the aluminum tube) of 105mm and the die overlap width (overlap width of adjacent die compression) of 8mm, the special die (aluminum tube pressing die) used to compress the aluminum tube and the steel-cored aluminum stranded wire is controlled to press down in stages along the axial direction of the aluminum tube at a preset interval of 8mm, completing a total of 12 pressing operations to uniformly compress the aluminum tube. The mating section (100mm long) of the aluminum tube and the steel-cored aluminum stranded wire is sequentially crimped with steel anchor-steel core, steel anchor-aluminum tube, and aluminum tube-steel-cored aluminum stranded wire, ultimately forming a complete finite element model of the transmission line steel-cored aluminum stranded wire that includes the assembly relationships, contact properties, and material characteristics of each component. The tension clamp is installed externally after the internal crimping of the steel anchor, aluminum tube, and steel-cored aluminum stranded wire, serving to secure the entire structure and prevent loosening. Together with the steel anchor and aluminum tube, it forms a complete crimping and fixing system. Then, the end of the steel anchor furthest from the crimping assembly (the steel anchor end) is fixed according to preset constraints. A uniformly increasing axial tensile load is applied to the free end of the steel-cored aluminum stranded wire furthest from the crimping end. The load-displacement curve and component contact state are monitored in real time using the finite element software's solution module. The maximum bearing tensile force when the component is about to detach, slip, or break is recorded, which is the calculated value of the transmission line's axial ultimate tensile strength for that iteration.

[0080] In this embodiment, by deeply binding the pressing motion parameters (including pressing path, speed, stroke, and spacing) of each step with the iterative optimization results, the real process logic of on-site pressing can be restored. This allows the finite element model to not only replicate the geometric dimensions of the component, but also accurately simulate the mechanical action and structural forming effect during the pressing process, making the subsequent axial ultimate tensile strength test results more consistent with actual working conditions.

[0081] In an exemplary embodiment, determining whether a preset stopping condition has been met based on the axial ultimate tensile strength, the optimization target, and the allowable relative error includes:

[0082] Based on the axial ultimate tensile strength and the optimization target, the relative error is obtained; if the absolute value of the relative error is greater than the allowable relative error, it is determined that the preset condition has not been met; if the absolute value of the relative error is not greater than the allowable relative error, it is determined that the preset condition has been met.

[0083] For example, based on the axial ultimate tensile strength and the optimization target, the relative error is obtained. The calculation formula is as follows:

[0084]

[0085] In this application, the relative error of the axial ultimate tensile strength is calculated. If the absolute value of the relative error is greater than the allowable relative error, it is determined that the preset condition has not been met; if the absolute value of the relative error is not greater than the allowable relative error, it is determined that the preset condition has been met. Furthermore, this application can pre-set the number of iterations. If the absolute value of the relative error is not greater than the allowable relative error, it continues to determine whether the current iteration number has reached the pre-set number of iterations. If it has, it is also determined that the preset condition has been met; if not, it is determined that the preset condition has not been met.

[0086] In this embodiment, the dual stopping condition design of relative error threshold judgment and iteration number threshold judgment avoids insufficient optimization due to insufficient iteration number and prevents invalid iteration due to consistently unacceptable error. This ensures that iterative optimization achieves a balance between efficiency and effectiveness, and ultimately outputs the optimal parameters that meet engineering requirements quickly, improving the practicality and reliability of the entire optimization process.

[0087] In one exemplary embodiment, the method further includes:

[0088] If the preset stopping condition is not met, the optimization parameters will be optimized in the next iteration.

[0089] For example, if the preset stopping condition is not met (i.e., the absolute value of the relative error is greater than the allowable relative error, or the current iteration number is not reached), the next iteration optimization process is automatically triggered: based on the axial limit tensile strength and parameter optimization results obtained in this iteration, combined with the previous iteration data (such as the relevant data of k-1 and k-2 iterations), the parameter optimization results of the next iteration are updated according to the established logic of calculating the dimensionless sensitivity matrix, solving the residual vector, and deriving the correction amount. Then, the complete process of parameterized modeling → model testing → stopping condition judgment is repeated until the stopping condition is met.

[0090] In this embodiment, when the stopping condition is not met, the system will automatically call the parameter optimization results and axial limit tensile force data of the previous iteration, and complete the dimensionless sensitivity matrix calculation, residual vector solution and correction amount derivation according to the established logic, and update the parameter optimization results for the next round. This design not only avoids the optimization interruption caused by the failure of a single iteration to meet the standard, but also improves the optimization efficiency and continuity through automated closed loop, ensuring that the final output parameter results are stable and in line with the actual needs of the project, and further enhances the practicality and reliability of the optimization process.

[0091] In one exemplary embodiment, such as Figure 2As shown, a parameter optimization method for steel-cored aluminum stranded wire in power transmission lines includes: setting optimization parameters, optimization objectives, and allowable relative errors for the steel-cored aluminum stranded wire in power transmission lines; performing iterative optimization on the optimization parameters; when the number of iterations is one, initializing the optimization parameters to obtain the parameter optimization result of the first iteration; when the number of iterations is two, initializing the correction amount to obtain the correction amount of the second iteration; and obtaining the parameter optimization result of the second iteration based on the parameter optimization result of the first iteration and the correction amount of the second iteration. When the number of iterations is not less than three, for any iteration optimization process, obtain the number k of the current iteration. Based on the parameter optimization results obtained in k-1 iterations, k-2 iterations, the axial limit tensile strength obtained in k-1 iterations, and k-2 iterations, calculate the dimensionless sensitivity matrix of the current iteration using the dimensionless sensitivity function. Based on the optimization objective and the axial limit tensile strength obtained in k-1 iterations, calculate the residual vector of the current iteration. Based on the dimensionless sensitivity matrix, sensitivity attenuation coefficient, and residual vector of the current iteration, obtain the correction amount of the current iteration using the correction formula. Based on the parameter optimization results obtained in k-1 iterations and the correction amount of the current iteration, obtain the parameter optimization result of the current iteration. For any iteration of parameter optimization, based on the results, the steel anchor pressing die is controlled to press down at a constant speed along a preset path perpendicular to the steel core axis, squeezing the overlapping section of the steel anchor and the steel core aluminum stranded wire to obtain a stable pressing structure between the steel anchor and the steel core. Based on the results, the anti-slip groove pressing die is controlled to press down in alignment with the anti-slip groove area of ​​the steel anchor, squeezing the anti-slip groove area of ​​the steel anchor and the corresponding contact section of the aluminum tube to obtain a mechanical interlocking structure between the steel anchor and the aluminum tube. Based on the results, the aluminum tube pressing die is controlled to press down step by step at preset intervals, uniformly squeezing the mating section between the aluminum tube and the steel core aluminum stranded wire to complete the pressing process of the steel anchor, aluminum tube, and steel core aluminum stranded wire, thus obtaining the transmission line steel core aluminum stranded wire model. The transmission line steel core aluminum stranded wire model is tested to obtain the axial ultimate tensile strength. Based on the axial ultimate tensile strength and the optimization target, the relative error is obtained. If the absolute value of the relative error is greater than the allowable relative error, it is determined that the preset condition has not been met; if the absolute value of the relative error is not greater than the allowable relative error, it is determined that the preset condition has been met. If the preset stopping condition is not met, the optimization parameters will be iterated again. If the preset stopping condition is met, the iterative optimization process ends, and the parameter optimization result of the corresponding number of iterations is taken as the completed parameter optimization result.

[0092] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0093] In one exemplary embodiment, such as Figure 3 As shown, a parameter optimization device for steel-cored aluminum stranded wire of a power transmission line is provided, including: a setting module 301, an optimization module 302, and a testing module 303, wherein:

[0094] The setting module is used to set the optimization parameters, optimization targets, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines.

[0095] The optimization module is used to iteratively optimize the optimization parameters and, based on the parameter optimization results obtained in each iteration, to establish the steel-cored aluminum stranded wire model of the transmission line corresponding to the number of iterations through the parametric finite element modeling script.

[0096] The testing module is used to test the steel-cored aluminum stranded wire model of the transmission line to obtain the axial ultimate tensile strength. Based on the axial ultimate tensile strength, the optimization target and the allowable relative error, it is determined whether the preset stopping condition has been met. If the preset stopping condition is met, the iterative optimization process ends and the parameter optimization result of the corresponding number of iterations is taken as the parameter optimization result of the optimization.

[0097] In one exemplary embodiment, the optimization module is further configured to:

[0098] When the number of iterations is not less than three, for any iteration optimization process, obtain the number k of the current iteration. Based on the parameter optimization results obtained in k-1 iterations, k-2 iterations, the axial limit tensile strength obtained in k-1 iterations, and k-2 iterations, calculate the dimensionless sensitivity matrix of the current iteration using the dimensionless sensitivity function. Based on the optimization objective and the axial limit tensile strength obtained in k-1 iterations, calculate the residual vector of the current iteration. Based on the dimensionless sensitivity matrix, sensitivity attenuation coefficient, and residual vector of the current iteration, obtain the correction amount of the current iteration using the correction formula. Based on the parameter optimization results obtained in k-1 iterations and the correction amount of the current iteration, obtain the parameter optimization result of the current iteration.

[0099] In one exemplary embodiment, the optimization module is further configured to:

[0100] When the number of iterations is one, the optimization parameters are initialized to obtain the parameter optimization result of the first iteration; when the number of iterations is two, the correction amount is initialized to obtain the correction amount of the second iteration; based on the parameter optimization result of the first iteration and the correction amount of the second iteration, the parameter optimization result of the second iteration is obtained.

[0101] In one exemplary embodiment, the optimization module is further configured to:

[0102] For any iteration of parameter optimization, based on the parameter optimization results, the steel anchor pressing die is controlled to press down at a uniform speed along a preset path perpendicular to the steel core axis, squeezing the overlapping section of the steel anchor and the steel core aluminum stranded wire to obtain a stable pressing structure between the steel anchor and the steel core; based on the parameter optimization results, the anti-slip groove pressing die is controlled to press down in alignment with the anti-slip groove area of ​​the steel anchor, squeezing the anti-slip groove area of ​​the steel anchor and the corresponding contact section of the aluminum tube to obtain a mechanical interlocking structure between the steel anchor and the aluminum tube; based on the parameter optimization results, the aluminum tube pressing die is controlled to press down step by step at preset intervals, uniformly squeezing the mating section between the aluminum tube and the steel core aluminum stranded wire to complete the pressing process of the steel anchor, aluminum tube and steel core aluminum stranded wire, and obtain the steel core aluminum stranded wire model of the transmission line.

[0103] In one exemplary embodiment, the test module is further configured to:

[0104] Based on the axial ultimate tensile strength and the optimization target, the relative error is obtained; if the absolute value of the relative error is greater than the allowable relative error, it is determined that the preset condition has not been met; if the absolute value of the relative error is not greater than the allowable relative error, it is determined that the preset condition has been met.

[0105] In one exemplary embodiment, the test module is further configured to:

[0106] If the preset stopping condition is not met, the optimization parameters will be optimized in the next iteration.

[0107] Each module in the aforementioned parameter optimization device for steel-cored aluminum stranded wire of transmission lines can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0108] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores parameter optimization results. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a parameter optimization method for steel-cored aluminum stranded wire in power transmission lines.

[0109] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0110] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0111] Set the optimization parameters, optimization objectives, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines;

[0112] The optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script.

[0113] The axial ultimate tensile strength was obtained by testing the steel-cored aluminum stranded wire model of the transmission line. Based on the axial ultimate tensile strength, the optimization target and the allowable relative error, it was determined whether the preset stopping condition was met. If the preset stopping condition was met, the iterative optimization process was terminated, and the parameter optimization result of the corresponding number of iterations was taken as the parameter optimization result of the optimization.

[0114] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0115] When the number of iterations is not less than three, for any iteration optimization process, obtain the number k of the current iteration. Based on the parameter optimization results obtained in k-1 iterations, k-2 iterations, the axial limit tensile strength obtained in k-1 iterations, and k-2 iterations, calculate the dimensionless sensitivity matrix of the current iteration using the dimensionless sensitivity function. Based on the optimization objective and the axial limit tensile strength obtained in k-1 iterations, calculate the residual vector of the current iteration. Based on the dimensionless sensitivity matrix, sensitivity attenuation coefficient, and residual vector of the current iteration, obtain the correction amount of the current iteration using the correction formula. Based on the parameter optimization results obtained in k-1 iterations and the correction amount of the current iteration, obtain the parameter optimization result of the current iteration.

[0116] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0117] When the number of iterations is one, the optimization parameters are initialized to obtain the parameter optimization result of the first iteration; when the number of iterations is two, the correction amount is initialized to obtain the correction amount of the second iteration; based on the parameter optimization result of the first iteration and the correction amount of the second iteration, the parameter optimization result of the second iteration is obtained.

[0118] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0119] For any iteration of parameter optimization, based on the parameter optimization results, the steel anchor pressing die is controlled to press down at a uniform speed along a preset path perpendicular to the steel core axis, squeezing the overlapping section of the steel anchor and the steel core aluminum stranded wire to obtain a stable pressing structure between the steel anchor and the steel core; based on the parameter optimization results, the anti-slip groove pressing die is controlled to press down in alignment with the anti-slip groove area of ​​the steel anchor, squeezing the anti-slip groove area of ​​the steel anchor and the corresponding contact section of the aluminum tube to obtain a mechanical interlocking structure between the steel anchor and the aluminum tube; based on the parameter optimization results, the aluminum tube pressing die is controlled to press down step by step at preset intervals, uniformly squeezing the mating section between the aluminum tube and the steel core aluminum stranded wire to complete the pressing process of the steel anchor, aluminum tube and steel core aluminum stranded wire, and obtain the steel core aluminum stranded wire model of the transmission line.

[0120] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0121] Based on the axial ultimate tensile strength and the optimization target, the relative error is obtained; if the absolute value of the relative error is greater than the allowable relative error, it is determined that the preset condition has not been met; if the absolute value of the relative error is not greater than the allowable relative error, it is determined that the preset condition has been met.

[0122] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0123] If the preset stopping condition is not met, the optimization parameters will be optimized in the next iteration.

[0124] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0125] Set the optimization parameters, optimization objectives, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines;

[0126] The optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script.

[0127] The axial ultimate tensile strength was obtained by testing the steel-cored aluminum stranded wire model of the transmission line. Based on the axial ultimate tensile strength, the optimization target and the allowable relative error, it was determined whether the preset stopping condition was met. If the preset stopping condition was met, the iterative optimization process was terminated, and the parameter optimization result of the corresponding number of iterations was taken as the parameter optimization result of the optimization.

[0128] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0129] When the number of iterations is not less than three, for any iteration optimization process, obtain the number k of the current iteration. Based on the parameter optimization results obtained in k-1 iterations, k-2 iterations, the axial limit tensile strength obtained in k-1 iterations, and k-2 iterations, calculate the dimensionless sensitivity matrix of the current iteration using the dimensionless sensitivity function. Based on the optimization objective and the axial limit tensile strength obtained in k-1 iterations, calculate the residual vector of the current iteration. Based on the dimensionless sensitivity matrix, sensitivity attenuation coefficient, and residual vector of the current iteration, obtain the correction amount of the current iteration using the correction formula. Based on the parameter optimization results obtained in k-1 iterations and the correction amount of the current iteration, obtain the parameter optimization result of the current iteration.

[0130] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0131] When the number of iterations is one, the optimization parameters are initialized to obtain the parameter optimization result of the first iteration; when the number of iterations is two, the correction amount is initialized to obtain the correction amount of the second iteration; based on the parameter optimization result of the first iteration and the correction amount of the second iteration, the parameter optimization result of the second iteration is obtained.

[0132] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0133] For any iteration of parameter optimization, based on the parameter optimization results, the steel anchor pressing die is controlled to press down at a uniform speed along a preset path perpendicular to the steel core axis, squeezing the overlapping section of the steel anchor and the steel core aluminum stranded wire to obtain a stable pressing structure between the steel anchor and the steel core; based on the parameter optimization results, the anti-slip groove pressing die is controlled to press down in alignment with the anti-slip groove area of ​​the steel anchor, squeezing the anti-slip groove area of ​​the steel anchor and the corresponding contact section of the aluminum tube to obtain a mechanical interlocking structure between the steel anchor and the aluminum tube; based on the parameter optimization results, the aluminum tube pressing die is controlled to press down step by step at preset intervals, uniformly squeezing the mating section between the aluminum tube and the steel core aluminum stranded wire to complete the pressing process of the steel anchor, aluminum tube and steel core aluminum stranded wire, and obtain the steel core aluminum stranded wire model of the transmission line.

[0134] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0135] Based on the axial ultimate tensile strength and the optimization target, the relative error is obtained; if the absolute value of the relative error is greater than the allowable relative error, it is determined that the preset condition has not been met; if the absolute value of the relative error is not greater than the allowable relative error, it is determined that the preset condition has been met.

[0136] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0137] If the preset stopping condition is not met, the optimization parameters will be optimized in the next iteration.

[0138] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0139] Set the optimization parameters, optimization objectives, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines;

[0140] The optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script.

[0141] The axial ultimate tensile strength was obtained by testing the steel-cored aluminum stranded wire model of the transmission line. Based on the axial ultimate tensile strength, the optimization target and the allowable relative error, it was determined whether the preset stopping condition was met. If the preset stopping condition was met, the iterative optimization process was terminated, and the parameter optimization result of the corresponding number of iterations was taken as the parameter optimization result of the optimization.

[0142] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0143] When the number of iterations is not less than three, for any iteration optimization process, obtain the number k of the current iteration. Based on the parameter optimization results obtained in k-1 iterations, k-2 iterations, the axial limit tensile strength obtained in k-1 iterations, and k-2 iterations, calculate the dimensionless sensitivity matrix of the current iteration using the dimensionless sensitivity function. Based on the optimization objective and the axial limit tensile strength obtained in k-1 iterations, calculate the residual vector of the current iteration. Based on the dimensionless sensitivity matrix, sensitivity attenuation coefficient, and residual vector of the current iteration, obtain the correction amount of the current iteration using the correction formula. Based on the parameter optimization results obtained in k-1 iterations and the correction amount of the current iteration, obtain the parameter optimization result of the current iteration.

[0144] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0145] When the number of iterations is one, the optimization parameters are initialized to obtain the parameter optimization result of the first iteration; when the number of iterations is two, the correction amount is initialized to obtain the correction amount of the second iteration; based on the parameter optimization result of the first iteration and the correction amount of the second iteration, the parameter optimization result of the second iteration is obtained.

[0146] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0147] For any iteration of parameter optimization, based on the parameter optimization results, the steel anchor pressing die is controlled to press down at a uniform speed along a preset path perpendicular to the steel core axis, squeezing the overlapping section of the steel anchor and the steel core aluminum stranded wire to obtain a stable pressing structure between the steel anchor and the steel core; based on the parameter optimization results, the anti-slip groove pressing die is controlled to press down in alignment with the anti-slip groove area of ​​the steel anchor, squeezing the anti-slip groove area of ​​the steel anchor and the corresponding contact section of the aluminum tube to obtain a mechanical interlocking structure between the steel anchor and the aluminum tube; based on the parameter optimization results, the aluminum tube pressing die is controlled to press down step by step at preset intervals, uniformly squeezing the mating section between the aluminum tube and the steel core aluminum stranded wire to complete the pressing process of the steel anchor, aluminum tube and steel core aluminum stranded wire, and obtain the steel core aluminum stranded wire model of the transmission line.

[0148] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0149] Based on the axial ultimate tensile strength and the optimization target, the relative error is obtained; if the absolute value of the relative error is greater than the allowable relative error, it is determined that the preset condition has not been met; if the absolute value of the relative error is not greater than the allowable relative error, it is determined that the preset condition has been met.

[0150] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0151] If the preset stopping condition is not met, the optimization parameters will be optimized in the next iteration.

[0152] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0153] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0154] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for optimizing the parameters of steel-cored aluminum stranded wire for power transmission lines, characterized in that, The method includes: Set the optimization parameters, optimization objectives, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines; The optimization parameters are iteratively optimized, and based on the parameter optimization results obtained in each iteration, a steel-cored aluminum stranded wire model for the corresponding number of iterations is established using a parametric finite element modeling script. The steel-cored aluminum stranded wire model of the transmission line is tested to obtain the axial ultimate tensile strength. Based on the axial ultimate tensile strength, the optimization target, and the allowable relative error, it is determined whether the preset stopping condition has been met. If the preset stopping condition is met, the iterative optimization process ends, and the parameter optimization result of the corresponding number of iterations is taken as the parameter optimization result after optimization is completed.

2. The method according to claim 1, characterized in that, The iterative optimization of the optimization parameters includes: When the number of iterations is not less than three, for any iteration optimization process, obtain the number of the current iteration k, and calculate the dimensionless sensitivity matrix of the current iteration based on the parameter optimization results obtained by the (k-1)th iteration, the parameter optimization results obtained by the (k-2)th iteration, the axial ultimate tensile strength obtained by the (k-1)th iteration, and the axial ultimate tensile strength obtained by the (k-2)th iteration through the dimensionless sensitivity function. Based on the optimization objective and the axial ultimate tensile strength obtained from the k-1 iterations, calculate the residual vector for the current iteration; Based on the dimensionless sensitivity matrix, sensitivity attenuation coefficient, and residual vector of the current iteration, the correction amount for the current iteration is obtained through the correction formula. Based on the parameter optimization results obtained from the (k-1)th iteration and the correction amount of the current iteration, the parameter optimization result of the current iteration is obtained.

3. The method according to claim 2, characterized in that, The method further includes: When the number of iterations is one, the optimization parameters are initialized to obtain the parameter optimization results of the first iteration; When the number of iterations is two, the correction amount is initialized to obtain the correction amount for the second iteration; based on the parameter optimization result of the first iteration and the correction amount of the second iteration, the parameter optimization result for the second iteration is obtained.

4. The method according to claim 1, characterized in that, The process of establishing a steel-cored aluminum stranded wire model for the corresponding number of iterations based on the parameter optimization results obtained from each iteration includes: For any iteration of the parameter optimization result, based on the parameter optimization result, the steel anchor pressing mold is controlled to press down at a constant speed along a preset path perpendicular to the steel core axis, extruding the overlapping section of the steel anchor and the steel core aluminum strand, to obtain a stable pressing structure between the steel anchor and the steel core. Based on the parameter optimization results, the anti-slip groove mold is controlled to press down on the anti-slip groove area of ​​the steel anchor, squeezing the anti-slip groove area of ​​the steel anchor and the corresponding contact section of the aluminum tube to obtain the mechanical interlocking structure between the steel anchor and the aluminum tube. Based on the parameter optimization results, the aluminum tube pressing die is controlled to press down step by step at a preset interval, uniformly extruding the mating section of the aluminum tube and the steel-cored aluminum stranded wire, completing the pressing process of the steel anchor, aluminum tube and steel-cored aluminum stranded wire, and obtaining the steel-cored aluminum stranded wire model of the transmission line.

5. The method according to claim 1, characterized in that, The step of determining whether a preset stopping condition has been met based on the axial ultimate tensile strength, the optimization target, and the allowable relative error includes: Based on the axial ultimate tensile strength and the optimization target, the relative error is obtained; If the absolute value of the relative error is greater than the allowable relative error, it is determined that the preset condition has not been met. If the absolute value of the relative error is not greater than the allowable relative error, then it is determined that the preset condition has been met.

6. The method according to claim 1, characterized in that, The method further includes: If the preset stopping condition is not met, the optimization parameters will be iterated and optimized again.

7. A parameter optimization device for steel-cored aluminum stranded wire in power transmission lines, characterized in that, The device includes: The setting module is used to set the optimization parameters, optimization targets, and allowable relative errors for steel-cored aluminum stranded wires in power transmission lines. The optimization module is used to iteratively optimize the optimization parameters and, based on the parameter optimization results obtained in each iteration, establish a steel-cored aluminum stranded wire model for the corresponding number of iterations using a parameterized finite element modeling script. The testing module is used to test the steel-cored aluminum stranded wire model of the transmission line to obtain the axial ultimate tensile strength; based on the axial ultimate tensile strength, the optimization target, and the allowable relative error, it is determined whether the preset stopping condition has been met; if the preset stopping condition is met, the iterative optimization process ends, and the parameter optimization result of the corresponding number of iterations is taken as the parameter optimization result after optimization is completed.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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